The document outlines a presentation on transceiver design for single-cell and multi-cell downlink multiuser MIMO systems. It discusses MSE uplink-downlink duality under imperfect CSI, showing that the sum MSE, user MSE, and symbol MSE are dual between the uplink and downlink channels. It demonstrates how to ensure the uplink and downlink MSE values are equal to each other by appropriately setting the transmit covariance matrices. The presentation also covers transceiver design algorithms for coordinated base station systems and generalized duality for multiuser MIMO systems.
1. The document derives formulas for variational Bayesian inference of correlated topic models (CTM).
2. It presents the generative process of CTM, which models correlations between topics using Gaussian distributions over topic proportions.
3. Variational inference is used to optimize an evidence lower bound, deriving update formulas for the variational distributions of topics, topic-word distributions, and correlations between topics.
This document summarizes Rajesh Gandham's PhD thesis defense on high-order numerical methods for ocean modeling applications. The thesis goals are to develop accurate PDE models, leverage many-core hardware architectures, and use efficient algorithm techniques. The document outlines work on two-dimensional shallow water modeling using discontinuous Galerkin methods, a pasiDG simulator implementation, and preliminary work on three-dimensional oceanic modeling. Performance results are shown for the pasiDG simulator running on GPUs and CPUs for the 2004 Indian Ocean tsunami test case.
A One-Pass Triclustering Approach: Is There any Room for Big Data?Dmitrii Ignatov
An efficient one-pass online algorithm for triclustering of binary data (triadic formal contexts) is proposed. This algorithm is a modified version of the basic algorithm for OAC-triclustering approach, but it has linear time and memory complexities with respect to the cardinality
of the underlying ternary relation and can be easily parallelized in order to be applied for the analysis of big datasets. The results of computer experiments show the efficiency of the proposed algorithm.
A copy of my slides from the SILO Seminar at UW Madison on our recent developments for the NEO-K-Means methods including new optimization routines and results.
1. The document derives formulas for variational Bayesian inference of correlated topic models (CTM).
2. It presents the generative process of CTM, which models correlations between topics using Gaussian distributions over topic proportions.
3. Variational inference is used to optimize an evidence lower bound, deriving update formulas for the variational distributions of topics, topic-word distributions, and correlations between topics.
This document summarizes Rajesh Gandham's PhD thesis defense on high-order numerical methods for ocean modeling applications. The thesis goals are to develop accurate PDE models, leverage many-core hardware architectures, and use efficient algorithm techniques. The document outlines work on two-dimensional shallow water modeling using discontinuous Galerkin methods, a pasiDG simulator implementation, and preliminary work on three-dimensional oceanic modeling. Performance results are shown for the pasiDG simulator running on GPUs and CPUs for the 2004 Indian Ocean tsunami test case.
A One-Pass Triclustering Approach: Is There any Room for Big Data?Dmitrii Ignatov
An efficient one-pass online algorithm for triclustering of binary data (triadic formal contexts) is proposed. This algorithm is a modified version of the basic algorithm for OAC-triclustering approach, but it has linear time and memory complexities with respect to the cardinality
of the underlying ternary relation and can be easily parallelized in order to be applied for the analysis of big datasets. The results of computer experiments show the efficiency of the proposed algorithm.
A copy of my slides from the SILO Seminar at UW Madison on our recent developments for the NEO-K-Means methods including new optimization routines and results.
Anti-differentiating approximation algorithms: A case study with min-cuts, sp...David Gleich
This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic.
This document outlines material and energy balances for different sections of a distillation column. It provides equations for:
1) Overall, component, and energy balances for the top, enriching, and stripping sections of the column, defining terms like reflux ratio, heat removed, and operating lines on diagrams.
2) Conditions for total reflux, where operating lines become vertical, and minimum reflux ratio, where the minimum length of line a is determined using trials to find where the tie line through F intersects ΔD.
3) Material and energy balances relate flow rates, temperatures, concentrations, and heat of different streams using terms like net heat out per mole and moles of component A out per total
Zero-Forcing Precoding and Generalized InversesDaniel Tai
This document discusses zero-forcing precoding and its relationship to generalized inverses. It examines this technique for multi-input single-output wireless systems under two power constraints: total transmit power and per-antenna power. The paper formulates the optimization problems for maximizing sum rate and fairness under these constraints. It presents the solutions using generalized inverses and simulations to evaluate the performance under different conditions.
This document summarizes quantization design techniques including Lloyd-Max quantizers and variable rate optimum quantizers. It discusses the problem setup for scalar quantization and outlines the local optimality conditions, alternating optimization approach, and dynamic programming approach for designing Lloyd-Max quantizers. It also covers the problem setup for variable rate optimum quantizer design subject to an entropy constraint, and describes analyzing this using a generalized Lloyd-Max algorithm.
Processing Reachability Queries with Realistic Constraints on Massive Network...BigMine
Massive graphs are ubiquitous in various application domains, such as social networks, road networks, communication networks, biological networks, RDF graphs, and so on. Such graphs are massive (for example, with hundreds of millions of nodes and edges or even more) and contain rich information (for example, node/edge weights, labels and textual contents). In such massive graphs, an important class of problems is to process various graph structure related queries. Graph reachability, as an example, asks whether a node can reach another in a graph. However, the large graph scale presents new challenges for efficient query processing.
In this talk, I will introduce two new yet important types of graph reachability queries: weight constraint reachability that imposes edge weight constraint on the answer path, and k-hop reachability that imposes a length constraint on the answer path. With such realistic constraints, we can find more meaningful and practically feasible answers. These two reachablity queries have wide applications in many real-world problems, such as QoS routing and trip planning.
Quantitative norm convergence of some ergodic averagesVjekoslavKovac1
The document summarizes quantitative estimates for the convergence of multiple ergodic averages of commuting transformations. Specifically, it presents a theorem that provides an explicit bound on the number of jumps in the Lp norm for double averages over commuting Aω actions on a probability space. The proof transfers the structure of the Cantor group AZ to R+ and establishes norm estimates for bilinear averages of functions on R2+. This allows bounding the variation of the double averages and proving the theorem.
This document summarizes fuzzy clustering and merging algorithms. It begins by introducing fuzzy weights that give less weight to outlier data points that are farther from cluster prototypes. It then describes the fuzzy c-means algorithm, which assigns data points membership weights between 0-1 for belonging to multiple clusters. The algorithm is presented to iteratively compute fuzzy weights and update cluster prototypes. Variations are also discussed, including using different weighting formulas and fuzzy set membership functions like Gaussian functions. Finally, a simple effective fuzzy clustering algorithm is outlined that initializes prototypes, performs k-means clustering, computes fuzzy weights and averages to update prototypes, and iterates until merging similar clusters.
Presentation of the paper:
Szymon Klarman and Thomas Meyer. Querying Temporal Databases via OWL 2 QL (with appendix). In Proceedings of the 8th International Conference on Web Reasoning and Rule Systems (RR-14), 2014.
The document describes a method called the "Four Russians method" to speed up Bayesian Hidden Markov Model (HMM) classification by exploiting repetition in long observation sequences. The key ideas are to break the observation sequence into blocks of length k and compute the forward variables only at block boundaries, and to sample the hidden state sequence block-by-block from the backward-forward distribution rather than the full backward distribution. This reduces the computational complexity from O(TN^2) to O(TNk/k^2) = O(TN/k).
This thesis examines the return interval distribution of extreme events in long memory time series that have two different scaling exponents. It first reviews long memory processes and their characterization using fractional autoregressive integrated moving average (ARFIMA) models. It then derives an analytical expression for the return interval distribution when the time series has two scaling exponents, as supported by numerical simulations. The thesis also considers a long memory probability process with two exponents and compares the return interval distribution to analytical results.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...VjekoslavKovac1
1. The document discusses variants of the Christ-Kiselev lemma and its application to maximal Fourier restriction estimates.
2. The Christ-Kiselev lemma allows block-diagonal and block-triangular truncations of operators while controlling their operator norms.
3. These lemmas can be used to prove maximal and variational estimates for the restriction of the Fourier transform to surfaces, which has applications in harmonic analysis.
ESL 4.4.3-4.5: Logistic Reression (contd.) and Separating HyperplaneShinichi Tamura
The presentation material for the reading club of Element of Statistical Learning by Hastie et al.
The contents of the sections cover
- Properties of logistic regression compared to least square s fitting
- Difference between logistic regression vs. linear discriminant analysis
- Rosenblatt's perceptron algorithm
- Derivation of optimal hyperplane, which offers the basis for SVM
-------------------------------------------------------------------------
研究室での『統計学習の基礎』(Hastieら著)の輪講用発表資料(ぜんぶ英語)です。
担当範囲は
・最小二乗法との類推で見るロジスティック回帰の特徴
・ロジスティック回帰と線形判別分析の比較
・ローゼンブラットのパーセプトロンアルゴリズム
・SVMの基礎となる最適分離超平面の導出
The document discusses Wasserstein GANs and improved training methods. It introduces Wasserstein GANs and discusses problems with training GANs using other distances like KL divergence. Wasserstein distance is defined and shown to be continuous and differentiable. The document outlines training Wasserstein GANs using Kantorovich-Rubinstein duality by having the discriminator produce 1-Lipschitz outputs. It then discusses problems with weight clipping and proposes an improved training method by constraining the discriminator's gradient norm to be less than or equal to 1.
This note gives a derivation of the variational posterior updates presented in the following paper:
Sato, Issei and Kurihara, Kenichi and Nakagawa, Hiroshi,
Practical Collapsed Variational Bayes Inference for Hierarchical Dirichlet Process,
in Proc. of KDD '12.
5G Communication (Full Video: https://www.youtube.com/watch?v=ICE2BGbQ_14)T. E. BOGALE
This document discusses potential technologies for 5G communication networks, including:
1. Network densification through ultra dense heterogeneous networks using small cells and multi-hop relaying to improve coverage and capacity.
2. Utilization of new spectrum bands, including millimeter wave frequencies above 6 GHz and visible light communication, to provide additional bandwidth for 5G.
3. Implementation of massive MIMO technology using large antenna arrays to focus transmissions and improve spectral efficiency.
The document presents a system model and problem formulation for user scheduling in massive MIMO OFDMA systems with hybrid analog-digital beamforming. The system considers a base station with N antennas but only Na < N RF chains serving multiple single-antenna mobile stations. The objective is to maximize the overall data rate by scheduling Kt mobile stations across subcarriers, subject to a per-subcarrier power constraint. For a single subcarrier, the problem is formulated as maximizing the sum rate of K scheduled users under a total power constraint, assuming Na = K RF chains. Two approaches are discussed: directly constraining the analog beamforming matrix or exploiting the solution from a digital scheduler using a clever decomposition method.
Anti-differentiating approximation algorithms: A case study with min-cuts, sp...David Gleich
This talk covers the idea of anti-differentiating approximation algorithms, which is an idea to explain the success of widely used heuristic procedures. Formally, this involves finding an optimization problem solved exactly by an approximation algorithm or heuristic.
This document outlines material and energy balances for different sections of a distillation column. It provides equations for:
1) Overall, component, and energy balances for the top, enriching, and stripping sections of the column, defining terms like reflux ratio, heat removed, and operating lines on diagrams.
2) Conditions for total reflux, where operating lines become vertical, and minimum reflux ratio, where the minimum length of line a is determined using trials to find where the tie line through F intersects ΔD.
3) Material and energy balances relate flow rates, temperatures, concentrations, and heat of different streams using terms like net heat out per mole and moles of component A out per total
Zero-Forcing Precoding and Generalized InversesDaniel Tai
This document discusses zero-forcing precoding and its relationship to generalized inverses. It examines this technique for multi-input single-output wireless systems under two power constraints: total transmit power and per-antenna power. The paper formulates the optimization problems for maximizing sum rate and fairness under these constraints. It presents the solutions using generalized inverses and simulations to evaluate the performance under different conditions.
This document summarizes quantization design techniques including Lloyd-Max quantizers and variable rate optimum quantizers. It discusses the problem setup for scalar quantization and outlines the local optimality conditions, alternating optimization approach, and dynamic programming approach for designing Lloyd-Max quantizers. It also covers the problem setup for variable rate optimum quantizer design subject to an entropy constraint, and describes analyzing this using a generalized Lloyd-Max algorithm.
Processing Reachability Queries with Realistic Constraints on Massive Network...BigMine
Massive graphs are ubiquitous in various application domains, such as social networks, road networks, communication networks, biological networks, RDF graphs, and so on. Such graphs are massive (for example, with hundreds of millions of nodes and edges or even more) and contain rich information (for example, node/edge weights, labels and textual contents). In such massive graphs, an important class of problems is to process various graph structure related queries. Graph reachability, as an example, asks whether a node can reach another in a graph. However, the large graph scale presents new challenges for efficient query processing.
In this talk, I will introduce two new yet important types of graph reachability queries: weight constraint reachability that imposes edge weight constraint on the answer path, and k-hop reachability that imposes a length constraint on the answer path. With such realistic constraints, we can find more meaningful and practically feasible answers. These two reachablity queries have wide applications in many real-world problems, such as QoS routing and trip planning.
Quantitative norm convergence of some ergodic averagesVjekoslavKovac1
The document summarizes quantitative estimates for the convergence of multiple ergodic averages of commuting transformations. Specifically, it presents a theorem that provides an explicit bound on the number of jumps in the Lp norm for double averages over commuting Aω actions on a probability space. The proof transfers the structure of the Cantor group AZ to R+ and establishes norm estimates for bilinear averages of functions on R2+. This allows bounding the variation of the double averages and proving the theorem.
This document summarizes fuzzy clustering and merging algorithms. It begins by introducing fuzzy weights that give less weight to outlier data points that are farther from cluster prototypes. It then describes the fuzzy c-means algorithm, which assigns data points membership weights between 0-1 for belonging to multiple clusters. The algorithm is presented to iteratively compute fuzzy weights and update cluster prototypes. Variations are also discussed, including using different weighting formulas and fuzzy set membership functions like Gaussian functions. Finally, a simple effective fuzzy clustering algorithm is outlined that initializes prototypes, performs k-means clustering, computes fuzzy weights and averages to update prototypes, and iterates until merging similar clusters.
Presentation of the paper:
Szymon Klarman and Thomas Meyer. Querying Temporal Databases via OWL 2 QL (with appendix). In Proceedings of the 8th International Conference on Web Reasoning and Rule Systems (RR-14), 2014.
The document describes a method called the "Four Russians method" to speed up Bayesian Hidden Markov Model (HMM) classification by exploiting repetition in long observation sequences. The key ideas are to break the observation sequence into blocks of length k and compute the forward variables only at block boundaries, and to sample the hidden state sequence block-by-block from the backward-forward distribution rather than the full backward distribution. This reduces the computational complexity from O(TN^2) to O(TNk/k^2) = O(TN/k).
This thesis examines the return interval distribution of extreme events in long memory time series that have two different scaling exponents. It first reviews long memory processes and their characterization using fractional autoregressive integrated moving average (ARFIMA) models. It then derives an analytical expression for the return interval distribution when the time series has two scaling exponents, as supported by numerical simulations. The thesis also considers a long memory probability process with two exponents and compares the return interval distribution to analytical results.
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
Operation cost reduction in unit commitment problem using improved quantum bi...IJECEIAES
Unit Commitment (UC) is a nonlinear mixed integer-programming problem. UC used to minimize the operational cost of the generation units in a power system by scheduling some of generators in ON state and the other generators in OFF state according to the total power outputs of generation units, load demand and the constraints of power system. This paper proposes an Improved Quantum Binary Particle Swarm Optimization (IQBPSO) algorithm. The tests have been made on a 10-units simulation system and the results show the improvement in an operation cost reduction after using the proposed algorithm compared with the ordinary Quantum Binary Particle Swarm Optimization (QBPSO) algorithm
Variants of the Christ-Kiselev lemma and an application to the maximal Fourie...VjekoslavKovac1
1. The document discusses variants of the Christ-Kiselev lemma and its application to maximal Fourier restriction estimates.
2. The Christ-Kiselev lemma allows block-diagonal and block-triangular truncations of operators while controlling their operator norms.
3. These lemmas can be used to prove maximal and variational estimates for the restriction of the Fourier transform to surfaces, which has applications in harmonic analysis.
ESL 4.4.3-4.5: Logistic Reression (contd.) and Separating HyperplaneShinichi Tamura
The presentation material for the reading club of Element of Statistical Learning by Hastie et al.
The contents of the sections cover
- Properties of logistic regression compared to least square s fitting
- Difference between logistic regression vs. linear discriminant analysis
- Rosenblatt's perceptron algorithm
- Derivation of optimal hyperplane, which offers the basis for SVM
-------------------------------------------------------------------------
研究室での『統計学習の基礎』(Hastieら著)の輪講用発表資料(ぜんぶ英語)です。
担当範囲は
・最小二乗法との類推で見るロジスティック回帰の特徴
・ロジスティック回帰と線形判別分析の比較
・ローゼンブラットのパーセプトロンアルゴリズム
・SVMの基礎となる最適分離超平面の導出
The document discusses Wasserstein GANs and improved training methods. It introduces Wasserstein GANs and discusses problems with training GANs using other distances like KL divergence. Wasserstein distance is defined and shown to be continuous and differentiable. The document outlines training Wasserstein GANs using Kantorovich-Rubinstein duality by having the discriminator produce 1-Lipschitz outputs. It then discusses problems with weight clipping and proposes an improved training method by constraining the discriminator's gradient norm to be less than or equal to 1.
This note gives a derivation of the variational posterior updates presented in the following paper:
Sato, Issei and Kurihara, Kenichi and Nakagawa, Hiroshi,
Practical Collapsed Variational Bayes Inference for Hierarchical Dirichlet Process,
in Proc. of KDD '12.
5G Communication (Full Video: https://www.youtube.com/watch?v=ICE2BGbQ_14)T. E. BOGALE
This document discusses potential technologies for 5G communication networks, including:
1. Network densification through ultra dense heterogeneous networks using small cells and multi-hop relaying to improve coverage and capacity.
2. Utilization of new spectrum bands, including millimeter wave frequencies above 6 GHz and visible light communication, to provide additional bandwidth for 5G.
3. Implementation of massive MIMO technology using large antenna arrays to focus transmissions and improve spectral efficiency.
The document presents a system model and problem formulation for user scheduling in massive MIMO OFDMA systems with hybrid analog-digital beamforming. The system considers a base station with N antennas but only Na < N RF chains serving multiple single-antenna mobile stations. The objective is to maximize the overall data rate by scheduling Kt mobile stations across subcarriers, subject to a per-subcarrier power constraint. For a single subcarrier, the problem is formulated as maximizing the sum rate of K scheduled users under a total power constraint, assuming Na = K RF chains. Two approaches are discussed: directly constraining the analog beamforming matrix or exploiting the solution from a digital scheduler using a clever decomposition method.
Orthogonal Faster than Nyquist Transmission for SIMO Wireless SystemsT. E. BOGALE
The document proposes a new Orthogonal Faster than Nyquist (OFTN) transmission scheme for SIMO wireless systems that can transmit more than one symbol per time interval, achieving higher spectral efficiency than existing OFDM. The proposed scheme splits the bandwidth into subbands and transmits symbols across subbands and time intervals. It is shown that up to P symbols can be transmitted in 3P-2 time intervals when there are N receive antennas, an improvement over OFDM. Numerical results demonstrate improved bit error rate and sum rate compared to OFDM, especially at high SNR. Open problems remaining include extending the approach to MISO systems and evaluating performance under different channel and system conditions.
Pilot Contamination Mitigation for Wideband Massive MIMO: Number of Cells Vs ...T. E. BOGALE
The document presents a pilot contamination mitigation technique for wideband massive MIMO systems. It proposes a three-step approach: 1) Allowing pilot transmission in the time domain, 2) Expressing sub-carrier channel estimates as linear combinations of received signals, and 3) Optimizing the number of cells, pilots, and linear combination terms to ensure unbounded signal-to-interference-plus-noise ratio (SINR). The main results show that the number of cells can be increased to L, where L is the number of multipath taps, allowing cancellation of pilot contamination. Simulation results demonstrate that the proposed approach achieves rates close to perfect channel state information.
MSE uplink-downlink duality of MIMO systems under imperfect CSIT. E. BOGALE
This document outlines a presentation on MSE duality under imperfect channel state information. It establishes three types of MSE duality - sum MSE duality, user-wise MSE duality, and symbol-wise MSE duality. As an application example, it examines a robust sum MSE minimization problem. An alternating optimization technique is proposed to solve this problem by decomposing precoders and decoders and formulating the power allocation as a geometric program at each step. Simulation results show the proposed robust design outperforms a non-robust design and that increased antenna correlation worsens sum AMSE.
Performance Analysis of Massive MIMO Downlink System with Imperfect Channel S...IJRES Journal
We investigate the ergodic sum rate and required transmit power of a single-cell massive
multiple-input multiple-output (MIMO) downlink system. The system considered in this paper is based on two
linear beamforming schemes, that is, maximum ratio transmission (MRT) beamforming and zero-forcing (ZF)
beamforming. What’s more, we use minimum mean square error (MMSE) channel estimation to get imperfect
channel state information (CSI). Compared with the perfect CSI case, both theoretical analysis and simulation
results show that the system performance is different when the imperfect CSI is taken into account.
The document discusses the key aspects of 5G technology, including its expected high speeds of up to 10GBps, lower battery consumption, and lower infrastructure costs. 5G is predicted to start being implemented in developing countries by the 2020s and will offer a wide range of new features like supporting over 60,000 connections simultaneously and enabling easier control of computers from handsets. However, many older devices may not be compatible with 5G networks and ensuring universal access to vast amounts of data will pose challenges.
The document discusses new handoff management techniques for device-to-device (D2D) communication in Long Term Evolution-Advanced (LTE-A) cellular networks. It first introduces D2D communication and its classification. It then describes the LTE-A architecture and protocol stack for supporting D2D, including the D2D function block and bearer management. Finally, it proposes using D2D communication to enable seamless handovers and reduce handover delays, outlining a high-level flow for D2D-enabled handovers.
This document provides a survey of device-to-device (D2D) communication. It begins with background on the increasing mobile data traffic and challenges it poses. It then classifies D2D technologies as inband or outband. Inband D2D can be network assisted or not, operating in cellular spectrum. Outband D2D operates in unlicensed spectrum. Challenges of D2D include interference management and resource allocation. Applications include offloading and public safety. While D2D promises increased capacity and proximity services, open challenges remain around interference, pricing, and standardization.
This document discusses direct mobile-to-mobile (D2D) communication as a paradigm for 5G networks. It defines D2D communication as allowing devices to communicate directly without routing through network infrastructure. D2D can occur either in licensed spectrum using cellular control or in unlicensed spectrum. The document outlines classifications of D2D, the functional block diagram, power control considerations, applications, and benefits like higher data rates, reliable communication, and power savings. It concludes that D2D communication aided by cellular networks can enhance resource reuse between devices and networks while expanding business opportunities.
Our porfolio in educational technology2Salido Noel
This document provides an overview of students' portfolio in educational technology. It contains 3 sections:
1. Introduction of the students, Noel M. Salido and Rhea A. Manzano, who are 3rd year Bachelor of Secondary Education students at Palawan State University.
2. Definitions and objectives of educational technology which aims to provide education on using technology for instruction and impart learning experiences.
3. Discussions of key aspects of educational technology including whether technology is a boon or bane, its systematic approach to teaching, roles in learning, and Edgar Dale's Cone of Experience model of learning.
Enabling D2D communication in mmWave 5G networks Bayar shahab
This document discusses enabling device-to-device (D2D) communications in millimeter-wave (mmWave) 5G cellular networks. It first describes mmWave bands which operate between 30-300 GHz and provide high data rates up to 10 Gbps but have short range. It then explains D2D communication which allows direct communication between devices. The document proposes enabling D2D in mmWave 5G networks using directional antennas to mitigate interference. It outlines the 5G network architecture combining 4G, mmWave base stations, and devices. Finally, it discusses the MAC layer and a resource sharing scheme to allocate non-interfering resources to concurrent transmitting links.
An overview about the new feature proposed for LTE Release 12 and beyond: Proximity Services (ProSe) / D2D.
It covers the D2D features: Discovery, Communication, Security and also shows some use-cases.
5G the Future of next Generation of communicationKarthik U
The document summarizes a technical seminar presentation on 5G technology. It discusses the objectives and introduction of 5G including its advantages over existing 4G systems such as higher data capacity up to 10 Gbps, lower latency of 1 millisecond, improved energy efficiency and more ubiquitous coverage. It describes the existing 4G system and its limitations. The proposed 5G system is outlined as delivering extreme broadband, ultra-robust and low latency connectivity through a combination of existing and novel radio access technologies integrated into a software-defined network architecture. Key aspects of 5G include possibilities enabled, versatile radio technologies used, its system of systems network architecture and a phased practical approach to deployment. Applications highlighted are unified global connectivity and the potential for
TCP Fairness for Uplink and Downlink Flows in WLANsambitlick
The document proposes a dual queue scheme at access points to improve fairness between uplink and downlink TCP flows in wireless local area networks. The scheme employs two queues - one for downlink TCP data packets and another for uplink TCP ACK packets. By selecting the queues with different probabilities, the access point can control the ratio of TCP data and ACK sending rates to achieve fairness. Simulation results show that the dual queue scheme is effective at resolving the unfairness problem in a simple way without modifying existing MAC protocols or requiring per-flow queueing.
Weighted Sum Rate Optimization for Downlink Multiuser MIMO Systems with per A...T. E. BOGALE
This document proposes a downlink-uplink duality approach to solve the weighted sum rate maximization problem for multiuser MIMO systems with per antenna power constraints. The duality is established by modifying the input covariance matrix of the dual uplink problem and formulating the noise covariance matrix as a fixed point function. Simulation results show that the proposed duality-based solution utilizes less total base station power than existing algorithms.
The document discusses MIMO (Multiple Input Multiple Output) systems. It motivates MIMO by explaining how system designers aim to achieve high data rates and quality while minimizing complexity, transmission power, and bandwidth. It describes MIMO antenna configurations including SISO and MIMO. MIMO systems use multiple transmit and receive antennas to achieve high capacity. The document outlines diversity as a design criterion for MIMO systems to achieve reliable reception. It also discusses Alamouti's space-time coding scheme and how MIMO can be combined with OFDM to further improve performance. In conclusions, MIMO brings us closer to gigabit speeds while also providing reliable communications.
Dokumen tersebut membahas tentang dasar-dasar ilmu hidrolika, yang menerangkan bahwa hidrolika mempelajari perilaku cairan baik dalam keadaan diam maupun bergerak. Dokumen juga menjelaskan hukum Pascal tentang tekanan cairan yang akan tersebar merata di ruang tertutup, serta bagaimana hukum Pascal diterapkan dalam sistem hidrolika untuk menghasilkan gaya yang lebih besar dengan menggunakan gaya yang le
Although there is near universal agreement on the customary norms governing armed conflict there has been no international discussion on applying these standards to the incorporation of Artificial Intelligence (AI) agents used in support of military operations. This brief aims to address that gap providing parameters for legal discussion on military use of A.I.
Authors: Thomas Wingfield, J.D., LL.M., Lydia Kostopoulos, PhD, Cyrus Hodes.
- The Future Society at Harvard Kennedy School of Government
This document provides data, formulae, and relationships for GCE Advanced Level and Advanced Subsidiary Physics A exams. It includes fundamental constants, conversion factors, mathematical equations, and formulae for mechanics, waves, fields, and modern physics. Students are provided this booklet during exams but must return it to the invigilator afterwards.
This document summarizes a study that applies a recently developed effective theory called SCETG to model jet quenching in heavy ion collisions at the LHC. SCETG allows for the unified treatment of vacuum and medium-induced parton showers. The authors establish an analytic connection between the QCD evolution approach and traditional energy loss approach in the soft gluon emission limit. They quantify uncertainties in implementing in-medium modifications to hadron production cross sections and find the coupling between jets and the medium can be constrained to better than 10% accuracy. Numerical comparisons between the medium-modified evolution approach and energy loss formalism for modeling RAA are also presented.
Markov chains can be used for economic modeling. A Markov chain is characterized by: (1) possible states of a system, (2) a transition matrix showing the probability of moving between states, and (3) initial state probabilities. The transition matrix specifies the one-step probabilities between each pair of states. Markov chains can converge to a stationary distribution over time. Recurrent states will be revisited, while transient states will not. Markov chains can model topics like industry investment, consumption/saving over a lifecycle, and regime-switching in economic time series.
The Goldberg-Coxeter construction takes two integers (k,l) a 3-or 4-valent plane graph and returns a 3- or 4-valent plane graph. This construction is useful in virus study, numerical analysis, architecture, chemistry and of course mathematics.
Here we consider the zigzags and central circuits of 3- or 4-valent plane graph. It turns out that we can define an algebraic construction of (k,l)-product that allows to find the length of the zigzags and central circuits in a compact way. All possible lengths of zigzags are determined by this (k,l)-product and the normal structure of the automorphism group allows to find them for some congruence conditions.
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Transceiver design for single-cell and multi-cell downlink multiuser MIMO systems
1. Transceiver design for single-cell and multi-cell
downlink multiuser MIMO systems
Tadilo Endeshaw Bogale
University Catholique de Louvain (UCL), ICTEAM
Dec. 2013
2. Presentation Outline
Presentation Outline
1 MSE uplink-downlink duality under imperfect CSI
MSE uplink-downlink duality under imperfect CSI
Application of AMSE duality
Simulation Results
Drawbacks and Looking ahead
2 Transceiver design for Coordinated BS Systems
Block diagram and Problem formulation
Proposed Algorithms
Simulation Results
Drawbacks and Looking ahead
3 Transceiver design for multiuser MIMO systems: Generalized duality
System Model and Problem Statements
Simulation Results
4 Thesis Conclusions
5 Future Research Directions
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 2 / 24
3. MSE duality MSE uplink-downlink duality under imperfect CSI
MSE uplink-downlink duality under imperfect CSI
(a)
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
(b)
H1
H2
HK
n
TH
V1
V2
VK
d
d1
d2
dK
Assumption: CSI model HH
k = HH
k + R
1/2
mk EH
wk R
1/2
bk
Objectives:
Exploit MSE duality (sum MSE, user MSE and symbol MSE duality)
between UL and DL channels
Apply duality to solve transceiver design problems
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 3 / 24
4. MSE duality MSE uplink-downlink duality under imperfect CSI
Sum MSE uplink-downlink duality
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
ξ
DL
k = tr{ISk
+ P
−1/2
k αk UH
k ΓDL
k Uk αk P
−1/2
k
−2ℜ{P
1/2
k GH
k Hk Uk αk P
−1/2
k }}
ΓDL
k = σ2
ek tr{Rbk GPGH
}Rmk +
HH
k GPGH
Hk + σ2
IMk
ξ
UL
k = tr{ISk
+ Q
−1/2
k αk GH
k ΓcGk αk Q
−1/2
k
−2ℜ{Q
−1/2
k αk GH
k Hk Uk Q
1/2
k }}
ΓUL
c = K
i=1(σ2
ei tr{Rmi Ui Qi UH
i }Rbi +
Hi Ui Qi UH
i HH
i ) + σ2
IN
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 4 / 24
5. MSE duality MSE uplink-downlink duality under imperfect CSI
Sum MSE uplink-downlink duality
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
ξ
DL
k = tr{ISk
+ P
−1/2
k αk UH
k ΓDL
k Uk αk P
−1/2
k
−2ℜ{P
1/2
k GH
k Hk Uk αk P
−1/2
k }}
ΓDL
k = σ2
ek tr{Rbk GPGH
}Rmk +
HH
k GPGH
Hk + σ2
IMk
ξ
UL
k = tr{ISk
+ Q
−1/2
k αk GH
k ΓcGk αk Q
−1/2
k
−2ℜ{Q
−1/2
k αk GH
k Hk Uk Q
1/2
k }}
ΓUL
c = K
i=1(σ2
ei tr{Rmi Ui Qi UH
i }Rbi +
Hi Ui Qi UH
i HH
i ) + σ2
IN
Given ξDL K
k=1 ξ
DL
k
We can ensure K
k=1 ξ
UL
k = ξDL
by setting Qk = ˜βα2
k P−1
k
with ˜β =
K
k=1 tr{Pk }
K
k=1
tr{P−1
k
αk }
K
k=1 tr{Qk } = K
k=1 tr{Pk }(Also met)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 4 / 24
6. MSE duality MSE uplink-downlink duality under imperfect CSI
Sum MSE uplink-downlink duality
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
ξ
DL
k = tr{ISk
+ P
−1/2
k αk UH
k ΓDL
k Uk αk P
−1/2
k
−2ℜ{P
1/2
k GH
k Hk Uk αk P
−1/2
k }}
ΓDL
k = σ2
ek tr{Rbk GPGH
}Rmk +
HH
k GPGH
Hk + σ2
IMk
ξ
UL
k = tr{ISk
+ Q
−1/2
k αk GH
k ΓcGk αk Q
−1/2
k
−2ℜ{Q
−1/2
k αk GH
k Hk Uk Q
1/2
k }}
ΓUL
c = K
i=1(σ2
ei tr{Rmi Ui Qi UH
i }Rbi +
Hi Ui Qi UH
i HH
i ) + σ2
IN
Given ξDL K
k=1 ξ
DL
k
We can ensure K
k=1 ξ
UL
k = ξDL
by setting Qk = ˜βα2
k P−1
k
with ˜β =
K
k=1 tr{Pk }
K
k=1
tr{P−1
k
αk }
K
k=1 tr{Qk } = K
k=1 tr{Pk }(Also met)
Given ξUL K
k=1 ξ
UL
k
We ensure K
k=1 ξ
DL
k = ξUL
by setting Pk = βα2
k Q−1
k
with β =
K
k=1 tr{Qk }
K
k=1
tr{Q−1
k
αk }
K
k=1 tr{Pk } = K
k=1 tr{Qk }(Also met)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 4 / 24
7. MSE duality MSE uplink-downlink duality under imperfect CSI
User MSE uplink-downlink duality
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
ξ
DL
k = tr{ISk
+ P
−1/2
k αk UH
k ΓDL
k Uk αk P
−1/2
k
−2ℜ{P
1/2
k GH
k Hk Uk αk P
−1/2
k }}
ΓDL
k = σ2
ek tr{Rbk GPGH
}Rmk +
HH
k GPGH
Hk + σ2
IMk
ξ
UL
k = tr{ISk
+ Q
−1/2
k αk GH
k ΓcGk αk Q
−1/2
k
−2ℜ{Q
−1/2
k αk GH
k Hk Uk Q
1/2
k }}
ΓUL
c = K
i=1(σ2
ei tr{Rmi Ui Qi UH
i }Rbi +
Hi Ui Qi UH
i HH
i ) + σ2
IN
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 5 / 24
8. MSE duality MSE uplink-downlink duality under imperfect CSI
User MSE uplink-downlink duality
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
ξ
DL
k = tr{ISk
+ P
−1/2
k αk UH
k ΓDL
k Uk αk P
−1/2
k
−2ℜ{P
1/2
k GH
k Hk Uk αk P
−1/2
k }}
ΓDL
k = σ2
ek tr{Rbk GPGH
}Rmk +
HH
k GPGH
Hk + σ2
IMk
ξ
UL
k = tr{ISk
+ Q
−1/2
k αk GH
k ΓcGk αk Q
−1/2
k
−2ℜ{Q
−1/2
k αk GH
k Hk Uk Q
1/2
k }}
ΓUL
c = K
i=1(σ2
ei tr{Rmi Ui Qi UH
i }Rbi +
Hi Ui Qi UH
i HH
i ) + σ2
IN
Given ξ
DL
k , ξ
UL
k = ξ
DL
k , K
k=1 tr{Qk } = K
k=1 tr{Pk }(ensured) by Qk = ˜βk α2
k P−1
k
where ˜T · [˜β1, . . . , ˜βK ]T
= σ2
[tr{P1}, . . . , tr{PK }]T
, ˜T is constant
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 5 / 24
9. MSE duality MSE uplink-downlink duality under imperfect CSI
User MSE uplink-downlink duality
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
ξ
DL
k = tr{ISk
+ P
−1/2
k αk UH
k ΓDL
k Uk αk P
−1/2
k
−2ℜ{P
1/2
k GH
k Hk Uk αk P
−1/2
k }}
ΓDL
k = σ2
ek tr{Rbk GPGH
}Rmk +
HH
k GPGH
Hk + σ2
IMk
ξ
UL
k = tr{ISk
+ Q
−1/2
k αk GH
k ΓcGk αk Q
−1/2
k
−2ℜ{Q
−1/2
k αk GH
k Hk Uk Q
1/2
k }}
ΓUL
c = K
i=1(σ2
ei tr{Rmi Ui Qi UH
i }Rbi +
Hi Ui Qi UH
i HH
i ) + σ2
IN
Given ξ
DL
k , ξ
UL
k = ξ
DL
k , K
k=1 tr{Qk } = K
k=1 tr{Pk }(ensured) by Qk = ˜βk α2
k P−1
k
where ˜T · [˜β1, . . . , ˜βK ]T
= σ2
[tr{P1}, . . . , tr{PK }]T
, ˜T is constant
Given ξ
UL
k , ξ
DL
k = ξ
UL
k , K
k=1 tr{Pk } = K
k=1 tr{Qk }(ensured) by Pk = βk α2
k Q−1
k
where T · [β1, . . . , βK ]T
= σ2
[tr{Q1}, . . . , tr{QK }]T
, T is constant
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 5 / 24
10. MSE duality Application of AMSE duality
Robust Weighted Sum MSE Minimization
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
minGk ,Uk ,αk ,Pk
K
k=1 τk ξ
DL
k
s.t K
k=1 tr{Pk } ≤ Pmax
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 6 / 24
11. MSE duality Application of AMSE duality
Robust Weighted Sum MSE Minimization
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
minGk ,Uk ,αk ,Pk
K
k=1 τk ξ
DL
k
s.t K
k=1 tr{Pk } ≤ Pmax
Case I : τk = 1, Rmk = I, Rbk = Rb, σ2
ek = σ2
e
⋄ Define Uk = Uk Qk UH
k
⋄ Optimize Uk (SDP problem)⋆
⋄ Get Uk and Qk from Uk
⋄ Update Rx by MMSE and get Gk , αk from Rx
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 6 / 24
12. MSE duality Application of AMSE duality
Robust Weighted Sum MSE Minimization
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
minGk ,Uk ,αk ,Pk
K
k=1 τk ξ
DL
k
s.t K
k=1 tr{Pk } ≤ Pmax
Case I : τk = 1, Rmk = I, Rbk = Rb, σ2
ek = σ2
e
⋄ Define Uk = Uk Qk UH
k
⋄ Optimize Uk (SDP problem)⋆
⋄ Get Uk and Qk from Uk
⋄ Update Rx by MMSE and get Gk , αk from Rx
⋄ Transfer to DL as Pk = βα2
k Q−1
k
where β =
K
k=1 tr{Qk }
K
k=1
tr{Q−1
k
αk }
⋄ Update Rx by MMSE
⋄ Get Uk and αk from Rx
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 6 / 24
13. MSE duality Application of AMSE duality
Robust Weighted Sum MSE Minimization
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
minGk ,Uk ,αk ,Pk
K
k=1 τk ξ
DL
k
s.t K
k=1 tr{Pk } ≤ Pmax
Case II : General τk , Rmk , Rbk , σ2
ek
⋄ Initialize Uk , Qk and get Gk , αk from MMSE Rx
⋄ Decompose Qk = qk
˜Qk , tr{ ˜Qk } = 1
⋄ Optimize qk (GP problem)⋆
⋄ Get Qk from qk and ˜Qk
⋄ Update Rx by MMSE and get Gk , αk from Rx
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 6 / 24
14. MSE duality Application of AMSE duality
Robust Weighted Sum MSE Minimization
(a)
d1
d2
dK
HH
2
HH
K αK
P
−1
2
2
P
−1
2
1
P
−1
2
K
n1
nK
n2
dK
d2
d1
= d GP
1
2
α1HH
1
α2
UH
1
UH
2
UH
K
(b)
d1
GH
d2
dK
Q
1
2
1
Q
1
2
2
Q
1
2
K
H1
H2
HK
n
dQ−1
2α
U1
U2
UK
minGk ,Uk ,αk ,Pk
K
k=1 τk ξ
DL
k
s.t K
k=1 tr{Pk } ≤ Pmax
Case II : General τk , Rmk , Rbk , σ2
ek
⋄ Initialize Uk , Qk and get Gk , αk from MMSE Rx
⋄ Decompose Qk = qk
˜Qk , tr{ ˜Qk } = 1
⋄ Optimize qk (GP problem)⋆
⋄ Get Qk from qk and ˜Qk
⋄ Update Rx by MMSE and get Gk , αk from Rx
⋄ Transfer to DL as Pk = βk α2
k Q−1
k
where T · [β1, . . . , βK ]T
=
σ2
[tr{Q1}, . . . , tr{QK }]T
T Constant
⋄ Update Rx by MMSE
⋄ Get Uk and αk from Rx
⋄ Switch to UL and iterate
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 6 / 24
15. MSE duality Simulation Results
Simulation Results
10 15 20 25 30 35
0
0.2
0.4
0.6
0.8
1
1.2
1.4
SNR (dB)
(a)
AveragesumMSE
GM (Na)
GM (Ro)
GM (Pe)
Alg I (Na)
Alg I (Ro)
Alg I (Pe)
10 15 20 25 30 35
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
SNR (dB)
(b)
AveragesumMSE
Na (ρ
b
= 0.25)
Ro (ρ
b
= 0.25)
Pe (ρ
b
= 0.25)
Na (ρ
b
= 0.75)
Ro (ρ
b
= 0.75)
Pe (ρ
b
= 0.75)
10 15 20 25 30 35
0
0.5
1
1.5
SNR (dB)
(c)
AveragesumMSE
Na (ρ
b
= 0.25, ρ
m
= 0.25)
Ro (ρ
b
= 0.25, ρ
m
= 0.25)
Pe (ρ
b
= 0.25, ρ
m
= 0.25)
Na (ρ
b
= 0.25, ρ
m
= 0.75)
Ro (ρ
b
= 0.25, ρ
m
= 0.75)
Pe (ρ
b
= 0.25, ρ
m
= 0.75)
Settings N = 4, K = 2, Mk = 2, Pmax = 10mw, τk = 1
Observations
⋄ Robust outperforms nonrobust
⋄ Perfect CSI gives the best AMSE
⋄ Large antenna correlation further
increases sum AMSE
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 7 / 24
16. MSE duality Drawbacks and Looking ahead
Drawbacks and Looking ahead
Drawbacks
The duality solve only total BS power based problems
The duality FAIL to solve Practically relevant per BS antenna
power based problems
Looking Ahead
No clue to resolve the drawback!!
Switch to distributed transceiver design for Coordinated BS
systems
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 8 / 24
17. Transceiver design for Coordinated BS Systems Block diagram and Problem formulation
Coordinated BS Block Diagram
Assumptions:
The lth BS precods the overall data d = [d1, · · · , dK ] by Bl
The kth MS uses the receiver Wk to recover its data dk
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 9 / 24
18. Transceiver design for Coordinated BS Systems Block diagram and Problem formulation
Coordinated BS Block Diagram
Assumptions:
The lth BS precods the overall data d = [d1, · · · , dK ] by Bl
The kth MS uses the receiver Wk to recover its data dk
ˆdk = WH
k ( L
l=1 HH
lk Bl d + nk )
= WH
k (HH
k Bd + nk )
where HH
k = [HH
1k , · · · , HH
Lk ]
B = [B1; · · · ; BL]
⋄ Interpreted as a gaint MIMO
⋄ Treated like conventional MIMO BUT with
per BS power constraint Or
per BS antenna power constraint
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 9 / 24
19. Transceiver design for Coordinated BS Systems Block diagram and Problem formulation
System Model and Problem Statement
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
max{Bk ,Wk }K
k=1
K
k=1
Sk
i=1 ωki Rki
s.t [
K
k=1 Bk BH
k ]n,n ≤ Pn, ∀n
Rki = log2 (ξ−1
ki )
ξki = wH
ki (HH
k BBH
Hk + σ2
k I)wki
−2ℜ{wH
ki HH
k bki } + 1
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 10 / 24
20. Transceiver design for Coordinated BS Systems Block diagram and Problem formulation
System Model and Problem Statement
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
max{Bk ,Wk }K
k=1
K
k=1
Sk
i=1 ωki Rki
s.t [
K
k=1 Bk BH
k ]n,n ≤ Pn, ∀n
Rki = log2 (ξ−1
ki )
ξki = wH
ki (HH
k BBH
Hk + σ2
k I)wki
−2ℜ{wH
ki HH
k bki } + 1
Reexpressed as
min{bs,ws}S
w=1
S
s=1 ξωs
s
s.t [
S
s=1 bsbH
s ]n,n ≤ Pn, ∀n
ξs = wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws
−2ℜ{wH
s
˜HH
s bs} + 1
⋄ Non linear and non convex
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 10 / 24
21. Transceiver design for Coordinated BS Systems Block diagram and Problem formulation
System Model and Problem Statement
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
max{Bk ,Wk }K
k=1
K
k=1
Sk
i=1 ωki Rki
s.t [
K
k=1 Bk BH
k ]n,n ≤ Pn, ∀n
Rki = log2 (ξ−1
ki )
ξki = wH
ki (HH
k BBH
Hk + σ2
k I)wki
−2ℜ{wH
ki HH
k bki } + 1
Reexpressed as
min{bs,ws}S
w=1
S
s=1 ξωs
s
s.t [
S
s=1 bsbH
s ]n,n ≤ Pn, ∀n
ξs = wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws
−2ℜ{wH
s
˜HH
s bs} + 1
⋄ Non linear and non convex
Existing iterative algorithm[1]
⋄ Solve this problem as it is
⋄ Complexity per iteration :
O( (N + S)(2NS + 1)2
(2S2
+ 2NS + S))
+O(K ˜M2.376
) + CGP
[1] Shi, S., Schubert, M., and Boche, H. ”Per-antenna power constrained rate optimization
for multiuser MIMO systems”, Proc. WSA, Belrin, Germany, Feb., 2008.
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 10 / 24
22. Transceiver design for Coordinated BS Systems Proposed Algorithms
Problem Reformulation
min{bs,ws}S
s=1
S
s=1 ξωs
s , s.t [
S
s=1 bsbH
s ]n,n ≤ Pn, ∀n
ξs = wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 11 / 24
23. Transceiver design for Coordinated BS Systems Proposed Algorithms
Problem Reformulation
min{bs,ws}S
s=1
S
s=1 ξωs
s , s.t [
S
s=1 bsbH
s ]n,n ≤ Pn, ∀n
ξs = wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1
Key facts
⋄
S
s=1 fs, fs > 0 ≡
min{νs}S
s=1
1
S
S
s=1 fsνs
S
s.t
S
s=1 νs = 1, νs ≥ 0
⋄ abω
, a, b > 0, 0 < ω < 1 ≡
minτ>0 κ(aγ
τ + bτµ
)
γ = 1
1−ω , µ = 1
ω − 1, κ = ωµ(1−ω)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 11 / 24
24. Transceiver design for Coordinated BS Systems Proposed Algorithms
Problem Reformulation
min{bs,ws}S
s=1
S
s=1 ξωs
s , s.t [
S
s=1 bsbH
s ]n,n ≤ Pn, ∀n
ξs = wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1
Key facts
⋄
S
s=1 fs, fs > 0 ≡
min{νs}S
s=1
1
S
S
s=1 fsνs
S
s.t
S
s=1 νs = 1, νs ≥ 0
⋄ abω
, a, b > 0, 0 < ω < 1 ≡
minτ>0 κ(aγ
τ + bτµ
)
γ = 1
1−ω , µ = 1
ω − 1, κ = ωµ(1−ω)
Reformulate WSR max problem as
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 11 / 24
25. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Centralized Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 12 / 24
26. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Centralized Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
⋄ For fixed B : Optimize ws, νs, τs (closed form solution)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 12 / 24
27. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Centralized Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
⋄ For fixed B : Optimize ws, νs, τs (closed form solution)
⋄ For fixed ws, νs, τs : Optimize bs (SDP problem)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 12 / 24
28. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Centralized Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
Repeat
⋄ For fixed B : Optimize ws, νs, τs (closed form solution)
⋄ For fixed ws, νs, τs : Optimize bs (SDP problem)
Until Convergence
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 12 / 24
29. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Centralized Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
Repeat
⋄ For fixed B : Optimize ws, νs, τs (closed form solution)
⋄ For fixed ws, νs, τs : Optimize bs (SDP problem)
Until Convergence
Computational complexity
Exist : O(K ˜M2.376
) + O( (N + S)(2NS + 1)2
(2S2
+ 2NS + S)) + CGP per ite
Prop : O(K ˜M2.376
) + O(
√
N + 1(2NS + 1)2
(2S2
+ 2NS)) per ite (Better!)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 12 / 24
30. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Distributed Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 13 / 24
31. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Distributed Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
⋄ For fixed B : Same as centralized
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 13 / 24
32. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Distributed Algorithm
Reformulated WSR max problem
minτs,νs,bs,ws
S
s=1 κs[νγs
s
τs
+ τµs
s (wH
s (˜HH
s BBH ˜Hs + ˜σ2
s I)ws − 2ℜ{wH
s
˜HH
s bs} + 1)]
s.t [
S
s=1 bsbH
s ]n,n ≤ pn,
S
s=1 νs = 1, νs > 0, τs > 0 ∀s, n
⋄ For fixed B : Same as centralized
For fixed ws, νs, τs
⋄ Formulate bs optimization as SDP
⋄ Get dual of SDP : ({λn ≥ 0}N
n=1 are dual variables)
⋄ Apply MFM and get λi iteratively by
⋄ λ⋆
i = |¯gi |/
√
pi , g⋆
i = λ(RRH
+ λ)−1
fi
where ¯gi is ith row of [g1, g2, · · · , gN ] (i.e., needs inner iteration)
R, fi are constants
⋄ Compute optimal bs by employing {λ⋆
i }N
i=1
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 13 / 24
33. Transceiver design for Coordinated BS Systems Proposed Algorithms
Proposed Distributed Algorithm
⋄ For fixed B : Same as centralized
For fixed ws, νs, τs
⋄ Formulate bs optimization as SDP
⋄ Get dual of SDP : ({λn ≥ 0}N
n=1 are dual variables)
⋄ Apply MFM and get λi iteratively by
⋄ λ⋆
i = |¯gi |/
√
pi , g⋆
i = λ(RRH
+ λ)−1
fi
where ¯gi is ith row of [g1, g2, · · · , gN ] (i.e., needs inner iteration)
R, fi are constants
⋄ Compute optimal bs by employing {λ⋆
i }N
i=1
Computational complexity
Exist : O(K ˜M2.376
) + O( (N + S)(2NS + 1)2
(2S2
+ 2NS + S)) + CGP per ite
Pro (cent) : O(K ˜M2.376
) + O(
√
N + 1(2NS + 1)2
(2S2
+ 2NS)) per ite
Pro (dist) : O(K ˜M2.376
) + inner ite × O(N2.376
) per ite (Much better!)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 13 / 24
34. Transceiver design for Coordinated BS Systems Simulation Results
Simulation Results for inner iteration
Large scale network: L = 25, K = 50, Mk = 2 at SNR = 10dB
2 4 6 8 10 12 14 16 18 20
0
20
40
60
80
100
120
140
Number of iterations
Objectivefunction
Small number of inner iteration is required
Indeed distributed needs less computation than centralized
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 14 / 24
35. Transceiver design for Coordinated BS Systems Simulation Results
Comparison of Proposed and Existing Algorithms
Set N = 4, L = 2, K = 4, Mk = 2, ω = [.6, .4, .5, .8, .25, .8, .46, .28]
5 10 15 20 25
4
5
6
7
8
9
10
11
12
Number of iterations
Weightedsumrate(bps/Hz)
SNR=10dB
Proposed centralized algorithm
Proposed distributed algorithm
Existing algorithm [1]
0 5 10 15 20
4
6
8
10
12
14
16
18
20
22
SNR (dB)
Weightedsumrate(bps/Hz)
Proposed centralized algorithm
Proposed distributed algorithm
Existing algorithm [1]
Proposed algorithms have faster convergence than existing
Proposed algorithms have slightly higher WSR than existing
Distributed algorithm achieves the same WSR as centralized
[1] Shi, S., Schubert, M., and Boche, H. ”Per-antenna power constrained rate optimization
for multiuser MIMO systems”, Proc. WSA, Belrin, Germany, Feb., 2008.
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 15 / 24
36. Transceiver design for Coordinated BS Systems Drawbacks and Looking ahead
Drawbacks and Looking ahead
Drawbacks:
The proposed distributed algorithm is problem dependent (i.e., for
each problem we need to formulate its Lagrangian dual problem).
Looking ahead
The WSR max problem can be analyzed like in a conventional
multiuser MIMO system with per antenna power constraint.
A clear relation between WSR and WSMSE is exploited.
Key observation of MSE duality: The role of transmitters and
receivers are interchanged.
Exploiting MSE duality for generalized power constraint should
help to get problem independent distributed algorithm for many
classes of transceiver design problems
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 16 / 24
37. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
System Model and Problem Statements
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
Objectives
To solve P1 and P2 by MSE duality approach
To show the benefits of the MSE duality solution approach
To show the extension of the duality for solving other transceiver
design problems
P1 : minBk ,Wk
K
k=1
Sk
i=1 ηki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki , ∀n, k, i
P2 : min{Bk ,Wk }K
k=1
max ρki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki , ∀n, k, i
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 17 / 24
38. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
Existing MSE Uplink-downlink Duality (Revisited)
(a)
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
(b)
H1
H2
HK
n
TH
V1
V2
VK
d
d1
d2
dK
The duality can maintain ξDL
ki = ξUL
ki
The duality cannot ensure [ K
k=1 Bk BH
k ]n,n ≤ ˘pn and bH
kibki ≤ ˘pki
P1 : min{Bk ,Wk }K
k=1
K
k=1
Sk
i=1 ηki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
P2 : min{Bk ,Wk }K
k=1
max ρki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 18 / 24
39. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
New MSE Downlink-Interference Duality
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
V1
V2
nI
1S1
nI
K1
nI
KSK
nI
11
tH
KSK
tH
K1
tH
1S1
tH
11
H111
H11S1
H21S1
H1KSK
H1K1
HKK1
HK1S1
H2K1
HKKSK
ˆdK1
ˆd11
VK
HK11
H2KSK
H211
d1
ˆdKSK
ˆd1S1
d2
dK
P1 : min{Bk ,Wk }K
k=1
K
k=1
Sk
i=1 ηki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 19 / 24
40. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
New MSE Downlink-Interference Duality
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
V1
V2
nI
1S1
nI
K1
nI
KSK
nI
11
tH
KSK
tH
K1
tH
1S1
tH
11
H111
H11S1
H21S1
H1KSK
H1K1
HKK1
HK1S1
H2K1
HKKSK
ˆdK1
ˆd11
VK
HK11
H2KSK
H211
d1
ˆdKSK
ˆd1S1
d2
dK
P1 : min{Bk ,Wk }K
k=1
K
k=1
Sk
i=1 ηki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
⋄ Initialize Bk and update Wk by MMSE
Repeat
Transformation (DL to Interference)
⋄ Set dI
ki ∼ (0, ηki ), nI
ki ∼ (0, Ψ + µki I), Ψ = diag(ψn)
⋄ Get ψn, µki iteratively
Key we show that ψn, µki > 0 always exist!
⋄ Set vki = wki and update tki by MMSE
Transformation (Interference to DL)
⋄ Set bki = βtki , β2
=
K
i=1
Si
j=1
ηij wH
ij
Ri wij
K
i=1
Si
j=1
tH
ij
(Ψ+µij I)tij
⋄ Update Wk by MMSE
Until convergence
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 19 / 24
41. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
New MSE Downlink-Interference Duality
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
V1
V2
nI
1S1
nI
K1
nI
KSK
nI
11
tH
KSK
tH
K1
tH
1S1
tH
11
H111
H11S1
H21S1
H1KSK
H1K1
HKK1
HK1S1
H2K1
HKKSK
ˆdK1
ˆd11
VK
HK11
H2KSK
H211
d1
ˆdKSK
ˆd1S1
d2
dK
P1 : min{Bk ,Wk }K
k=1
K
k=1
Sk
i=1 ηki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
⋄ Initialize Bk and update Wk by MMSE
Repeat
Transformation (DL to Interference)
⋄ Set dI
ki ∼ (0, ηki ), nI
ki ∼ (0, Ψ + µki I), Ψ = diag(ψn)
⋄ Get ψn, µki iteratively
Key we show that ψn, µki > 0 always exist!
⋄ Set vki = wki and update tki by MMSE
Transformation (Interference to DL)
⋄ Set bki = βtki , wki =
vki
β
, β2
=
K
i=1
Si
j=1
ηij wH
ij
Ri wij
K
i=1
Si
j=1
tH
ij
(Ψ+µij I)tij
⋄ Decompose Bk = Gk P
1/2
k
, Wk = Gk P
−1/2
k
αk
⋄ Optimize Pk (increases convergence speed)
⋄ Again update Bk = Gk P
1/2
k
and Wk by MMSE
Until convergence
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 19 / 24
42. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
New MSE Downlink-Interference Duality
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
V1
V2
nI
1S1
nI
K1
nI
KSK
nI
11
tH
KSK
tH
K1
tH
1S1
tH
11
H111
H11S1
H21S1
H1KSK
H1K1
HKK1
HK1S1
H2K1
HKKSK
ˆdK1
ˆd11
VK
HK11
H2KSK
H211
d1
ˆdKSK
ˆd1S1
d2
dK
P2 : min{Bk ,Wk }K
k=1
max ρki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 19 / 24
43. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
New MSE Downlink-Interference Duality
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
V1
V2
nI
1S1
nI
K1
nI
KSK
nI
11
tH
KSK
tH
K1
tH
1S1
tH
11
H111
H11S1
H21S1
H1KSK
H1K1
HKK1
HK1S1
H2K1
HKKSK
ˆdK1
ˆd11
VK
HK11
H2KSK
H211
d1
ˆdKSK
ˆd1S1
d2
dK
P2 : min{Bk ,Wk }K
k=1
max ρki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
⋄ Initialize Bk and update Wk by MMSE
Repeat
Transformation (DL to Interference)
⋄ Set dI
ki ∼ (0, 1), nI
ki ∼ (0, Ψ + µki I), Ψ = diag(ψn)
⋄ Get ψn, µki iteratively
⋄ Get ¯β2
[ ¯β2
11, · · · ¯β2
KSK
] as ¯β2
= ¯Zx
x = [ψ1, · · · , ψN , µ11, · · · , µKSK
], ¯Z is constant
⋄ Set vki = ¯βki wki and update tki by MMSE
Transformation (Interference to DL)
⋄ Get β2
[β2
11, · · · β2
KSK
] as β2
= Zx, Z is constant
Key we show that ψn, µki , ¯β2
ki , β2
ki > 0 always exist!
⋄ Set bki = βki tki and update Wk by MMSE
Until convergence
Unbalanced weighted MSE
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 19 / 24
44. Transceiver design for multiuser MIMO systems: Generalized duality System Model and Problem Statements
New MSE Downlink-Interference Duality
d2
d1 HH
1
HH
2
HH
K
WH
2
n1
nK
n2
WH
K
WH
1
d1
d2
dK
= d B
dK
V1
V2
nI
1S1
nI
K1
nI
KSK
nI
11
tH
KSK
tH
K1
tH
1S1
tH
11
H111
H11S1
H21S1
H1KSK
H1K1
HKK1
HK1S1
H2K1
HKKSK
ˆdK1
ˆd11
VK
HK11
H2KSK
H211
d1
ˆdKSK
ˆd1S1
d2
dK
P2 : min{Bk ,Wk }K
k=1
max ρki ξDL
ki
s.t [
K
k=1 Bk BH
k ]n,n ≤ ˘pn,
bH
ki bki ≤ ˘pki
⋄ Initialize Bk and update Wk by MMSE
Repeat
Transformation (DL to Interference)
⋄ Set dI
ki ∼ (0, 1), nI
ki ∼ (0, Ψ + µki I), Ψ = diag(ψn)
⋄ Get ψn, µki iteratively
⋄ Get ¯β2
[ ¯β2
11, · · · ¯β2
KSK
] as ¯β2
= ¯Zx
x = [ψ1, · · · , ψN , µ11, · · · , µKSK
], ¯Z is constant
⋄ Set vki = ¯βki wki and update tki by MMSE
Transformation (Interference to DL)
⋄ Get β2
[β2
11, · · · β2
KSK
] as β2
= Zx, Z is constant
Key we show that ψn, µki , ¯β2
ki , β2
ki > 0 always exist!
⋄ Set bki = βki tki , wki = vki /βki and decompose
Bk = Gk P
1/2
k
, Wk = Gk P
−1/2
k
αk
⋄ Optimize Pk , −Ensures balanced weighted MSE
−Increases convergence speed
⋄ Again update Bk = Gk P
1/2
k
and Wk by MMSE
Until convergence
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 19 / 24
45. Transceiver design for multiuser MIMO systems: Generalized duality Simulation Results
Simulation Results
10 15 20 25 30 35
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SNR (dB)
WeightedsumMSE
Proposed Duality
Algorithm in [1]
−25 −20 −15 −10 −5 0
7.5
8
8.5
9
9.5
10
σav
2
(dB)
TotalBSpower
Proposed Duality
Algorithm in [1]
10 15 20 25 30 35
0
0.05
0.1
0.15
0.2
0.25
SNR (dB)
MaximumsymbolMSE
Proposed Duality
Algorithm in [1]
−25 −20 −15 −10 −5 0
7
7.5
8
8.5
9
9.5
10
σ
av
2
(dB)
TotalBSpower
Proposed Duality
Algorithm in [1]
Settings N = 4, K = 2, Mk = 2, ˘pki = 2.5mw, ˘pn = 2.5mw, ηki = ρki = 1
[1] Shi, S., Schubert, M., Vucic, N., and Boche, H. ”MMSE Optimization with Per-Base-Station Power
Constraints for Network MIMO Systems”, Proc. IEEE ICC, Beijing, China, May, 2008.
P1
P2
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 20 / 24
46. Transceiver design for multiuser MIMO systems: Generalized duality Simulation Results
Simulation Results
10 15 20 25 30 35
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
SNR (dB)
WeightedsumMSE
Proposed Duality
Algorithm in [1]
−25 −20 −15 −10 −5 0
7.5
8
8.5
9
9.5
10
σav
2
(dB)
TotalBSpower
Proposed Duality
Algorithm in [1]
10 15 20 25 30 35
0
0.05
0.1
0.15
0.2
0.25
SNR (dB)
MaximumsymbolMSE
Proposed Duality
Algorithm in [1]
−25 −20 −15 −10 −5 0
7
7.5
8
8.5
9
9.5
10
σ
av
2
(dB)
TotalBSpower
Proposed Duality
Algorithm in [1]
Settings N = 4, K = 2, Mk = 2, ˘pki = 2.5mw, ˘pn = 2.5mw, ηki = ρki = 1
[1] Shi, S., Schubert, M., Vucic, N., and Boche, H. ”MMSE Optimization with Per-Base-Station Power
Constraints for Network MIMO Systems”, Proc. IEEE ICC, Beijing, China, May, 2008.
P1
P2
Complexity (P1)
Proposed duality O(N2.376
) + O(KM2.376
) + CGP (≡ Linear programming)
Algorithm in [1] O( (N + KM + 1)(2MKN + 1)2
(2(MK)2
+ 4NMK)) + O(KM2.376
)
Complexity (P2)
Proposed duality O(N2.376
) + O(KM2.376
) + CGP (≡ Linear programming)
Algorithm in [1] O( (N + KM + 1)(2MKN + 1)2
(2(MK)2
+ 4NMK)) + O(KM2.376
)
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 20 / 24
47. Thesis Conclusions
Thesis Conclusions
In this PhD work, we accomplish the following main tasks:
We generalize the existing MSE duality to handle many practically
relevant transceiver design problems.
For stochastic robust design MSE-based problems, the duality can
be extended straightforwardly to imperfect CSI scenario.
For all of considered problems, the proposed duality algorithms
require less total BS power (and complexity) compared to the
existing solution approach which does not employ duality
The relationship between WSMSE and WSR problems have been
exploited. Consequently, the complicated nonlinear WSR problem
can be examined by its equivalent linear WSMSE problem
We also develop distributed transceiver design algorithms to solve
weighted sum rate and MSE optimization problems
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 21 / 24
48. Future Research Directions
Future Research Directions
All of our algorithms are linear but suboptimal. So getting linear
and optimal algorithm is still an open research topic (one
approach could be to extend the well known Majorization theory to
Multiuser MIMO setup).
The proposed general duality is valid only for perfect CSI and
imperfect CSI with stochastic robust design. The extension of the
proposed duality to imperfect CSI with worst-case robust design is
open for future research.
In all of our distributive algorithms, we assume that the global
channel knowledge is available at the central controller (or at all
BSs) prior to optimization. Thus, developing distributed algorithm
with local CSI knowledge is also an open research direction
The robust rate and SINR-based problems (i.e, in stochastic
design approach) have not been examined. Hence, solving such
problems is open research topic.
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 22 / 24
49. Future Research Directions
Selected List of Publications I
T. E. Bogale, B. K. Chalise, and L. Vandendorpe, Robust
transceiver optimization for downlink multiuser MIMO systems,
IEEE Tran. Sig. Proc. 59 (2011), no. 1, 446 – 453.
T. E. Bogale and L. Vandendorpe, MSE uplink-downlink duality of
MIMO systems with arbitrary noise covariance matrices, 45th
Annual conference on Information Sciences and Systems (CISS)
(Baltimore, MD, USA), 23 – 25 Mar. 2011, pp. 1 – 6.
T. E. Bogale and L. Vandendorpe, Weighted sum rate optimization
for downlink multiuser MIMO coordinated base station systems:
Centralized and distributed algorithms, IEEE Trans. Signal
Process. 60 (2011), no. 4, 1876 – 1889.
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 23 / 24
50. Future Research Directions
Selected List of Publications II
, Weighted sum rate optimization for downlink multiuser
MIMO systems with per antenna power constraint: Downlink-uplink
duality approach, IEEE International Conference On Acuostics,
Speech and Signal Processing (ICASSP) (Kyoto, Japan), 25 – 30
Mar. 2012, pp. 3245 – 3248.
, Linear transceiver design for downlink multiuser MIMO
systems: Downlink-interference duality approach, IEEE Trans. Sig.
Process. 61 (2013), no. 19, 4686 – 4700.
Tadilo (PhD defense (UCL)) Transceiver design Dec. 2013 24 / 24