I present the Multiple Input and Multiple Output (MIMO) channel capacity. Capacity regions for multiuser MIMO broadcast and multiple access channel are shown. Also, the duality relationship between multiple access and broadcast channel is presented.
PgRouting is an open source library that provides routing functionality for PostgreSQL and PostGIS. It allows calculating the shortest path between points on a road network. Costs in the network can be made dynamic to model real-world factors like traffic, conditions, restrictions. This allows pgRouting to find alternate routes automatically in response to changes rather than requiring pre-calculated networks.
The document discusses combinatorial optimization problems including the travelling salesman problem, which aims to find the shortest route to visit all cities in a graph exactly once. It defines local and global optima, with local optima being the best solution in a neighborhood and global optima being the best overall solution. The document also defines convex sets as sets where the line segment between any two points is contained in the set, and lists some properties of convex sets.
Presentation 2(power point presentation) dis2016Daniel Omunting
(1) The document discusses constructing a CMOS static diagram based on the Boolean equations X1 = (A+B+C)D and X2 = (AB) + (CD).
(2) For X1 = (A+B+C)D, the pull-up network is (A·B·C) + D and the pull-down network is (A+B+C)D.
(3) For X2 = (AB) + (CD), the pull-up network is (A+B)·(C+D) and the pull-down network is (AB) + (CD).
This lesson introduces students to using properties of real numbers to simplify expressions. It discusses the inverse, identity, commutative, and associative properties of addition and multiplication. Examples are provided to demonstrate the identity property of multiplication and addition, the commutative properties of multiplication and addition, and the associative properties of multiplication and addition. The objective is for students to use these properties to simplify expressions involving real numbers.
This lesson introduces students to basic arithmetic operations like identity, commutative, and associative properties. It provides examples of applying these properties to multiplication and addition. Students are asked to simplify expressions using the different properties and solve systems of equations to find the values of variables.
The document contains three mathematical expressions that can be simplified: a) combines like terms to simplify a fraction into a single term, b) combines all terms containing A and B, and c) combines terms based on their variables to reduce the number of unique terms.
The document discusses using dynamic programming to solve economic dispatch problems in electrical power systems. It explains that dynamic programming can be used to find the minimum cost generation dispatch by treating the problem as a multistage decision process. An illustrative example is provided of applying dynamic programming to an economic dispatch problem with 5 generation stages and 12 possible generation combinations.
The document discusses using dynamic programming to solve the economic dispatch problem in electrical power systems. It explains that dynamic programming can be used to solve other complex optimization problems as well. It provides an illustrative example of how to apply dynamic programming to determine the optimal generation dispatch from a set of power plants to meet demand at least cost. The example breaks the problem down into stages moving from plant to plant to determine the minimum cost solution.
PgRouting is an open source library that provides routing functionality for PostgreSQL and PostGIS. It allows calculating the shortest path between points on a road network. Costs in the network can be made dynamic to model real-world factors like traffic, conditions, restrictions. This allows pgRouting to find alternate routes automatically in response to changes rather than requiring pre-calculated networks.
The document discusses combinatorial optimization problems including the travelling salesman problem, which aims to find the shortest route to visit all cities in a graph exactly once. It defines local and global optima, with local optima being the best solution in a neighborhood and global optima being the best overall solution. The document also defines convex sets as sets where the line segment between any two points is contained in the set, and lists some properties of convex sets.
Presentation 2(power point presentation) dis2016Daniel Omunting
(1) The document discusses constructing a CMOS static diagram based on the Boolean equations X1 = (A+B+C)D and X2 = (AB) + (CD).
(2) For X1 = (A+B+C)D, the pull-up network is (A·B·C) + D and the pull-down network is (A+B+C)D.
(3) For X2 = (AB) + (CD), the pull-up network is (A+B)·(C+D) and the pull-down network is (AB) + (CD).
This lesson introduces students to using properties of real numbers to simplify expressions. It discusses the inverse, identity, commutative, and associative properties of addition and multiplication. Examples are provided to demonstrate the identity property of multiplication and addition, the commutative properties of multiplication and addition, and the associative properties of multiplication and addition. The objective is for students to use these properties to simplify expressions involving real numbers.
This lesson introduces students to basic arithmetic operations like identity, commutative, and associative properties. It provides examples of applying these properties to multiplication and addition. Students are asked to simplify expressions using the different properties and solve systems of equations to find the values of variables.
The document contains three mathematical expressions that can be simplified: a) combines like terms to simplify a fraction into a single term, b) combines all terms containing A and B, and c) combines terms based on their variables to reduce the number of unique terms.
The document discusses using dynamic programming to solve economic dispatch problems in electrical power systems. It explains that dynamic programming can be used to find the minimum cost generation dispatch by treating the problem as a multistage decision process. An illustrative example is provided of applying dynamic programming to an economic dispatch problem with 5 generation stages and 12 possible generation combinations.
The document discusses using dynamic programming to solve the economic dispatch problem in electrical power systems. It explains that dynamic programming can be used to solve other complex optimization problems as well. It provides an illustrative example of how to apply dynamic programming to determine the optimal generation dispatch from a set of power plants to meet demand at least cost. The example breaks the problem down into stages moving from plant to plant to determine the minimum cost solution.
L8 Tolls (Transportation and Logistics & Dr. Anna Nagurney)Hossam Shafiq I
This document discusses tolls and toll collection policies for transportation networks. It begins by introducing the concepts of system-optimal (S-O) and user-optimized (U-O) flow patterns on a network. It then explains that imposing tolls can modify travel costs as perceived by users to make the S-O flow pattern the same as the U-O pattern. There are two main types of toll policies discussed: link toll policies that charge per link, and path toll policies that charge per path. The document provides the mathematical formulations and conditions needed to determine tolls that make the S-O and U-O patterns identical. It also briefly discusses electronic toll collection and the use of toll roads worldwide.
The document discusses a logarithmic equation for calculating parking rates based on an hourly rate and constant. It also mentions the linear equation y=mx+c. The document thanks the reader.
This document contains solutions to two questions about image processing techniques. Question 1 involves implementing Sobel edge detection using 4 convolution masks and calculating the approximate gradient magnitude. Question 2 involves implementing a corner detector by building a neighborhood matrix around each pixel, computing its singular values via SVD, and highlighting locations where the second singular value exceeds a threshold after removing neighboring duplicates.
L9 Modeling Extensions (Transportation and Logistics & Dr. Anna Nagurney)Hossam Shafiq I
1) The document discusses modeling extensions for transportation models, including extended models to handle two-way traffic and intersections, and multimodal models to handle networks with multiple transportation modes.
2) It also discusses modeling elastic demand, where transportation demand between origins and destinations can change based on levels of congestion. The elastic demand model introduces demand functions and formulations to capture this effect.
3) Solutions to the elastic demand problem include transforming it into a fixed demand problem that can be solved using existing algorithms, or representing it as a network problem using excess demand formulations.
Parameterized convolutional neural networks for aspect level classificationJunya Kamura
This document describes the architecture of a convolutional neural network (CNN) model with three components: a generator network CNNg, a discriminator network CNNt, and a sentence encoder network CNNs. CNNg produces an initial representation θg which is input to CNNt to generate θt. θt and the input text representations v are then input to CNNs to generate the final encoded representation θs. The networks involve convolutions using weight matrices w and biases b applied to windows of the input with pooling to generate the respective outputs θg, θt, and θs.
1. The document discusses simplification of Sum of Products (SoP) expressions using Karnaugh maps. It provides two examples of SoP expressions and their simplification using K-maps.
2. The first example simplifies the expression A'B'C'+A'B'C+ABC'+AB'C' to AB+AC using a 4-variable K-map.
3. The second example simplifies the expression B'C'+AB'+ABC'+AB'CD'+A'B'C'D+AB'CD to AB'C'D+AB'C'D'+A'B'C'D+A'B'C'D'+AB'
This document discusses solving transportation problems to maximize profit rather than minimize cost. It provides 3 methods for solving these types of problems: 1) Convert it to a minimization problem by multiplying the profit matrix by -1. 2) Subtract all profits from the highest profit. 3) Solve it directly as a maximization problem by allocating to highest profit cells and checking for non-positive cell evaluations. It then works through an example problem using these steps to find the optimal solution maximizing total profit.
This document discusses optimal transport and how it can be used to model complex systems. Optimal transport finds the most efficient way to transport mass from an initial distribution to a final distribution given a cost matrix, and can be formulated as an energy minimization problem with constraints. The solution is found using the Sinkhorn-Knopp algorithm, which scales rows and columns iteratively. Optimal transport has applications in distribution matching, interpolation, domain adaptation, and modeling developmental landscapes. It provides a simple framework for comparing distributions with constraints and competition between parts.
This math problem involves dividing numbers. Part (a) divides 84 by 9 to get C3 = 9. Part (b) divides 504 by 3 and then by 9 to get A9 = 18. Part (c) cubes the number 93 to get A3 = 729.
This MATLAB code analyzes a coin image to identify and count coins. It loads and preprocesses the image, finds coin boundaries, labels separate coins, measures the area of each coin region, and annotates the image with the value (5 cents or 10 cents) and running total based on coin area.
The document discusses a math problem about modeling traffic congestion using a function. It provides the function f(t) and asks students to analyze properties of the traffic jam like its length at different times, when it is longest, and how quickly it increases and decreases. It also includes artwork created by students depicting traffic jams and the solutions to the math questions about the modeled congestion based on the given function.
L1 Transportation Planning Process (Transportation and Logistics & Dr. Anna N...Hossam Shafiq I
This document summarizes the transportation planning process presented in a lecture by Dr. Anna Nagurney. The planning process involves 4 phases: 1) collecting base year inventory data on existing networks and traffic, 2) building mathematical models to understand relationships between planning factors, 3) using the models to forecast future traffic, and 4) evaluating potential network designs based on traffic predictions and cost-benefit analyses. The core three-stage model involves trip generation, trip distribution, and traffic assignment models to predict traffic flows.
L10 The Extended Model (Transportation and Logistics & Dr. Anna Nagurney)Hossam Shafiq I
This document summarizes a lecture on the extended model in transportation networks. The extended model generalizes the standard model by allowing link costs to depend on the entire vector of link flows, not just the individual link flow. The total link cost functions must have a symmetric Jacobian matrix for an optimization formulation to exist. If the Jacobian is also positive definite, there is a unique flow pattern. The lecture provides examples of how to represent costs in an extended model and conditions for equilibrium.
The document discusses the concepts of average and instantaneous rate of change. It defines average velocity as the change in distance over the change in time. Instantaneous rate of change is defined as the limit as the change in time approaches 0 of the change in the function over the change in time. This gives the slope of the tangent line. It provides an example of finding the derivative of f(x)=x^2 by taking the limit as h approaches 0 of (f(x+h)-f(x))/h.
This document contains questions for an exam on applied mathematics for engineers. It has three parts:
Part A contains 10 short answer questions worth 2 marks each. Questions cover topics like defining terms related to chains and generalized eigenvalues.
Part B has two longer answer questions worth 13 marks each. One question involves finding the number of generalized eigenvectors for a given matrix.
Part C contains two questions worth 15 marks each. One asks to show a given functional is stationary for given functions, the other asks to derive moments and the moment generating function of the Poisson distribution.
The document discusses options for locating a new stadium in the Research Triangle region of North Carolina, which includes the cities of Raleigh, Durham, and Chapel Hill.
Option 1 would place the stadium the same distance from each city, requiring new highways to be built from each city to the stadium. Option 2 would place the stadium equidistant from the existing highways, requiring resurfacing of the existing highways and building new connector highways.
Using a digital geometry environment (DGE) to analyze the options, the responder determined that Option 2 would be cheaper, costing an estimated $7.73 million compared to $9.57 million for Option 1.
The document discusses Karnaugh maps (K-maps), which are a tool for representing and simplifying Boolean functions with up to six variables. K-maps arrange the variables in a grid with cells representing minterms or maxterms. Adjacent cells that are both 1s can be combined to eliminate variables. The document provides examples of constructing K-maps from Boolean expressions and using them to find minimum sum of products (SOP) and product of sums (POS) expressions.
VTU CBCS E&C 5th sem Information theory and coding(15EC54) Module -3 notesJayanth Dwijesh H P
This document contains formulas and definitions related to information theory and coding. It defines key terms such as entropy, joint entropy, equivocation, mutual information, and channel capacity. Formulas are provided for calculating the entropy of input and output symbols, joint entropy, equivocation, mutual information, channel capacity, source efficiency, channel efficiency, and channel redundancy. The document serves as a reference for these important information theory concepts and the mathematical formulas used to quantify them.
VTU E&C,TCE CBCS[NEW]5th Sem Information Theory and Coding Module-3 notes(15&...Jayanth Dwijesh H P
INFORMATION THEORY AND CODING
B.E., V Semester, Electronics & Communication
Engineering / Telecommunication Engineering
[As per Choice Based Credit System (CBCS) scheme]
Subject Code:15EC54 and 17EC54
Module-3
Information Channels: Communication Channels ( Section 4.4 of Text 1). Channel Models, Channel Matrix, Joint probabilty Matrix, Binary Symmetric Channel, System Entropies, Mutual Information, Channel Capacity, Channel Capacity of : Binary Symmetric Channel, Binary Erasure Channel, Muroga,s Theorem, Contineuos Channels (Sections 4.2, 4.3, 4.4, 4.6, 4.7 of Text 3).
Question paper pattern:
The question paper will have ten questions
Each full question consists of 16marks.
There will be 2 full questions (with a maximum of four sub questions) from each module.
Each full question will have sub questions covering all the topics under a Module.
The students will have to answer 5 full questions, selecting one full question From each module.
Text Book:
1. Information Theory and Coding, Muralidhar Kulkarni , K.S. Shivaprakasha, Wiley India Pvt. Ltd, 2015, ISBN:978-81-265-5305-1
2. Digital and analog communication systems, K. Sam Shanmugam, John Wiley India Pvt. Ltd, 1996.
3. Digital communication, Simon Haykin, John Wiley India Pvt. Ltd, 2008.
Reference Books:
1. ITC and Cryptography, Ranjan Bose, TMH, II edition, 2007
2. Principles of digital communication, J. Das, S. K. Mullick, P. K. Chatterjee, Wiley, 1986 - Technology & Engineering
3. Digital Communications – Fundamentals and Applications, Bernard Sklar, Second Edition, Pearson Education, 2016, ISBN: 9780134724058.
4. Information Theory and Coding, K.N.Hari bhat, D.Ganesh Rao, Cengage Learning, 2017.
VTC-location based channel estimation for massive full-dimensional MIMO systemsQian Han
This document proposes a two-dimensional location-based channel estimation method for massive full-dimensional MIMO systems. It uses an intra-cell pilot reuse scheme where users with the same pilots can be distinguished by their unique azimuth and elevation angles of arrival. The method applies a 2D FFT to the pilot-aided channel estimates followed by a 2D window function in the angle domain to isolate the different users. Simulation results show the proposed method outperforms conventional pilot-aided estimation and that 3D MIMO can improve sum capacity and serve more users compared to traditional MIMO systems.
L8 Tolls (Transportation and Logistics & Dr. Anna Nagurney)Hossam Shafiq I
This document discusses tolls and toll collection policies for transportation networks. It begins by introducing the concepts of system-optimal (S-O) and user-optimized (U-O) flow patterns on a network. It then explains that imposing tolls can modify travel costs as perceived by users to make the S-O flow pattern the same as the U-O pattern. There are two main types of toll policies discussed: link toll policies that charge per link, and path toll policies that charge per path. The document provides the mathematical formulations and conditions needed to determine tolls that make the S-O and U-O patterns identical. It also briefly discusses electronic toll collection and the use of toll roads worldwide.
The document discusses a logarithmic equation for calculating parking rates based on an hourly rate and constant. It also mentions the linear equation y=mx+c. The document thanks the reader.
This document contains solutions to two questions about image processing techniques. Question 1 involves implementing Sobel edge detection using 4 convolution masks and calculating the approximate gradient magnitude. Question 2 involves implementing a corner detector by building a neighborhood matrix around each pixel, computing its singular values via SVD, and highlighting locations where the second singular value exceeds a threshold after removing neighboring duplicates.
L9 Modeling Extensions (Transportation and Logistics & Dr. Anna Nagurney)Hossam Shafiq I
1) The document discusses modeling extensions for transportation models, including extended models to handle two-way traffic and intersections, and multimodal models to handle networks with multiple transportation modes.
2) It also discusses modeling elastic demand, where transportation demand between origins and destinations can change based on levels of congestion. The elastic demand model introduces demand functions and formulations to capture this effect.
3) Solutions to the elastic demand problem include transforming it into a fixed demand problem that can be solved using existing algorithms, or representing it as a network problem using excess demand formulations.
Parameterized convolutional neural networks for aspect level classificationJunya Kamura
This document describes the architecture of a convolutional neural network (CNN) model with three components: a generator network CNNg, a discriminator network CNNt, and a sentence encoder network CNNs. CNNg produces an initial representation θg which is input to CNNt to generate θt. θt and the input text representations v are then input to CNNs to generate the final encoded representation θs. The networks involve convolutions using weight matrices w and biases b applied to windows of the input with pooling to generate the respective outputs θg, θt, and θs.
1. The document discusses simplification of Sum of Products (SoP) expressions using Karnaugh maps. It provides two examples of SoP expressions and their simplification using K-maps.
2. The first example simplifies the expression A'B'C'+A'B'C+ABC'+AB'C' to AB+AC using a 4-variable K-map.
3. The second example simplifies the expression B'C'+AB'+ABC'+AB'CD'+A'B'C'D+AB'CD to AB'C'D+AB'C'D'+A'B'C'D+A'B'C'D'+AB'
This document discusses solving transportation problems to maximize profit rather than minimize cost. It provides 3 methods for solving these types of problems: 1) Convert it to a minimization problem by multiplying the profit matrix by -1. 2) Subtract all profits from the highest profit. 3) Solve it directly as a maximization problem by allocating to highest profit cells and checking for non-positive cell evaluations. It then works through an example problem using these steps to find the optimal solution maximizing total profit.
This document discusses optimal transport and how it can be used to model complex systems. Optimal transport finds the most efficient way to transport mass from an initial distribution to a final distribution given a cost matrix, and can be formulated as an energy minimization problem with constraints. The solution is found using the Sinkhorn-Knopp algorithm, which scales rows and columns iteratively. Optimal transport has applications in distribution matching, interpolation, domain adaptation, and modeling developmental landscapes. It provides a simple framework for comparing distributions with constraints and competition between parts.
This math problem involves dividing numbers. Part (a) divides 84 by 9 to get C3 = 9. Part (b) divides 504 by 3 and then by 9 to get A9 = 18. Part (c) cubes the number 93 to get A3 = 729.
This MATLAB code analyzes a coin image to identify and count coins. It loads and preprocesses the image, finds coin boundaries, labels separate coins, measures the area of each coin region, and annotates the image with the value (5 cents or 10 cents) and running total based on coin area.
The document discusses a math problem about modeling traffic congestion using a function. It provides the function f(t) and asks students to analyze properties of the traffic jam like its length at different times, when it is longest, and how quickly it increases and decreases. It also includes artwork created by students depicting traffic jams and the solutions to the math questions about the modeled congestion based on the given function.
L1 Transportation Planning Process (Transportation and Logistics & Dr. Anna N...Hossam Shafiq I
This document summarizes the transportation planning process presented in a lecture by Dr. Anna Nagurney. The planning process involves 4 phases: 1) collecting base year inventory data on existing networks and traffic, 2) building mathematical models to understand relationships between planning factors, 3) using the models to forecast future traffic, and 4) evaluating potential network designs based on traffic predictions and cost-benefit analyses. The core three-stage model involves trip generation, trip distribution, and traffic assignment models to predict traffic flows.
L10 The Extended Model (Transportation and Logistics & Dr. Anna Nagurney)Hossam Shafiq I
This document summarizes a lecture on the extended model in transportation networks. The extended model generalizes the standard model by allowing link costs to depend on the entire vector of link flows, not just the individual link flow. The total link cost functions must have a symmetric Jacobian matrix for an optimization formulation to exist. If the Jacobian is also positive definite, there is a unique flow pattern. The lecture provides examples of how to represent costs in an extended model and conditions for equilibrium.
The document discusses the concepts of average and instantaneous rate of change. It defines average velocity as the change in distance over the change in time. Instantaneous rate of change is defined as the limit as the change in time approaches 0 of the change in the function over the change in time. This gives the slope of the tangent line. It provides an example of finding the derivative of f(x)=x^2 by taking the limit as h approaches 0 of (f(x+h)-f(x))/h.
This document contains questions for an exam on applied mathematics for engineers. It has three parts:
Part A contains 10 short answer questions worth 2 marks each. Questions cover topics like defining terms related to chains and generalized eigenvalues.
Part B has two longer answer questions worth 13 marks each. One question involves finding the number of generalized eigenvectors for a given matrix.
Part C contains two questions worth 15 marks each. One asks to show a given functional is stationary for given functions, the other asks to derive moments and the moment generating function of the Poisson distribution.
The document discusses options for locating a new stadium in the Research Triangle region of North Carolina, which includes the cities of Raleigh, Durham, and Chapel Hill.
Option 1 would place the stadium the same distance from each city, requiring new highways to be built from each city to the stadium. Option 2 would place the stadium equidistant from the existing highways, requiring resurfacing of the existing highways and building new connector highways.
Using a digital geometry environment (DGE) to analyze the options, the responder determined that Option 2 would be cheaper, costing an estimated $7.73 million compared to $9.57 million for Option 1.
The document discusses Karnaugh maps (K-maps), which are a tool for representing and simplifying Boolean functions with up to six variables. K-maps arrange the variables in a grid with cells representing minterms or maxterms. Adjacent cells that are both 1s can be combined to eliminate variables. The document provides examples of constructing K-maps from Boolean expressions and using them to find minimum sum of products (SOP) and product of sums (POS) expressions.
VTU CBCS E&C 5th sem Information theory and coding(15EC54) Module -3 notesJayanth Dwijesh H P
This document contains formulas and definitions related to information theory and coding. It defines key terms such as entropy, joint entropy, equivocation, mutual information, and channel capacity. Formulas are provided for calculating the entropy of input and output symbols, joint entropy, equivocation, mutual information, channel capacity, source efficiency, channel efficiency, and channel redundancy. The document serves as a reference for these important information theory concepts and the mathematical formulas used to quantify them.
VTU E&C,TCE CBCS[NEW]5th Sem Information Theory and Coding Module-3 notes(15&...Jayanth Dwijesh H P
INFORMATION THEORY AND CODING
B.E., V Semester, Electronics & Communication
Engineering / Telecommunication Engineering
[As per Choice Based Credit System (CBCS) scheme]
Subject Code:15EC54 and 17EC54
Module-3
Information Channels: Communication Channels ( Section 4.4 of Text 1). Channel Models, Channel Matrix, Joint probabilty Matrix, Binary Symmetric Channel, System Entropies, Mutual Information, Channel Capacity, Channel Capacity of : Binary Symmetric Channel, Binary Erasure Channel, Muroga,s Theorem, Contineuos Channels (Sections 4.2, 4.3, 4.4, 4.6, 4.7 of Text 3).
Question paper pattern:
The question paper will have ten questions
Each full question consists of 16marks.
There will be 2 full questions (with a maximum of four sub questions) from each module.
Each full question will have sub questions covering all the topics under a Module.
The students will have to answer 5 full questions, selecting one full question From each module.
Text Book:
1. Information Theory and Coding, Muralidhar Kulkarni , K.S. Shivaprakasha, Wiley India Pvt. Ltd, 2015, ISBN:978-81-265-5305-1
2. Digital and analog communication systems, K. Sam Shanmugam, John Wiley India Pvt. Ltd, 1996.
3. Digital communication, Simon Haykin, John Wiley India Pvt. Ltd, 2008.
Reference Books:
1. ITC and Cryptography, Ranjan Bose, TMH, II edition, 2007
2. Principles of digital communication, J. Das, S. K. Mullick, P. K. Chatterjee, Wiley, 1986 - Technology & Engineering
3. Digital Communications – Fundamentals and Applications, Bernard Sklar, Second Edition, Pearson Education, 2016, ISBN: 9780134724058.
4. Information Theory and Coding, K.N.Hari bhat, D.Ganesh Rao, Cengage Learning, 2017.
VTC-location based channel estimation for massive full-dimensional MIMO systemsQian Han
This document proposes a two-dimensional location-based channel estimation method for massive full-dimensional MIMO systems. It uses an intra-cell pilot reuse scheme where users with the same pilots can be distinguished by their unique azimuth and elevation angles of arrival. The method applies a 2D FFT to the pilot-aided channel estimates followed by a 2D window function in the angle domain to isolate the different users. Simulation results show the proposed method outperforms conventional pilot-aided estimation and that 3D MIMO can improve sum capacity and serve more users compared to traditional MIMO systems.
Multiuser MIMO Gaussian Channels: Capacity Region and DualityShristi Pradhan
In this paper, I present the MIMO channel for single user case, discuss the decomposition of MIMO into parallel independent channels, and estimate the MIMO channel capacity. Then, I discuss on computation of capacity region for multiuser MIMO broadcast and multiple access channel and plot capacity regions for two users case. I conclude by showing the duality relationship between the multiple access and broadcast channel and show its significance for numerical standpoint.
Presentation at IEEE WCNC 2018 on simple asymptotic bounds on channel estimation and prediction. This work is the presentation of the following paper: https://sfx.aub.aau.dk/sfxaub?sid=pureportal&doi=10.1109/WCNC.2018.8377005.
This document introduces channel models and channel capacity. It defines a binary symmetric channel (BSC) as a channel with input and output sets of {0,1} and a crossover probability p that an input bit is flipped. A discrete memoryless channel is characterized by a conditional probability matrix relating discrete inputs to outputs. Channel types include single-input single-output, single-input multiple-output, multiple-input single-output, and multiple-input multiple-output. Channel capacity is the maximum mutual information between input and output, achieved by optimizing the input distribution. Capacity examples include relay channels and multiple access channels. The BSC capacity is 1-H(p) where H(p) is the entropy function
This document discusses discrete memoryless channels and their capacity. It defines a discrete memoryless channel as having inputs from a finite alphabet that are transmitted independently through the channel. The output depends only on the current input and not past inputs. It presents the probability model and transition matrix for modeling a discrete memoryless channel. It also describes binary channels specifically and binary symmetric channels where the error probability between any two inputs is the same. The goal is to determine the maximum error-free transmission rate, or capacity, of a discrete memoryless channel.
This three day course is intended for practicing systems engineers who want to learn how to apply model-driven systems Successful systems engineering requires a broad understanding of the important principles of modern spacecraft communications. This three-day course covers both theory and practice, with emphasis on the important system engineering principles, tradeoffs, and rules of thumb. The latest technologies are covered. <p>
ECCV2008: MAP Estimation Algorithms in Computer Vision - Part 2zukun
The document discusses image segmentation using minimum cut (st-mincut) algorithms. It describes how to formulate image segmentation as an energy minimization problem and construct a graph such that the minimum cut of the graph corresponds to the minimum of the energy function. Maximum flow algorithms, such as Ford-Fulkerson and Dinic's algorithm, can then be used to find the minimum cut and optimal segmentation. Reparameterization of the energy function does not change the minimum cut.
This document provides information on designing satellite communication links. It discusses key factors that influence system design such as frequency band, propagation effects, and multiple access techniques. The performance objectives and parameters of earth stations and satellites are outlined. Methods for calculating noise temperature, link budgets, and overall C/N ratio are presented. The document provides examples of designing links using different satellite systems and frequency bands.
EE402B Radio Systems and Personal Communication Networks-Formula sheetHaris Hassan
Programmes in which available:
Masters of Engineering - Electrical and Electronic
Engineering. Masters of Engineering - Electronic
Engineering and Computer Science. Master of Science -
Communication Systems and Wireless Networking.
Master of Science - Smart Telecom and Sensing
Networks. Master of Science - Photonic Integrated
Circuits, Sensors and Networks
To enable an extension of knowledge in fundamental data communications to radio communications and networks widely adopted
in modern telecommunications systems. To provide understanding of radio wave utilisation, channel loss properties, mobile
communication technologies and network protocol architecture applied to practical wireless systems
This document introduces information theory and channel capacity models. It discusses several channel models including the binary symmetric channel (BSC), binary erasure channel, and additive white Gaussian noise channel. It explains how channel capacity is defined as the maximum rate of error-free transmission and derives the capacity for some basic channels. The document also covers channel coding techniques like interleaving that can improve performance by converting burst errors into random errors.
Machine Learning and Stochastic Geometry: Statistical Frameworks Against Unce...Koji Yamamoto
The document summarizes a tutorial on using machine learning techniques like deep reinforcement learning and stochastic geometry for wireless local area networks (WLANs). It discusses:
1) Key aspects of IEEE 802.11ax and the next-generation 802.11be standard for WLANs.
2) How deep reinforcement learning can be used for channel allocation in dense WLANs to address issues like throughput starvation.
3) How stochastic geometry can be used to model and analyze WLAN topology and performance without simulations.
The tutorial covers using these machine learning techniques for performance optimization and modeling of WLANs under uncertainty.
The document discusses network flows and algorithms for finding maximum flows in networks. It begins by defining a flow network as a directed graph with a source, sink, and edge capacities. The maximum flow problem is to find the maximum amount of flow that can be sent from the source to the sink respecting capacity constraints. The Ford-Fulkerson algorithm uses augmenting paths to iteratively increase the flow value. It runs in O(mC) time where m is edges and C is total capacity. The maximum flow value equals the minimum cut capacity, proven using residual graphs. Later sections discuss improvements like capacity scaling and preflow-push algorithms. Bipartite matching is also shown to reduce to a maximum flow problem.
- The document discusses the capacity of MIMO Gaussian channels with amplitude-limited inputs. It derives upper and lower bounds on the channel capacity.
- It summarizes prior work on the capacity of scalar and multiple access channels with amplitude constraints. However, extending those results to MIMO systems is not possible due to differences in dimensionality.
- Upper bounds are derived by optimizing over the smallest rectangle enclosing the feasible input region. Lower bounds are derived using a smaller inscribed rectangle.
- Asymptotic bounds are also derived for low and high noise scenarios by approximating the optimal input distribution.
- The gap between upper and lower bounds decreases with lower noise variance and higher dimensionality.
Gate 2013 complete solutions of ec electronics and communication engineeringmanish katara
The document is a sample paper for GATE 2013 that contains 25 multiple choice questions related to engineering topics like logic gates, vector fields, impulse response of systems, diodes, IC technology, and more. Each question is followed by a brief explanation of the answer. The questions cover a range of fundamental concepts in areas like signals and systems, electronics, semiconductor devices, and mathematics.
The document is a sample paper for GATE 2013 that contains 25 multiple choice questions related to engineering topics like logic gates, vector fields, impulse response of systems, diodes, IC technology, and more. Each question is followed by a brief explanation of the answer. The questions cover a range of fundamental concepts in areas like signals and systems, electronics, semiconductor devices, and mathematics.
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1. Mul$user
MIMO
Gaussian
Channels:
Capacity
Regions
and
Duality
Shris$
Pradhan
University
of
Bri.sh
Columbia
December
2011
2. Mul$ple
Input
Mul$ple
Output
(MIMO)
y = Hx + n
MIMO
Model
Transmit
Precoding
and
Receiver
Shaping
Singular
Value
Decomposi$on
(SVD):
H =UΣV H
y = Σx + n
Mul$plexing
gain
=
Rank
of
H
=
RH
Power
Noise
1 2 3
Subchannel
Water
Level
MIMO
Channel
Capacity
C = max
p(x)
I(X;Y) = max
p(x)
[H(Y)− H(Y | X)]
C = max
Pi:ΣiPi≤P
log2 1+
Piγi
P
#
$
%
&
'
(
i
∑ γ = SNR
Pi
P
=
1
γ0
−
1
γi
0
γi ≥γo
γi <γ0
#
$
%%
&
%
%
C = log
γi
γ0
!
"
#
$
%
&
i:γi≥γ0
∑
3. Mul$user
MIMO
MAC
and
BC
Broadcast
(Downlink)
Channel
Capacity
0 1 2 3 4 5 6 7 8 9
0
1
2
3
4
5
6
7
8
9
R1 [bps/Hz]
R2[bps/Hz]
BC
Mul$ple
Access
(Uplink)
Channel
Capacity
0 1 2 3 4 5 6 7 8
0
1
2
3
4
5
6
7
8
R1 (bps)
R2(bps)
Capacity Region of MAC
Duality
of
MAC
and
BC
CBC (P;g1,g2 ) = CMAC (P1,P − P1;g1,g2 )
0≤P1≤P
CMAC (P1,P2;g1,g2 ) = CBC
P1
α
+ P2;αg1,g2
!
"
#
$
%
&
α>0
R1 = log I + H1
H
Q1H1
R2 = log I + H1
H
Q1H1 + H2
H
Q2H2 − R1
CDPC (P, H) = Co R(π,Σi )
π,Σi
"
#
$$
%
&
''