Blind PNLMS Adaptive Algorithm for SIMO FIR Channel Estimation
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Proposed a new MCPNLMS algorithm which showed significant performance improvement in case of random channels
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This document summarizes a B.Tech project thesis defense on distributed learning and adaptation in cognitive radio. The project aimed to develop a decentralized learning system to map secondary users to idle primary network channels without collision. The methodology used a novel Distributed Learning and Adaptive algorithm (DALA) policy based on stochastic learning and an Upper Confidence Bound algorithm. Simulation results showed the DALA policy converged channel selection probabilities and secondary users were assigned different channels. Mini-batch stochastic learning improved performance over DALA by reducing regret. Thresholding channel probabilities provided faster convergence. Future work may include variable step sizes, dynamic channel availability, and new ranking schemes.
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...
Projected Barzilai-Borwein Methods Applied to Distributed Compressive Spectru...Polytechnique Montreal
Cognitive radio allows unlicensed (cognitive) users to use licensed frequency bands by exploiting spectrum sensing techniques to detect whether or not the licensed (primary) users are present. In this paper, we present a compressed sensing applied to spectrum-occupancy detection in wide-band applications. The collected analog signals from each cognitive radio (CR) receiver at a fusion center are transformed to discrete-time signals by using analog-to-information converter (AIC) and then employed to calculate the autocorrelation. For signal reconstruction, we exploit a novel approach to solve the optimization problem consisting of minimizing both a quadratic (l2) error term and an l1-regularization term. In specific, we propose the Basic gradient projection (GP) and projected Barzilai-Borwein (PBB) algorithm to offer a better performance in terms of the mean squared error of the power spectrum density estimate and the detection probability of licensed signal occupancy.What's hot (20)
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Similar to Blind PNLMS Adaptive Algorithm for SIMO FIR Channel Estimation
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These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
Performance Analysis of MIMO–OFDM for PCHBF , RELAY Technique with MMSE For T...
Performance Analysis of MIMO–OFDM for PCHBF , RELAY Technique with MMSE For T...Sri Manakula Vinayagar Engineering College
MIMO – OFDM
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Partially Connected Hybrid Beamforming TechniqueResearch paper (channel_estimation)
The document proposes a new channel estimation algorithm called ICEA-DA for MIMO-OFDM systems. ICEA-DA uses least squares estimation with discrete Fourier transform and integrates it with a differential evolution technique. It is placed at the central part of the MIMO-OFDM system between the transmitter and receiver blocks. The proposed method is evaluated for a 2x4 MIMO system using QPSK modulation. Results show ICEA-DA provides better performance than existing techniques in terms of bit error rate, peak signal-to-noise ratio, channel capacity, and symbol error rate.
Hybrid Adaptive Channel Estimation Technique in Time and Frequency Domain for...
This document summarizes a research paper that proposes a hybrid adaptive channel estimation technique for MIMO-OFDM wireless communication systems. The technique integrates channel estimation in the time domain and frequency domain. It uses column-based time domain estimation initially and switches to row-based frequency domain estimation when channel quality is good and bit error rate is low. It switches back to time domain estimation when channel quality worsens. The goal is to adaptively estimate the channel under different conditions. Experimental results showed the proposed hybrid technique outperformed other methods.
IRJET- Compressed Sensing based Modified Orthogonal Matching Pursuit in DTTV ...
This document discusses a modified orthogonal matching pursuit algorithm used for channel estimation in digital terrestrial television systems. It proposes using compressed sensing based channel estimation at the receiver to eliminate sparse information. Thresholding is used to remove noise from the channel estimation and improve signal quality. Simulation results show that bit error rate decreases when the received signal power from different transmitters is almost equal.
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...
A novel and efficient mixed-signal compressed sensing for wide-band cognitive...Polytechnique Montreal
In cognitive radio (CR) networks, unlicensed (cognitive) users can exploit the licensed frequency bands by using spectrum sensing techniques to identify spectrum holes. This paper proposes a distributed compressive spectrum sensing scheme, in which the modulated wide-band converter can apply compressed sensing (CS) directly to analog signals at the sub-Nyquist rate and the central fusion receives signals from multiple CRs and exploits the multiple-measurements-vectors (MMV) subspace pursuit (M-SP) algorithm to jointly reconstruct the spectral support of the wide-band signal. This support is then used to detect whether the licensed bands are occupy or not. Finally, extensive simulation results show the advantages of the proposed scheme. Besides, we also compare the performance of M-SP with M-orthogonal matching pursuit (M-OMP) algorithms.MIMO Channel Estimation Using the LS and MMSE Algorithm
Wireless Communication Technology has developed over the past few yearsfor other objectives.The Multiple InputMultiple Output (MIMO) is one of techniques that is used to enhancethe data rates, in which multiple antennas are employed both the transmitter and receiver. Multiple signals are transmitted from different antennas at the transmitter using the same frequency and separated space. Various channel estimation techniques are employed in order to judge the physical effects of the medium present. In this paper, we analyze and implementvarious estimation techniques for MIMO Systems such as Least Squares (LS), Minimum Mean Square Error (MMSE),these techniques are therefore compared to effectively estimate the channel in MIMO System. The results demonstrate that SNR required to support different values of bit error rate varies depending on different low correlation between the transmitting and the receiving antennas .In addition, it is illustrated that when the number of transmitter and receiver antennas increases, the performance of TBCE schemes significantly improves. The Same behavior isalso observed for MIMO system. Performance of both MMSE and LSestimation are the same for allkinds of modulation at small value of SNR but the more we increase the SNR value the more performance gap goes on increasing.
F43063841
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
International Journal of Engineering and Science Invention (IJESI)
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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Intersymbol interference caused by multipath in band limited frequency selective time dispersive channels distorts the transmitted signal, causing bit error at receiver. ISI is the major obstacle to high speed data transmission over wireless channels. Channel estimation is a technique used to combat the intersymbol interference. The objective of this paper is to improve channel estimation accuracy in MIMO-OFDM system by using modified variable step size leaky Least Mean Square (MVSSLLMS) algorithm proposed for MIMO OFDM System. So we are going to analyze Bit Error Rate for different signal to noise ratio, also compare the proposed scheme with standard LMS channel estimation method.
Enhancement of New Channel Equalizer Using Adaptive Neuro Fuzzy Inference System
The document proposes a new channel equalizer based on adaptive neuro-fuzzy inference system (ANFIS) to reduce intersymbol interference over communication channels. Simulation results show the proposed equalizer has better bit error rate performance than multilayer perceptron and least mean square equalizers, and comparable performance to radial basis function network and optimal Bayesian equalizers, but with lower structural complexity than RBFN. The ANFIS equalizer combines the learning ability of neural networks and the reasoning of fuzzy systems to adaptively equalize linear and nonlinear channels.
Enhancement of New Channel Equalizer Using Adaptive Neuro Fuzzy Inference System
The rapidly increasing need for information communication requires higher speed data transmission over the existing channels. The data rate over these channels is limited mainly by Inter Symbol Interference (ISI). Channel equalizers are used to reduce the effect of ISI. In this paper, a new equalizer based on Adaptive Neuro-Fuzzy Inference System is presented. The performance of the proposed equalizer is evaluated for both linear as well as non-linear channels in terms of bit-error rate for different noise powers. Simulation results show that the proposed equalizer has better Bit Error Rate (BER) performance compared to multi-layer perceptron and least mean square equalizers. However, its BER performance is slightly poorer than that of radial basis function network and optimal Bayesian equalizer but is better in terms of structural complexity.
Unit iv wcn main
The document summarizes key concepts in equalization and diversity techniques used in mobile communication systems. It discusses linear equalizers like transversal filters and lattice filters. Nonlinear equalizers covered include decision feedback equalization (DFE) and maximum likelihood sequence estimation (MLSE). DFE uses a feedforward filter and feedback filter to cancel intersymbol interference. MLSE estimates sequences using a trellis channel model and the Viterbi algorithm. Diversity techniques like spatial, frequency and time diversity are also introduced to mitigate fading effects.
Application of compressed sampling
The document discusses applying compressed sampling (CS) techniques for spectrum sensing and channel estimation in cognitive radio (CR) networks. It first provides background on CS theory, noting that signals can be reconstructed from fewer samples than required by Nyquist's theorem if the signal is sparse. It then proposes a compressed spectrum sensing scheme to detect wideband spectrum using sub-Nyquist sampling. After sensing, it formalizes the notion of sparse multipath channels and discusses estimating such channels using orthogonal matching pursuit. The effectiveness of these CS-based approaches is demonstrated through comparisons with conventional sensing and estimation methods.
Eqalization and diversity
This document provides an overview of equalization and diversity techniques used in mobile communication systems. It discusses how equalization is used to compensate for intersymbol interference caused by multipath fading. Linear equalizers aim to minimize mean square error, while nonlinear equalizers also use decision feedback. Common equalizer structures include transversal filters adapted using algorithms like LMS or RLS. Diversity techniques like spatial or frequency diversity compensate for fading by combining signals from multiple antennas or frequency bands. Channel coding adds redundancy to improve link performance in the presence of errors.
Ieee 2013 matlab abstracts part b
This document provides contact information for VENSOFT Technologies and describes 25 MATLAB projects for the 2013-2014 academic year related to signal processing topics such as phase noise estimation in MIMO systems, distributed averaging algorithms, channel estimation, computation of the moment generating function for lognormal distributions, compressed sensing of EEG data, and compressed sensing for wireless monitoring of fetal ECG signals. The contact for projects is provided as VENSOFT Technologies, their website, and a phone number.
L010628894
This document discusses performance of matching algorithms for signal approximation. It begins by introducing matching pursuit algorithms like Orthogonal Matching Pursuit (OMP) and Stagewise Orthogonal Matching Pursuit (StOMP) which are greedy algorithms that approximate sparse signals. It then describes the Non-Negative Least Squares algorithm which solves non-negative least squares problems. Finally, it discusses Extranious Equivalent Detection (EED), a modification of OED that incorporates non-negativity of representations by using a non-negative optimization technique instead of orthogonal projection.
Error Rate Analysis of MIMO System Using V Blast Detection Technique in Fadin...
Wireless communication system with multi- antenna arrays has been a field of intensive analysis on the last years. The appliance of multiple sending antennas and Receiving Antennas either side will considerably enhance the data rate and rate. The review of the performance limitations of MIMO system becomes vital since it will provide lot ideas in understanding and planning the important life MIMO systems. Vertical Bell Laboratories layered space Time (V-BLAST). The thought behind Multiple Input and Multiple Output system is that the signals on the transmitter antennas at one finish and also the receiver antennas at the opposite finish are correlative in such how that the performance (Bit Error Rate or BER) or the info rate (bits/sec) of the wireless communication system for every MIMO subscriber are improved. During this paper we tend to are proposing a technique that evaluates the performance of V-BLAST MIMO system in several thought of Rayleigh attenuation surroundings to urge higher performance of the system. In V- BLAST MIMO system a number of linear detection techniques will be used for interference cancellation. At this point we are using MMSE-IC for the same. Our expected system provide higher error rate performance with the used of matched filter at receiver aspect .The projected system compared within the presence of AWGN. Now matched filter applied on V- BLAST MIMO with MMSE-IC system in fading diversity surroundings.
Performance evaluation of high mobility OFDM channel estimation techniques
In wireless communication, Orthogonal Frequency Division Multiplexing (OFDM) has been adopted due to its robustness to multipath fading and high data rate transmissions. At the other hand, the performance of OFDM systems severely degraded due to multi-path fading and Doppler frequency shifts in mobile systems, which causes inter-carrier-interference (ICI). Thus, Estimation of channel parameters is required at the receiver using a pre designed estimator where pilot tones are inserted in each OFDM symbol. In this paper, a random pilot data are generated and inserted in each OFDM symbol at equally spaced locations. The performance test of Least Square (LS) and Linear Minimum Mean Square (LMMSE) estimation methods are proposed with Discrete Fourier Transform (DFT) based on both LS and LMMSE, where different ITU channel models are considered in order to compare their performance for data transmission in high mobile systems with different Doppler frequencies exceeds 200 Hz and minimal number of pilots.
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MIMO Channel Estimation Using the LS and MMSE Algorithm
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International Journal of Engineering and Science Invention (IJESI)
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Enhancement of New Channel Equalizer Using Adaptive Neuro Fuzzy Inference System
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Blind PNLMS Adaptive Algorithm for SIMO FIR Channel Estimation
- 1. GROUP MEMBERS: ISHTIAQUE AL MAHMUD (022469) AHSANUR RAHMAN (022464) JABED HASAN (022473) PROJECT SUPERVISOR: Prof. Md. KAMRUL HASAN EEE, BUET Blind PNLMS Adaptive Algorithm for SIMO FIR Channel Estimation
- 8. Performance curve for FSSLMS Performance Index, Number of channel, M=5 Length of impulse response, L =32 SNR=high µ=1
- 9. VSSLMS Algorithm Variable step size is defined as ,
- 10. Performance curve for VSSLMS Performance Index,
- 11. Comparative Study Number of channel, M=5 Length of impulse response, L =32
- 13. MCNLMS Algorithm Update equation for a single channel, Variable step size is defined as, Where, error vector,
- 14. MCPNLMS Algorithm Where, Update Equation for single channel
- 15. Comparative Study (Random channels) Channel number, M=3 Channel length, L=32 SNR=20db
- 16. Comparative Study (Random channels) Channel number, M=5 Channel length, L=32 SNR=20db
- 17. Comparative Study (Acoustic channels) Channel number, M=5 Channel length, L=32 SNR=20db
- 18. Comparative Study (Acoustic channels) Channel number, M=5 Channel length, L=128 SNR=20db
- 19. Approximation of MCPNLMS Algorithm Update Equation for single channel where Variable step size is defined as,
- 20. Comparative Study (Random channels) Channel number, M=3 Channel length, L=32 SNR=20db
- 21. Comparative Study (Random channels) Channel number, M=3 Channel length, L=64 SNR=20db
- 22. Comparative Study (Random channels) Channel number, M=5 Channel length, L=32 SNR=20db
- 23. Comparative Study (Random channels) Channel number, M=5 Channel length, L=64 SNR=20db
- 24. Comparative Study (Random channels) Channel number, M=8 Channel length, L=64 SNR=20db