Adaptive vector quantization in SVD MIMO system backward link with limited number of active sub channels

This paper presents combination of Channel Optimized Vector Quantization based on LBG algorithm and sub channel power allocation for MIMO systems with Singular Value Decomposition and limited number of active sub channels. Proposed algorithm is designed to enable maximal throughput with bit error rate bellow some tar- get level in case of backward channel capacity limitation. Presence of errors effect in backward channel is also considered.

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A New Compression Scheme Based on Adaptive Vector Quantization and Singular Value Decomposition

International Review on Computers and Software (IRECOS) ◽

10.15866/irecos.v11i5.9236 ◽

2016 ◽

Vol 11 (5) ◽

pp. 445 ◽

Cited By ~ 3

Author(s):

Imene Soussi ◽

Mohamed Ouslim

Keyword(s):

Singular Value Decomposition ◽

Vector Quantization ◽

Singular Value ◽

Compression Scheme ◽

Adaptive Vector Quantization ◽

Value Decomposition

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Improved Iterative Coordinated Beamforming Based on Singular Value Decomposition for Multiuser Mimo Systems With Limited Feedforward

Journal of Applied Research and Technology ◽

10.22201/icat.16656423.2011.9.03.429 ◽

2011 ◽

Vol 9 (03) ◽

Author(s):

L. Soriano-Equigua ◽

J. Sánchez-García ◽

C.B. Chae ◽

R. W. Heath Jr

Keyword(s):

Singular Value Decomposition ◽

Bit Error Rate ◽

Error Rate ◽

Base Station ◽

Wireless Channel ◽

Singular Value ◽

Coordinated Beamforming ◽

Original Algorithm ◽

Value Decomposition ◽

Number Of Iterations

Coordinated beamforming based on singular value decomposition is an iterative method to jointly optimize the transmit beamformers and receive combiners, to achieve high levels of sum rates in the downlink of multiuser systems, by exploiting the multi-dimensional wireless channel created by multiple transmit and receive antennas. The optimization is done at the base station and the quantized beamformers are sent to the users through a low rate link.In this work, we propose to optimize this algorithm by reducing the number of iterations and improving its uncoded bit error rate performance. Simulation results show that our proposal achieves a better bit error rate with a lower number of iterations than the original algorithm.

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Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture

Electronics ◽

10.3390/electronics10010034 ◽

2020 ◽

Vol 10 (1) ◽

pp. 34

Author(s):

Michele Alessandrini ◽

Giorgio Biagetti ◽

Paolo Crippa ◽

Laura Falaschetti ◽

Lorenzo Manoni ◽

...

Keyword(s):

Embedded Systems ◽

Singular Value Decomposition ◽

Embedded System ◽

Mimo Systems ◽

Singular Value ◽

Measurement Unit ◽

Comprehensive Treatment ◽

Mathematical Tool ◽

Value Decomposition ◽

Speed Accuracy

Singular value decomposition (SVD) is a central mathematical tool for several emerging applications in embedded systems, such as multiple-input multiple-output (MIMO) systems, data analytics, sparse representation of signals. Since SVD algorithms reduce to solve an eigenvalue problem, that is computationally expensive, both specific hardware solutions and parallel implementations have been proposed to overcome this bottleneck. However, as those solutions require additional hardware resources that are not in general available in embedded systems, optimized algorithms are demanded in this context. The aim of this paper is to present an efficient implementation of the SVD algorithm on ARM Cortex-M. To this end, we proceed to (i) present a comprehensive treatment of the most common algorithms for SVD, providing a fairly complete and deep overview of these algorithms, with a common notation, (ii) implement them on an ARM Cortex-M4F microcontroller, in order to develop a library suitable for embedded systems without an operating system, (iii) find, through a comparative study of the proposed SVD algorithms, the best implementation suitable for a low-resource bare-metal embedded system, (iv) show a practical application to Kalman filtering of an inertial measurement unit (IMU), as an example of how SVD can improve the accuracy of existing algorithms and of its usefulness on a such low-resources system. All these contributions can be used as guidelines for embedded system designers. Regarding the second point, the chosen algorithms have been implemented on ARM Cortex-M4F microcontrollers with very limited hardware resources with respect to more advanced CPUs. Several experiments have been conducted to select which algorithms guarantee the best performance in terms of speed, accuracy and energy consumption.

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Fast-Convergence Singular Value Decomposition for Tracking Time-Varying Channels in Massive Mimo Systems

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2018.8462248 ◽

2018 ◽

Author(s):

Pei-Yun Tsai ◽

Yi Chang ◽

Jian-Lin Li

Keyword(s):

Singular Value Decomposition ◽

Massive Mimo ◽

Mimo Systems ◽

Singular Value ◽

Fast Convergence ◽

Time Varying ◽

Tracking Time ◽

Value Decomposition ◽

Time Varying Channels

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Bit Error Rate (BER) Performance of MIMO Systems in M-QAM with Nonlinear Effect

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b2642.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 265-267

Keyword(s):

Bit Error Rate ◽

Error Rate ◽

Mimo Systems ◽

Mimo System ◽

High Amplitude ◽

Transmitter Diversity ◽

Receiver Diversity ◽

Alamouti Scheme ◽

Amplifier Model ◽

Modulation Techniques

Out object in this paper it to study, the effect of nonlinearity on the bit error rate (BER) of MIMO systems in M-QAM modulation techniques. We consider Saleh’s model (power amplifier model) for the nonlinearity, and apply the nonlinear model on MIMO system with receiver diversity and transmitter diversity. For transmitter diversity, the Space-Time Block Coding (STBC) based on Alamouti scheme is used to provide transmits diversity for two transmitting antennas. The results show that, if there is a high variation in the amplitude of the M- QAM symbols, there will behigh effect of nonlinearity that causes high BER especially for high amplitude symbols at high SNR.

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Performance Enhancement in SU and MU MIMO-OFDM Technique for Wireless Communication: A Review

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v7i5.pp2459-2467 ◽

2017 ◽

Vol 7 (5) ◽

pp. 2459

Author(s):

S.N. Raut ◽

R.M. Jalnekar

Keyword(s):

Bit Error Rate ◽

Error Rate ◽

Performance Enhancement ◽

Mimo Systems ◽

Mimo System ◽

Wireless Systems ◽

Multipath Fading ◽

Data Rate ◽

Spatial Multiplexing ◽

Rician Fading

<p class="Default">The consistent demand for higher data rates and need to send giant volumes of data while not compromising the quality of communication has led the development of a new generations of wireless systems. But range and data rate limitations are there in wireless devices. In an attempt to beat these limitations, Multi Input Multi Output (MIMO) systems will be used which also increase diversity and improve the bit error rate (BER) performance of wireless systems. They additionally increase the channel capacity, increase the transmitted data rate through spatial multiplexing, and/or reduce interference from other users. MIMO systems therefore create a promising communication system because of their high transmission rates without additional bandwidth or transmit power and robustness against multipath fading. This paper provides the overview of Multiuser MIMO system. A detailed review on how to increase performance of system and reduce the bit error rate (BER) in different fading environment e.g. Rayleigh fading, Rician fading, Nakagami fading, composite fading.</p>

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Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622021500310 ◽

2021 ◽

pp. 1-19

Author(s):

Taushif Anwar ◽

V. Uma ◽

Gautam Srivastava

Keyword(s):

Singular Value Decomposition ◽

Collaborative Filtering ◽

Error Rate ◽

Recommendation System ◽

Recommendation Systems ◽

Matrix Decomposition ◽

Cold Start ◽

Singular Value ◽

Decomposition Approach ◽

Value Decomposition

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD)[Formula: see text] for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system’s performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD[Formula: see text], Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100[Formula: see text]K, MovieLens 1[Formula: see text]M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation ([Formula: see text]) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100[Formula: see text]K dataset ([Formula: see text], [Formula: see text]). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items.

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