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

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

Serbian Journal of Electrical Engineering ◽

10.2298/sjee0403113i ◽

2004 ◽

Vol 1 (3) ◽

pp. 113-123

Author(s):

Predrag Ivanis ◽

Dusan Drajic

Keyword(s):

Singular Value Decomposition ◽

Vector Quantization ◽

Bit Error Rate ◽

Error Rate ◽

Mimo Systems ◽

Mimo System ◽

Singular Value ◽

Lbg Algorithm ◽

Adaptive Vector Quantization ◽

Value Decomposition

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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Theoretical and computational improvements to the algebraic method for discovering cooperative rigid-unit modes

Journal of Applied Crystallography ◽

10.1107/s1600576721009341 ◽

2021 ◽

Vol 54 (6) ◽

Author(s):

Branton J. Campbell ◽

Harold T. Stokes ◽

Tyler B. Averett ◽

Shae Machlus ◽

Christopher J. Yost

Keyword(s):

Singular Value Decomposition ◽

Computational Efficiency ◽

Quantitative Measure ◽

Algebraic Method ◽

Singular Value ◽

Original Algorithm ◽

Variable Block ◽

Rigid Unit Modes ◽

Numerical Robustness ◽

Value Decomposition

A linear-algebraic algorithm for identifying rigid-unit modes in networks of interconnected rigid units has recently been demonstrated. This article presents a series of enhancements to the original algorithm, which greatly improve its conceptual simplicity, numerical robustness, computational efficiency and interpretability. The improvements include the efficient isolation of constraints, the observation of variable-block separability, the use of singular value decomposition and a quantitative measure of solution inexactness.

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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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