A User Selection Algorithm Based on the Combination of Block Diagonalization and SLNR Maximization

2013 ◽  
Vol 347-350 ◽  
pp. 2474-2478
Author(s):  
Wei Hong Fu ◽  
Cheng Wang ◽  
Nai An Liu ◽  
Qing Liang Kong ◽  
Wei Xin Tian
Keyword(s):  
User Selection ◽  
Rate Performance ◽  
Block Diagonalization ◽  
Selection Algorithm ◽  
Signal Noise ◽  
Performance Loss ◽  
Single Antenna ◽  
Sum Rate ◽  
Simulation Results ◽  
Noise Ratio

In this paper, a new precoding scheme is proposed based on the combination of Block Diagonalization (BD) and SLNR (Signal Leakage Noise Ratio) maximization. Then a new user selection algorithm is proposed based on the joint precoding scheme. BD precoding will cause performance loss in the single antenna terminals when the number of terminal antenna is inconsistent. The algorithm we proposed can overcome the drawback by using the maximum SLNR for single-antenna users and BD precoding for multi-antenna users respectively. Simulation results show that the proposed algorithm will enhance the system sum-rate performance significantly when SNR (Signal Noise Ratio) over 5dB. The performance improves by 30% when SNR reaches 20dB.

scholarly journals Sum Rate Analysis for Massive MIMO Downlink With MRC Beamforming and User Selection

2021 ◽  
Vol 2113 (1) ◽  
pp. 012025
Author(s):  
Yiyang Wu ◽  
Chang Chang ◽  
Fei Xie ◽  
Dacheng Ju ◽  
Yilun Pan
Keyword(s):  
Channel Model ◽  
Base Station ◽  
Transmission Model ◽  
User Selection ◽  
Selection Algorithm ◽  
User Scheduling ◽  
Downlink System ◽  
Sum Rate ◽  
Simulation Results ◽  
Mimo Downlink

Abstract Average allocation of data rate to each user is inefficient since the resource a base station can allocate is limited. Thus, user selection and user scheduling need to be applied into multi-user massive multiple-input multiple-output (MIMO) downlink system. In this paper, we mainly focus on the methods of user selection. First, we establish a downlink system model including transmission model and channel model. Then, two user-rate based user selection algorithms via the signal-to-interference-plus-noise-ratio (SINR) are proposed, where the SINR is generated by MRC beamforming. Finally, simulation results are provided to compare the performance of two proposed algorithms and their fairness towards selected users. In the simulation results, location-based selection algorithm and random selection algorithm are jointly compared. The second proposed algorithm possesses the highest total sum-rate and is the optimal algorithms among the four algorithms.

scholarly journals Location-dependent User Selection Based on Sum Rate Approximation in Large System Regime for Massive MIMO

2018 ◽  
Vol 218 ◽  
pp. 03010 ◽  
Author(s):  
Vera Noviana Sulistyawan ◽  
Rina Pudji Astuti ◽  
Arfianto Fahmi
Keyword(s):  
Urban Areas ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Path Loss ◽  
Large System ◽  
User Selection ◽  
Rate Performance ◽  
Selection Scheme ◽  
Multiple User ◽  
Sum Rate

Massive MIMO with multiple BS antennas can give simultaneous service for multiple user equipments (UEs) that are widely considered in massive connectivity to meet high data rate requirements. User selection is critical to optimize the overall performance of MIMO systems in various scenarios and has been extensively studied in cellular networks to guarantee service for users. In the previous study, location-dependent user selection (LUS) had extremely low computational complexity which is capable to enhance sum rate performance, but there are many environmental condition assumptions that make this algorithm does not reflect real conditions. In this research, we proposed modified LUS with approximations of sum rate in large system regimes by adding the sum ergodic of the distance from one user to another which enhance sum rate performance. In addition, we vary the user environment that was ignored in previous research by varying the path loss exponent values. In this research, we focus modify on sub-urban areas with each UEs having different environmental conditions. The selection scheme is equipped with spatial correlation fading on the transmitter side MIMO antenna. The simulation shows an increase in sum rate between 0.0012 to 0.3935 in perfect CSI. For the imperfect CSI with antenna correlation coefficient for power at 30 dBm is 0.5 when 32x64 antennas is 14 optimal active UEs with sum rate is 23.4207 bps/Hz. For cases where the user is located in different positions with different environmental circumstances, with 32x64 antennas showing the highest sum rate is 24.8436 bps/Hz with 17 optimal UEs.

A User Selection Algorithm Providing Maximum Sum-Rate for Multiuser MIMO Systems

2010 ◽  
Vol E93-B (5) ◽  
pp. 1302-1305 ◽  
Author(s):  
Taeyoul OH ◽  
Seungheon HYEON ◽  
Hyunsung GO ◽  
Seungwon CHOI
Keyword(s):  
Mimo Systems ◽  
User Selection ◽  
Selection Algorithm ◽  
Multiuser Mimo ◽  
Sum Rate ◽  
Multiuser Mimo Systems

Application of Weighted Projection PM Algorithm in Estimation of DOA

2013 ◽  
Vol 300-301 ◽  
pp. 746-749
Author(s):  
Wei Wei Hu ◽  
Chang Ming Wang ◽  
Ai Jun Zhang
Keyword(s):  
Signal Subspace ◽  
Steering Vector ◽  
Signal Noise ◽  
Noise Subspace ◽  
Propagator Method ◽  
Simulation Results ◽  
Noise Ratio ◽  
The Relationship

In order to improve the decreasing resolution ability of Propagator Method (PM) algorithm under the environments like low signal noise ratio and small number of snapshots, a new weighted projection PM algorithm is proposed in this paper. This algorithm orthogonalizes noise subspace to get a new one, gains the signal subspace with the relationship between it and noise subspace, and weights the signal subspace and noise subspace with values gained by projecting integral value of steering vector in the field around the signals to each element of subspace. Simulation results show that the proposed method can keep computation simple, and also can decrease signal noise ratio threshold and snapshots threshold, so it has the better resolution ability and higher precision in snapshot deficient and low signal noise ratio scenario.

An Improved Precoding Algorithm for LTE-Advanced CoMP System

2013 ◽  
Vol 427-429 ◽  
pp. 2519-2522
Author(s):  
Qiong Wang ◽  
Zhao Xia Zhang ◽  
Jia Liu
Keyword(s):  
Performance Improvement ◽  
Block Diagonalization ◽  
Communication Quality ◽  
Zero Forcing ◽  
Cell Edge ◽  
Lte Advanced ◽  
Simulation Results ◽  
Noise Ratio ◽  
Inter Cell Interference

In LTE-Advanced (LTE-A) system, coordinated multi-point (CoMP) technology can reduce inter-cell interference effectively, and improve the communication quality of the cell edge users. The main purpose of this paper is to optimize the precoding algorithm and enhance the overall cell throughput in LTE-A CoMP downlink. Based on CoMP-JP, we focus on zero-forcing (ZF), block diagonalization (BD) and signal-to-leakage-plus-noise-ratio (SLNR). We propose an improved precoding algorithm (ZF-SLNR) which combines the advantages of ZF and SLNR . Simulation results suggest that ZF-SLNR algorithm provides appreciable performance improvement.

A Modified Least Significant Bit Embedding with Error Correction

2013 ◽  
Vol 284-287 ◽  
pp. 3256-3259
Author(s):  
Hsin Ying Liang ◽  
Chia Hsin Cheng ◽  
Cheng Ying Yang ◽  
Kun Fu Zhang
Keyword(s):  
Error Correction ◽  
Multilevel Coding ◽  
Least Significant Bit ◽  
Signal Noise ◽  
Error Correction Capability ◽  
Simulation Results ◽  
Noise Ratio ◽  
Similar Peak

This paper proposes a modified Least significant bit (LSB) embedding capable of both a high embedding payload and error correction. The method proposed in this paper combines the techniques of both LSB embedding and multilevel coding to produce stego images with error correction capability and high embedding payloads. The proposed method divides cover work into multiple blocks, and each LSB for all the pixels in each block is considered a layer. Reed-Muller codes are used to encode cipher and embed data into every layer. LSB embedding has no inherent capability to correct errors in cipher extraction, but the proposed method can correct some errors according to the error correction capability of multilevel coding. Compared with LSB embedding, simulation results show that the proposed method has a similar peak signal noise ratio (PSNR) and embedding payload. The peak signal noise ratio (PSNR) exceeds 40 dB by using our proposed method. Additionally, our proposed method offers significantly superior embedding payloads and error correction capabilities.

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