scholarly journals Learned Conjugate Gradient Descent Network for Massive MIMO Detection

2020 ◽  
Vol 68 ◽  
pp. 6336-6349
Author(s):  
Yi Wei ◽  
Ming-Min Zhao ◽  
Mingyi Hong ◽  
Min-Jian Zhao ◽  
Ming Lei
Keyword(s):  
Conjugate Gradient ◽  
Massive Mimo ◽  
Gradient Descent ◽  
Mimo Detection

Performance analysis of various massive MIMO detection algorithms in 5G Wireless technologies

2019 ◽  
Vol 10 ◽  
Author(s):  
Shihab Jimaa ◽  
Jawahir Al-Ali
Keyword(s):  
Massive Mimo ◽  
Spectrum Efficiency ◽  
System Capacity ◽  
Network Coverage ◽  
Mimo Detection ◽  
Infinity Norm ◽  
Detection Algorithms ◽  
Alternating Direction ◽  
5G Systems ◽  
Efficient Power

Background: The 5G will lead to a great transformation in the mobile telecommunications sector. Objective: The huge challenges being faced by wireless communications such as the increased number of users have given a chance for 5G systems to be developed and considered as an alternative solution. The 5G technology will provide a higher data rate, reduced latency, more efficient power than the previous generations, higher system capacity, and more connected devices. Method: It will offer new different technologies and enhanced versions of the existing ones, as well as new features. 5G systems are going to use massive MIMO (mMIMO), which is a promising technology in the development of these systems. Furthermore, mMIMO will increase the wireless spectrum efficiency and improve the network coverage. Result: In this paper we present a brief survey on 5G and its technologies, discuss the mMIMO technology with its features and advantages, review the mMIMO capacity and energy efficiency and also presents the recent beamforming techniques. Conclusion: Finally, simulation of adopting different mMIMO detection algorithms are presented, which shows the alternating direction method of multipliers (ADMM)-based infinity-norm (ADMIN) detector has the best performance.

scholarly journals A Low Complexity Precoding Algorithm Based on Parallel Conjugate Gradient for Massive MIMO Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 54010-54017 ◽  
Author(s):  
Geng Chen ◽  
Qingtian Zeng ◽  
Xiaomei Xue ◽  
ZhengQuan Li
Keyword(s):  
Conjugate Gradient ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity

One-Bit Quantized Massive MIMO Detection Based on Variational Approximate Message Passing

2018 ◽  
Vol 66 (9) ◽  
pp. 2358-2373 ◽  
Author(s):  
Zhaoyang Zhang ◽  
Xiao Cai ◽  
Chunguang Li ◽  
Caijun Zhong ◽  
Huaiyu Dai
Keyword(s):  
Message Passing ◽  
Massive Mimo ◽  
Mimo Detection ◽  
Approximate Message Passing

scholarly journals MODERN METHODS OF BUNDLE ADJUSTMENT ON THE GPU

2016 ◽  
Vol III-3 ◽  
pp. 43-50 ◽  
Author(s):  
R. Hänsch ◽  
I. Drude ◽  
O. Hellwich
Keyword(s):  
Conjugate Gradient ◽  
Gradient Descent ◽  
Optimization Problem ◽  
Error Rates ◽  
Bundle Adjustment ◽  
Test Cases ◽  
Current State ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Nonlinear Conjugate Gradient

The task to compute 3D reconstructions from large amounts of data has become an active field of research within the last years. Based on an initial estimate provided by structure from motion, bundle adjustment seeks to find a solution that is optimal for all cameras and 3D points. The corresponding nonlinear optimization problem is usually solved by the Levenberg-Marquardt algorithm combined with conjugate gradient descent. While many adaptations and extensions to the classical bundle adjustment approach have been proposed, only few works consider the acceleration potentials of GPU systems. This paper elaborates the possibilities of time and space savings when fitting the implementation strategy to the terms and requirements of realizing a bundler on heterogeneous CPUGPU systems. Instead of focusing on the standard approach of Levenberg-Marquardt optimization alone, nonlinear conjugate gradient descent and alternating resection-intersection are studied as two alternatives. The experiments show that in particular alternating resection-intersection reaches low error rates very fast, but converges to larger error rates than Levenberg-Marquardt. PBA, as one of the current state-of-the-art bundlers, converges slower in 50 % of the test cases and needs 1.5-2 times more memory than the Levenberg- Marquardt implementation.

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