Abstract
Massive multiple-input-multiple-output (MIMO) is one of the key technologies in the fifth generation (5G) cellular communication systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) presents a near-optimal performance. Due to the required direct matrix inverse, however, the MMSE detection algorithm becomes computationally very expensive, especially when the number of users is large. For achieving the high detection accuracy as well as reducing the computational complexity in massive MIMO systems, we propose an improved Jacobi iterative algorithm by accelerating the convergence rate in the signal detection process.Specifically, the steepest descent (SD) method is utilized to achieve an efficient searching direction. Then, the whole-correction method is applied to update the iterative process. As the result, the fast convergence and the low computationally complexity of the proposed Jacobi-based algorithm are obtained and proved. Simulation results also demonstrate that the proposed algorithm performs better than the conventional algorithms in terms of the bit error rate (BER) and achieves a near-optimal detection accuracy as the typical MMSE detector, but utilizing a small number of iterations.
Highlights
Massive multiple-input-multiple-output (MIMO) is an emerging technology for communication application which contributes a promising technology for the wireless sensor networks (WSNs) [1,2,3]and the fifth generation (5G) wireless communications [4]
We provide the simulation results on the bit error rate (BER) performance to compare the proposed algorithm with the conventional algorithms
We propose an improved Jacobi iterative algorithm in signal detection in the massive
Summary
Massive multiple-input-multiple-output (MIMO) is an emerging technology for communication application which contributes a promising technology for the wireless sensor networks (WSNs) [1,2,3]. The inverse of the large-dimensional covariance matrices required by MMSE would result in a prohibitively high complexity. Given this challenge, low-complexity approximate matrix inversion has drawn substantial attention. Its performance suffers from a significant loss with the scaling up massive MIMO and only marginal reduction in complexity can be achieved Another category is based on the iterative algorithms derived from linear equations. Based iterative algorithm is one of the typical algorithms and utilizes the most up-to-date values at each iteration, leading to the faster convergence rate and lower complexity than NSE detection algorithm [16]. Motivated by the above concerns, we focus our attention on further improving the convergence rate and calculation accuracy of the Jacobi algorithm with low-complexity calculation. IK denotes the K × K identify matrix, [a b] denotes the inner product of the vectors, and k · k stands for the Euclidean norm of a vector
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