Efficient Soft-Output Gauss–Seidel Data Detector for Massive MIMO Systems

Abstract

For massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection has been shown to achieve near-optimal performance but suffers from excessively high complexity due to the large-scale matrix inversion. Being matrix inversion free, detection algorithms based on the Gauss-Seidel (GS) method have been proved more efficient than conventional Neumann series expansion (NSE) based ones. In this paper, an efficient GS-based soft-output data detector for massive MIMO and a corresponding VLSI architecture are proposed. To accelerate the convergence of the GS method, a new initial solution is proposed. Several optimizations on the VLSI architecture level are proposed to further reduce the processing latency and area. Our reference implementation results on a Xilinx Virtex-7 XC7VX690T FPGA for a 128 base-station antenna and 8 user massive MIMO system show that our GSbased data detector achieves a throughput of 732 Mb/s with closeto-MMSE error-rate performance. Our implementation results demonstrate that the proposed solution has advantages over existing designs in terms of complexity and efficiency, especially under challenging propagation conditions. Index Terms-Massive MIMO, minimum-mean square error (MMSE), Gauss-Seidel method, soft-output data detection, VLSI. I. INTRODUCTIONT HE transmission of multiple data streams concurrently and in the same frequency band, which is known as multiple-input multiple-output (MIMO) technology, enables higher data rates compared to traditional single-input singleoutput (SISO) wireless communication systems [2]. Due to the combined effect of the ever-growing mobile data traffic and the scarcity of favorable radio spectrum in the low-loss frequency range, massive MIMO is widely believed to be a core technology for upcoming fifth generation (5G) wireless communication systems [3]. Massive MIMO promises higher data rates, improved spectral efficiency, better link reliability, and coverage over small-scale MIMO systems [4][5][6][7][8][9]. Compared to SISO systems, multiple interfering messages/symbols are transmitted concurrently in MIMO systems and expected to be separated at the receiver side with the contamination of noise or interference [10]. In the case of massive MIMO, however, this data detection operation entails excessively high computational

Low-complexity signal detection and precoding algorithms for multiuser massive MIMO systems

“…Table 1 compares the computational complexity of the proposed algorithm to other, recently published massive MIMO data detectors, namely conjugate gradient (CG)based detector [17], Improved GS [18], ADMIN detector [19], OCDBOX [20], and Richardson detector [21].…”

“…Recently, several works have been published to improve the performance of both signal detection and precoding algorithms for multi-users massive MIMO mobile communication systems. In order to reduce the high complexity due to the explicit inverse of the channel matrix, Yin et al [17] proposed an algorithm based on conjugate gradient (CG) for both data detection and precoding, whereas Zhang et al [18] presented an efficient soft-output data detection algorithm based on Gauss-Seidel method. For large scale MIMO systems, the Gram matrix is diagonally dominant and thus the initial solution of the Gauss-Seidel method is chosen as a 2-term Neumann series expansion, which efficiently speeds up the convergence of the algorithm.…”

A Low Complexity Near-Optimal Iterative Linear Detector for Massive MIMO in Realistic Radio Channels of 5G Communication Systems

“…To obtain fast convergence rate and hence, reduce the complexity, initialization is mandatory in GS detector. If the initial values are not well known, they can be considered zeros [ 57 ]. The GS detector is not desired in parallel implementation because of the internal sequential iterations structure [ 58 ].…”

A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

“…In addition, Gauss-Seidal (GS) based approximate LMMSE detection methods have been proposed to avoid large-scale matrix inversion [28]- [30]. The proposed GS based detectors enable low error rate at low-complexity.…”