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.
A novel, experimental massive channel matrix model for indoor scenarios is proposed. In this model, the hand-held effects of smartphone users are taken into consideration, as well as the correlation and coupling at transmitters and receivers in the conventional models. Hence, the proposed model is more applicable in real environments. Measurements at 3.5 GHz are carried out in a typical indoor scenario with a massive multiple-input multiple-output (MIMO) testbed to serve as a base station and an eight-antenna handset to mimic a smartphone. Channel performance predicted by the proposed model is in good agreement with the measurement results. It is highlighted that the proposed model is more accurate than the traditional massive MIMO channel matrix models. INDEX TERMSChannel matrix model, indoor, massive MIMO, propagation. I. INTRODUCTION The Massive Multiple-Input Multiple-Output (MIMO) has established itself as a key technology for emerging wireless communication systems such as 5G [1], [2]. It can remarkably improve system performance including spectral and power efficiency [3], [4]. The performance of massive MIMO highly depends on the characteristics of the wireless channel. Hence, deep physical comprehension of the propagation mechanisms and accurate channel models for massive MIMO are becoming increasingly important [5]-[7]. In particular, channel matrix models can be employed to characterize a lot of channel performances. They are also important basis for the evolution of standardized models such as WINNER and 3GPP SCM. One of the most commonly used channel matrix models is the Kronecker model, where channel characteristics are dependent on the Kronecker product of linkend correlation matrices [8], [9]. This model has advantage in simplicity but is proved to be inaccurate as the effects between link-ends are ignored. This deficiency is solved in Weichselberger model [10], [11], where both the correlation matrices and the effects between link-ends are taken intoThe associate editor coordinating the review of this manuscript and approving it for publication was Kostas P. Peppas.
In order to reduce the computational complexity of the inverse matrix in the regularized zero-forcing (RZF) precoding algorithm, this paper expands and approximates the inverse matrix based on the truncated Kapteyn series expansion and the corresponding low-complexity RZF precoding algorithm is obtained. In addition, the expansion coefficients of the truncated Kapteyn series in our proposed algorithm are optimized, leading to further improvement of the convergence speed of the precoding algorithm under the premise of the same computational complexity as the traditional RZF precoding. Moreover, the computational complexity and the downlink channel performance in terms of the average achievable rate of the proposed RZF precoding algorithm and other RZF precoding algorithms with typical truncated series expansion approaches are analyzed, and further evaluated by numerical simulations in a large-scale single-cell multiple-input-multiple-output (MIMO) system. Simulation results show that the proposed improved RZF precoding algorithm based on the truncated Kapteyn series expansion performs better than other compared algorithms while keeping low computational complexity.
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