Achieving high spectral efficiency in realistic massive multiple-input multiple-output (M-MIMO) systems entail a significant increase in implementation complexity, especially with respect to data detection. Linear minimum mean-squared error (LMMSE) can achieve near-optimal performance but involves computationally expensive large-scale matrix inversions. This paper proposes a novel computationally efficient data detection algorithm based on the modified Richardson method. We first propose an antenna-dependent approach for the robust initialization of the Richardson method. It is shown that the proposed initializer outperforms the existing initialization schemes by a large margin. Then, the Chebyshev acceleration technique is proposed to overcome the sensitivity of the Richardson method to relaxation parameter while simultaneously enhancing its convergence rate. We demonstrate that the proposed algorithm mitigates multiuser interference and offers significant performance gains via the iterative cancellation of the bias term by prior estimation. Hence, each step of the iteration routine gives a new and better estimate of the solution. An asymptotic expression for the average convergence rate is also derived in this paper. The numerical results show that the proposed algorithm outperforms the existing methods and achieves near-LMMSE performance with a significantly reduced computational complexity.

Large-scale multiple-input multiple-output (LS-MIMO) is one of the promising technologies beyond the 5G cellular system in which large antenna arrays at the base station (BS) improve the system capacity and energy-efficiency. However, the large number of antennas at the BS makes it challenging to design low-complexity high-performance data detectors. Thus, a number of iterative detection methods, such as Gauss-Seidel and conjugate gradient, are introduced to achieve complexity-performance tradeoff. However, their performance deteriorates for the systems with small BS-to-user antenna ratio or for the channels that exhibit correlation. This paper proposes a new efficient iterative detection algorithm based on the improved Gauss-Seidel iteration to address this problem. The proposed method performs one conjugate gradient iteration that enables better performance with less number of iterations. A new hybrid iteration is introduced and a low-complexity initial estimation is utilised to enhance detection accuracy while reducing the complexity further. In addition, a novel preconditioning technique is proposed to maintain the benefits of the proposed detector in correlated MIMO channels. It is mathematically demonstrate that the proposed detector achieves low approximated error. Theoretical analysis and numerical results show that the proposed algorithm provides a faster convergence rate compared to conventional methods.

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Signal detection is a major challenge in massive multiple‐input multiple‐output (MIMO) wireless systems due to array of hundreds of antennas. Linear minimum mean square error (MMSE) enables near‐optimal detection performance in massive MIMO but suffers from unbearable computational complexity due to complicated matrix inversions. To address this problem, we propose a novel low‐complexity signal detector based on joint steepest descent (SD) and non‐stationary Richardson (NSR) iteration method. The SD is applied to get an efficient searching direction for the following NSR method to enhance the performance. The key idea of the proposed algorithm is to utilize a combination of the scaled‐diagonal initialization and the system‐ and iteration‐dependent acceleration mechanism, so that the convergence can be significantly speeded up. An antenna‐ and eigenvalue‐based scaling parameter is introduced for the proposed detector to further improve the error‐rate performance. We also provide convergence guarantees for the proposed technique. Numerical results demonstrate that the proposed joint detection approach attains superior performance compared to existing iterative approaches and provides a lower computational complexity than the conventional MMSE detector.