Low-complexity Linear Precoding Scheme Research Articles

Two‐Stage Precoding Based on Overlapping User Grouping Approach in IoT‐Oriented 5G MU‐MIMO Systems

Downlink transmission techniques for multiuser (MU) multiple‐input multiple‐output (MIMO) systems have been comprehensively studied during the last two decades. The well‐known low complexity linear precoding schemes are currently deployed in long‐term evolution (LTE) networks. However, these schemes exhibit serious shortcomings in scenarios when users’ channels are strongly correlated. The nonlinear precoding schemes show better performance, but their complexity is prohibitively high for a real‐time implementation. Two‐stage precoding schemes, proposed in the standardization process for 5G new radio (5G NR), combine these two approaches and present a reasonable trade‐off between computational complexity and performance degradation. Before applying the precoding procedure, users should be properly allocated into beamforming subgroups. Yet, the optimal solution for user selection problem requires an exhaustive search which is infeasible in practical scenarios. Suboptimal user grouping approaches have been mostly focused on capacity maximization through greedy user selection. Recently, overlapping user grouping concept was introduced. It ensures that each user is scheduled in at least one beamforming subgroup. To the best of our knowledge, the existing two‐stage precoding schemes proposed in literature have not considered overlapping user grouping strategy that solves user selection, ordering, and coverage problem simultaneously. In this paper, we present a two‐stage precoding technique for MU‐MIMO based on the overlapping user grouping approach and assess its computational complexity and performance in IoT‐oriented 5G environment. The proposed solution deploys two‐stage precoding in which linear zero forcing (ZF) precoding suppresses interference between the beamforming subgroups and nonlinear Tomlinson‐Harashima precoding (THP) mitigates interuser interference within subgroups. The overlapping user grouping approach enables additional capacity improvement, while ZF‐THP precoding attains balance between the capacity gains and suffered computational complexity. The proposed algorithm achieves up to 45% higher MU‐MIMO system capacity with lower complexity order in comparison with two‐stage precoding schemes based on legacy user grouping strategies.

Low-Complexity SSOR-Based Precoding for Massive MIMO Systems

With the increase of the number of base station (BS) antennas in massive multiple-input multiple-output (MIMO) systems, linear precoding schemes are able to achieve the near-optimal performance, and thus are more attractive than nonlinear precoding techniques. However, conventional linear precoding schemes such as zero-forcing (ZF) precoding involve the matrix inversion of large size with high computational complexity, especially in massive MIMO systems. To reduce the complexity, in this letter, we propose a low-complexity linear precoding scheme based on the symmetric successive over relaxation (SSOR) method. Moreover, we propose a simple way to approximate the optimal relaxation parameter of the SSOR-based precoding by exploiting the channel property of asymptotical orthogonality in massive MIMO systems. We show that the proposed SSOR-based precoding can reduce the complexity of the classical ZF precoding by about one order of magnitude without performance loss, and it also outperforms the recently proposed linear approximate precoding schemes in typical fading channels.

A Linear Precoding Scheme for Downlink Multiuser MIMO Precoding Systems

In this letter, we propose a low complexity linear precoding scheme for downlink multiuser MIMO precoding systems where there is no limit on the number of multiple antennas employed at both the base station and the users. In the proposed algorithm, we can achieve the precoder in two steps. In the first step, we balance the multiuser interference (MUI) and noise by carrying out a novel channel extension approach. In the second step, we further optimize the system performance assuming parallel SU MIMO channels. Simulation results show that the proposed algorithm can achieve elaborate performance while offering lower computational complexity.