Abstract
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.
Highlights
Cellular Internet of things (IoT) has been recognized as a key enabler for digital transformation and automation of almost all industries
To evaluate the performance of the proposed two-stage ZFTHP precoding based on overlapping user grouping approach (OUG zero forcing (ZF)-Tomlinson-Harashima precoding (THP) algorithm), we compared the MU-multiple-input multiple-output (MIMO) system capacity for this algorithm with the linear OUG-Greedy ZF algorithm [8] and two-stage block diagonalization (BD)-THP precoding based on optimized K-means clustering (K -means BD-THP) [21]
For the sake of completeness, we show simulation results for zero forcing with user selection (ZFS) [7] and THP precoding [12], combined with the overlapping user grouping strategy (OUG THP) that defines practical lower and upper bound of the MU-MIMO capacity region for this particular case, Table 3: Computational complexity of all observed algorithms comprising the user grouping and precoding
Summary
Cellular Internet of things (IoT) has been recognized as a key enabler for digital transformation and automation of almost all industries. In [21], Trifan et al proposed two-stage BD-THP precoding scheme based on the optimized K-means clustering with the imposed cluster size constraint and a distance metric based on the angles between users This approach does not provide information on the channel separation between users associated with different clusters. Instead of further modification of K-means clustering, like in [21], we here adopt the overlapping user grouping method from OUG-Greedy algorithm This algorithm considers both user selection and user ordering in order to maximize MU-MIMO system capacity and to ensure that users with the favorable channel conditions are assigned to multiple beamforming subgroups simultaneously.
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