journal article·2021Open access

Improved Hybrid Beamforming for mmWave Multi-User Massive MIMO

Ji-Sung Jung, Won-Seok Lee, Yeong-Rong Lee, Jaeho Kim, Hyoung-Kyu Song

Computers, Materials & Continua· Volume 67· Issue 3

Abstract

Massive multiple input multiple output (MIMO) has become essential for the increase of capacity as the millimeter-wave (mmWave) communication is considered. Also, hybrid beamforming systems have been studied since full-digital beamforming is impractical due to high cost and power consumption of the radio frequency (RF) chains. This paper proposes a hybrid beamforming scheme to improve the spectral efficiency for multi-user MIMO (MU-MIMO) systems. In a frequency selective fading environment, hybrid beamforming schemes suffer from performance degradation since the analog precoder performs the same precoding for all subcarriers. To mitigate performance degradation, this paper uses the average channel covariance matrix for all subcarriers and considers an iterative algorithm to design analog precoder using approximation techniques. The analog precoder is iteratively updated for each column until it converges. The proposed scheme can reduce errors in the approximating process of the overall spectral efficiency. This scheme can be applied to fully-connected and partially-connected structures. The simulation results show that spectral efficiency performance for the proposed scheme is better than the conventional schemes. The proposed scheme can achieve similar performance with full-digital beamforming by using a sufficiently large number of RF chains. Also, this paper shows that the proposed scheme outperforms other schemes in the frequency selective fading environment. This performance improvement can be achieved in both structures.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Millimeter-Wave Propagation and Modeling · Advanced MIMO Systems Optimization · Microwave Engineering and Waveguides

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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