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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2023年 4卷 518-533页

作者： Aswathylakshmi, P. Ganti, Radha Krishna Indian Inst Technol Madras Dept Elect Engn Chennai 600036 India

Massive multiple-input-multiple-output (MIMO) is a key enabler for obtaining higher data rates in the next generation wireless technology. While it has the power to transform cellular communication, with potential for spatial diversity and multiplexing, a bottleneck that often gets overlooked is the fronthaul capacity. The fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries in-phase and quadrature (IQ) samples to the Baseband Unit (BBU) of the base station can throttle the network capacity/speed if appropriate data compression techniques are not applied, particularly in the uplink. This paper proposes an iterative technique for fronthaul load reduction in the uplink for massive MIMO systems utilizing the convolution structure of the received signals. The proposed algorithm provides compression ratios of about 30- 50x. This work provides extensive analysis of the performance of the proposed method for a plethora of practical scenarios and constraints, such as different channel parameters and models, receive antenna correlation, and under imperfect channel information. It also discusses the numerical convergence and complexity of the proposed algorithm and compares the performance against other existing compression techniques.

关键词： Uplink Massive MIMO matrix decomposition Receiving antennas Antenna arrays OFDM Iterative methods 5G networks fronthaul compression iterative technique alternating minimization blind matrix deconvolution

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Fronthaul Compression for Uplink Massive MIMO using matrix Decomposition

Fronthaul Compression for Uplink Massive MIMO using Matrix D...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Aswathylakshmi, P. Ganti, Radha Krishna Indian Inst Technol Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

ISBN: (纸本)9781665442664

Massive MIMO opens up attractive possibilities for next generation wireless systems with its large number of antennas offering spatial diversity and multiplexing gain. However, the fronthaul link that connects a massive MIMO Remote Radio Head (RRH) and carries IQ samples to the Baseband Unit (BBU) of the base station can throttle the network capacity/speed if appropriate data compression techniques are not applied. In this paper, we propose an iterative technique for fronthaul load reduction in the uplink for massive MIMO systems that utilizes the convolution structure of the received signals. We use an alternating minimisation algorithm for blind deconvolution of the received data matrix that provides compression ratios of 30-50. In addition, the technique presented here can be used for blind decoding of OFDM signals in massive MIMO systems.

关键词： Massive MIMO 5G networks fronthaul compression iterative technique alternating minimisation blind matrix deconvolution

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