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arXiv · 1905.03160

Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms

Jesus Rodriguez Sanchez·Fredrik Rusek·Ove Edfors·Muris Sarajlic·Liang Liu
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Abstract

Algorithms for Massive MIMO uplink detection and downlink precoding typically rely on a centralized approach, by which baseband data from all antenna modules are routed to a central node in order to be processed. In the case of Massive MIMO, where hundreds or thousands of antennas are expected in the base-station, said routing becomes a bottleneck since interconnection throughput is limited. This paper presents a fully decentralized architecture and an algorithm for Massive MIMO uplink detection and downlink precoding based on the Stochastic Gradient Descent (SGD) method, which does not require a central node for these tasks. Through a recursive approach and very low complexity operations, the proposed algorithm provides a good trade-off between performance, interconnection throughput and latency. Further, our proposed solution achieves significantly lower interconnection data-rate than other architectures, enabling future scalability.

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Jesus Rodriguez Sanchez, Fredrik Rusek, Ove Edfors, Muris Sarajlic, Liang Liu. 2019-05-08. Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms. https://doi.org/10.1109/tsp.2020.2964496

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