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.
@article{arxiv.1905.03160,
title = {Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms},
author = {Jesus Rodriguez Sanchez and Fredrik Rusek and Ove Edfors and Muris Sarajlic and Liang Liu},
journal= {arXiv preprint arXiv:1905.03160},
year = {2020}
}
Comments
Manuscript accepted for publication in IEEE Transactions on Signal Processing