Decentralized Massive MIMO Processing Exploring Daisy-chain Architecture and Recursive Algorithms
arXiv:1905.03160 · doi:10.1109/TSP.2020.2964496
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.
arXiv commentManuscript accepted for publication in IEEE Transactions on Signal Processing
References in corpus (4)
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Cited by in corpus (5)
- Distributed Receivers for Extra-Large Scale MIMO Arrays: A Message Passing Approach
- Cell-free Massive MIMO with Sequential Fronthaul Architecture and Limited Memory Access Points
- Distributed and Scalable Uplink Processing for LIS: Algorithm, Architecture, and Design Trade-offs
- Processing Distribution and Architecture Tradeoff for Large Intelligent Surface Implementation
- Decentralized Linear MMSE Equalizer Under Colored Noise for Massive MIMO Systems