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

## TL;DR

This paper introduces a decentralized Massive MIMO processing architecture and recursive algorithms based on SGD, reducing interconnection data-rate and latency, thus enabling scalable and efficient uplink detection and downlink precoding.

## Contribution

It proposes a novel decentralized architecture with recursive SGD algorithms for Massive MIMO, eliminating the need for a central node and improving scalability.

## Key findings

- Achieves lower interconnection data-rate compared to centralized architectures.
- Provides a good balance between performance, latency, and interconnection throughput.
- Enables scalable Massive MIMO processing with recursive algorithms.

## 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.