# Decentralized Baseband Processing for Massive MU-MIMO Systems

## TL;DR

This paper introduces a decentralized processing architecture for massive MU-MIMO systems, reducing complexity and data transfer bottlenecks by partitioning the antenna array and developing local algorithms, enabling scalable high-performance wireless communication.

## Contribution

It proposes a novel decentralized baseband processing architecture with algorithms that operate on local information, improving scalability and efficiency for large MU-MIMO systems.

## Key findings

- Decentralized algorithms require less inter-cluster communication.
- The architecture scales efficiently to thousands of antennas.
- Performance trade-offs are characterized between error rate, complexity, and bandwidth.

## Abstract

Achieving high spectral efficiency in realistic massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems requires computationally-complex algorithms for data detection in the uplink (users transmit to base-station) and beamforming in the downlink (base-station transmits to users). Most existing algorithms are designed to be executed on centralized computing hardware at the base-station (BS), which results in prohibitive complexity for systems with hundreds or thousands of antennas and generates raw baseband data rates that exceed the limits of current interconnect technology and chip I/O interfaces. This paper proposes a novel decentralized baseband processing architecture that alleviates these bottlenecks by partitioning the BS antenna array into clusters, each associated with independent radio-frequency chains, analog and digital modulation circuitry, and computing hardware. For this architecture, we develop novel decentralized data detection and beamforming algorithms that only access local channel-state information and require low communication bandwidth among the clusters. We study the associated trade-offs between error-rate performance, computational complexity, and interconnect bandwidth, and we demonstrate the scalability of our solutions for massive MU-MIMO systems with thousands of BS antennas using reference implementations on a graphic processing unit (GPU) cluster.