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 thesis proposes novel decentralized baseband processing architectures that alleviate 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 those decentralized architectures, 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 first propose a decentralized consensus-sharing architecture. With this architecture, each cluster performs local baseband processing in parallel and then shares their local results with little amount of data transfer to compute a global consensus at a centralized processing element; the consensus is then broadcast to each cluster for another round of local update. After a few rounds of local update and consensus sharing, a converged global consensus result is obtained. Under this architecture, we solve uplink data detection and downlink beamforming problems using alternating direction method of multipliers (ADMM) and conjugate gradient methods in a decentralized manner, and show superb error-rate performance that has minimum loss compared to centralized solutions.

To reduce the data transfer latency across clusters, we further propose a decentralized feedforward architecture that only requires one-shot message passing among clusters to arrive at global detection or beamforming results. With this architecture, we develop multiple variations of detection and beamforming algorithms with non-linear or linear local solvers, and with partially or fully decentralization schemes, that realize trade-offs between error-rate performance, computational complexity, and interconnect bandwidth.

To evaluate the hardware efficiency of our proposed methods, we implement above decentralized detection and beamforming algorithms on multi-GPU systems using parallel and distributed programming techniques to optimize the data rate performance. Our implementations achieve less than 1ms latency and over 1Gbps data throughput on a high-end multi-GPU platform, and demonstrate high scalability to support hundreds to thousands of antennas for massive MU-MIMO systems.