  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.
