  The Internet of things (IoT) holds much commercial potential and could
facilitate distributed multiple-input multiple-output (MIMO) communication in
future systems. We study a distributed reception scenario in which a
transmitter equipped with multiple antennas sends multiple streams via spatial
multiplexing to a large number of geographically separated single antenna
receive nodes. The receive nodes then quantize their received signals and
forward the quantized received signals to a receive fusion center. With global
channel knowledge and forwarded quantized information from the receive nodes,
the fusion center attempts to decode the transmitted symbols. We assume the
transmit vector consists of phase shift keying (PSK) constellation points, and
each receive node quantizes its received signal with one bit for each of the
real and imaginary parts of the signal to minimize the transmission overhead
between the receive nodes and the fusion center. Fusing this data is a
non-trivial problem because the receive nodes cannot decode the transmitted
symbols before quantization. Instead, each receive node processes a single
quantity, i.e., the received signal, regardless of the number of transmitted
symbols. We develop an optimal maximum likelihood (ML) receiver and a
low-complexity zero-forcing (ZF)-type receiver at the fusion center. Despite
its suboptimality, the ZF-type receiver is simple to implement and shows
comparable performance with the ML receiver in the low signal-to-noise ratio
(SNR) regime but experiences an error rate floor at high SNR. It is shown that
this error floor can be overcome by increasing the number of receive nodes.
Hence, the ZF-type receiver would be a practical solution for distributed
reception with spatial multiplexing in the era of the IoT where we can easily
have a large number of receive nodes.
