Massive MIMO and C-RAN are two promising techniques for implementing futurewireless communication systems, where a large number of antennas are deployedeither being co-located at the base station (BS) or totally distributed atseparate sites called remote radio heads (RRHs). In this paper, we consider ageneral antenna deployment design for wireless networks, termed multi-antennaC-RAN, where a flexible number of antennas can be equipped at each RRH to moreeffectively balance the performance and fronthaul complexity trade-off beyondthe conventional massive MIMO and single-antenna C-RAN. Under the uplinkcommunication setup, we propose a new "spatial-compression-and-forward (SCF)"scheme, where each RRH first performs a linear spatial filtering to denoise andmaximally compress its received signals from multiple users to a reduced numberof dimensions, then conducts uniform scalar quantization over each of theresulting dimensions in parallel, and finally sends the total quantized bits tothe baseband unit (BBU) via a finite-rate fronthaul link for joint informationdecoding. Under this scheme, we maximize the minimumsignal-to-interference-plus-noise ratio (SINR) of all users at the BBU by ajoint resource allocation over the wireless transmission and fronthaul links.Specifically, each RRH determines its own spatial filtering solution in adistributed manner to reduce the signalling overhead with the BBU, while theBBU jointly optimizes the users' transmit power, the RRHs' fronthaul bitsallocation, and the BBU's receive beamforming with fixed spatial filters atindividual RRHs. Through numerical results, it is shown that given a totalnumber of antennas to be deployed, multi-antenna C-RAN with the proposed SCFand joint optimization significantly outperforms both massive MIMO andsingle-antenna C-RAN under practical fronthaul capacity constraints.
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