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The use of a large excess of service antennas brings a variety of performancebenefits to distributed MIMO C-RAN, but the corresponding high fronthaul dataloads can be problematic in practical systems with limited fronthaul capacity.In this work we propose the use of lossy dimension reduction, applied locallyat each remote radio head (RRH), to reduce this fronthaul traffic. We firstconsider the uplink, and the case where each RRH applies a linear dimensionreduction filter to its multi-antenna received signal vector. It is shown thatunder a joint mutual information criteria, the optimal dimension reductionfilters are given by a variant of the conditional Karhunen-Loeve transform,with a stationary point found using block co-ordinate ascent. These filters arethen modified such that each RRH can calculate its own dimension reductionfilter in a decentralised manner, using knowledge only of its own instantaneouschannel and network slow fading coefficients. We then show that in TDD systemsthese dimension reduction filters can be re-used as part of a two-stage reduceddimension downlink precoding scheme. Analysis and numerical results demonstratethat the proposed approach can significantly reduce both uplink and downlinkfronthaul traffic whilst incurring very little loss in MIMO performance.

Distributed Dimension Reduction for Distributed Massive MIMO C-RAN with Finite Fronthaul Capacity