  This work studies distributed compression for the uplink of a cloud radio
access network where multiple multi-antenna base stations (BSs) are connected
to a central unit, also referred to as cloud decoder, via capacity-constrained
backhaul links. Since the signals received at different BSs are correlated,
distributed source coding strategies are potentially beneficial, and can be
implemented via sequential source coding with side information. For the problem
of compression with side information, available compression strategies based on
the criteria of maximizing the achievable rate or minimizing the mean square
error are reviewed first. It is observed that, in either case, each BS requires
information about a specific covariance matrix in order to realize the
advantage of distributed source coding. Since this covariance matrix depends on
the channel realizations corresponding to other BSs, a robust compression
method is proposed for a practical scenario in which the information about the
covariance available at each BS is imperfect. The problem is formulated using a
deterministic worst-case approach, and an algorithm is proposed that achieves a
stationary point for the problem. Then, BS selection is addressed with the aim
of reducing the number of active BSs, thus enhancing the energy efficiency of
the network. An optimization problem is formulated in which compression and BS
selection are performed jointly by introducing a sparsity-inducing term into
the objective function. An iterative algorithm is proposed that is shown to
converge to a locally optimal point. From numerical results, it is observed
that the proposed robust compression scheme compensates for a large fraction of
the performance loss induced by the imperfect statistical information.
Moreover, the proposed BS selection algorithm is seen to perform close to the
more complex exhaustive search solution.
