  The Cloud-Radio Access Network (C-RAN) cellular architecture relies on the
transfer of complex baseband signals to and from a central unit (CU) over
digital fronthaul links to enable the virtualization of the baseband processing
functionalities of distributed radio units (RUs). The standard design of
digital fronthauling is based on either scalar quantization or on more
sophisticated point to-point compression techniques operating on baseband
signals. Motivated by network-information theoretic results, techniques for
fronthaul quantization and compression that improve over point-to-point
solutions by allowing for joint processing across multiple fronthaul links at
the CU have been recently proposed for both the uplink and the downlink. For
the downlink, a form of joint compression, known in network information theory
as multivariate compression, was shown to be advantageous under a
non-constructive asymptotic information-theoretic framework. In this paper,
instead, the design of a practical symbol-by-symbol fronthaul quantization
algorithm that implements the idea of multivariate compression is investigated
for the C-RAN downlink. As compared to current standards, the proposed
multivariate quantization (MQ) only requires changes in the CU processing while
no modification is needed at the RUs. The algorithm is extended to enable the
joint optimization of downlink precoding and quantization, reduced-complexity
MQ via successive block quantization, and variable-length compression.
Numerical results, which include performance evaluations over standard cellular
models, demonstrate the advantages of MQ and the merits of a joint optimization
with precoding.
