  This work considers the downlink of a cloud radio access network (C-RAN), in
which a control unit (CU) encodes confidential messages, each of which is
intended for a user equipment (UE) and is to be kept secret from all the other
UEs. As per the C-RAN architecture, the encoded baseband signals are quantized
and compressed prior to the transfer to distributed radio units (RUs) that are
connected to the CU via finite-capacity fronthaul links. This work argues that
the quantization noise introduced by fronthaul quantization can be leveraged to
act as "artificial" noise in order to enhance the rates achievable under
secrecy constraints. To this end, it is proposed to control the statistics of
the quantization noise by applying multivariate, or joint, fronthaul
quantization/compression at the CU across all outgoing fronthaul links.
Assuming wiretap coding, the problem of jointly optimizing the precoding and
multivariate compression strategies, along with the covariance matrices of
artificial noise signals generated by RUs, is formulated with the goal of
maximizing the weighted sum of achievable secrecy rates while satisfying per-RU
fronthaul capacity and power constraints. After showing that the artificial
noise covariance matrices can be set to zero without loss of optimaliy, an
iterative optimization algorithm is derived based on the concave convex
procedure (CCCP), and some numerical results are provided to highlight the
advantages of leveraging quantization noise as artificial noise.
