  In this paper, we study feedback optimization problems that maximize the
users' signal to interference plus noise ratio (SINR) in a two-cell MIMO
broadcast channel. Assuming the users learn their direct and interfering
channels perfectly, they can feed back this information to the base stations
(BSs) over the uplink channels. The BSs then use the channel information to
design their transmission scheme. Two types of feedback are considered: analog
and digital. In the analog feedback case, the users send their unquantized and
uncoded CSI over the uplink channels. In this context, given a user's fixed
transmit power, we investigate how he/she should optimally allocate it to feed
back the direct and interfering (or cross) CSI for two types of base station
cooperation schemes, namely, Multi-Cell Processing (MCP) and Coordinated
Beamforming (CBf). In the digital feedback case, the direct and cross link
channel vectors of each user are quantized separately, each using RVQ, with
different size codebooks. The users then send the index of the quantization
vector in the corresponding codebook to the BSs. Similar to the feedback
optimization problem in the analog feedback, we investigate the optimal bit
partitioning for the direct and interfering link for both types of cooperation.
We focus on regularized channel inversion precoding structures and perform our
analysis in the large system limit in which the number of users per cell ($K$)
and the number of antennas per BS ($N$) tend to infinity with their ratio
$\beta=\frac{K}{N}$ held fixed.
