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We investigate the optimal power allocation and optimal precoding for amulti-cell-processing (MCP) framework with limited cooperation. In particular,we consider two base stations(BSs) which maximize the achievable rate for twousers connecting to each BS and sharing channel state information (CSI). Wepropose a two way channel estimation or prediction process. Such framework haspromising outcomes in terms of feedback reduction and acheivable rates movingthe system from one with unkown CSI at the transmitter to a system withinstantanous CSI at both sides of the communication. We derive new extentionsof the fundamental relation between the gradient of the mutual information andthe MMSE for the conditional and non-conditional mutual information.Capitalizing on such relations, we provide the optimal power allocation andoptimal precoding designs with respect to the estimated channel and MMSE. Thedesigns introduced are optimal for multiple access (MAC) Gaussian coherenttime-varying fading channels with general inputs and can be specialized tomultiple input multiple output (MIMO) channels by decoding interference. Theimpact of interference on the capacity is quantified by the gradient of themutual information with respect to the power, channel, and error covariance ofthe interferer. We provide two novel distributed MCP algorithms that providethe solutions for the optimal power allocation and optimal precoding for the ULand DL with a two way channel estimation to keep track of the channelvariations over blocks of data transmission. Therefore, we provide a novelsolution that allows with limited cooperation: a significant reduction in theCSI feedback from the receiver to the transmitter, and timely optimal designsof the precoding and power allocation.

Multi-Cell Processing with Limited Cooperation: A Novel Framework to Timely Designs and Reduced CSI Feedback with General Inputs