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

We investigate the optimal power allocation and optimal precoding for a multi-cell-processing (MCP) framework with limited cooperation. In particular, we consider two base stations(BSs) which maximize the achievable rate for two users connecting to each BS and sharing channel state information (CSI). We propose a two way channel estimation or prediction process. Such framework has promising outcomes in terms of feedback reduction and acheivable rates moving the system from one with unkown CSI at the transmitter to a system with instantanous CSI at both sides of the communication. We derive new extentions of the fundamental relation between the gradient of the mutual information and the MMSE for the conditional and non-conditional mutual information. Capitalizing on such relations, we provide the optimal power allocation and optimal precoding designs with respect to the estimated channel and MMSE. The designs introduced are optimal for multiple access (MAC) Gaussian coherent time-varying fading channels with general inputs and can be specialized to multiple input multiple output (MIMO) channels by decoding interference. The impact of interference on the capacity is quantified by the gradient of the mutual information with respect to the power, channel, and error covariance of the interferer. We provide two novel distributed MCP algorithms that provide the solutions for the optimal power allocation and optimal precoding for the UL and DL with a two way channel estimation to keep track of the channel variations over blocks of data transmission. Therefore, we provide a novel solution that allows with limited cooperation: a significant reduction in the CSI feedback from the receiver to the transmitter, and timely optimal designs of the precoding and power allocation.Comment: Submitted to IEEE Transactions on Signal Processing, 201

On the Degrees-of-Freedom of the K-user Distributed Broadcast Channel

We study the Degrees-of-Freedom (DoF) in a wireless setting in which K Transmitters (TXs) aim at jointly serving K users. The performance is studied when the TXs are faced with a distributed Channel State Information (CSI) configuration in which each TX has access to its own multi-user imperfect channel estimate based on which it designs its transmit coefficients. The channel estimates are not only imperfectly acquired but they are also imperfectly shared between the TXs. Our first contribution consists of computing a genie-aided upper bound for the DoF of that setting. Our main contribution is then to develop a new robust transmission scheme that leverages the different qualities of CSI available at the TXs to improve the achieved DoF. We show the surprising result that there is a CSI regime, coined the Weak-CSIT regime, in which the genie-aided upper bound is achieved by the proposed transmission scheme. Interestingly, the optimal DoF in the Weak-CSIT regime only depends on the CSI quality at the best informed TX and not on the CSI quality at all other TXs.Comment: 34 pages, 7 figure

Centralized & Distributed Deep Reinforcement Learning Methods for Downlink Sum-Rate Optimization

For a multi-cell, multi-user, cellular network downlink sum-rate maximization through power allocation is a nonconvex and NP-hard optimization problem. In this paper, we present an effective approach to solving this problem through single- and multi-agent actor-critic deep reinforcement learning (DRL). Specifically, we use finite-horizon trust region optimization. Through extensive simulations, we show that we can simultaneously achieve higher spectral efficiency than state-of-the-art optimization algorithms like weighted minimum mean-squared error (WMMSE) and fractional programming (FP), while offering execution times more than two orders of magnitude faster than these approaches. Additionally, the proposed trust region methods demonstrate superior performance and convergence properties than the Advantage Actor-Critic (A2C) DRL algorithm. In contrast to prior approaches, the proposed decentralized DRL approaches allow for distributed optimization with limited CSI and controllable information exchange between BSs while offering competitive performance and reduced training times.Comment: Accepted for publication in IEEE Transactions on Wireless Communication