Distributed Filter Design and Power Allocation for Small-CellMIMO Networks

Abstract

The deluge of wireless data traffic catalyzed by the growing number of data-intensive deviceshas motivated the deployment of small-cell in fifth-generation (5G) networks. A primary challengefor deploying dense small-cell networks comes from the lack of practical techniques that efficientlyhandle the increased network interference at a low cost. This has aroused considerable interest inthe distributed precoder/combiner coordination techniques that leverage the channel reciprocity,while relying on the local channel state information (CSI) available at each communication end. Inthis thesis, a distributed approach is proposed to the problem of signal-to-interference-plus-noise-ratio (SINR)-guaranteed power minimization (SGPM) for multicell multiuser (MCMU) multiple-input multiple-output (MIMO) systems. Unlike prior SGPM approaches, the proposed techniqueis based on solving necessary and sufficient optimality conditions, which are derived by decom-posing the original problem into forward and backward (FB) subproblems, while ensuring thestrong duality of each subproblem. The proposed distributed SGPM algorithm makes use of FBadaptation and Jacobi recursion for iterative filter design and power allocation, respectively. Asufficient condition for the feasibility of the proposed distributed algorithm is analyzed, based onthe application of the matrix inverse-positive theory. Unlike the existing fully distributed FB filterupdate algorithms, the proposed approach guarantees target SINR performance as well as its convergence to a stationary point. Simulation results illustrate the enhanced power efficiency with theperformance guarantees of the proposed method compared to the existing distributed techniques.