Joint Time Allocation and Power Control in Multi-Cell Networks with Load Coupling: Energy Saving and Rate Improvement

TL;DR

This paper proposes distributed algorithms for joint time allocation and power control in multi-cell networks with load coupling, achieving energy savings and rate improvements through convex optimization and theoretical analysis.

Contribution

It introduces a novel approach to optimize power and time allocation in multi-cell networks with load coupling, providing closed-form solutions and convergence proofs.

Findings

Distributed algorithm for power minimization is globally optimal.

Suboptimal distributed algorithm for rate maximization with proven convergence.

Numerical results show significant improvements over uniform power allocation.

Abstract

In this paper, we consider the problems of minimizing sum power and maximizing sum rate for multi-cell networks with load coupling, where coupling relation occurs among cells due to inter-cell interference. This coupling relation is characterized by the signal-to-interference-and-noise-ratio (SINR) coupling model with cell load vector and cell power vector as the variables. Due to the nonlinear SINR coupling model, the optimization problems for multi-cell networks with load coupling is nonconvex. To solve these nonconvex problems, we first consider the optimization problems for single-cell networks. Through variable transformations, the optimization problems can be equivalently transformed into convex problems. By solving the Karush-Kuhn-Tucker (KKT), the optimal solutions to power minimization and rate maximization problems can be obtained in closed form. Based on the theoretical findings of optimization problems for single-cell networks, we develop a distributed time allocation and power control algorithm with low complexity for sum power minimization in multi-cell networks. This algorithm is proved to be convergent and globally optimal by using the properties of standard interference function. For sum rate optimization in multi-cell networks, we also provide a distributed algorithm which yields suboptimal solution. Besides, the convergence for this distributed algorithm is proved. Numerical results illustrate the theoretical findings, showing the superiority of our solutions compared to the conventional solution of allocating uniform power for users in the same cell.