Joint Optimization Methods for Nonconvex Resource Allocation Problems of Decode-and-Forward Relay-Based OFDM Networks

Abstract

Generally, resource allocation for multicarrier cooperation communication networks includes subcarrier and power allocation; however, it is difficult to solve because of the 0-1 integer programming of subcarrier allocation, which makes the problem nonconvex. This paper focuses on solving the nonconvex problems and provides a general solution to the resource allocation for relay-enhanced multicarrier systems. An established scenario, namely, a decode-and-forward (DF) relay-assisted orthogonal frequency-division multiplexing (OFDM) system, is considered, and we formulate the resource allocation as a joint subcarrier pairing, assignment, and power allocation problem, in which heterogeneous users' data rate requirements are also considered. To make the expression of the proposed optimization approaches for the original maximization problem clear, a four-step methodology is given. First, we transform the original nonconvex problem into a standard convex problem by imposing a new constraint of subcarrier allocation index, regardless of the convexity of the objective function. Furthermore, we prove that the optimum resource allocation algorithm (ORAA) for the dual problem of the transformed optimization problem is equivalent to the optimization of the original function. Subsequently, the optimum solution could be obtained by the golden section search method and the iterative resource allocation algorithm. Finally, a suboptimal resource allocation algorithm (SRAA) that solves the primal problem in an asymptotic manner is proposed. Simulation results illustrate that our proposed SRAA achieves comparable performance to the ORAA with satisfied complexity.