Subcarrier Pairing and Power Allocation With Interference Management in Cognitive Relay Networks Based on Genetic Algorithms

Abstract

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This paper considers the resource allocation for an orthogonal-frequency-division-multiplexing (OFDM)-based cognitive decode-and-forward (DF) relay network. Our objective is to maximize the sum rate (over subcarriers) of the cognitive-radio user with the interference introduced to the primary users (PUs) being managed. The optimization is over subcarrier pairing and power allocation, which leads to a mixed-integer programming (MIP) problem. To resolve this complicated MIP problem at a reasonable cost, we adopt the heterogeneous genetic algorithm (HGA) framework. The main motivation for the HGA framework comes from the idea that it can reduce the impact of extra assumptions made in previous works to simplify the problem. In our HGA, the chromosome is divided into an integer string for subcarrier pairing and a real-number string for power allocation. Two new initialization methods of these chromosomes, which are motivated by the convex optimization theory, are proposed. New crossover and mutation schemes are also devised to accommodate these new chromosomes, as well as to manage the interference to the PUs. Furthermore, we also propose a two-stage low-complexity genetic algorithm, which separately determines the proper subcarrier pairs and power allocations. Our simulations show that our HGAs and the two-stage algorithm provide competing performance, compared with similar state-of-the-art works.