Cross-Layer Optimization and Link Adaptation in Cognitive Radios

Abstract

Cognitive Radio (CR) systems are smart systems capable of sensing the surrounding radio environment and adapting their operating parameters in order to efficiently utilize the available radio spectrum. To reach this goal, different transmission parameters across the Open Systems Interconnection (OSI) layers, such as transmit power, modulation scheme, and packet length, should be optimized. This chapter discusses the Adaptive Discrete Particle Swarm Optimization (ADPSO) algorithm as an efficient algorithm for optimizing and adapting CR operating parameters from physical, MAC, and network layers. In addition, the authors present two extensions for the proposed algorithm. The first one is Automatic Repeat reQuest-ADPSO (ARQ-ADPSO) for efficient spectrum utilization. The second one is merging ARQ-ADPSO and Case-Based Reasoning (CBR) algorithms for autonomous link adaptation under dynamic radio environment. The simulation results show improvements in the convergence time, signaling overhead, and spectrum utilization compared to the well-known optimization algorithms such as the Genetic Algorithm (GA).