Cross-Layer Optimization and Link Adaptation in Cognitive Radios

Cross-Layer Optimization and Link Adaptation in Cognitive Radios

Ali H. Mahdi (University of Baghdad, Iraq & Technische Universität Ilmenau, Germany) and Mohamed A. Kalil (Suez University, Egypt)
DOI: 10.4018/978-1-4666-6571-2.ch026
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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).
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Optimization Algorithms

The optimization algorithms have been used to optimize the CR performance by determining the best link configuration between CR (Fette, 2009). The optimization algorithm uses objectives to validate the proposed link configuration under current radio environment. In this section, we present a detailed description of the most popular works with their applications, strengths and weaknesses.

Key Terms in this Chapter

Cross-Layer Optimization (CLO): Is an approach that coordinates the resources allocated to different layers across the Open Systems Interconnection layers in order to achieve globally optimal network performance.

Particle Swarm Optimization (PSO): Is an evolutionary algorithm that can be applied to solve optimization problems by using an iterative method to improve a certain solution.

Artificial Intelligence (AI): Is the human-like intelligence method exhibited by machines or software program.

Genetic Algorithm (GA): Is a biologically inspired search technique used to solve optimization problems.

Cognitive Radio (CR): Is an intelligent wireless communication system that is aware of its surrounding environment, and can adapt its internal states according to the statistical variations of the radio environment by making corresponding real-time changes in some operating parameters to provide highly reliable communications and to enhance utilization of the radio spectrum.

Case-Based Reasoning (CBR): Is the process of solving new problems based on the solutions of similar previous problems.

Automatic Repeat Request (ARQ): Is an error-control protocol for data transmission that uses acknowledgements and timeouts to achieve reliable data transmission.

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