Cognition is a high level mental faculty of the brain that includes functions like adaptation, learning, deciding, and others. Accordingly, a Cognitive Radio must have capabilities that mimic such Cognitive functions. As one of the fundamental cognitive abilities of the radio, this paper proposes a novel adaptation method; which uses Real-coded Genetic Algorithms (RGA) to adapt physical layer radio parameters in response to varying environmental conditions and different user services. The adaptation method is applied in a single objective optimization setting -that's the minimization of BER. Minimum transmitted EIRP levels of the resulting solutions are achieved by using a special Power Limiting Algorithm (PLA) which increments the maximum transmitted allowable EIRP levels during the engine run, if it experienced a slow convergence towards the optimal required solution. Results have indicated the success of the engine in adapting the physical layer radio parameters in response to varying environmental conditions and different user services to minimize resulting link BER, with the minimum possible transmitted EIRP levels.

Long Term Evolution (LTE) developed by Third Generation Partnership Project (3GPP), is the access part of the Evolved Packet System (EPS). LTE physical layer is based on Orthogonal Frequency Division Multiple Access (OFDMA) with Quadrature Amplitude Modulation (QAM). Although there has been lots of enhancements in the LTE physical layer, yet higher order modulation schemes were not introduced in the specification until Release 12. This paper investigates the performance of L TE with 256-QAM which was introduced in the 3GPP standard in release 12.3 aiming to enhance the spectral efficiency of the system and increase the peak data rates. Bit Error Rate (BER) values were populated for the probable Signal to Noise Ratio (SNR) operating ranges. The results are based on a MATLAB system model simulation. The results demonstrate that LTE-A can adopt the 256-QAM higher order modulation especially for nomadic users.

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Cognitive Radio (CR) is the future of radio systems giving authorities and end users unprecedented capabilities, and completely new services expectations. This paper aims at introducing an evolutionary novel Cognitive Radio adaptation engine architecture inspired from theories developed in cognitive sciences. Adaptation is a fundamental feature of CR necessary for many applications and use cases such as dynamic spectrum access, and emergency and disaster relief communication systems. The proposed architecture employs meta-heuristic techniques to dynamically and autonomously self-adapt to external varying stimuli, in order to reach some objectives. The implemented architecture is shown to be suitable for applications of emergency and disaster relief communication systems.

Adaptation is one of the fundamental functionalities of Cognitive Radio Systems (CRS). Adaptation refers to theability of the radio to adapt its operating parameters in response to varying stimuli.Choice of the best parameter set of the radio to achieve certain objectives in shortest time possible remains one of the most challenging tasks in Cognitive Radio (CR) research.One possible approach to adaptation engine design is based on utilizing Genetic Algorithms (GA) which invoke a combination of exploration and exploitation processes to perform random and directed searches for semi-optimal solutions in the possible solution space. However, conventional Binary-coded Genetic Algorithms (BGA) based adaptation engines used frequently in CR research,are criticized for their slow convergence and response times. Accordingly, Real-coded Genetic Algorithms (RGA) -a specific type of GA -have been implemented in our work, to address this problem. RGA alleviates many of the disadvantages of conventional BGA based implementations. This paper focuses on RGA based adaptation engine implementations' performance assessment compared to conventional BGA based implementations. Performance assessment results indicate that RGA based implementation does demonstrate a superior performance over BGA based implementations; in achieving the best configuration to minimize the link BER with minimum possible transmitted EIRP levels; in the shortest time possible.