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Genetic algorithms are well suited for optimization problems involving large search spaces. In this paper, we present several approaches designed to enhance the convergence time and/or improve the performance results of genetic algorithm-based search engine for cognitive radio networks, including techniques such as population adaptation, variable quantization, variable adaptation, and multi-objective...
In this paper, we propose a novel spectrum allocation approach for distributed cognitive radio networks. Cognitive radio systems are capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance system and network performance. Using this technology, our proposed approach optimizes each individual wireless device and its single-hop...
In this paper, we present three novel priority-based spectrum allocation techniques for enabling dynamic spectrum access (DSA) networking for non-contiguous orthogonal frequency division multiplexing (NC-OFDM) transmission. With each communication link in the network possessing a specified pair of bit error rate (BER) and throughput requirements for supporting a specific application, the proposed...
In this paper, we propose two optimization approaches based on genetic algorithms in addition to several assisting mechanisms for making these approaches more efficient and robust in actual network implementations. Using the fact that a cognitive radio is capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance system (and potentially...
We propose a novel distributed cross-layer optimization approach for cognitive radio wireless networks. A cognitive radio is a wireless device that is capable of sensing the prevailing environmental conditions and automatically adapting its operating parameters in order to enhance the performance of both the system and potentially the whole network. Given the lack of a centralized intelligence to...
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