Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Shannon-theoretic approach to a Gaussian cellular multiple-access channel with fading
IEEE Transactions on Information Theory
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Efficient use of available spectrum is of concern to future wireless network planners. Although global cooperation at access points (APs) maximizes sum rate, for large networks this assumption is too complex to implement. We partition the wireless network into localized jointly decoded cells implemented as fixed size clusters. This is an alternative model which is more practical and improves efficiency of current systems. Conventionally, frequency allocation using interference avoidance maximizes spectrum usage. However, with clusters, careful allocation of interference needs to be explored. This is done using flexible bin based frequency allocation and applying heuristic tools. This work is the first known attempt to analyze uplink capacity of bin based fixed cluster cellular systems using genetic algorithms. To implement this, we derive an expression for the uplink capacity of bin based fixed clusters. We then input this as a fitness function to a modified simple genetic algorithm to compute a good fit for our bin allocation problem. We deduce that for sparsely distributed APs and large cluster sizes, rates close to that of a joint processor are achievable. Moreover, decreasing AP density for small cluster sizes (inter cell distance greater than 5 km for 7 cell-cluster) has insignificant effect on sum rate performance. However, with a nominal increase in the number of bins available for transmission, for dense system, the per-cell sum rate of a clustered cellular system can reach close to that of a hyper receiver using a genetic algorithm.