TY - GEN
T1 - Optimization of uplink sum-rate for bin based clustered cellular system using a genetic algorithm
AU - Majid, Muhammad Imran
AU - Imran, Muhammad Ali
AU - Hoshyar, Reza
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Channel capacity
KW - Cooperative communications
KW - Dynamic resource allocation
KW - Genetic algorithm
KW - Network information theory
UR - https://www.scopus.com/pages/publications/77955148088
U2 - 10.1145/1815396.1815628
DO - 10.1145/1815396.1815628
M3 - Conference contribution
AN - SCOPUS:77955148088
SN - 9781450300629
T3 - IWCMC 2010 - Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
SP - 1016
EP - 1020
BT - IWCMC 2010 - Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
T2 - 6th International Wireless Communications and Mobile Computing Conference, IWCMC 2010
Y2 - 28 June 2010 through 2 July 2010
ER -