TY - GEN
T1 - A genetic algorithm for joint resource allocation in cooperative cognitive radio networks
AU - Yang, Wei
AU - Ban, Dongsong
AU - Liang, Weifa
AU - Dou, Wenhua
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2011
Y1 - 2011
N2 - Existing literature in Cooperative Cognitive Radio Networks (CCRNs) always assumed a scenario where only one Primary User (PU) and several Secondary Users (SUs) coexist. However, in practice, multi-PUs and multi-SUs always coexist and the number of SUs is usually greater than that of PUs. Under such complex yet real scenarios, we assume that each PU not only allows a set of SUs to access its pre-allocated channel, but can leverage some of these SUs to improve its transmission rate via cooperative technologies. We consider a joint channel allocation and cooperation set partition problem in CCRNs, in which we aim to allocate a channel and assign a cooperation set that consists of several SUs for each PU, such that for a given period of time, the average transmission rates gained by all the users achieve maximum proportional fairness. We formulate the problem as a 0-1 non-linear programming model. Due to its NP-hardness, we propose a suboptimal Centralized Genetic Algorithm (CGA) for the problem. Extensive simulations demonstrate that CGA not only converges rapidly, but is shown to perform as well as 92% of the optimal solution delivered by brutal search, in terms of the fitness that reflects the fairness degree of the transmission performance gained by all the users. © 2011 IEEE.
AB - Existing literature in Cooperative Cognitive Radio Networks (CCRNs) always assumed a scenario where only one Primary User (PU) and several Secondary Users (SUs) coexist. However, in practice, multi-PUs and multi-SUs always coexist and the number of SUs is usually greater than that of PUs. Under such complex yet real scenarios, we assume that each PU not only allows a set of SUs to access its pre-allocated channel, but can leverage some of these SUs to improve its transmission rate via cooperative technologies. We consider a joint channel allocation and cooperation set partition problem in CCRNs, in which we aim to allocate a channel and assign a cooperation set that consists of several SUs for each PU, such that for a given period of time, the average transmission rates gained by all the users achieve maximum proportional fairness. We formulate the problem as a 0-1 non-linear programming model. Due to its NP-hardness, we propose a suboptimal Centralized Genetic Algorithm (CGA) for the problem. Extensive simulations demonstrate that CGA not only converges rapidly, but is shown to perform as well as 92% of the optimal solution delivered by brutal search, in terms of the fitness that reflects the fairness degree of the transmission performance gained by all the users. © 2011 IEEE.
KW - channel allocation
KW - cooperation set partition
KW - cooperative cognitive radio networks
UR - https://www.scopus.com/pages/publications/80052515127
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-80052515127&origin=recordpage
U2 - 10.1109/IWCMC.2011.5982411
DO - 10.1109/IWCMC.2011.5982411
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781424495399
T3 - IWCMC 2011 - 7th International Wireless Communications and Mobile Computing Conference
SP - 167
EP - 172
BT - IWCMC 2011 - 7th International Wireless Communications and Mobile Computing Conference
T2 - 7th International Wireless Communications and Mobile Computing Conference, IWCMC 2011
Y2 - 4 July 2011 through 8 July 2011
ER -