In Cognitive radio ad hoc networks （CRAHNs）, the secondary users （SUs） or cognitive radio nodes （CRs） are always equipped with limited energy and have a high error probability of data transmission. To address th...In Cognitive radio ad hoc networks （CRAHNs）, the secondary users （SUs） or cognitive radio nodes （CRs） are always equipped with limited energy and have a high error probability of data transmission. To address this issue, we first describe the network utility under energy constraint as a max-min model, where the re-transmission strategy with network coding is employed. Additionally, the expression of retransmission probability is presented in terms of power and bit error rate （BER）. Moreover, since the max-min model is non-convex in both objective and constraints, we use a normal- form game to find a near-optimal solution. The simulation results show that the proposed approach could achieve a higher network utility than the compared approaches.展开更多
基金This work was supported in part by the Research Fund for the Doctoral Program of Higher Education of China under Grant 20122304130002,the Natural Science Foundation in China under Grant 61370212,the Fundamental Research Fund for the Central Universities under Grant HEUCFZ1213 and HEUCF100601
文摘In Cognitive radio ad hoc networks （CRAHNs）, the secondary users （SUs） or cognitive radio nodes （CRs） are always equipped with limited energy and have a high error probability of data transmission. To address this issue, we first describe the network utility under energy constraint as a max-min model, where the re-transmission strategy with network coding is employed. Additionally, the expression of retransmission probability is presented in terms of power and bit error rate （BER）. Moreover, since the max-min model is non-convex in both objective and constraints, we use a normal- form game to find a near-optimal solution. The simulation results show that the proposed approach could achieve a higher network utility than the compared approaches.
