黎海涛, 刘长军. 同时收发的机载认知网络优化设计[J]. 北京工业大学学报, 2019, 45(1): 33-41. DOI: 10.11936/bjutxb2018010008
    引用本文: 黎海涛, 刘长军. 同时收发的机载认知网络优化设计[J]. 北京工业大学学报, 2019, 45(1): 33-41. DOI: 10.11936/bjutxb2018010008
    LI Haitao, LIU Changjun. Optimal Design of Airborne Cognitive Network Based on Simultaneous Transmit and Receiver[J]. Journal of Beijing University of Technology, 2019, 45(1): 33-41. DOI: 10.11936/bjutxb2018010008
    Citation: LI Haitao, LIU Changjun. Optimal Design of Airborne Cognitive Network Based on Simultaneous Transmit and Receiver[J]. Journal of Beijing University of Technology, 2019, 45(1): 33-41. DOI: 10.11936/bjutxb2018010008

    同时收发的机载认知网络优化设计

    Optimal Design of Airborne Cognitive Network Based on Simultaneous Transmit and Receiver

    • 摘要: 针对由主网和次网构成的分层机载认知网络,研究了采用同时收发认知抗干扰技术的网络节点优化设计.首先,基于能量检测法推导出衰落信道中多跳认知网络的虚警概率和检测概率,并据此计算得到次网认知无线电(cognitive radio,CR)吞吐量.然后,以次网CR总吞吐量最大和发射功率最低为目标,建立感知时间、判决阈值和发射功率约束的多目标优化模型,并用拟牛顿法和对数罚函数法求解该优化问题.仿真结果表明,每一组初始化的目标函数值经优化后均可得其Pareto最优解,最优解对应的感知时间、信道判决阈值和信道发射功率是唯一的.

       

      Abstract: For the hierarchical airborne cognitive network that is composed of primary net(PNet) and secondary net(SNet), an optimal design of hierarchical airborne network, which is equipped with cognitive anti-jamming radio, was investigated based on simultaneous transmit and receive(STAR). First, the false alarm and detection probability of the multihop cognitive network in the fading channel were derived based on the energy detection method(ED), then the throughput of the secondary net CRs was calculated. Furthermore, a multi-objective optimization model constraint by sensing time, decision threshold and transmitting power was constructed to maximize the throughputs and minimize the transmitting power of the secondary net CRs, an approach was presented based on quasi-Newton method and a logarithm penalty function method to solve this optimization problem. The simulation results show that the Pareto optimal solution can be obtained after the optimization of each set of initialized target functions, and the optimization solution is corresponding to unique sensing time, channel decision threshold and channel transmit power.

       

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