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Partial gradient based cooperative sensing in distributed cognitive radio ad-hoc networks

机译:分布式认知无线电自组织网络中基于局部梯度的协作感知

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In ad hoc networks, the unused spectrum resources can be effectively sensed by cognitive radio technology, and transmission performance will be improved. Cooperative spectrum sensing is necessary because a single node cannot detect the existence of primary user accurately due to shadowing, noise and fading in wireless channels. Gradient based cooperative sensing (GBCS) is a widely used scheme in distributed cooperative spectrum sensing. However, as the nodes of cognitive radio ad-hoc networks increasing, the efficiency of GBCS degrades because the complexity rises. To solve the problem, a partial-GBCS (F-GBCS) scheme is proposed in this paper. The ROC performance and energy consumption are balanced by SNR-based adaptive threshold and optimal cooperative numbers. Simulation results show that under the proposed scheme, the value of object function, which evaluates the comprehensive performance of the algorithm, is improved by 37%, and the robustness of cooperative spectrum sensing is guaranteed in large-scale cognitive ad hoc networks. Meanwhile, the interference to primary users is suppressed and equipment power consumption is reduced.
机译:在ad hoc网络中,可以通过认知无线电技术有效地感测未使用的频谱资源,并提高传输性能。合作频谱感测是必要的,因为由于无线信道中的阴影,噪声和衰落,单个节点无法准确检测主要用户的存在。基于梯度的协作感知(GBCS)是分布式协作频谱感知中广泛使用的方案。但是,随着认知无线电自组织网络的节点增加,由于复杂度增加,GBCS的效率下降。为了解决这个问题,本文提出了一种部分GBCS(F-GBCS)方案。 ROC性能和能耗通过基于SNR的自适应阈值和最佳协作数来平衡。仿真结果表明,在该方案下,评价算法综合性能的目标函数值提高了37%,在大规模认知自组织网络中保证了协作频谱感知的鲁棒性。同时,抑制了对主要用户的干扰并降低了设备功耗。

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