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Energy Efficient Collaborative Beamforming for Reducing Sidelobe in Wireless Sensor Networks

机译:用于减少无线传感器网络中Sidelobe的节能协同波束形成

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Collaborative beamforming (CB) in wireless sensor networks (WSNs) based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance the energy efficiency of sensor nodes. However, a VNAA cannot be pre-designed like the conventional antenna arrays due to the randomly deployed sensor nodes, thereby causing a high sidelobe level (SLL) which increases the interferences. In this article, we formulate a hybrid discrete and continuous optimization problem (HDCOP) for reducing the maximum SLL. HDCOP requires to solve both the discrete and the continuous problems simultaneously, and we propose both centralized and consensus-based distributed CB strategies for solving HDCOP. For the centralized strategy, we convert HDCOP into two sub-optimization problems, and propose a discrete cuckoo search (CS) algorithm for the node location selection optimization and a continuous CS algorithm to optimize the excitation current weights of the selected nodes. For the distributed strategy, we propose a parallel distributed CS algorithm to solve the discrete and continuous parts of HDCOP simultaneously. Moreover, we propose two operating mechanisms based on these two algorithms. Simulation results verify the effectiveness of the proposed strategies for reducing the maximum SLL of CB in WSNs. Moreover, the proposed CB strategies have better performance in terms of the energy efficiency compared with other approaches such as the cross-entropy optimization-based method.
机译:基于虚拟节点天线阵列(VNAA)的无线传感器网络(WSN)中的协作波束成形(CB)可以增加传输距离并增强传感器节点的能量效率。然而,由于随机展开的传感器节点,因此不能预先设计VNAA,从而导致增加干扰的高侧链电平(SLL)。在本文中,我们制定了混合离散和连续优化问题(HDCOP),用于减少最大SLL。 HDCOP需要同时解决离散和持续的问题,并提出了用于解决HDCOP的集中和共识的分布式CB策略。对于集中式策略,我们将HDCOP转换为两个子优化问题,并提出了一种用于节点位置选择优化的离散Cuckoo搜索(CS)算法和连续的CS算法来优化所选节点的激励电流权重。对于分布式策略,我们提出了一种并行分布式CS算法,可以同时解决HDCOP的离散和连续部分。此外,我们提出了基于这两种算法的两个操作机制。仿真结果验证了拟议策略减少WSN中CB最大SLL的有效性。此外,与基于跨熵优化的方法等其他方法相比,所提出的CB策略在能效方面具有更好的性能。

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