Reseach On Energy Efficient Sidelobe Level Suppression Optimization In Distributed Collaborative Beamforming

The resource constrained networks such as the wireless sensor networks(WSN)are usually composed of a large number of distributed nodes that transmit power and energy.These nodes usually transmit data to the base station(BS)by using multi-hop method.However,the multi-hop communications will lead to higher routing overhead and increase the probability of communication failure,thereby reducing the reliability of communications.In addition,energy limitation is also a significant feature of WSN.Thus,to improve the energy efficiency of the nodes is of great significance.Distributed collaborative beamforming(CB)is an effective method to enhance the energy efficiency of WSN.The sensor nodes can form a virtual node antenna array(VNAA)and use CB to generate a beam with high gain toward the BS so that avoiding multi-hop communications.Using CB can not only reduce the communication delay,but also obtain the energy gain consumption and prolong the network lifetime.However,due to the random distribution of nodes in WSN,the deterioration of beam patterns caused by the location errors is usually considered as the main problem of CB in WSNs.In addition,the excitation current of each node in the VNAA is another important factor that directly affects the sidelobe of the beam pattern.A set of optimal excitation currents can effectively suppress the maximum sidelobe level(SLL)and reduce the transmit power.Thus,how to determine the optimal location of nodes,the optimal excitation current and then reduce the maximum SLL as well as the energy consumption of the nodes is the key issue of CB.Therefore,this paper focuses on how to effectively suppress the maximum SLL and to enhance the energy efficiency of the nodes.The main contributions and innovations are as follows:(1)The suppression and optimization of the sidelobe level of the antenna array beam pattern.The SLL suppression and optimization method of the normal antenna array is an important and basic work for the beam pattern optimization of CB.Thus,we propose an enhanced biogeography-based optimization(EBBO)algorithm for optimizing the maximum SLL and controlling the nulls of circular antenna array(CAA),a hierarchy cuckoo search(HCS)algorithm for reducing the maximum SLL of the large scale antenna array in 5G,and a biogeography-based optimization with local search(BBOLS)for the optimization of the power pattern in wireless power transmission.(2)Node selection optimizations in CB based on different geometric models.A sidelobe control by node selection algorithm(SCNSA)is proposed to reduce the maximum SLL of the beam pattern of VNAA in CB.The algorithm first shows a method for calculating the optimal energy efficient number of the array nodes based on the linear antenna array(LAA).Then,an array node selection algorithm and a excitation current optimization method based on the cuckoo search(CS)are given.Moreover,a node selection optimization algorithm(NSOA)based on the circular antenna array(CAA)is proposed.NSOA first gives the number of the energy optimal array nodes based on the virtual CAA.Then,the optimal positions and excitation currents of the elements of the CAA is determined by using the firefly algorithm(FA),and this CAA is used to guide the selection of the VNAA.Finally,an array node selection method is given.In addition,A sidelobe and energy optimization array node selection(SEOANS)algorithm based on the concentric circular antenna array(CCAA)is proposed.First,SEOANS gives the calculation method for the number of the energy optimal array nodes.Second,the algorithm shows that how to use the CCAA as the guide array to select the nodes of the VNAA.Third,we show a cuckoo search-chicken swarm optimization(CSCSO)algorithm to optimize the excitation current of the selected array nodes.Finally,SEOANS gives the fault tolerance mechanism of the algorithm.(3)The SLL suppression method for CB based on the hybrid discretecontinuous optimization.First,we formulate the node selection and excitation current optimization into a hybrid discrete and continuous optimization problem(HDCOP),which requires simultaneous search for the discrete and continuous solutions.Second,a centralized strategy and a distributed consensus strategy are proposed,respectively,to solve the formulated optimization problem.In the centralized strategy,the HDCOP is divided into two sub optimization problems,and we give a improved discrete CS algorithm(IDCSA)and an chaotic hierarchy cuckoo search algorithm(CHCSA)to solve the discrete and the continuous parts of these two problems,respectively.For the distributed consensus stragegy,a distributed parallel cuckoo search algorithm(DPCSA)is established to synchronously solve the discrete and continuous parts of the HDCOP.Finally,the operation mechanism and energy consumption analysis of the two strategies are presented.