An anti-jamming hybrid beamforming system designed based on support vector machine algorithm
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摘要: 针对复杂电磁环境下毫米波阵列的空间辐射干扰抑制问题,设计了一种低成本的自适应波束成形系统。首先建立了复杂电磁环境下通信系统模型,建立目标函数。然后利用机器学习中支持向量机算法将原非凸约束问题转化为二阶锥优化问题,获得理想编码矢量。最后利用梯度追踪算法对理想波束矢量进行稀疏重构,完成抗干扰波束的低复杂度实现。仿真结果表明,提出的波束成形系统能够对干扰作出有效抑制,能提升通信质量。Abstract: A low-cost adaptive beamforming system is designed to solve the space radiation interference problem of millimeter wave communication in complex electromagnetic environment. Firstly, the communication system model in complex electromagnetic environment and the objective function are established. Then, the support vector machine algorithm in machine learning is used to simplify the objective function to obtain the ideal coding vector. Finally, the gradient pursuit algorithm is used to build sparse reconstruction of the ideal beam vector to achieve low-cost anti-jamming beam. Simulation results show that the proposed beamforming system can effectively suppress the interference and improve the communication quality.
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Key words:
- anti-jamming /
- beamforming /
- support vector machine /
- sparse reconstruct /
- gradient pursuit
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表 1 基于GP的波束成形算法
Table 1. GP Algorithm
input $ {{\boldsymbol{w}}_i} $,$ {N_{{\text{RF}}}} $,${ { {{\boldsymbol{A}}} }_Q}$ ① ${ { { {\boldsymbol{F} } } }_{ {\text{RF} }(i)} } = \left[ { \cdot } \right];$ ② $ {{\boldsymbol{F}}_{{\text{res}}(i)}} = {{\boldsymbol{w}}_i}; $ ③ for $ j \leqslant {N_{{\text{RF}}}} $ do ④ ${ { {\boldsymbol{ \varPsi } } } } = { {\boldsymbol{A} } } _Q^*{ {\boldsymbol{F} }_{ {\text{res} }(i)} }$ ⑤ $k = \arg \max {(\boldsymbol{\varPsi} {\boldsymbol{\varPsi} ^*})_{l,l} },l = 1,\cdots ,{N_{ {A_Q} } }$ ⑥ ${ {\boldsymbol{F} }_{ {\text{RF} }(i)} } = \left[ { { {\boldsymbol{F} }_{ {\text{RF} }(i)} }\left| { { {\boldsymbol{A} }_Q}(:,k)} \right.} \right];$ ⑦ ${\boldsymbol{d} } = {{{\boldsymbol{F}}} _{ {\text{RF(} }i{\text{)} } }^{\text{H} }{ {\boldsymbol{F} }_{ {\text{res(} }i{\text{)} } } }ext{)} }$ ⑧ $g = \dfrac{ { {\boldsymbol{F} }_{ {\text{res(} }i{\text{)} } }^{\text{H} }{ {\boldsymbol{F} }_{ {\text{RF} }(i)} }{\boldsymbol{d} } } }{ {\left\| { { {\boldsymbol{F} }_{ {\text{RF} }(i)} }{\boldsymbol{d} } } \right\|_{{F} }^2} }$ ⑨ ${ {\boldsymbol{v} }_{ {\text{BB} }(i)} } = \left[ { { {\boldsymbol{v} }_{ {\text{BB} }(i)} }\left| 0 \right.} \right] + g*{\boldsymbol{d} }$ ⑩ ${ {\boldsymbol{F} }_{ {\text{res} }(i)} } = { {\boldsymbol{F} }_{ {\text{res} }(i)} } - g{ {\boldsymbol{F} }_{ {\text{RF} }(i)} }{\boldsymbol{d} }$ ⑪ end for ⑫ ${ {\boldsymbol{v} }_{ {\text{BB} }(i)} } = {P_i}\dfrac{ { { {\boldsymbol{v} }_{ {\text{BB} }(i)} } } }{ { { {\left\| { { {\boldsymbol{F} }_{ {\text{RF} }(i)} }{ {\boldsymbol{v} }_{ {\text{BB} }(i)} } } \right\|}_{{F} } } }}$ end -
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