An anti-jamming hybrid beamforming system designed based on support vector machine algorithm

Lei Xu; Yuejin Zang; Xinyao Zhou; Guanfa Liu; Lisha Zhou

doi:10.11884/HPLPB202133.210235

Journals >High Power Laser and Particle Beams >Volume 33 >Issue 12 >Page 123020 > Article

High Power Laser and Particle Beams
Vol. 33, Issue 12, 123020 (2021)

An anti-jamming hybrid beamforming system designed based on support vector machine algorithm

Lei Xu, Yuejin Zang, Xinyao Zhou, Guanfa Liu, and Lisha Zhou

Author Affiliations

Shanghai Institute of Mechanical and Electrical Engineering, Shanghai 210000, China

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DOI: 10.11884/HPLPB202133.210235 Cite this Article

Lei Xu, Yuejin Zang, Xinyao Zhou, Guanfa Liu, Lisha Zhou. An anti-jamming hybrid beamforming system designed based on support vector machine algorithm[J]. High Power Laser and Particle Beams, 2021, 33(12): 123020 Copy Citation Text

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Fig. 1. Structure of hybrid beamforming system

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Fig. 2. Communication network of muti-link

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Fig. 3. SINR vs total transmitted power, the number of interference is 2

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Fig. 4. SINR vs total transmitted power, the number of interference is 6

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Fig. 5. Running timevs number of iteration

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Fig. 6. Running time vs number of antennas

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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

Table 1. GP Algorithm

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