机译:深度学习使得在每天线功率约束下的下行波束形成优化:算法和实验示范
Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England;
Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore;
Univ Durham Dept Engn Durham DH1 3LE England;
Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England;
Loughborough Univ Wolfson Sch Mech Elect & Mfg Engn Loughborough LE11 3TU Leics England;
UCL Dept Elect & Elect Engn London WC1E 6BT England;
Array signal processing; Signal to noise ratio; Interference; Training; Downlink; Wireless communication; Optimization; Deep learning; beamforming; multiple-input-single-output (MISO); signal-to-interference-plus-noise ratio (SINR) balancing; per-antenna power constraints;
机译:深度学习在每天线功率约束下,使得下行链路波束形成优化:算法和实验演示
机译:MISO NOMA下行链路波束形成优化,具有每个天线功率约束
机译:在每个天线功率约束和均等速率指标下的多用户MISO下行链路系统中的迫零波束成形
机译:具有每个天线功率限制的下行链路波束成形和功率控制
机译:具有每个天线功率约束的下行链路广播信道中的输入优化。
机译:粒子群优化动态变异人工免疫系统和引力搜索算法辅助的线性约束最小方差自适应波束成形的空转向
机译:深度学习使得在每天线功率约束下的下行波束形成优化:算法和实验示范
机译:下行发送波束成形算法综述