论文标题
通过多任务学习，用于RIS辅助的Terahertz Mimo网络的合作梁选择
Cooperative Beam Selection for RIS-Aided Terahertz MIMO Networks via Multi-Task Learning
论文作者
论文摘要
可重新配置的智能表面（RIS）已被铸造为减轻阻塞脆弱性并增强Terahertz（THZ）通信的覆盖能力的有希望的替代方法。由于收发器和RIS的大规模数组元素，基于代码书的波束形成可以以计算有效的方式使用。但是，用于模拟波束形成的代码字选择是一种棘手的组合优化（CO）问题。为此，通过将CO问题作为分类问题，开发了基于多任务学习的模拟束选择（MTL-ABS）框架，以同时在收发和RIS中同时实现合作束选择。此外，剩余网络和自我注意力的机制用于对抗网络降解和矿山内在的THZ通道特征。最后，从区块的角度分析了网络收敛，数值结果表明，与基于启发式搜索的对应物相比，MTL-ABS框架大大降低了光束选择开销，并且达到接近最佳的总和结果。
Reconfigurable intelligent surface (RIS) have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz (THz) communications. Owing to large-scale array elements at transceivers and RIS, the codebook based beamforming can be utilized in a computationally efficient manner. However, the codeword selection for analog beamforming is an intractable combinatorial optimization (CO) problem. To this end, by taking the CO problem as a classification problem, a multi-task learning based analog beam selection (MTL-ABS) framework is developed to implement cooperative beam selection concurrently at transceivers and RIS. In addition, residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features. Finally, the network convergence is analyzed from a blockwise perspective, and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.
