Reconfigurable intelligent surface (RIS) and hybrid beamforming have been envisioned as promising alternatives to alleviate blockage vulnerability and enhance coverage capability for terahertz (THz) multi-user multiple-input multiple-output systems that suffer from severe propagation attenuation and poor diffraction. Considering that the joint beamforming with large-scale array elements at transceivers and RIS is extremely complicated, the codebook based beamforming can be employed in a computationally efficient manner. However, the codeword selection for analog beamforming is an intractable combinatorial optimization (CO) problem. To this end, an iterative alternating search (IAS) algorithm is developed to achieve a near-optimal sum-rate performance with low computational complexity in contrast with the optimal exhaustive search algorithm. According to the THz channel dataset generated by the IAS algorithm, a multi-task learning based analog beam selection (MTL-ABS) framework is developed to further decrease the computation overhead. Specifically, we take the CO problem as a multi-task classification problem and implement multiple beam selection tasks at transceivers and RIS simultaneously. Remarkably, residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features. Finally, blockwise convergence analysis and numerical results demonstrate the effectiveness of the MTL-ABS framework over search based counterparts.
翻译:重新配置智能表面(RIS)和混合波束成形,被认为是大有希望的替代方法,可以缓解阻塞脆弱性,提高受严重传播衰减和差分分差影响、多用户多用户多输入多输出输出系统的覆盖能力。考虑到在收发器和RIS中与大型阵列元素联成的光束极为复杂,基于光束成形的编码手册可以以计算效率的方式加以使用。但是,模拟波形成形的编码选择是一个棘手的组合优化问题。为此,开发了迭代交替搜索(IAS)算法,以取得接近最佳的优化和低计算率的超速和超低计算率的计算率功能。根据IAS算法生成的THz频道数据集,正在开发一个基于模拟波段选择的多任务学习框架(MTL-ABS),以进一步减少计算间接费用。具体地说,我们把CO问题视为一个多任务分类问题,并实施了多任务交错调调调调调,为此,在最后的阵列内置网络上,同时展示了基于内存的搜索网和内部降解结果的自我分析。