论文标题
低复杂性SLP：无反转的，可行的ADMM方法
Low Complexity SLP: An Inversion-Free, Parallelizable ADMM Approach
论文作者
论文摘要
我们提出了一种平行的建设性干扰（CI）基于符号级的预编码（SLP）方法，用于在多源多输入单输出（MU-MISO）系统的下行链路中进行大规模连通性，仅在每个处理器单元和有限的信息交换之间使用局部通道状态信息（CSI）。通过对功率最小化（PM）SLP问题进行重新调整并利用相应重新制定的可分离性，通过使用封闭式解决方案的ADMM框架将原始问题分解为几个平行的子问题，从而大大降低了计算复杂性。根据自适应参数调整策略来加速收敛速率，可以得出确保所提出方法收敛的充分条件。为避免大维矩阵逆操作，通过使用标准近端项并利用单数值分解（SVD）提出了有效的算法。此外，采用代理近端项以完全消除矩阵倒置，并最终获得了平行的无反向SLP（PIF-SLP）算法。数值结果验证了我们上面的推导，并证明所提出的PIF-SLP算法可以显着降低计算复杂性与最先进的算法相比。
We propose a parallel constructive interference (CI)-based symbol-level precoding (SLP) approach for massive connectivity in the downlink of multiuser multiple-input single-output (MU-MISO) systems, with only local channel state information (CSI) used at each processor unit and limited information exchange between processor units. By reformulating the power minimization (PM) SLP problem and exploiting the separability of the corresponding reformulation, the original problem is decomposed into several parallel subproblems via the ADMM framework with closed-form solutions, leading to a substantial reduction in computational complexity. The sufficient condition for guaranteeing the convergence of the proposed approach is derived, based on which an adaptive parameter tuning strategy is proposed to accelerate the convergence rate. To avoid the large-dimension matrix inverse operation, an efficient algorithm is proposed by employing the standard proximal term and by leveraging the singular value decomposition (SVD). Furthermore, a prox-linear proximal term is adopted to fully eliminate the matrix inversion, and a parallel inverse-free SLP (PIF-SLP) algorithm is finally obtained. Numerical results validate our derivations above, and demonstrate that the proposed PIF-SLP algorithm can significantly reduce the computational complexity compared to the state-of-the-arts.
