过载 NOMA 系统中的鲁棒符号检测
Robust Symbol Detection in Overloaded NOMA Systems
AI总结：
针对过载 NOMA 系统，提出基于 L0 正则化最大似然、二次变换和 QCQP-1 的低复杂度 IDLS 检测器，并扩展为适应噪声、不完美 CSI 与硬件损伤的 Robust IDLS，性能优于 LMMSE、SOAV 和 SCSR。
摘要：
我们提出了一个框架，用于为多维过载 NOMA 系统设计低复杂度、高性能接收机。该框架建立在离散输入检测问题的一种新型压缩感知（CS）正则化最大似然表述之上，其中引入 L0 范数以迫使解遵循预定的离散符号星座。与以往许多文献不同，该方法并未松弛为 L1 范数，而是采用连续且渐近精确的表达式进行近似，且无需借助并行干扰消除。由此得到的表述，其目标函数是凹函数与凸函数之比的和；随后通过二次变换对其进行紧密凸化，使其解能够通过一个简单闭式表达式的迭代获得，该表达式与经典零迫（ZF）接收机的表达式非常相似。通过进一步将上述问题转化为带有一个凸约束的二次约束二次规划（QCQP-1），可以证明，在迭代算法每一步中使用的最优正则化参数是一对以闭式给出的矩阵的最大广义特征值。由此得到的方法称为 IDLS，随后被扩展以处理若干实际相关因素，例如噪声条件、不完美 CSI 和硬件损伤，从而产生 Robust IDLS 算法。仿真结果表明，所提出的技术显著优于 LMMSE 等经典接收机，以及 SOAV 和 SCSR 检测器等近期基于 CS 的替代方案。
英文摘要：
We present a framework for the design of low-complexity and high-performance receivers for multidimensional overloaded NOMA systems. The framework is built upon a novel compressive sensing (CS) regularized maximum likelihood formulation of the discrete-input detection problem, in which the L0-norm is introduced to enforce adherence of the solution to the prescribed discrete symbol constellation. Unlike much of preceding literature, the method is not relaxed into the L1-norm, but rather approximated with a continuous and asymptotically exact expression without resorting to parallel interference cancellation. The objective function of the resulting formulation is thus a sum of concave-over-convex ratios, which is then tightly convexized via the quadratic transform, such that its solution can be obtained via the iteration of a simple closed-form expression that closely resembles that of the classic zero-forcing (ZF) receiver. By further transforming the aforementioned problem into a quadratically constrained quadratic program with one convex constraint (QCQP-1), the optimal regularization parameter to be used at each step of the iterative algorithm is then shown to be the largest generalized eigenvalue of a pair of matrices which are given in closed-form. The method so obtained, referred to as the IDLS, is then extended to address several factors of practical relevance, such as noisy conditions, imperfect CSI, and hardware impairments, thus yielding the Robust IDLS algorithm. Simulation results show that the proposed art significantly outperforms both classic receivers, such as the LMMSE, and recent CS-based alternatives, such as the SOAV and the SCSR detectors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
