In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform. Partially linear multiuser detection, which combines the robustness of linear detection with the power of nonlinear methods, has been proposed for a massive connectivity scenario with the non-orthogonal multiple access (NOMA). This is a promising approach, but detecting payloads within a received orthogonal frequency division multiplexing (OFDM) radio frame requires the execution of a large number of inner product operations, which are the main computational burden of the algorithm. Although inner-product operations consist of simple kernel evaluations, their vast number poses a challenge in ultra-low latency (ULL) applications, because the time needed for computing the inner products might exceed the sub-millisecond latency requirement. To address this problem, this study demonstrates the acceleration of the inner-product operations through massive parallelization. The result is a GPU-accelerated real-time OFDM receiver that enables sub-millisecond latency detection to meet the requirements of 5th generation (5G) and beyond ultra-reliable and low latency communications (URLLC) systems. Moreover, the parallelization and acceleration techniques explored and demonstrated in this study can be extended to many other signal processing algorithms in Hilbert spaces, such as those based on projection onto convex sets (POCS) and adaptive projected subgradient method (APSM) algorithms. Experimental results and comparisons with the state-of-art confirm the effectiveness of our techniques.
翻译:在这一可行性研究中,我们实施了最近提出的在GPU加速平台上复制核心Hilbert空间(RKHSs)的局部线性多用户检测算法。部分线性多用户检测算法,将线性检测的稳健性与非线性方法的力量结合起来,是为大规模连通情景和非横向多重访问(NOMA)提出的。这是一个很有希望的方法,但在接收或分频多重重叠(OFDM)无线电框架内检测有效载荷需要执行大量内部产品操作,这是算法的主要计算负担。虽然内产品操作包括简单的内核评估,但是其大量多用户检测在超低线性拉特度(ULLL)应用中构成挑战,因为计算内产所需的时间可能超过二级通量要求。为解决这一问题,这项研究表明,通过大规模平行平行化(GPU-加速实时DM接收器)的实时实时接收器,从而使得亚线性内核电解(5级平流率快速度测试)的快速度和快速递增速度技术满足了(LiLial-CS)的预测(Listral-Lialal-de)的预测,这些超快化技术的预测(5-Lial-Lial-lavical-Lial-Lial-Lislation-assilal-de)的快速探测和高级技术的预测,这些技术的预测的预测的预测的预测的预测的预测,可以满足了。