  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.
