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Found 4 papers, 1 papers with code
Parallel APSM for Fast and Adaptive Digital SIC in Full-Duplex Transceivers with Nonlinearity
This paper presents a kernel-based adaptive filter that is applied for the digital domain self-interference cancellation (SIC) in a transceiver operating in full-duplex (FD) mode.
GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access
Non-orthogonal multiple access (NOMA) is an interesting technology that enables massive connectivity as required in future 5G and 6G networks.
GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems
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.
Machine Learning-Based Adaptive Receive Filtering: Proof-of-Concept on an SDR Platform
As an alternative to conventional methods, this paper proposes and demonstrates a low-complexity practical Machine Learning (ML) based receiver that achieves similar (and at times better) performance to the SIC receiver.
