Interference-Aware Super-Constellation Design for NOMA
This addresses a challenge in implementing NOMA for next-generation wireless systems, offering a novel method to enhance performance, though it appears incremental as it builds on existing autoencoder and NOMA concepts.
The paper tackled the problem of high bit error rates in non-orthogonal multiple access (NOMA) with finite-alphabet inputs due to inter-user interference, by using autoencoders to design interference-aware super-constellations that improve bit error rates and remove the need for successive interference cancellation.
Non-orthogonal multiple access (NOMA) has gained significant attention as a potential next-generation multiple access technique. However, its implementation with finite-alphabet inputs faces challenges. Particularly, due to inter-user interference, superimposed constellations may have overlapping symbols leading to high bit error rates when successive interference cancellation (SIC) is applied. To tackle the issue, this paper employs autoencoders to design interference-aware super-constellations. Unlike conventional methods where superimposed constellation may have overlapping symbols, the proposed autoencoder-based NOMA (AE-NOMA) is trained to design super-constellations with distinguishable symbols at receivers, regardless of channel gains. The proposed architecture removes the need for SIC, allowing maximum likelihood-based approaches to be used instead. The paper presents the conceptual architecture, loss functions, and training strategies for AE-NOMA. Various test results are provided to demonstrate the effectiveness of interference-aware constellations in improving the bit error rate, indicating the adaptability of AE-NOMA to different channel scenarios and its promising potential for implementing NOMA systems