An SCMA Receiver for 6G NTN based on Multi-Task Learning

TL;DR

This paper proposes an AI-based multi-task learning receiver for SCMA in 6G non-terrestrial networks, significantly improving error rates and energy efficiency over traditional methods.

Contribution

It introduces a novel AI-driven SCMA receiver tailored for 6G NTN, demonstrating enhanced performance and lower energy requirements compared to existing algorithms.

Findings

Achieves 10% BLER at 3.5dB lower Eb/N0 than benchmarks.

Reduces uplink energy consumption for reliable communication.

Highlights complexity challenges for satellite implementation.

Abstract

Future 6G networks are envisioned to enhance the user experience in a multitude of different ways. The unification of existing terrestrial networks with non-terrestrial network (NTN) components will provide users with ubiquitous connectivity. Multi-access edge computing (MEC) will enable low-latency services, with computations performed closer to the end users, and distributed learning paradigms. Advanced multiple access schemes, such as sparse code multiple access (SCMA), can be employed to efficiently move data from edge nodes to spaceborne MEC servers. However, the non-orthogonal nature of SCMA results in interference, limiting the effectiveness of traditional SCMA receivers. Hence, NTN links should be protected with robust channel codes, significantly reducing the uplink throughput. Thus, we investigate the application of artificial intelligence (AI) to SCMA receivers for 6G NTNs. We train an AI model with multi-task learning to optimally separate and receive superimposed SCMA signals. Through link level simulations, we evaluate the block error rate (BLER) and the aggregated theoretical throughput achieved by the AI model as a function of the received energy per bit over noise power spectral density ratio (Eb/N0). We show that the proposed receiver achieves a target 10% BLER with 3.5dB lower Eb/N0 with respect to the benchmark algorithm. We conclude the assessment discussing the complexity-related challenges to the implementation of the AI model on board of a low earth orbit satellite.