Semantic Feature Division Multiple Access for Multi-user Digital Interference Networks

TL;DR

This paper introduces a novel semantic feature division multiple access (SFDMA) scheme utilizing deep learning to enable multiple users to transmit simultaneously over the same resources, reducing interference and improving performance in 5G networks.

Contribution

It proposes a new SFDMA paradigm with discrete semantic representations, a theoretical ABG formula relating inference accuracy and power, and adaptive power control methods for multi-user interference networks.

Findings

SFDMA achieves approximate orthogonal transmission in interference networks.

The ABG formula links inference accuracy with transmission power.

Simulations demonstrate the effectiveness and superiority of SFDMA.

Abstract

With the ever-increasing user density and quality of service (QoS) demand,5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extracts multi-user semantic information into discrete representations in distinguishable semantic subspaces, which enables multiple users to transmit simultaneously over the same time-frequency resources. Furthermore, based on a robust information bottleneck, we design a SFDMA based multi-user digital semantic interference network for inference tasks, which can achieve approximate orthogonal transmission. Moreover, we propose a SFDMA based multi-user digital semantic interference network for image reconstruction tasks, where the discrete outputs of the semantic encoders of the users are approximately orthogonal, which significantly reduces multi-user interference. Furthermore, we propose an Alpha-Beta-Gamma (ABG) formula for semantic communications, which is the first theoretical relationship between inference accuracy and transmission power. Then, we derive adaptive power control methods with closed-form expressions for inference tasks. Extensive simulations verify the effectiveness and superiority of the proposed SFDMA.