  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.
