Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference

• URL: http://arxiv.org/abs/2302.14702v3
• Date: Fri, 23 Feb 2024 21:54:33 GMT
• Title: Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference
• Authors: Tilahun M. Getu, Walid Saad, Georges Kaddoum, and Mehdi Bennis
• Abstract summary: We study the performance limits of a popular text SemCom system named DeepSC in the presence of (multi-interferer) RFI. We show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC's practical limits and a lower bound on its outage probability under multi-interferer RFI.
• Score: 89.91583691993071
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although deep learning (DL)-enabled semantic communication (SemCom) has emerged as a 6G enabler by minimizing irrelevant information transmission -- minimizing power usage, bandwidth consumption, and transmission delay, its benefits can be limited by radio frequency interference (RFI) that causes substantial semantic noise. Such semantic noise's impact can be alleviated using an interference-resistant and robust (IR$^2$) SemCom design, though no such design exists yet. To stimulate fundamental research on IR2 SemCom, the performance limits of a popular text SemCom system named DeepSC are studied in the presence of (multi-interferer) RFI. By introducing a principled probabilistic framework for SemCom, we show that DeepSC produces semantically irrelevant sentences as the power of (multi-interferer) RFI gets very large. We also derive DeepSC's practical limits and a lower bound on its outage probability under multi-interferer RFI, and propose a (generic) lifelong DL-based IR$^2$ SemCom system. We corroborate the derived limits with simulations and computer experiments, which also affirm the vulnerability of DeepSC to a wireless attack using RFI.

Related papers

• RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach [10.626169088908867]
  Current AI-based semantic communication methods require digital hardware for implementation. RIS-based semantic communications offer appealing features, such as light-speed computation, low computational power requirements, and the ability to handle multiple tasks simultaneously.
  arXiv  Detail & Related papers  (2023-12-01T12:15:49Z)
• Dynamic Sparsity Is Channel-Level Sparsity Learner [91.31071026340746]
  Dynamic sparse training (DST) is a leading sparse training approach. Channel-aware dynamic sparse (Chase) seamlessly translates the promise of unstructured dynamic sparsity to channel-level sparsity. Our approach translates unstructured sparsity to channel-wise sparsity.
  arXiv  Detail & Related papers  (2023-05-30T23:33:45Z)
• Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach [74.25870052841226]
  A digital twin (DT) leverages a virtual representation of the physical world, along with communication (e.g., 6G), computing, and artificial intelligence (AI) technologies to enable many connected intelligence services. Wireless systems can exploit the paradigm of semantic communication (SC) for facilitating informed decision-making under strict communication constraints. A novel framework called causal semantic communication (CSC) is proposed for DT-based wireless systems.
  arXiv  Detail & Related papers  (2023-04-25T00:15:00Z)
• Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces [56.35676570414731]
  The emerging technology of Reconfigurable Intelligent Surfaces (RISs) is provisioned as an enabler of smart wireless environments. RISs offer a highly scalable, low-cost, hardware-efficient, and almost energy-neutral solution for dynamic control of the propagation of electromagnetic signals over the wireless medium. One of the major challenges with the envisioned dense deployment of RISs in such reconfigurable radio environments is the efficient configuration of multiple metasurfaces.
  arXiv  Detail & Related papers  (2022-05-08T06:21:33Z)
• Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access [65.21117658030235]
  This work proposes a novel design for a practical RSMA receiver based on model-based deep learning (MBDL) methods. The MBDL receiver is evaluated in terms of uncoded Symbol Error Rate (SER), throughput performance through Link-Level Simulations (LLS) and average training overhead. Results reveal that the MBDL outperforms by a significant margin the SIC receiver with imperfect CSIR.
  arXiv  Detail & Related papers  (2022-05-02T12:23:55Z)
• Reconfigurable Intelligent Surface Enabled Spatial Multiplexing with Fully Convolutional Network [40.817290717344534]
  Reconfigurable surface (RIS) is an emerging technology for wireless communication systems. We propose to apply a fully convolutional network (WSNFC) to solve this problem. We design a set of channel features that includes both cascaded channels via the RIS and the direct channel.
  arXiv  Detail & Related papers  (2022-01-08T14:16:00Z)
• Phase Configuration Learning in Wireless Networks with Multiple Reconfigurable Intelligent Surfaces [50.622375361505824]
  Reconfigurable Intelligent Surfaces (RISs) are highly scalable technology capable of offering dynamic control of electro-magnetic wave propagation. One of the major challenges with RIS-empowered wireless communications is the low-overhead dynamic configuration of multiple RISs. We devise low-complexity supervised learning approaches for the RISs' phase configurations.
  arXiv  Detail & Related papers  (2020-10-09T05:35:27Z)
• Reinforcement Learning for Mitigating Intermittent Interference in Terahertz Communication Networks [4.999585439793266]
  Uncoordinated transmissions by a large number of users can cause substantial interference in terahertz networks. New framework based on reinforcement learning is proposed that uses an adaptive multi-thresholding strategy.
  arXiv  Detail & Related papers  (2020-03-10T16:28:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.