Sequence Spreading-Based Semantic Communication Under High RF Interference

• URL: http://arxiv.org/abs/2501.12502v1
• Date: Tue, 21 Jan 2025 21:08:40 GMT
• Title: Sequence Spreading-Based Semantic Communication Under High RF Interference
• Authors: Hazem Barka, Georges Kaddoum, Mehdi Bennis, Md Sahabul Alam, Minh Au,
• Abstract summary: We propose a novel approach based on integrating sequence spreading techniques with SemCom to enhance system robustness against adverse conditions. We also propose a novel signal refining network (SRN) to refine the received signal after despreading and equalization. The proposed network eliminates the need for computationally intensive end-to-end (E2E) training while improving performance metrics.
• Score: 42.00742666044546
• License:
• Abstract: In the evolving landscape of wireless communications, semantic communication (SemCom) has recently emerged as a 6G enabler that prioritizes the transmission of meaning and contextual relevance over conventional bit-centric metrics. However, the deployment of SemCom systems in industrial settings presents considerable challenges, such as high radio frequency interference (RFI), that can adversely affect system performance. To address this problem, in this work, we propose a novel approach based on integrating sequence spreading techniques with SemCom to enhance system robustness against such adverse conditions and enable scalable multi-user (MU) SemCom. In addition, we propose a novel signal refining network (SRN) to refine the received signal after despreading and equalization. The proposed network eliminates the need for computationally intensive end-to-end (E2E) training while improving performance metrics, achieving a 25% gain in BLEU score and a 12% increase in semantic similarity compared to E2E training using the same bandwidth.

Related papers

• Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
  Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks. Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance. We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
  arXiv  Detail & Related papers  (2025-01-20T04:26:21Z)
• AoI in Context-Aware Hybrid Radio-Optical IoT Networks [8.467370216900107]
  We study hybrid IoT networks that employ Optical Communication (OC) as a reinforcement medium to Radio Frequency (RF) We adopt a multi-objective optimization strategy to balance the collection of the throughput with the minimization of energy and the frequency of switching between technologies. Simulation results show that the OC supplementary integration alongside enhances the network's overall performances and significantly reduces the Mean AoI and Peak AoI.
  arXiv  Detail & Related papers  (2024-12-17T13:48:25Z)
• UAV Virtual Antenna Array Deployment for Uplink Interference Mitigation in Data Collection Networks [71.23793087286703]
  Unmanned aerial vehicles (UAVs) have gained considerable attention as a platform for establishing aerial wireless networks and communications. This paper explores a novel uplink interference mitigation approach based on the collaborative beamforming (CB) method in multi-UAV network systems.
  arXiv  Detail & Related papers  (2024-12-09T12:56:50Z)
• OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
  This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
  arXiv  Detail & Related papers  (2024-06-07T09:20:30Z)
• Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels [11.108614988357008]
  We propose an interference-robust semantic communication (IRSC) scheme for general multiple-input multiple-output (MIMO) interference channels. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches.
  arXiv  Detail & Related papers  (2024-04-10T11:40:22Z)
• Adaptive Resource Allocation for Semantic Communication Networks [34.189531352110386]
  This paper investigates the quality of service for semantic communication networks, including the semantic quantization efficiency (SQE) and transmission latency. A problem maximizing the overall effective SC-QoS is formulated by jointly the transmit beamforming the base station, the bits semantic representation the subchannel assignment, and the semantic resource allocation. Our design can effectively combat semantic noise and achieve superior performance in wireless communications compared to several benchmark schemes.
  arXiv  Detail & Related papers  (2023-12-02T09:12:12Z)
• A Wireless AI-Generated Content (AIGC) Provisioning Framework Empowered by Semantic Communication [53.78269720999609]
  This paper proposes a semantic communication (SemCom)-empowered AIGC (SemAIGC) generation and transmission framework. Specifically, SemAIGC integrates diffusion models within the semantic encoder and decoder to design a workload-adjustable transceiver. Simulations verify the superiority of our proposed SemAIGC framework in terms of latency and content quality compared to conventional approaches.
  arXiv  Detail & Related papers  (2023-10-26T18:05:22Z)
• Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interference [89.91583691993071]
  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.
  arXiv  Detail & Related papers  (2023-02-15T05:43:08Z)

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.