Resource Allocation for Semantic Communication under Physical-layer
Security
- URL: http://arxiv.org/abs/2312.04155v1
- Date: Thu, 7 Dec 2023 09:12:26 GMT
- Title: Resource Allocation for Semantic Communication under Physical-layer
Security
- Abstract summary: This paper proposes a joint optimization algorithm for total latency and utility.
We incorporate the secrecy rate, a physical-layer security method, into the optimization problem.
- Score: 13.221806733868975
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Semantic communication is deemed as a revolution of Shannon's paradigm in the
six-generation (6G) wireless networks. It aims at transmitting the extracted
information rather than the original data, which receivers will try to recover.
Intuitively, the larger extracted information, the longer latency of semantic
communication will be. Besides, larger extracted information will result in
more accurate reconstructed information, thereby causing a higher utility of
the semantic communication system. Shorter latency and higher utility are
desirable objectives for the system, so there will be a trade-off between
utility and latency. This paper proposes a joint optimization algorithm for
total latency and utility. Moreover, security is essential for the semantic
communication system. We incorporate the secrecy rate, a physical-layer
security method, into the optimization problem. The secrecy rate is the
communication rate at which no information is disclosed to an eavesdropper.
Experimental results demonstrate that the proposed algorithm obtains the best
joint optimization performance compared to the baselines.
Related papers
- Privacy-Preserving Semantic Communications via Multi-Task Learning and Adversarial Perturbations [49.494000480610225]
This paper presents a deep learning-based semantic communication framework.<n>It supports multiple receiver tasks while explicitly limiting semantic leakage to an eavesdropper.<n>A perturbation layer is successful in reducing semantic leakage even when the legitimate link is trained only for its own task.
arXiv Detail & Related papers (2025-12-30T20:19:49Z) - InfoCom: Kilobyte-Scale Communication-Efficient Collaborative Perception with Information Bottleneck [14.691852809650323]
InfoCom is an information-aware framework establishing the pioneering theoretical foundation for communication-efficient collaborative perception.<n>Experiments across multiple datasets demonstrate that InfoCom achieves near-lossless perception while reducing communication overhead from megabyte to kilobyte-scale, representing 440-fold and 90-fold reductions per agent compared to Where2comm and ERMVP, respectively.
arXiv Detail & Related papers (2025-12-11T05:51:02Z) - Security-Aware Joint Sensing, Communication, and Computing Optimization in Low Altitude Wireless Networks [83.84711311344918]
Integrated sensing, communications, and computing (I SCC) is one of the core parts of Low-Altitude Wireless Networks (LAWNs)<n>This paper studies joint performance optimization of I SCC while considering secrecyness of the communications.<n>We propose a deep Q-network (DQN)-based multi-objective evolutionary algorithm, which adaptively selects evolutionary operators according to the evolving optimization objectives.
arXiv Detail & Related papers (2025-11-03T11:06:41Z) - Optimization of Private Semantic Communication Performance: An Uncooperative Covert Communication Method [25.072439238973473]
A server extracts and transmits the semantic information, i.e., the meaning of image data, to a user over several time slots.<n>An attacker seeks to detect and eavesdrop the semantic transmission to acquire details of the original image.<n>A friendly jammer is deployed to transmit jamming signals to interfere the attacker so as to hide the transmitted semantic information.<n>We propose a prioritised sampling assisted twin delayed deep deterministic policy gradient algorithm to jointly determine the transmitted semantic information and the transmit power per time slot.
arXiv Detail & Related papers (2025-08-11T03:31:05Z) - Shuffling for Semantic Secrecy [12.708217189207828]
We devise a novel semantic security communication system wherein the random shuffling pattern plays the role of the shared secret key.<n>The proposed random shuffling method also exhibits its flexibility in working for the existing semantic communication system as a plugin.
arXiv Detail & Related papers (2025-07-10T03:42:17Z) - Diffusion-aided Task-oriented Semantic Communications with Model Inversion Attack [8.014010065113707]
We propose DiffSem, a diffusion-aided framework for task-oriented semantic communication.<n>DiffSem integrates a transmitter-side self-noising mechanism that adaptively regulates semantic content while compensating for channel noise.<n>Our experiments demonstrate that DiffSem enables the legitimate receiver to achieve higher accuracy, thereby validating the superior performance of the proposed framework.
arXiv Detail & Related papers (2025-06-24T05:21:27Z) - Distributionally Robust Wireless Semantic Communication with Large AI Models [120.29419104482793]
6G wireless systems are expected to support massive volumes of data with ultra-low latency.<n> conventional bit-level transmission strategies cannot support the efficiency and adaptability required by modern, data-intensive applications.<n>The concept of semantic communication (SemCom) addresses this limitation by focusing on transmitting task-relevant semantic information instead of raw data.
arXiv Detail & Related papers (2025-05-28T04:03:57Z) - Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks [55.32199894495722]
We investigate an LMM-based vehicle AI assistant using a Large Language and Vision Assistant (LLaVA)<n>To reduce computational demands and shorten response time, we optimize LLaVA's image slicing to selectively focus on areas of utmost interest to users.<n>We construct a Visual Question Answering (VQA) dataset for traffic scenarios to evaluate effectiveness.
arXiv Detail & Related papers (2025-05-05T07:18:47Z) - Semantic Communication based on Generative AI: A New Approach to Image Compression and Edge Optimization [1.450405446885067]
This thesis integrates semantic communication and generative models for optimized image compression and edge network resource allocation.
The communication infrastructure can benefit to significant improvements in bandwidth efficiency and latency reduction.
Results demonstrate the potential of combining generative AI and semantic communication to create more efficient semantic-goal-oriented communication networks.
arXiv Detail & Related papers (2025-02-01T21:48:31Z) - Semantic Communication Networks Empowered Artificial Intelligence of Things [2.590720801978138]
This paper presents a comprehensive survey of security and privacy threats across various layers of semantic communication systems.
We identify critical open issues in this burgeoning field warranting further investigation.
arXiv Detail & Related papers (2024-07-04T14:39:28Z) - Semantic Entropy Can Simultaneously Benefit Transmission Efficiency and Channel Security of Wireless Semantic Communications [55.54210451136529]
We propose SemEntropy to explore semantics of data for both adaptive transmission and physical layer encryption.
We show that SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission.
arXiv Detail & Related papers (2024-02-05T12:25:02Z) - 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) - Generative AI-aided Joint Training-free Secure Semantic Communications
via Multi-modal Prompts [89.04751776308656]
This paper proposes a GAI-aided SemCom system with multi-model prompts for accurate content decoding.
In response to security concerns, we introduce the application of covert communications aided by a friendly jammer.
arXiv Detail & Related papers (2023-09-05T23:24:56Z) - Cognitive Semantic Communication Systems Driven by Knowledge Graph:
Principle, Implementation, and Performance Evaluation [74.38561925376996]
Two cognitive semantic communication frameworks are proposed for the single-user and multiple-user communication scenarios.
An effective semantic correction algorithm is proposed by mining the inference rule from the knowledge graph.
For the multi-user cognitive semantic communication system, a message recovery algorithm is proposed to distinguish messages of different users.
arXiv Detail & Related papers (2023-03-15T12:01:43Z) - Knowledge Enhanced Semantic Communication Receiver [7.171974845607281]
We propose a knowledge enhanced semantic communication framework in which the receiver can more actively utilize the facts in the knowledge base for semantic reasoning and decoding.
Specifically, we design a transformer-based knowledge extractor to find relevant factual triples for the received noisy signal.
Extensive simulation results on the WebNLG dataset demonstrate that the proposed receiver yields superior performance on top of the knowledge graph enhanced decoding.
arXiv Detail & Related papers (2023-02-13T01:49:51Z) - Performance Optimization for Semantic Communications: An Attention-based
Reinforcement Learning Approach [187.4094332217186]
A semantic communication framework is proposed for textual data transmission.
A metric of semantic similarity (MSS) that jointly captures the semantic accuracy and completeness of the recovered text is proposed.
arXiv Detail & Related papers (2022-08-17T11:39:16Z) - Neuro-Symbolic Artificial Intelligence (AI) for Intent based Semantic
Communication [85.06664206117088]
6G networks must consider semantics and effectiveness (at end-user) of the data transmission.
NeSy AI is proposed as a pillar for learning causal structure behind the observed data.
GFlowNet is leveraged for the first time in a wireless system to learn the probabilistic structure which generates the data.
arXiv Detail & Related papers (2022-05-22T07:11:57Z) - Federated Reinforcement Learning at the Edge [1.4271989597349055]
Modern cyber-physical architectures use data collected from systems at different physical locations to learn appropriate behaviors and adapt to uncertain environments.
This paper considers a setup where multiple agents need to communicate efficiently in order to jointly solve a reinforcement learning problem over time-series data collected in a distributed manner.
An algorithm for achieving communication efficiency is proposed, supported with theoretical guarantees, practical implementations, and numerical evaluations.
arXiv Detail & Related papers (2021-12-11T03:28:59Z) - Federated Learning over Wireless IoT Networks with Optimized
Communication and Resources [98.18365881575805]
Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention.
It is of interest to investigate fast responding and accurate FL schemes over wireless systems.
We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
arXiv Detail & Related papers (2021-10-22T13:25:57Z)
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.