Adaptive Resource Allocation for Semantic Communication Networks
- URL: http://arxiv.org/abs/2312.01081v1
- Date: Sat, 2 Dec 2023 09:12:12 GMT
- Title: Adaptive Resource Allocation for Semantic Communication Networks
- Authors: Lingyi Wang, Wei Wu, Fuhui Zhou, Zhaohui Yang, Zhijin Qin
- Abstract summary: 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.
- Score: 34.189531352110386
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic communication, recognized as a promising technology for future
intelligent applications, has received widespread research attention. Despite
the potential of semantic communication to enhance transmission reliability,
especially in low signal-to-noise (SNR) environments, the critical issue of
resource allocation and compatibility in the dynamic wireless environment
remains largely unexplored. In this paper, we propose an adaptive semantic
resource allocation paradigm with semantic-bit quantization (SBQ) compatibly
for existing wireless communications, where the inaccurate environment
perception introduced by the additional mapping relationship between semantic
metrics and transmission metrics is solved. In order to investigate the
performance of semantic communication networks, the quality of service for
semantic communication (SC-QoS), including the semantic quantization efficiency
(SQE) and transmission latency, is proposed for the first time. A problem of
maximizing the overall effective SC-QoS is formulated by jointly optimizing the
transmit beamforming of the base station, the bits for semantic representation,
the subchannel assignment, and the bandwidth resource allocation. To address
the non-convex formulated problem, an intelligent resource allocation scheme is
proposed based on a hybrid deep reinforcement learning (DRL) algorithm, where
the intelligent agent can perceive both semantic tasks and dynamic wireless
environments. Simulation results demonstrate that our design can effectively
combat semantic noise and achieve superior performance in wireless
communications compared to several benchmark schemes. Furthermore, compared to
mapping-guided paradigm based resource allocation schemes, our proposed
adaptive scheme can achieve up to 13% performance improvement in terms of
SC-QoS.
Related papers
- Take What You Need: Flexible Multi-Task Semantic Communications with Channel Adaptation [51.53221300103261]
This article introduces a novel channel-adaptive and multi-task-aware semantic communication framework based on a masked auto-encoder architecture.
A channel-aware extractor is employed to dynamically select relevant information in response to real-time channel conditions.
Experimental results demonstrate the superior performance of our framework compared to conventional methods in tasks such as image reconstruction and object detection.
arXiv Detail & Related papers (2025-02-12T09:01:25Z) - 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) - Bridging Neural Networks and Wireless Systems with MIMO-OFDM Semantic Communications [31.886033455714]
This article focuses on the practical impacts of power amplifier (PA) nonlinearity and peak-to-average power ratio (PAPR) variations in a semantic communication system.
By addressing key limitations in existing designs, we provide actionable insights for advancing semantic communications in practical wireless environments.
arXiv Detail & Related papers (2025-01-28T06:07:39Z) - 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) - IRS-Enhanced Secure Semantic Communication Networks: Cross-Layer and Context-Awared Resource Allocation [30.000606717755833]
The challenge of eavesdropping poses a formidable threat to semantic privacy due to the open nature of wireless communications.
In this paper, intelligent reflective surface (IRS)-enhanced secure semantic communication (IRS-SSC) is proposed to guarantee the physical layer security from a task-oriented semantic perspective.
We propose a novel semantic awared state space (SCA-SS) to fusion the high-dimensional semantic space and the observable system state space.
arXiv Detail & Related papers (2024-11-04T05:40:30Z) - Hypergame Theory for Decentralized Resource Allocation in Multi-user Semantic Communications [60.63472821600567]
A novel framework for decentralized computing and communication resource allocation in multiuser SC systems is proposed.
The challenge of efficiently allocating communication and computing resources is addressed through the application of Stackelberg hyper game theory.
Simulation results show that the proposed Stackelberg hyper game results in efficient usage of communication and computing resources.
arXiv Detail & Related papers (2024-09-26T15:55:59Z) - Agent-driven Generative Semantic Communication with Cross-Modality and Prediction [57.335922373309074]
We propose a novel agent-driven generative semantic communication framework based on reinforcement learning.
In this work, we develop an agent-assisted semantic encoder with cross-modality capability, which can track the semantic changes, channel condition, to perform adaptive semantic extraction and sampling.
The effectiveness of the designed models has been verified using the UA-DETRAC dataset, demonstrating the performance gains of the overall A-GSC framework.
arXiv Detail & Related papers (2024-04-10T13:24:27Z) - Dynamic Relative Representations for Goal-Oriented Semantic Communications [13.994922919058922]
semantic and effectiveness aspects of communications will play a fundamental role in 6G wireless networks.
In latent space communication, this challenge manifests as misalignment within high-dimensional representations where deep neural networks encode data.
This paper presents a novel framework for goal-oriented semantic communication, leveraging relative representations to mitigate semantic mismatches.
arXiv Detail & Related papers (2024-03-25T17:48:06Z) - Reasoning with the Theory of Mind for Pragmatic Semantic Communication [62.87895431431273]
A pragmatic semantic communication framework is proposed in this paper.
It enables effective goal-oriented information sharing between two-intelligent agents.
Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits.
arXiv Detail & Related papers (2023-11-30T03:36:19Z) - 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) - Wireless Resource Management in Intelligent Semantic Communication
Networks [15.613654766345702]
We address the user association (UA) and bandwidth allocation problems in an ISC-enabled heterogeneous network (ISC-HetNet)
We propose a two-stage solution, including a programming method to obtain an objective, and a algorithm in the second stage to reach the optimality of UA and BA.
arXiv Detail & Related papers (2022-02-15T18:28:28Z)
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.