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Resource Allocation for the Training of Image Semantic Communication Networks

Yang Li, Xinyu Zhou, Jun Zhao

TL;DR

The paper tackles the challenge of training image SemCom models under resource constraints by proposing a distributed framework where the base station and mobile devices collaboratively train DL-based SemCom models. It formulates a joint time-energy optimization problem and develops an adaptable resource allocation algorithm that decouples the problem into tractable subproblems assessed via convex optimization (KKT) and fractional programming with Newton-like methods. The approach yields global optimality for the subproblems and converges under the analyzed conditions, with extensive simulations showing reduced latency and energy while maintaining or improving PSNR on CIFAR-10. The work provides practical insights for deploying SemCom in resource-limited edge networks and highlights potential extensions to fading channels and DRL-based strategies for scalability.

Abstract

Semantic communication is a new paradigm that aims at providing more efficient communication for the next-generation wireless network. It focuses on transmitting extracted, meaningful information instead of the raw data. However, deep learning-enabled image semantic communication models often require a significant amount of time and energy for training, which is unacceptable, especially for mobile devices. To solve this challenge, our paper first introduces a distributed image semantic communication system where the base station and local devices will collaboratively train the models for uplink communication. Furthermore, we formulate a joint optimization problem to balance time and energy consumption on the local devices during training while ensuring effective model performance. An adaptable resource allocation algorithm is proposed to meet requirements under different scenarios, and its time complexity, solution quality, and convergence are thoroughly analyzed. Experimental results demonstrate the superiority of our algorithm in resource allocation optimization against existing benchmarks and discuss its impact on the performance of image semantic communication systems.

Resource Allocation for the Training of Image Semantic Communication Networks

TL;DR

The paper tackles the challenge of training image SemCom models under resource constraints by proposing a distributed framework where the base station and mobile devices collaboratively train DL-based SemCom models. It formulates a joint time-energy optimization problem and develops an adaptable resource allocation algorithm that decouples the problem into tractable subproblems assessed via convex optimization (KKT) and fractional programming with Newton-like methods. The approach yields global optimality for the subproblems and converges under the analyzed conditions, with extensive simulations showing reduced latency and energy while maintaining or improving PSNR on CIFAR-10. The work provides practical insights for deploying SemCom in resource-limited edge networks and highlights potential extensions to fading channels and DRL-based strategies for scalability.

Abstract

Semantic communication is a new paradigm that aims at providing more efficient communication for the next-generation wireless network. It focuses on transmitting extracted, meaningful information instead of the raw data. However, deep learning-enabled image semantic communication models often require a significant amount of time and energy for training, which is unacceptable, especially for mobile devices. To solve this challenge, our paper first introduces a distributed image semantic communication system where the base station and local devices will collaboratively train the models for uplink communication. Furthermore, we formulate a joint optimization problem to balance time and energy consumption on the local devices during training while ensuring effective model performance. An adaptable resource allocation algorithm is proposed to meet requirements under different scenarios, and its time complexity, solution quality, and convergence are thoroughly analyzed. Experimental results demonstrate the superiority of our algorithm in resource allocation optimization against existing benchmarks and discuss its impact on the performance of image semantic communication systems.
Paper Structure (30 sections, 3 theorems, 57 equations, 10 figures, 2 tables, 3 algorithms)

This paper contains 30 sections, 3 theorems, 57 equations, 10 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. DL-enabled Semantic Communication Model
  4. Resource Allocation in SemCom Networks
  5. Comparison between Our Work and Related Studies
  6. System Model
  7. Training of Semantic Communication Network
  8. Computation and Transmission Model
  9. SemCom Performance Analysis
  10. Joint Optimization Problem Formulation
  11. Problem Formulation
  12. Challenges and Strategies of Solving Problem $\mathbb{P}_1$
  13. Solutions to the joint optimization problem
  14. Solution to $\mathbb{P}_3$
  15. Solution to SUB2
  16. ...and 15 more sections

Key Result

Lemma 1

For Problem $\mathbb{P}_4$, we have:

Figures (10)

  • Figure 1: System model.
  • Figure 2: Interploted surfaces and fitted results for CIFAR-10. Specifically, each data point (i.e., different SNR and compression rate) was run ten times, and the average accuracy was taken to reduce experimental error.
  • Figure 3: Time, energy, and their weighted sum under different total bandwidth $B_{\textnormal{total}}$.
  • Figure 4: Time, energy, and their weighted sum under different maximum transmission power $p_n^{\textnormal{max}}$.
  • Figure 5: Time, energy, and their weighted sum under different maximum frequency $f_n^{\textnormal{max}}$.
  • ...and 5 more figures

Theorems & Definitions (7)

  • Lemma 1
  • proof
  • Remark 1
  • Lemma 2
  • proof
  • Theorem 1
  • proof