Energy-Efficient Image Semantic Communication: Architecture Design and Optimal Joint Allocation of Communication and Computation Resources

Semantic communication is an emerging paradigm with significant potential for image transmission. However, resource-efficient architecture design and resource allocation in this field have not received adequate research attention. This paper proposes a resource-efficient multi-branch semantic communication architecture based on saliency detection, aimed at optimizing computational efficiency in image transmission. The architecture leverages models with varying capacities to process regions of images with different complexities. We further address the problem of multi-user uplink semantic communication and resource allocation, focusing on minimizing the total energy consumption for communication and computation. The optimization problem, subject to user demand, computation, delay, and transmission power constraints, is non-convex due to the coupling of variables, making it challenging to solve. To tackle this, we introduce a two-level decomposition approach. The lower-level problem, given a fixed compression rate, is solved using Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission power and computation frequency. The upper-level problem, which optimizes the compression rate, is reformulated as a monotone optimization problem for efficient solution finding. Numerical results demonstrate that the proposed architecture significantly reduces computational resource usage while maintaining image quality, and the resource allocation strategy effectively minimizes energy consumption, outperforming baseline schemes in terms of energy efficiency.