Semantic-Aware Resource Allocation in Constrained Networks with Limited User Participation

TL;DR

This work addresses resource allocation for semantic communications under tight bandwidth and power constraints in IoT-like networks. It models semantic transmission as graph-based feature extraction guided by device-specific knowledge bases and selects a subset of semantic triplets to maximize a semantic efficiency objective, under transmission time and BER constraints. The authors propose a two-stage solution: first derive closed-form power/bandwidth allocations under BER constraints, then apply an alternating optimization that alternates between sub-graph and user selection using convex relaxations and LPs, with a convergence guarantee to a local optimum. Simulations demonstrate that the proposed semantic-aware strategy outperforms fixed-bandwidth, random-bandwidth, and traditional approaches, achieving higher semantic efficiency and enabling efficient IoT-scale semantic communications.

Abstract

Semantic communication has gained attention as a key enabler for intelligent and context-aware communication. However, one of the key challenges of semantic communications is the need to tailor the resource allocation to meet the specific requirements of semantic transmission. In this paper, we focus on networks with limited resources where devices are constrained to transmit with limited bandwidth and power over large distance. Specifically, we devise an efficient strategy to select the most pertinent semantic features and participating users, taking into account the channel quality, the transmission time, and the recovery accuracy. To this end, we formulate an optimization problem with the goal of selecting the most relevant and accurate semantic features over devices while satisfying constraints on transmission time and quality of the channel. This involves optimizing communication resources, identifying participating users, and choosing specific semantic information for transmission. The underlying problem is inherently complex due to its non-convex nature and combinatorial constraints. To overcome this challenge, we efficiently approximate the optimal solution by solving a series of integer linear programming problems. Our numerical findings illustrate the effectiveness and efficiency of our approach in managing semantic communications in networks with limited resources.

Figures (5)

• Figure 1: Semantic Communication Model
• Figure 2: Importance score per triplet for different contexts
• Figure 3: Value of LHS of constraint (11b)
• Figure 4: SE per time threshold for different scenarios
• Figure 5: SE per time threshold for different $P_{max}$