Semantic and Goal-oriented Wireless Network Coverage: The Area of Effectiveness
Mattia Merluzzi, Giuseppe Di Poce, Paolo Di Lorenzo
TL;DR
This work tackles the inadequacy of traditional radio-coverage maps for next-generation networks that host edge AI and semantic services. It introduces Area of Effectiveness (AoE), a goal-oriented coverage notion that jointly accounts for communication and in-network computing to quantify end-to-end performance via metrics like goal-effectiveness and end-to-end delay $D_{loop}$. Through an indoor factory ray-tracing scenario with co-located AI models, the paper demonstrates that AoE-based planning and joint connect-compute policies yield larger effective coverage and lower costs than conventional RSS-based or isolated approaches. The results motivate dynamic AoE adaptation and semantic model alignment as key directions for practical and efficient semantic 6G deployments.
Abstract
Assessing wireless coverage is a fundamental task for public network operators and private deployments, whose goal is to guarantee quality of service across the network while minimizing material waste and energy consumption. These maps are usually built through ray tracing techniques and/or channel measurements that can be consequently translated into network Key Performance Indicators (KPIs), such as capacity or throughput. However, next generation networks (e.g., 6G) typically involve beyond communication resources, towards services that require data transmission, but also processing (local and remote) to perform complex decision making in real time, with the best balance between performance, energy consumption, material waste, and privacy. In this paper, we introduce the novel concept of areas of effectiveness, which goes beyond the legacy notion of coverage, towards one that takes into account capability of the network of offering edge Artificial Intelligence (AI)-related computation. We will show that radio coverage is a poor indicator of real system performance, depending on the application and the computing capabilities of network and devices. This opens new challenges in network planning, but also resource orchestration during operation to achieve the specific goal of communication.