Semantic and Goal-oriented Wireless Network Coverage: The Area of Effectiveness
Mattia Merluzzi, Giuseppe Di Poce, Paolo Di Lorenzo

TL;DR
This paper introduces the concept of areas of effectiveness in wireless networks, emphasizing the importance of AI-enabled computation capabilities over traditional coverage metrics for next-generation networks like 6G.
Contribution
It proposes a novel framework that considers both communication and computational resources, moving beyond traditional coverage assessments to optimize network performance for AI-driven services.
Findings
Radio coverage alone is insufficient to predict system performance.
Network planning must incorporate computational capabilities for effective AI services.
Resource orchestration should balance communication, computation, and energy use.
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…
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Taxonomy
TopicsICT Impact and Policies
