Wireless Context Engineering for Efficient Mobile Agentic AI and Edge General Intelligence
Changyuan Zhao, Jiacheng Wang, Yunting Xu, Geng Sun, Dusit Niyato, Zan Li, Abbas Jamalipour, Dong In Kim

TL;DR
This paper introduces wireless context engineering to enhance edge AI performance in wireless networks by selectively integrating relevant contextual information, demonstrated through a beam prediction case study under sensing constraints.
Contribution
It extends context engineering principles to wireless systems, proposing a Wireless Context Communication Framework (WCCF) for adaptive context management at the edge.
Findings
WCCF improves beam prediction accuracy under sensing constraints.
Context augmentation enhances inference without increasing model complexity.
Framework provides a systematic approach for wireless context management.
Abstract
Future wireless networks demand increasingly powerful intelligence to support sensing, communication, and autonomous decision-making. While scaling laws suggest improving performance by enlarging model capacity, practical edge deployments are fundamentally constrained by latency, energy, and memory, making unlimited model scaling infeasible. This creates a critical need to maximize the utility of limited inference-time inputs by filtering redundant observations and focusing on high-impact data. In large language models and generative artificial intelligence (AI), context engineering has emerged as a key paradigm to guide inference by selectively structuring and injecting task-relevant information. Inspired by this success, we extend context engineering to wireless systems, providing a systematic way to enhance edge AI performance without increasing model complexity. In dynamic…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Advanced Wireless Communication Technologies · IoT Networks and Protocols
