Wireless Personal Agent: Extending Wireless Intelligence from Networks to Terminals
2026-06-22 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors created WISPA, a smart system that helps wireless devices manage their resources based on what the user prefers and needs. Instead of only focusing on network performance like speed or coverage, WISPA uses a big language model to understand user habits and preferences over time. It separates quick, simple decisions from more complex, slower learning processes to work well even on devices with limited power. The authors tested WISPA on a campus commute route and showed it adapts to how individual users connect to the network.
wireless networksresource managementlarge language modelsuser preferencesterminal-sideonline executoroffline reflectionnetwork optimizationpersonalized service
Authors
Jiedan Tan, Fang Liu, Jingwen Tong, Shengli Zhang, Jun Zhang, Wing Shing Wong
Abstract
Wireless networks are evolving from connectivity-oriented infrastructures into intelligent and personalized service platforms. Existing wireless intelligence remains centered on network-side optimization, improving objectives such as throughput, latency, and coverage. Nevertheless, besides network performance, wireless intelligence also depends on user-perceived experience via application context, mobility routine, service cost, privacy preference, and long-term usage behavior. This article proposes WISPA, a Wireless Intelligent Self-evolving Personal Agent framework for automated terminal-side resource management based on large language model (LLM)-based agent. To overcome the resource constraints on terminals, WISPA decouples the latency-sensitive online resource execution from offline LLM agent reflection. In this way, a lightweight online executor makes deterministic resource decisions using interpretable preference parameters; While an offline LLM agent analyzes terminal-side traces, refines user profiles, and updates online preference parameters for subsequent decisions. At last, we demonstrate the practical applicability and benefits of WISPA for terminal-side resource allocations on a campus commute route. Numerical results show that WISPA learns user-specific connection styles and adapts access decisions as preferences change.