安全節能無線代理式 AI 網路

• Multi-Agent Architecture: Supervisor agent dynamically assigns reasoning tasks to selected agents while unselected agents perform friendly jamming to prevent eavesdropping, creating a collaborative security model[1] • Optimization Framework: Energy minimization problem formulated with three coupled variables—agent selection, base station (BS) beamforming, and transmission power—subject to latency and accuracy constraints[1] • Solution Schemes: ASC (Alternating Sequential Convex) uses ADMM (Alternating Direction Method of Multipliers), SDR (Semidefinite Relaxation), and SCA (Successive Convex Approximation) iteratively; LAW (LLM-Augmented Workflow) integrates LLM optimizer into agentic decision loop[1] • LLM Integration: Qwen-based system validates the approach on public benchmarks, demonstrating semantic understanding of network optimization tasks through natural language reasoning[1] • Physical Layer Security: Agentic AI derives optimal secure beamforming strategies from noisy multi-user wireless channel environments, improving secrecy rate in dynamic scenarios[1] • Semantic Steganography: Proposed scheme includes protective signal generation and modulation onto training symbols for CSI estimation, masking signal fluctuations while preserving sensing performance[1] • Real-Time Data Processing: Brain component continuously ingests and analyzes real-time network data, performs intelligent state inference, generates evolutionary strategies, and translates them into granular control directives across network layers[1]

The convergence of agentic AI with energy-efficient wireless networks addresses two critical industry challenges: operational cost reduction and autonomous network management. This research validates that multi-agent systems can achieve significant energy savings (59.1%) while maintaining security and quality of service constraints—directly supporting telecom operators' sustainability and operational efficiency goals. As 2026 marks the transition from research to early real-world deployments[3], frameworks like this provide theoretical foundations for production systems. The integration of LLMs into agentic workflows suggests future wireless networks will operate with semantic understanding of optimization objectives, enabling more adaptive and context-aware resource allocation. Physical layer security through friendly jamming agents represents a paradigm shift from reactive to proactive threat mitigation. Industry adoption will likely accelerate as vendors (Ericsson, Nokia, AT&T, Telefónica) operationalize agentic AI capabilities, potentially creating competitive advantages in network efficiency, customer experience, and security posture. Standardization of agentic AI frameworks and governance models will become critical as autonomous decision-making scales across global telecom infrastructure.