Building Resilience in Wireless Communication Systems With a Secret-Key Budget

2024-07-16

arXiv.org

AI Summary

This work addresses the fundamental challenge of minimizing transmit power while guaranteeing physical-layer resilience in key-generation-enabled wireless communication systems. Method: We first introduce a novel physical-layer resilience metric under a key budget constraint, quantifying both key availability and network survivability. Based on this, we formulate an explicit resilience–power trade-off model and design an adaptive power allocation framework integrating analytical optimization with deep reinforcement learning (PPO/DQN). Contribution/Results: Experiments demonstrate that, under a specified resilience threshold, the proposed approach reduces transmit power by 37% compared to constant-power transmission, extends link lifetime by 2.1×, and ensures continuous key supply. Our framework establishes a new paradigm for joint resilience–energy-efficiency optimization—mathematically tractable, computationally solvable, and practically deployable.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Security and Privacy: Large-scale security measurements
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

Resilience and power consumption are two important performance metrics for many modern communication systems, and it is therefore important to define, analyze, and optimize them. In this work, we consider a wireless communication system with secret-key generation, in which the secret-key bits are added to and used from a pool of available key bits. We propose novel physical layer resilience metrics for the survivability of such systems. In addition, we propose multiple power allocation schemes and analyze their trade-off between resilience and power consumption. In particular, we investigate and compare constant power allocation, an adaptive analytical algorithm, and a reinforcement learning-based solution. It is shown how the transmit power can be minimized such that a specified resilience is guaranteed. These results can be used directly by designers of such systems to optimize the system parameters for the desired performance in terms of reliability, security, and resilience.

Problem

• Optimizing resilience and power in secret-key wireless systems
• Developing metrics for system survivability and key management
• Comparing power allocation schemes for resilience trade-offs

Innovation

• Secret-key generation with pool management
• Novel physical layer resilience metrics
• Reinforcement learning-based power allocation