Secure Energy Efficient Wireless Transmission: A Finite v/s Infinite-Horizon RL Solution

TL;DR

This paper develops and compares finite and infinite-horizon reinforcement learning algorithms for optimizing energy-efficient secure wireless transmission with energy harvesting, demonstrating superior performance of the finite-horizon approach.

Contribution

It introduces the FHJPA algorithm for finite-horizon RL in secure wireless energy harvesting networks and compares it with existing greedy and infinite-horizon algorithms.

Findings

FHJPA outperforms GA and IHJPA in secrecy energy efficiency.

Performance gap between FHJPA and IHJPA decreases with longer horizons.

FHJPA reduces computational time by 16.6% compared to IHJPA.

Abstract

In this paper, a joint optimal allocation of transmit power at the source and jamming power at the destination is proposed to maximize the average secrecy energy efficiency (SEE) of a wireless network within a finite time duration. The destination transmits the jamming signal to improve secrecy by utilizing full-duplex capability. The source and destination both have energy harvesting (EH) capability with limited battery capacity. Due to the Markov nature of the system, the problem is formulated as a finite-horizon reinforcement learning (RL) problem. We propose the finite-horizon joint power allocation (FHJPA) algorithm for the finite-horizon RL problem and compare it with a low-complexity greedy algorithm (GA). An infinite-horizon joint power allocation (IHJPA) algorithm is also proposed for the corresponding infinite-horizon problem. A comparative analysis of these algorithms is carried out in terms of SEE, expected total transmitted secure bits, and computational complexity. The results show that the FHJPA algorithm outperforms the GA and IHJPA algorithms due to its appropriate modelling in finite horizon transmission. When the source node battery has sufficient energy, the GA can yield performance close to the FHJPA algorithm despite its low-complexity. When the transmission time horizon increases, the accuracy of the infinite-horizon model improves, resulting in a reduced performance gap between FHJPA and IHJPA algorithms. The computational time comparison shows that the FHJPA algorithm takes $16.6$ percent less time than the IHJPA algorithm.