A Learning Theoretic Approach to Energy Harvesting Communication System Optimization

Abstract: A point-to-point wireless communication system in which the transmitter is equipped with an energy harvesting device and a rechargeable battery, is studied. Both the energy and the data arrivals at the transmitter are modeled as Markov processes. Delay-limited communication is considered assuming that the underlying channel is block fading with memory, and the instantaneous channel state information is available at both the transmitter and the receiver. The expected total transmitted data during the transmitter's activation time is maximized under three different sets of assumptions regarding the information available at the transmitter about the underlying stochastic processes. A learning theoretic approach is introduced, which does not assume any a priori information on the Markov processes governing the communication system. In addition, online and offline optimization problems are studied for the same setting. Full statistical knowledge and causal information on the realizations of the underlying stochastic processes are assumed in the online optimization problem, while the offline optimization problem assumes non-causal knowledge of the realizations in advance. Comparing the optimal solutions in all three frameworks, the performance loss due to the lack of the transmitter's information regarding the behaviors of the underlying Markov processes is quantified.

• The paper introduces a reinforcement learning approach using Q-learning to optimize data transmission in energy harvesting wireless systems.
• It compares online dynamic programming and offline MILP methods to benchmark performance under stochastic energy and data arrivals.
• The study shows that system parameters like battery capacity and energy arrival probability enable learning-based policies to reach 90% to 99% of the optimal solution.

A Learning Theoretic Approach to Energy Harvesting Communication System Optimization

The paper explores the optimization of a point-to-point wireless communication system equipped with energy harvesting (EH) capabilities. The core focus is on maximizing the expected total transmitted data during the activation period of a transmitter that benefits from renewable energy sources and rechargeable battery support.

Problem Context and Approach

The researchers model both energy and data arrivals at the transmitter as first-order discrete-time Markov processes. They acknowledge the challenges posed by these stochastic processes, such as sporadic energy availability and variable data arrival rates. The work focuses on optimizing the system's operation under different assumptions of information availability regarding these processes.

To address the problem, a learning theoretic approach is introduced that operates without a priori information about the Markov processes governing the energy harvesting and changeable communication environment. This is contrasted with two distinct scenarios:

1. Online Optimization: Full statistical knowledge and causal information regarding the stochastic processes are assumed to be known. Here, dynamic programming (DP) techniques are utilized, specifically the policy iteration (PI) method, to find the optimal transmission policy.
2. Offline Optimization: Where non-causal knowledge of the processes is available in advance. It is tackled using a mixed integer linear programming (MILP) framework, with solutions derived through the branch-and-bound (BAB) method and linear program relaxations.

The study's major innovation lies in the application of reinforcement learning (RL), particularly Q-learning, to enable the transmitter to learn the optimal transmission policy via interaction with the environment.

Key Findings and Results

• Theoretical Quantification: The paper quantifies the performance loss when the transmitter lacks complete information about the system's stochastic structures. It defines the theoretical upper bounds in network performance achievable under offline optimization scenarios, against which other approaches' performances are compared.
• Performance Metrics and Algorithm Convergence: It establishes strong numerical results showing that the Q-learning algorithm's performance converges to that of the online approach as the learning time increases. During simulations, the adaptive learning-based algorithm achieved substantial performance, covering 90% to 99% of the optimal solution provided by the online DP approach for various parameter settings.
• Effect of System Parameters: The study illustrates the impact of key parameters, such as battery capacity and energy packet arrival probability, on system performance. It highlights that higher average energy arrival rates improve overall system performance due to better adaptation capabilities by the learning algorithms.

Implications and Future Directions

The presented learning theoretic approach offers a practical method for optimizing EH communication systems in the absence of complete statistical knowledge. This holds significant potential for real-world deployment, where environmental dynamics and energy patterns can change unpredictably.

The implications of this work support the growing practicality of energy-aware communication networks, particularly in sensor networks and Internet of Things (IoT) applications. Future research could focus on extending these methods to more complex networks, such as multi-user or multi-hop scenarios, and integrating more advanced machine learning techniques for enhanced adaptability and robustness.

In summary, this paper contributes a valuable framework for adapting point-to-point wireless communication systems to dynamic energy environments using reinforcement learning, demonstrating robust performance across varied system conditions.