Throughput Optimal Policies for Energy Harvesting Wireless Transmitters with Non-Ideal Circuit Power (1204.3818v2)

Abstract: Characterizing the fundamental tradeoffs for maximizing energy efficiency (EE) versus spectrum efficiency (SE) is a key problem in wireless communication. In this paper, we address this problem for a point-to-point additive white Gaussian noise (AWGN) channel with the transmitter powered solely via energy harvesting from the environment. In addition, we assume a practical on-off transmitter model with non-ideal circuit power, i.e., when the transmitter is on, its consumed power is the sum of the transmit power and a constant circuit power. Under this setup, we study the optimal transmit power allocation to maximize the average throughput over a finite horizon, subject to the time-varying energy constraint and the non-ideal circuit power consumption. First, we consider the off-line optimization under the assumption that the energy arrival time and amount are a priori known at the transmitter. Although this problem is non-convex due to the non-ideal circuit power, we show an efficient optimal solution that in general corresponds to a two-phase transmission: the first phase with an EE-maximizing on-off power allocation, and the second phase with a SE-maximizing power allocation that is non-decreasing over time, thus revealing an interesting result that both the EE and SE optimizations are unified in an energy harvesting communication system. We then extend the optimal off-line algorithm to the case with multiple parallel AWGN channels, based on the principle of nested optimization. Finally, inspired by the off-line optimal solution, we propose a new online algorithm under the practical setup with only the past and present energy state information (ESI) known at the transmitter.

• The paper presents a two-phase power allocation strategy that first maximizes energy efficiency using on-off transmission and then enhances spectrum efficiency via non-decreasing power allocation.
• It utilizes a point-to-point AWGN channel model and extends the approach to multiple channels with nested scalar optimization.
• An innovative online algorithm based on causal energy information achieves near-optimal throughput, closely approximating the performance of the ideal offline strategy.

Analysis of "Throughput Optimal Policies for Energy Harvesting Wireless Transmitters with Non-Ideal Circuit Power"

The paper by Jie Xu and Rui Zhang explores the critical issue of optimizing the throughput of wireless communication systems that rely on energy harvesting, specifically in the context where transmitters must grapple with non-ideal circuit power consumption. This inquiry is pivotal for enhancing energy efficiency (EE) and spectrum efficiency (SE) in green wireless communications.

Summary

The authors focus on a point-to-point additive white Gaussian noise (AWGN) channel model, where the transmitter is powered entirely by energy harvested from environmental sources such as solar or wind. Acknowledging that real-world wireless transmitters consume power not only for transmission but also for other circuitry (non-ideal circuit power), the paper addresses the challenge of power allocation to maximize average throughput within a finite time horizon, constrained by the stochastic nature of energy availability.

The optimal power allocation problem is first addressed in an offline context, assuming that both the timing and quantity of energy arrivals are known in advance. This scenario reveals a two-phase optimal transmission policy: an initial phase focusing on maximizing EE with an on-off power allocation strategy, and a subsequent phase targeting SE maximization, characterized by a non-decreasing power allocation over time. This unified approach to EE and SE optimization highlights the novel integration of strategies traditionally treated separately.

The paper expands this offline solution approach to handle multiple parallel AWGN channels under a common energy harvesting constraint, employing nested optimization methods to transform the multi-dimensional problem into a more tractable scalar optimization task.

Building on the insights gained from the offline solution, the authors propose an innovative online algorithm that operates with only the causal (past and present) energy state information. This online strategy is vital for real-world applications, where energy availability is not deterministic.

Key Numeric Results and Insights

The optimal offline strategy's performance is demonstrated to vary from the proposed online algorithm, with simulations indicating a close approximation of the throughput upper bound even with limited information. For instance, in a simulated environment with specific channel and energy arrival parameters, the online algorithm achieved a throughput of 61.61 Mbits, just 1.53 Mbits shy of the optimal offline policy’s 63.14 Mbits.

The authors' method outperforms other heuristically designed online algorithms, underscoring its efficacy.

Implications and Future Directions

The results have significant implications for energy harvesting wireless systems, particularly in designing protocols that can adapt efficiently to the unpredictable energy supplies afforded by renewable sources. The unification of EE and SE optimization strategies may inform future protocol designs across the industry that aim for greener, more efficient communication networks.

Theoretical implications include a foundation for further exploration of non-ideal circuit power effects in stochastic environments, potentially extending to broader communication models beyond AWGN channels.

Future research could explore extending these optimization strategies to dynamically changing network topologies or integrating machine learning techniques to predict energy availability, thereby enhancing the adaptability of online algorithms.

This paper contributes to the growing body of work aimed at marrying energy autonomy with communication efficiency, pivotal for achieving sustainable growth in wireless communication infrastructures.