Abstract
In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpresure algorithms. Specifically, we propose two policies: (i) the Stochastic Backpressure with Energy Harvesting (SBP-EH), in which a node's routing-scheduling decisions are determined by the difference between the Lagrange multipliers associated to their queue stability constraints and their neighbors'; and (ii) the Stochastic Soft Backpressure with Energy Harvesting (SSBP-EH), an improved algorithm where the routing-scheduling decision is of a probabilistic nature. For both policies, we show that given sustainable data and energy arrival rates, the stability of the data queues over all network nodes is guaranteed. Numerical results corroborate the stability guarantees and illustrate the minimal gap in performance that our policies offer with respect to classical ones which work with an unlimited energy supply.
Highlights
Providing wireless devices with Energy Harvesting (EH) capabilities enables them to acquire energy from their surroundings
NUMERICAL RESULTS we conduct numerical experiments aimed at evaluating the performance of the proposed Stochastic Backpressure with Energy Harvesting (SBP-EH) and Stochastic Soft Backpressure with Energy Harvesting (SSBP-EH) policies
In this work, we have generalized the stochastic family of backpressure policies to energy harvesting networks
Summary
Providing wireless devices with Energy Harvesting (EH) capabilities enables them to acquire energy from their surroundings. The appearance of multiple interconnected devices powered by energy harvesting results in communication networks formed by self-sustainable and perpetually communicating nodes In such scenarios, there is the necessity of designing efficient routing and scheduling algorithms that explicitly take into account the energy harvesting process. Non-causal policies are typically designed under the assumption of independent and identically distributed (i.i.d.) or Markov energy harvesting and data arrival processes, and Lyapunov optimization techniques are used to derive their queue stability results. When the nodes are powered by energy harvesting, the previous works [23] and [24] considered a similar problem, which consists in finding admission control and resource allocation policies that satisfy network stability and energy causality while attaining close-to-optimal performance.
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