Abstract

This paper investigates resource scheduling in a wireless communication system operating with energy harvesting (EH)-based devices and perfect channel state information (CSI). The aim is to minimize the packet loss that occurs when the buffer is overflowed or when the queued packet is older than a certain pre-defined threshold. So, we consider a strict delay constraint rather than an average delay constraint. The associated optimization problem is modeled as the Markov decision process (MDP) where the actions are the number of packets sent on the known channel at each slot. The optimal deterministic offline policy is exhibited through dynamic programming techniques, i.e., value iteration (VI) algorithm. We show that the gain in the number of transmitted packets and the consumed energy is substantial compared to: 1) a naive policy which forces the system to send the maximum number of packets using the available energy in the battery; 2) two variants of the previous policy that take into account the buffer state; and 3) a policy optimized with an average delay constraint. Finally, we evaluate our optimal policy under imperfect CSI scenario where only an estimate of the channel state is available.

Highlights

  • Energy harvesting (EH) technology has emerged recently as a promising solution to improve the energy efficiency and self-sustainability of 5G mobile and IoT networks

  • We evaluate numerically the optimal policy obtained by resolving Problem 1

  • We have addressed resource scheduling problem under energy harvesting capabilities with strict delay constraint and perfect Channel State Information (CSI)

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Summary

Introduction

Energy harvesting (EH) technology has emerged recently as a promising solution to improve the energy efficiency and self-sustainability of 5G mobile and IoT networks. While relying on renewable energy sources in their surrounding environments, the mobile devices can harvest energy to perform their communications and operational tasks. The stochastic energy harvesting process and the energy storage constraints in addition to the time-varying nature of the wireless channels bring new design challenges in EH communications making the optimization of the transmission policies a more difficult task. Efficient resource scheduling of mobile devices need to adapt the transmission rate and power to the dynamic levels of the available energy and the channels in order to ensure the users quality of service (QoS) and the system sustainability

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