9 research outputs found
Optimal Online Transmission Policy for Energy-Constrained Wireless-Powered Communication Networks
This work considers the design of online transmission policy in a
wireless-powered communication system with a given energy budget. The system
design objective is to maximize the long-term throughput of the system
exploiting the energy storage capability at the wireless-powered node. We
formulate the design problem as a constrained Markov decision process (CMDP)
problem and obtain the optimal policy of transmit power and time allocation in
each fading block via the Lagrangian approach. To investigate the system
performance in different scenarios, numerical simulations are conducted with
various system parameters. Our simulation results show that the optimal policy
significantly outperforms a myopic policy which only maximizes the throughput
in the current fading block. Moreover, the optimal allocation of transmit power
and time is shown to be insensitive to the change of modulation and coding
schemes, which facilitates its practical implementation.Comment: 7 pages, accepted by ICC 2019. An extended version of this paper is
accepted by IEEE TW
Optimal finite horizon sensing for wirelessly powered devices
We are witnessing a significant advancements in the sensor technologies which has enabled a broad spectrum of applications. Often, the resolution of the produced data by the sensors significantly affects the output quality of an application. We study a sensing resolution optimization problem for a wireless powered device (WPD) that is powered by wireless power transfer (WPT) from an access point (AP). We study a class of harvest-first-transmit-later type of WPT policy, where an access point (AP) first employs RF power to recharge the WPD in the down-link, and then, collects the data from the WPD in the up-link. The WPD optimizes the sensing resolution, WPT duration and dynamic power control in the up-link to maximize an application dependant utility at the AP. The utility of a transmitted packet is only achieved if the data is delivered successfully within a finite time. Thus, we first study a finite horizon throughput maximization problem by jointly optimizing the WPT duration and power control. We prove that the optimal WPT duration obeys a time-dependent threshold form depending on the energy state of the WPD. In the subsequent data transmission stage, the optimal transmit power allocations for the WPD is shown to posses a channel-dependent fractional structure. Then, we optimize the sensing resolution of the WPD by using a Bayesian inference based multi armed bandit problem with fast convergence property to strike a balance between the quality of the sensed data and the probability of successfully delivering it
Joint Energy and SINR Coverage in Spatially Clustered RF-powered IoT Network
Owing to the ubiquitous availability of radio-frequency (RF) signals, RF
energy harvesting is emerging as an appealing solution for powering IoT
devices. In this paper, we model and analyze an IoT network which harvests RF
energy and receives information from the same wireless network. In order to
enable this operation, each time slot is partitioned into charging and
information reception phases. For this setup, we characterize two performance
metrics: (i) energy coverage and (ii) joint signal-to-interference-plus-noise
(SINR) and energy coverage. The analysis is performed using a realistic spatial
model that captures the spatial coupling between the locations of the IoT
devices and the nodes of the wireless network (referred henceforth as the IoT
gateways), which is often ignored in the literature. In particular, we model
the locations of the IoT devices using a Poisson cluster process (PCP) and
assume that some of the clusters have IoT gateways (GWs) deployed at their
centers while the other GWs are deployed independently of the IoT devices. The
level of coupling can be controlled by tuning the fraction of total GWs that
are deployed at the cluster centers. Due to the inherent intractability of
computing the distribution of shot noise process for this setup, we propose two
accurate approximations, using which the aforementioned metrics are
characterized. Multiple system design insights are drawn from our results. For
instance, we demonstrate the existence of optimal slot partitioning that
maximizes the system throughput. In addition, we explore the effect of the
level of coupling between the locations of the IoT devices and the GWs on this
optimal slot partitioning. Particularly, our results reveal that the optimal
value of time duration for the charging phase increases as the level of
coupling decreases.Comment: To appear in IEEE Transactions on Green Communications and Networkin
On optimal policies in full-duplex wireless powered communication networks
The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance
Stochastic Optimization of Energy Harvesting Wireless Communication Networks
Energy harvesting from environmental energy sources (e.g., sunlight) or from man-made
sources (e.g., RF energy) has been a game-changing paradigm, which enabled the possibility
of making the devices in the Internet of Things or wireless sensor networks operate
autonomously and with high performance for years or even decades without human
intervention. However, an energy harvesting system must be correctly designed to achieve
such a goal and therefore the energy management problem has arisen and become a critical
aspect to consider in modern wireless networks. In particular, in addition to the hardware
(e.g., in terms of circuitry design) and application point of views (e.g., sensor deployment),
also the communication protocol perspective must be explicitly taken into account; indeed,
the use of the wireless communication interface may play a dominant role in the energy
consumption of the devices, and thus must be correctly designed and optimized. This
analysis represents the focus of this thesis.
Energy harvesting for wireless system has been a very active research topic in the past
decade. However, there are still many aspects that have been neglected or not completely
analyzed in the literature so far. Our goal is to address and solve some of these new
problems using a common stochastic optimization setup based on dynamic programming.
In particular, we formulate both the centralized and decentralized optimization problems
in an energy harvesting network with multiple devices, and discuss the interrelations
between these two schemes; we study the combination of environmental energy harvesting
and wireless energy transfer to improve the transmission rate of the network and achieve a
balanced situation; we investigate the long-term optimization problem in wireless powered
communication networks, in which the receiver supplies wireless energy to the terminal
nodes; we deal with the energy storage inefficiencies of the energy harvesting devices,
and show that traditional policies may be strongly suboptimal in this context; finally, we
investigate how it is possible to increase secrecy in a wireless link where a third malicious
party eavesdrops the information transmitted by an energy harvesting node