961 research outputs found

    Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags

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    The Internet of things (IoT) is expected to connect physical objects and end-users using technologies such as wireless sensor networks and radio frequency identification (RFID). In addition, it will employ a wireless multi-hop backhaul to transfer data collected by a myriad of devices to users or applications such as digital twins operating in a Metaverse. A critical issue is that the number of packets collected and transferred to the Internet is bounded by limited network resources such as bandwidth and energy. In this respect, IoT networks have adopted technologies such as time division multiple access (TDMA), signal interference cancellation (SIC) and multiple-input multiple-output (MIMO) in order to increase network capacity. Another fundamental issue is energy. To this end, researchers have exploited radio frequency (RF) energy-harvesting technologies to prolong the lifetime of energy constrained sensors and smart devices. Specifically, devices with RF energy harvesting capabilities can rely on ambient RF sources such as access points, television towers, and base stations. Further, an operator may deploy dedicated power beacons that serve as RF-energy sources. Apart from that, in order to reduce energy consumption, devices can adopt ambient backscattering communication technologies. Advantageously, backscattering allows devices to communicate using negligible amount of energy by modulating ambient RF signals. To address the aforementioned issues, this thesis first considers data collection in a two-tier MIMO ambient RF energy-harvesting network. The first tier consists of routers with MIMO capability and a set of source-destination pairs/flows. The second tier consists of energy harvesting devices that rely on RF transmissions from routers for energy supply. The problem is to determine a minimum-length TDMA link schedule that satisfies the traffic demand of source-destination pairs and energy demand of energy harvesting devices. It formulates the problem as a linear program (LP), and outlines a heuristic to construct transmission sets that are then used by the said LP. In addition, it outlines a new routing metric that considers the energy demand of energy harvesting devices to cope with routing requirements of IoT networks. The simulation results show that the proposed algorithm on average achieves 31.25% shorter schedules as compared to competing schemes. In addition, the said routing metric results in link schedules that are at most 24.75% longer than those computed by the LP

    Optimal finite horizon sensing for wirelessly powered devices

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    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

    Energy efficient resource allocation for future wireless communication systemsy

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    Next generation of wireless communication systems envisions a massive number of connected battery powered wireless devices. Replacing the battery of such devices is expensive, costly, or infeasible. To this end, energy harvesting (EH) is a promising technique to prolong the lifetime of such devices. Because of randomness in amount and availability of the harvested energy, existing communication techniques require revisions to address the issues specific to EH systems. In this thesis, we aim at revisiting fundamental wireless communication problems and addressing the future perspective on service based applications with the specific characteristics of the EH in mind. In the first part of the thesis, we address three fundamental problems that exist in the wireless communication systems, namely; multiple access strategy, overcoming the wireless channel, and providing reliability. Since the wireless channel is a shared medium, concurrent transmissions of multiple devices cause interference which results in collision and eventual loss of the transmitted data. Multiple access protocols aim at providing a coordination mechanism between multiple transmissions so as to enable a collision free medium. We revisit the random access protocol for its distributed and low energy characteristics while incorporating the statistical correlation of the EH processes across two transmitters. We design a simple threshold based policy which only allows transmission if the battery state is above a certain threshold. By optimizing the threshold values, we show that by carefully addressing the correlation information, the randomness can be turned into an opportunity in some cases providing optimal coordination between transmitters without any collisions. Upon accessing the channel, a wireless transmitter is faced with a transmission medium that exhibits random and time varying properties. A transmitter can adapt its transmission strategy to the specific state of the channel for an efficient transmission of information. This requires a process known as channel sensing to acquire the channel state which is costly in terms of time and energy. The contribution of the channel sensing operation to the energy consumption in EH wireless transmitters is not negligible and requires proper optimization. We developed an intelligent channel sensing strategy for an EH transmitter communicating over a time-correlated wireless channel. Our results demonstrate that, despite the associated time and energy cost, sensing the channel intelligently to track the channel state improves the achievable long-term throughput significantly as compared to the performance of those protocols lacking this ability as well as the one that always senses the channel. Next, we study an EH receiver employing Hybrid Automatic Repeat reQuest (HARQ) to ensure reliable end-to-end communications. In inherently error-prone wireless communications systems, re-transmissions triggered by decoding errors have a major impact on the energy consumption of wireless devices. We take into account the energy consumption induced by HARQ to develop simple-toimplement optimal algorithms that minimizes the number of retransmissions required to successfully decode the packet. The large number of connected edge devices envisioned in future wireless technologies enable a wide range of resources with significant sensing capabilities. The ability to collect various data from the sensors has enabled many exciting smart applications. Providing data at a certain quality greatly improves the performance of many of such applications. However, providing high quality is demanding for energy limited sensors. Thus, in the second part of the thesis, we optimize the sensing resolution of an EH wireless sensor in order to efficiently utilize the harvested energy to maximize an application dependent utilit

    Distributed Cooperative Communications and Wireless Power Transfer

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    In telecommunications, distributed cooperative communications refer to techniques which allow different users in a wireless network to share or combine their information in order to increase diversity gain or power gain. Unlike conventional point-to-point communications maximizing the performance of the individual link, distributed cooperative communications enable multiple users to collaborate with each other to achieve an overall improvement in performance, e.g., improved range and data rates. The first part of this dissertation focuses the problem of jointly decoding binary messages from a single distant transmitter to a cooperative receive cluster. The outage probability of distributed reception with binary hard decision exchanges is compared with the outage probability of ideal receive beamforming with unquantized observation exchanges. Low- dimensional analysis and numerical results show, via two simple but surprisingly good approximations, that the outage probability performance of distributed reception with hard decision exchanges is well-predicted by the SNR of ideal receive beamforming after subtracting a hard decision penalty of slightly less than 2 dB. These results, developed in non-asymptotic regimes, are consistent with prior asymptotic results (for a large number of nodes and low per-node SNR) on hard decisions in binary communication systems. We next consider the problem of estimating and tracking channels in a distributed transmission system with multiple transmitters and multiple receivers. In order to track and predict the effective channel between each transmit node and each receive node to facilitate coherent transmission, a linear time-invariant state- space model is developed and is shown to be observable but nonstabilizable. To quantify the steady-state performance of a Kalman filter channel tracker, two methods are developed to efficiently compute the steady-state prediction covariance. An asymptotic analysis is also presented for the homogenous oscillator case for systems with a large number of transmit and receive nodes with closed-form results for all of the elements in the asymptotic prediction covariance as a function of the carrier frequency, oscillator parameters, and channel measurement period. Numeric results confirm the analysis and demonstrate the effect of the oscillator parameters on the ability of the distributed transmission system to achieve coherent transmission. In recent years, the development of efficient radio frequency (RF) radiation wireless power transfer (WPT) systems has become an active research area, motivated by the widespread use of low-power devices that can be charged wirelessly. In this dissertation, we next consider a time division multiple access scenario where a wireless access point transmits to a group of users which harvest the energy and then use this energy to transmit back to the access point. Past approaches have found the optimal time allocation to maximize sum throughput under the assumption that the users must use all of their harvested power in each block of the harvest-then-transmit protocol. This dissertation considers optimal time and energy allocation to maximize the sum throughput for the case when the nodes can save energy for later blocks. To maximize the sum throughput over a finite horizon, the initial optimization problem is separated into two sub-problems and finally can be formulated into a standard box- constrained optimization problem, which can be solved efficiently. A tight upper bound is derived by relaxing the energy harvesting causality. A disadvantage of RF-radiation based WPT is that path loss effects can significantly reduce the amount of power received by energy harvesting devices. To overcome this problem, recent investigations have considered the use of distributed transmit beamforming (DTB) in wireless communication systems where two or more individual transmit nodes pool their antenna resources to emulate a virtual antenna array. In order to take the advantages of the DTB in the WPT, in this dissertation, we study the optimization of the feedback rate to maximize the energy efficiency in the WPT system. Since periodic feedback improves the beamforming gain but requires the receivers to expend energy, there is a fundamental tradeoff between the feedback period and the efficiency of the WPT system. We develop a new model to combine WPT and DTB and explicitly account for independent oscillator dynamics and the cost of feedback energy from the receive nodes. We then formulate a Normalized Weighted Mean Energy Harvesting Rate (NWMEHR) maximization problem to select the feedback period to maximize the weighted averaged amount of net energy harvested by the receive nodes per unit of time as a function of the oscillator parameters. We develop an explicit method to numerically calculate the globally optimal feedback period

    Joint Transmission and Energy Transfer Policies for Energy Harvesting Devices with Finite Batteries

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    One of the main concerns in traditional Wireless Sensor Networks (WSNs) is energy efficiency. In this work, we analyze two techniques that can extend network lifetime. The first is Ambient \emph{Energy Harvesting} (EH), i.e., the capability of the devices to gather energy from the environment, whereas the second is Wireless \emph{Energy Transfer} (ET), that can be used to exchange energy among devices. We study the combination of these techniques, showing that they can be used jointly to improve the system performance. We consider a transmitter-receiver pair, showing how the ET improvement depends upon the statistics of the energy arrivals and the energy consumption of the devices. With the aim of maximizing a reward function, e.g., the average transmission rate, we find performance upper bounds with and without ET, define both online and offline optimization problems, and present results based on realistic energy arrivals in indoor and outdoor environments. We show that ET can significantly improve the system performance even when a sizable fraction of the transmitted energy is wasted and that, in some scenarios, the online approach can obtain close to optimal performance.Comment: 16 pages, 12 figure
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