1,360 research outputs found
Wirelessly Powered Backscatter Communication Networks: Modeling, Coverage and Capacity
Future Internet-of-Things (IoT) will connect billions of small computing
devices embedded in the environment and support their device-to-device (D2D)
communication. Powering this massive number of embedded devices is a key
challenge of designing IoT since batteries increase the devices' form factors
and battery recharging/replacement is difficult. To tackle this challenge, we
propose a novel network architecture that enables D2D communication between
passive nodes by integrating wireless power transfer and backscatter
communication, which is called a wirelessly powered backscatter communication
(WP-BackCom) network. In the network, standalone power beacons (PBs) are
deployed for wirelessly powering nodes by beaming unmodulated carrier signals
to targeted nodes. Provisioned with a backscatter antenna, a node transmits
data to an intended receiver by modulating and reflecting a fraction of a
carrier signal. Such transmission by backscatter consumes orders-of-magnitude
less power than a traditional radio. Thereby, the dense deployment of
low-complexity PBs with high transmission power can power a large-scale IoT. In
this paper, a WP-BackCom network is modeled as a random Poisson cluster process
in the horizontal plane where PBs are Poisson distributed and active ad-hoc
pairs of backscatter communication nodes with fixed separation distances form
random clusters centered at PBs. The backscatter nodes can harvest energy from
and backscatter carrier signals transmitted by PBs. Furthermore, the
transmission power of each node depends on the distance from the associated PB.
Applying stochastic geometry, the network coverage probability and transmission
capacity are derived and optimized as functions of backscatter parameters,
including backscatter duty cycle and reflection coefficient, as well as the PB
density. The effects of the parameters on network performance are
characterized.Comment: 28 pages, 11 figures, has been submitted to IEEE Trans. on Wireless
Communicatio
Throughput Maximization for UAV-Aided Backscatter Communication Networks
This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks
Data Collection in Two-Tier IoT Networks with Radio Frequency (RF) Energy Harvesting Devices and Tags
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
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