2,414 research outputs found
Spatial Throughput Maximization of Wireless Powered Communication Networks
Wireless charging is a promising way to power wireless nodes' transmissions.
This paper considers new dual-function access points (APs) which are able to
support the energy/information transmission to/from wireless nodes. We focus on
a large-scale wireless powered communication network (WPCN), and use stochastic
geometry to analyze the wireless nodes' performance tradeoff between energy
harvesting and information transmission. We study two cases with battery-free
and battery-deployed wireless nodes. For both cases, we consider a
harvest-then-transmit protocol by partitioning each time frame into a downlink
(DL) phase for energy transfer, and an uplink (UL) phase for information
transfer. By jointly optimizing frame partition between the two phases and the
wireless nodes' transmit power, we maximize the wireless nodes' spatial
throughput subject to a successful information transmission probability
constraint. For the battery-free case, we show that the wireless nodes prefer
to choose small transmit power to obtain large transmission opportunity. For
the battery-deployed case, we first study an ideal infinite-capacity battery
scenario for wireless nodes, and show that the optimal charging design is not
unique, due to the sufficient energy stored in the battery. We then extend to
the practical finite-capacity battery scenario. Although the exact performance
is difficult to be obtained analytically, it is shown to be upper and lower
bounded by those in the infinite-capacity battery scenario and the battery-free
case, respectively. Finally, we provide numerical results to corroborate our
study.Comment: 15 double-column pages, 8 figures, to appear in IEEE JSAC in February
2015, special issue on wireless communications powered by energy harvesting
and wireless energy transfe
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Signal and System Design for Wireless Power Transfer : Prototype, Experiment and Validation
A new line of research on communications and signals design for Wireless
Power Transfer (WPT) has recently emerged in the communication literature.
Promising signal strategies to maximize the power transfer efficiency of WPT
rely on (energy) beamforming, waveform, modulation and transmit diversity, and
a combination thereof. To a great extent, the study of those strategies has so
far been limited to theoretical performance analysis. In this paper, we study
the real over-the-air performance of all the aforementioned signal strategies
for WPT. To that end, we have designed, prototyped and experimented an
innovative radiative WPT architecture based on Software-Defined Radio (SDR)
that can operate in open-loop and closed-loop (with channel acquisition at the
transmitter) modes. The prototype consists of three important blocks, namely
the channel estimator, the signal generator, and the energy harvester. The
experiments have been conducted in a variety of deployments, including
frequency flat and frequency selective channels, under static and mobility
conditions. Experiments highlight that a channeladaptive WPT architecture based
on joint beamforming and waveform design offers significant performance
improvements in harvested DC power over conventional
single-antenna/multiantenna continuous wave systems. The experimental results
fully validate the observations predicted from the theoretical signal designs
and confirm the crucial and beneficial role played by the energy harvester
nonlinearity.Comment: Accepted to IEEE Transactions on Wireless Communication
Distributed Optimization in Energy Harvesting Sensor Networks with Dynamic In-network Data Processing
Energy Harvesting Wireless Sensor Networks (EH- WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and net- working algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sens- ing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and eval- uate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs
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