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|Title:||Transmission strategies for wireless networks powered by wireless energy transfer and energy harvesting||Authors:||Yang, Gang||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Wireless communication systems||Issue Date:||2015||Source:||Yang, G. (2015). Transmission strategies for wireless networks powered by wireless energy transfer and energy harvesting. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Energy harvesting (EH) and radio-frequency (RF) based wireless energy transfer (WET) are two promising techniques to address the lifetime bottlenecks of energy-constrained wireless devices. The EH devices harness energy from ambient energy sources (e.g., solar power) which typically depend on the environment and are thus unstable and uncontrollable. Unlike EH, RF-based WET enables an energy receiver (ER) to harvest energy remotely from dedicated RF signals radiated by an energy transmitter (ET). Hence, WET provides a more stable and controllable energy source. This thesis studies wireless transmission strategies for both wireless powered communication networks (WPCN) and energy-harvesting wireless sensor networks (EHWSN). One major challenge for a WPCN is to increase the efficiency of WET and support longer operating range, due to significant path loss over distance. The first part of this thesis exploits energy beamforming by using the estimated channel state information (CSI) to achieve high-efficiency WET, and further investigates the resource allocation problem for maximizing the harvested energy. We first consider WET from a multi-antenna beamforming ET to a single-antenna ER, in a traditional radio communication system. The ER estimates the downlink (i.e., ET-to-ER radio link) CSI (D-CSI) by receiving a training pilot, and feeds the estimated D-CSI back to the ET for beamforming. The optimal energy beamformer is derived. Then, we optimize the resources to maximize the transfered energy for two scenarios: dynamic-length pilot and fixed-length pilot. For the former, we perform dynamic time allocation for channel estimation (CE) and WET. The optimal pilot length is obtained online by solving a dynamic programming problem. The optimal policy is shown to depend only on the channel estimate power. For the latter, we perform static time allocation for CE and WET. The optimal pilot length is obtained in closed-form by offline optimization. Moreover, the optimal power allocation is derived for both scenarios too. We also discuss the extensions of WET to multiple ERs, WET in time-varying channels, and WET-based utility maximization. Then, we consider WET from a multi-antenna beamforming ET to multiple ERs in a backscatter communication system like radio-frequency-identification (RFID). For such systems, the ERs (or RFID tags) scatter back a portion of the incident signal to the ET (or RFID reader). The acquisition of the D-CSI at the ET is challenging, since the ERs are typically too energy-and-hardware constrained to estimate or feed back the D-CSI. We propose that the ET leverages on its observed backscatter signals to estimate the backscatter-channel (i.e., ET-to-ER-to-ET radio link) state information (BS-CSI), and uses it for downlink energy beamforming. The harvested energy is analyzed and found to be affected by the unknown uplink (i.e., ER-to-ET radio link) channel. Furthermore, we optimize the channel-training energy and the energy allocation weights to achieve weighted-sum-energy (WSE) maximization or proportional-fair-energy (PFE) maximization. The optimal solution for WSE maximization is shown to use only one energy beam. For PFE maximization, it is shown to be a biconvex problem, and we propose a block-coordinate-descent based algorithm to find the close-to-optimal solution. The second part of this thesis optimizes the throughput of a WPCN in which the harvested energy is used for energy-hungry uplink data transmissions. We focus on a WET-powered massive multiple-input-multiple-output (MIMO) (MM) communication system consisting of a hybrid data-and-energy access point (H-AP) equipped with a large number of antennas and multiple single-antenna users, since more transmit antennas enables higher beamforming gain. To optimize the throughput and ensure rate fairness, the minimum rate among all users is maximized. The asymptotically optimal solutions are obtained. We define the massive MIMO degree-of-rate-gain (MM-DoRG) as the asymptotic uplink rate normalized by the logarithm of the number of antennas equipped at the H-AP. The proposed WET-MM communication system is shown to be optimal in terms of MM-DoRG, i.e., it achieves the same MM-DoRG as the case with ideal CE. Moreover, the WET-MM communication system achieves the best possible rate fairness among users, as users asymptotically achieve a common rate. The third part of this thesis studies the data transmission and recovery for an EHWSN. In a wireless sensor network, one critical but challenging task is for the fusion center (FC) to recover the data of all sensors by using minimal transmission resources. All EH sensors also have different amount of energy available for transmissions, due to different EH rates. If the sensor data is spatially correlated and thus sparse in some domain, we propose an energy-aware wireless compressive sensing (WCS) approach in which each sensor transmits independently with some probability and adapts the transmit power to its harvested energy, and the FC recovers the sensor data by performing compressive sensing (CS) decoding. We provide theoretical guarantees on the number of measurements required for reliable recovery in such a system, by showing that the non-Gaussian and non-i.i.d. sensing matrix satisfies the restricted isometry property (RIP). Using large deviations theory, we show that the number of measurements required for the RIP to hold is not sensitive to the inhomogeneity of received signal-to-noise-ratios, when the number of sensors is large.||URI:||http://hdl.handle.net/10356/65540||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
checked on Sep 28, 2020
checked on Sep 28, 2020
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