Online Power and Time Allocation in MIMO Uplink Transmissions Powered by RF Wireless Energy Transfer

Liang, Kai; Zhao, Liqiang; Yang, Kun; Chu, Xiaoli

doi:10.1109/tvt.2017.2651949

Cited by 21 publications

(15 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [5], a RFpowered massive multiple input multiple output (MIMO) system adopted slotted transmissions, in which each slot was divided into three phases for channel estimation, downlink power transmission, and uplink data transmission, respectively. In [6], an online power and time allocation algorithm was studied for a WET powered MIMO system with its focus on energy receiving sensitivity with a finite battery capacity. Robust resource allocation methods in non-linear EH model based MIMO WPCNs were investigated in [7].…”

Section: A Related Workmentioning

confidence: 99%

Non-Uniform Deployment of Power Beacons in Wireless Powered Communication Networks

Liang

Zhao

Zheng

et al. 2019

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

In wireless powered communication networks (WPCNs), base station (BS) and power beacons (PBs) can offer supplement power for uplink transmission of user equipments (UEs). However, aggregate power consumption of massively deployed PBs may exceed that of a BS. We propose a non-uniform deployment scheme for PBs in WPCNs, where a cell is divided into inner and outer areas, such that BS and PBs can cooperate to power UEs. To be more specific, a BS located in the center of a cell provides downlink power supply for the inner area UEs and uplink information decoding for all the UEs in the cell; while the PBs power UEs in the outer area. With multiple antennas, maximum ratio transmission and maximum ratio combining are adopted for downlink energy beamforming and IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. XX, NO. XX, MONTH XX 2019 2 uplink information reception. Considering a finite area of the network, we derive the distribution of the distance from a non-center-located UE to its nearest PB in the outer area. An optimization problem is formulated to minimize total average power consumption, while satisfying BS average transmission power constraint and coverage probability threshold. Moreover, coverage probability is derived for performance evaluation. Numerical results show that the power consumption of the proposed scheme is reduced significantly compared to PB-only WPCNs. Index Terms-Wireless powered communication network; Wireless energy transfer; Non-uniform deployment.

show abstract

Section: A Related Workmentioning

confidence: 99%

Non-Uniform Deployment of Power Beacons in Wireless Powered Communication Networks

Liang

Zhao

Zheng

et al. 2019

IEEE Trans. Wireless Commun.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Here, E(·) and Tr(·) denote statistical expectation and the trace of a matrix, respectively. By ignoring the negligible receiver noise power for energy harvesting, the amount of energy harvested at device k under a linear energy harvesting model 1 [7], [8], [10], [13], [20] can be expressed as…”

Section: A System Modelmentioning

confidence: 99%

“…From (8), it is observed that when DL WPT is activated, the optimal W is rank one and independent of the users' initial energy E I k . This suggests that to maximize WSR, the optimal energy beamforming direction does not depend on the initial energy levels of the devices, E I k .…”

Section: B Optimal Resource Allocationmentioning

confidence: 99%

“…Recently, wireless-powered communication networks (WPCNs) have been proposed in [7] where a dedicated power station (PS) first broadcasts energy signals to devices for downlink (DL) WPT and then the devices exploit the harvested energy for uplink wireless information transmission (UL WIT). WPCNs have been extended to different practical scenarios such as multi-antenna [8], [9], multi-carrier [10], [11], secure relay [12], and cognitive networks [13], [14]. In particular, a distributed power control algorithm is proposed in [11] to maximize the user energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generalized Wireless-Powered Communications: When to Activate Wireless Power Transfer?

Zhang

et al. 2019

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

Wireless-powered communication network (WPCN) is a key technology to power energy-limited massive devices, such as on-board wireless sensors in autonomous vehicles, for Internet-of-Things (IoT) applications. Conventional WPCNs rely only on dedicated downlink wireless power transfer (WPT), which is practically inefficient due to the significant energy loss in wireless signal propagation. Meanwhile, ambient energy harvesting is highly appealing as devices can scavenge energy from various existing energy sources (e.g., solar energy and cellular signals). Unfortunately, the randomness of the availability of these energy sources cannot guarantee stable communication services. Motivated by the above, we consider a generalized WPCN where the devices can not only harvest energy from a dedicated multipleantenna power station (PS), but can also exploit stored energy stemming from ambient energy harvesting. Since the dedicated WPT consumes system resources, if the stored energy is sufficient, WPT may not be needed to maximize the weighted sum rate (WSR). To analytically characterize this phenomenon, we derive the condition for WPT activation and reveal how it is affected by the different system parameters. Subsequently, we further derive the optimal resource allocation policy for the cases that WPT is activated and deactivated, respectively. In particular, it is found that when WPT is activated, the optimal energy beamforming at the PS does not depend on the devices' stored energy, which is shown to lead to a new unfairness issue. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed optimal resource allocation.Index Terms-Energy-constrained IoT networks, when to activate WPT, energy beamforming, optimal resource allocation.

show abstract

“…2) Multiple Access: While single-device scenarios have been considered in [16], [19], [22], [23], [25], WPCNs may also be used for multiple WDs [2], [13], [14], [17], [18]. When multiple devices are considered, multiple access techniques should be utilized for the UL channels.…”

Section: ) Duplexingmentioning

confidence: 99%

Maximizing Ergodic Throughput in Wireless Powered Communication Networks

Ahmadian

Park

2018

2018 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

In this paper, we consider a frequency-division duplexing (FDD) multiple-user multiple-input-singleoutput (MU-MISO) wireless-powered communication network (WPCN) consisting of one hybrid dataand-energy access point (HAP) with multiple antennas which coordinates energy/information transfer to/from several single-antenna wireless devices (WD). Typically, in such a system, wireless energy transfer (WET) requires such techniques as energy beamforming (EB) for efficient transfer of energy to the WDs. Yet, efficient EB can only be accomplished if channel state information (CSI) is available to the transmitter, which, in FDD systems is only achieved through uplink (UL) feedback. Therefore, while in our scheme we use the downlink (DL) channels for WET only, the UL channel frames are split into two phases: the CSI feedback phase during which the WDs feed CSI back to the HAP and the WIT phase where the HAP performs wireless information transmission (WIT) via space-division-multipleaccess (SDMA). To ensure rate fairness among the WDs, this paper maximizes the minimum WIT data rate among the WDs. Using an iterative solution, the original optimization problem can be relaxed into two sub-problems whose convexity conditions are derived. Finally, the behavior of this system when the number of HAP antennas increases is analyzed. Simulation results verify the truthfulness of our analysis. Index TermsCSI feedback, WPCN, energy beamforming, throughput, rate fairness, doubly near-far effect, MU-MISO I. INTRODUCTION The limited lifetime of battery-powered wireless devices (WD)s in a conventional wireless communication network has always been a fundamental bottleneck for which the only solu-This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. 2 tion has been to manually replace or recharge the batteries after depletion. Yet, there exists applications where doing so is laborious or even impractical 1 . Wireless-powered communication (WPC) has recently emerged as a promising solution to prolong the lifetime of such energyconstrained WDs [1], [2]. WPC can be used in a variety of applications, such as IOT networks, RFID systems, and wireless sensor networks (WSNs) [3], [4]. It uses RF-enabled wireless energy transfer (WET) [1] as a means to wirelessly supply the energy of WDs, thus enabling them to function seamlessly without the need of battery replacement/ recharging. WET can be defined as the use of electromagnetic (EM) waves to transfer energy from an energy transmitter (ET) to an energy receiver (ER) over the air [2].As with many new technologies, WET raises its own issues however. First, since the power signal from the transmitter is severely attenuated over distance, the problem of transferring sufficient energy over even moderately large distances is not trivial. Second, in many cases there are multiple ERs which could all be mobile. Therefore, the scheme needs to be adaptable to multiple receivers and at the same time r...

show abstract

Online Power and Time Allocation in MIMO Uplink Transmissions Powered by RF Wireless Energy Transfer

Cited by 21 publications

References 38 publications

Non-Uniform Deployment of Power Beacons in Wireless Powered Communication Networks

Non-Uniform Deployment of Power Beacons in Wireless Powered Communication Networks

Generalized Wireless-Powered Communications: When to Activate Wireless Power Transfer?

Maximizing Ergodic Throughput in Wireless Powered Communication Networks

Contact Info

Product

Resources

About