scholarly journals SWIPT in mMIMO system with non-linear energy-harvesting terminals: protocol design and performance optimization

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Kui Xu ◽  
Ming Zhang ◽  
Jie Liu ◽  
Nan Sha ◽  
Wei Xie ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Performance Optimization ◽  
Power Transmission ◽  
Base Station ◽  
Second Phase ◽  
Wireless Power ◽  
Half Duplex ◽  
Non Linear ◽  
Two Phases ◽  
And Performance

Abstract In this paper, we design the simultaneous wireless information and power transfer (SWIPT) protocol for massive multi-input multi-output (mMIMO) system with non-linear energy-harvesting (EH) terminals. In this system, the base station (BS) serves a set of uplink fixed half-duplex (HD) terminals with non-linear energy harvester. Considering the non-linearity of practical energy-harvesting circuits, we adopt the realistic non-linear EH model rather than the idealistic linear EH model. The proposed SWIPT protocol can be divided into two phases. The first phase is designed for terminals EH and downlink training. A beam domain energy beamforming method is employed for the wireless power transmission. In the second phase, the BS forms the two-layer receive beamformers for the reception of signals transmitted by terminals. In order to improve the spectral efficiency (SE) of the system, the BS transmit power- and time-switching ratios are optimized. Simulation results show the superiority of the proposed beam-domain SWIPT protocol on SE performance compared with the conventional mMIMO SWIPT protocols.

scholarly journals System Performance Analysis for an Energy Harvesting IoT System Using a DF/AF UAV-Enabled Relay with Downlink NOMA under Nakagami-m Fading

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 285
Author(s):  
Anh-Nhat Nguyen ◽  
Van Nhan Vo ◽  
Chakchai So-In ◽  
Dac-Binh Ha
Keyword(s):  
Energy Harvesting ◽  
System Performance ◽  
Base Station ◽  
Second Phase ◽  
Power Splitting ◽  
Amplify And Forward ◽  
Decode And Forward ◽  
Adaptive Power ◽  
Outage Probabilities ◽  
Two Phases

This paper investigates system performance in the Internet of Things (IoT) with an energy harvesting (EH) unmanned aerial vehicle (UAV)-enabled relay under Nakagami-m fading, where the time switching (TS) and adaptive power splitting (APS) protocols are applied for the UAV. Our proposed system model consists of a base station (BS), two IoT device (ID) clusters (i.e., a far cluster and a near cluster), and a multiantenna UAV-enabled relay (UR). We adopt a UR-aided TS and APS (U-TSAPS) protocol, in which the UR can dynamically optimize the respective power splitting ratio (PSR) according to the channel conditions. To improve the throughput, the nonorthogonal multiple access (NOMA) technique is applied in the transmission of both hops (i.e., from the BS to the UR and from the UR to the ID clusters). The U-TSAPS protocol is divided into two phases. In the first phase, the BS transmits a signal to the UR. The UR then splits the received signal into two streams for information processing and EH using the APS scheme. In the second phase, the selected antenna of the UR forwards the received signal to the best far ID (BFID) in the far cluster and the best near ID (BNID) in the near cluster using the decode-and-forward (DF) or amplify-and-forward (AF) NOMA scheme. We derive closed-form expressions for the outage probabilities (OPs) at the BFID and BNID with the APS ratio under imperfect channel state information (ICSI) to evaluate the system performance. Based on these derivations, the throughputs of the considered system are also evaluated. Moreover, we propose an algorithm for determining the nearly optimal EH time for the system to minimize the OP. In addition, Monte Carlo simulation results are presented to confirm the accuracy of our analysis based on simulations of the system performance under various system parameters, such as the EH time, the height and position of the UR, the number of UR antennas, and the number of IDs in each cluster.

scholarly journals Aerodock (a smart, autonomous charging and docking station for unmanned aerial vehicles)

2022 ◽  
Vol 2161 (1) ◽  
pp. 012058
Author(s):  
Laaboni Mukerjee ◽  
Mukul Yadav ◽  
Amit Choraria ◽  
Atharv Tendolkar ◽  
Arjun Hariharan ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Unmanned Aerial Vehicles ◽  
Performance Optimization ◽  
Wireless Power Transfer ◽  
External Factors ◽  
Battery Management ◽  
Human Intervention ◽  
Wireless Power ◽  
Aerial Vehicles ◽  
And Performance

Abstract The COVID-19 pandemic has laid bare the need for contactless operations. While unmanned aerial vehicles (UAVs) are being developed to aid humans in countless domains, the need for effective battery management and performance optimization remains a huge task. The proposed solution, the “AeroDock”, aims to tackle these challenges by using wireless power transfer (WPT) technology coupled with smart monitoring of the drone’s health. The performance and hardware checks are assessed at the user end via cloud computing and IoT technology. This system is contact-less, safe, reliable and its usage is not affected by external factors. Thus, the AeroDock is a smart docking station for UAVs which eliminates the need for human intervention in effective charging and maintenance.

scholarly journals On Throughput for UAV Relay Assisted for Use in Disaster Communications

2021 ◽  
Author(s):  
Van Vo Nhan ◽  
Dang Ngoc Cuong ◽  
Tran Ban Thach ◽  
Hung Tran
Keyword(s):  
System Performance ◽  
Multiple Access ◽  
Base Station ◽  
Closed Form Expression ◽  
Second Phase ◽  
Form Expression ◽  
Decode And Forward ◽  
Disaster Communications ◽  
Aerial Vehicle ◽  
Two Phases

In this paper, the system performance of an energy harvesting (EH) unmanned aerial vehicle (UAV) system for use in disasters was investigated. The communication protocol was divided into two phases. In the first phase, a UAV relay (UR) harvested energy from a power beacon (PB). In the second phase, a base station (BS) transmitted the signal to the UR using non-orthogonal multiple access (NOMA); then, the UR used its harvested energy from the first phase to transfer the signal to two sensor clusters, i.e., low-priority and high-priority clusters, via the decode-and-forward (DF) technique. A closed-form expression for the throughput of the cluster heads of these clusters was derived to analyze the system performance. Monte Carlo simulations were employed to verify our approach.

scholarly journals Lexicographic Repair Under Querying Prioritised DL-Lite Knowledge Bases

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Telli Abdelmoutia ◽  
Hamdi Ghassen ◽  
Omri Nazih
Keyword(s):  
Experimental Study ◽  
Execution Time ◽  
Knowledge Bases ◽  
Point Of View ◽  
Second Phase ◽  
Two Phases ◽  
And Performance ◽  
Data Collections ◽  
Different Levels

This article discusses the issue of inconsistency in responses from various DL-Lite knowledge bases. This inconsistency problem is at the origin of several sources of assertions with different levels of reliability. The various solutions proposed in the literature that have to do with retrieving an exhaustive and coherent list of responses are not satisfactory from the point of view of reliability and performance. The solution that we present to solve this problem is articulated around two phases: the first phase consists of interrogating the different knowledge bases to retrieve all of the possible answers, which may be inconsistent and/or contradictory, and the second phase consists in repairing these inconsistencies and/or contradictions. To do this, we propose an approach based on three algorithms that we developed in this framework: a first algorithm for non-defeat repair, a second algorithm for lexicographic repair and a third algorithm for non-defeat repair based on lexicography of possible inconsistent responses. The experimental study carried out on the different data collections, as well as the analysis of the results obtained, confirm the performance of our approach as well as its efficiency in regards to productivity and complexity in terms of execution time.

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