Resource Allocation for Energy Harvesting-Powered D2D Communication Underlaying UAV-Assisted Networks

Wang, Haichao; Wang, Jinlong; Ding, Guoru; Le, Wang; Tsiftsis, Theodoros A.; Sharma, Prabhat Kumar

doi:10.1109/tgcn.2017.2767203

Cited by 173 publications

(105 citation statements)

References 30 publications

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“…We propose an algorithm to solve the EE maximization (11). The initial point (θ (0) , p (0) ) for (11) is easily located by random search such that it satisfies the constraints in problem (7). Solve the (11) for the optimal solution (θ (κ+1) , p (κ+1) ).…”

Section: Joint Harvesting Time and Power Allocation (Jhtpa) For mentioning

confidence: 99%

“…The D2D pairs are randomly distributed in the coverage network and the maximum distance between D2D-Tx and D2D-Rx is 50m. Similar to [7], the channel power gain between D2D-Tx and D2D-Rx is modelled as…”

Section: Implementationsmentioning

confidence: 99%

“…Other simulation parameters in the considered D2D UAV network are provided in Table I as [7], [13]. The excessive attenuation factor γ = 20 dB…”

Section: Implementationsmentioning

confidence: 99%

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Real-Time Optimal Resource Allocation for Embedded UAV Communication Systems

Nguyen

Tuan

2019

IEEE Wireless Commun. Lett.

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We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.

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Section: Joint Harvesting Time and Power Allocation (Jhtpa) For mentioning

confidence: 99%

Section: Implementationsmentioning

confidence: 99%

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Real-Time Optimal Resource Allocation for Embedded UAV Communication Systems

Nguyen

Tuan

2019

IEEE Wireless Commun. Lett.

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“…(τ τ τ , p, h, θ) and r 2,(κ) j(k) (τ τ τ , p, h, θ) are defined in(18) and(24), respectively. Algorithm 2 DPC-based algorithm for max-min rate optimization problem (9) Initialization: Set κ := 0 and a feasible point (τ τ τ (0) , p (0) , θ (0) , h (0) ) for constraints (1), (8b), (8c),…”

mentioning

confidence: 99%

“…. , K} and r(κ) k (τ τ τ , p, h, θ) is defined in(18). Algorithm 3 outlines the steps to solve the max-min rate optimization problem(10).…”

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confidence: 99%

UAV-Enabled Communication Using NOMA

Nasir

Tuan

Duong

et al. 2019

IEEE Trans. Commun.

202

127

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Unmanned aerial vehicles (UAVs) can be deployed as flying base stations (BSs) to leverage the strength of line-of-sight connections and effectively support the coverage and throughput of wireless communication.This paper considers a multiuser communication system, in which a single-antenna UAV-BS serves a large number of ground users by employing non-orthogonal multiple access (NOMA). The max-min rate optimization problem is formulated under total power, total bandwidth, UAV altitude, and antenna beamwdith constraints. The objective of max-min rate optimization is non-convex in all optimization variables, i.e. UAV altitude, transmit antenna beamwidth, power allocation and bandwidth allocation for multiple users. A pathfollowing algorithm is proposed to solve the formulated problem. Next, orthogonal multiple access (OMA) and dirty paper coding (DPC)-based max-min rate optimization problems are formulated and respective pathfollowing algorithms are developed to solve them. Numerical results show that NOMA outperforms OMA and achieves rates similar to those attained by DPC. In addition, a clear rate gain is observed by jointly optimizing all the parameters rather than optimizing a subset of parameters, which confirms the desirability of their joint optimization. Index TermsA. A. Nasir is with the 2 Unmanned aerial vehicle (UAV), non-orthogonal multiple access (NOMA), orthogonal multiple access (OMA), dirty paper coding (DPC), non-convex optimization, throughput. I. INTRODUCTION Unmanned aerial vehicles (UAVs) can assist normal communication networks by acting as flying base stations (UAV-BSs) and taking care of traffic demand in exceptional situations, e.g., sports events, concerts, disaster position, military situations, traffic congestion, etc. [1]-[6]. UAVs can also function as temporary hotspots or relay nodes for connections between the safe area and disaster areas [7]-[9]. Ground users served by the UAV-BSs can expect line-of-sight (LoS) air-to-ground communication. Thus, UAV-enabled communication can be efficient in supporting the coverage and throughput of wireless communications [10], [11]. UAV-enabled communication networks have recently gained significant interests and are actively investigated in open literature. Thanks to the flexibility of UAV deployment, the coverage area, throughput, and energy efficiency of UAV-enabled communication can be improved by UAV placement [12]-[14], beamwidth control [15], [16], and power allocation [1], [17], [18].Unlike conventional cellular communication, which operates in a rich scattering environment that supports multi-antenna array transmission for spatial diversity, UAV-enabled downlink communication exhibits much poorer scattering and as such a single-antenna UAV is most desired. To be served by the same UAV over the same time, multiple users must share the communication bandwidth.Usually each user is assigned an individual bandwidth channel so its achievable rate is very sensitive to the number of users sharing the same bandwidth. Naturally one may think to assig...

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D2Dwith Energy Harvesting Capabilities

Chang

2020

Wiley 5G Ref

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Device‐to‐device (D2D) communication is envisioned as a key enabling technology for fifth‐generation (5G) wireless systems and the Internet of Things (IoT). D2D promises increased resource utilization, throughput, and coverage, and decreased delays and power consumption, by offloading traffic from the centralized infrastructure to autonomous direct device‐to‐device transmissions. With the rise of D2D and IoT, which comprise typically small, battery‐powered devices, it is essential to consider the energy efficiency and sustainability of future D2D networks. Energy harvesting (EH) has emerged as a promising green and sustainable energy solution for D2D and IoT. EH technologies represent harnessing energy from ambient renewable energy sources and converting the harvested environmental energy into usable electrical energy via wireless power transfer (WPT) technologies. Among various renewable sources, radio frequency (RF) signals are more reliable and allow for simultaneous wireless information and power transfer (SWIPT), and therefore are of particular interest. EH‐aided D2D communication raises new resource allocation and interference management considerations nonexistent in conventional D2D communication, since cochannel interference, a main performance‐limiting factor in underlaid D2D networks, could be converted into useful energy. Issues such as power allocation/control, beamforming design, time allocation, and spectrum allocation, with the consideration of EH‐empowered D2D devices, have been investigated for EH‐aided D2D networks.

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Resource Allocation for Energy Harvesting-Powered D2D Communication Underlaying UAV-Assisted Networks

Cited by 173 publications

References 30 publications

Real-Time Optimal Resource Allocation for Embedded UAV Communication Systems

Real-Time Optimal Resource Allocation for Embedded UAV Communication Systems

UAV-Enabled Communication Using NOMA

D2Dwith Energy Harvesting Capabilities

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