UAV-Assisted Cooperative &amp; Cognitive NOMA: Deployment, Clustering, and Resource Allocation

Arzykulov, Sultangali; Çelik, Abdulkadir; Nauryzbayev, Galymzhan; Eltawil, Ahmed M.

doi:10.1109/tccn.2021.3105133

Cited by 38 publications

(23 citation statements)

References 65 publications

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“…SensRate is proposed for lowaltitude UAVs. The underlying applications include traffic offloading in hotspot areas [2]- [6], [48], [49], data collection [7], [8] and UAV-enabled mobile edge computing [9], [10], considering the UAV altitudes and velocities in this paper. For example, DroneFi [2] proposes drone hotspots flying below 20 m. Tethered UAVs have recently attracted attention for network coverage [6] with limited altitudes and velocities.…”

Section: Discussionmentioning

confidence: 99%

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Xiao¹,

Wang²,

Jiang³

2022

Preprint

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Propelled by multi-user MIMO (MU-MIMO) technology, unmanned aerial vehicles (UAVs) as mobile hotspots have recently emerged as an attractive wireless communication paradigm. Rate adaptation (RA) becomes indispensable to enhance UAV communication robustness against UAV mobilityinduced channel variances. However, existing MU-MIMO RA algorithms are mainly designed for ground communications with relatively stable channel coherence time, which incurs channel measurement staleness and sub-optimal rate selections when coping with highly dynamic air-to-ground links. In this paper, we propose SensRate, a new uplink MU-MIMO RA algorithm dedicated for low-altitude UAVs, which exploits inherent onboard sensors used for flight control with no extra cost. We propose a novel channel prediction algorithm that utilizes sensorestimated flight states to assist channel direction prediction for each client and estimate inter-user interference for optimal rates. We provide an implementation of our design using a commercial UAV and show that it achieves an average throughput gain of 1.24× and 1.28× compared with the bestknown RA algorithm for 2-and 3-antenna APs, respectively.

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Section: Discussionmentioning

confidence: 99%

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Xiao¹,

Wang²,

Jiang³

2022

Preprint

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“…Another new technique to assist the calculating and communication in IoT networks is the deployment of unmanned aerial vehicles (UAVs), which are easy to be used and provide flexible ability. Moreover, the price of UAV is becoming cheaper and cheaper, which inspires a lot of applications in practice [14,15]. For the MEC system, the UAVs can rescue the data calculating with higher priority through some intelligent path routing and scheduling, which exploits the incremental system resources due to the usage of UAVs.…”

Section: Introductionmentioning

confidence: 99%

UAV-Aided Multiuser Mobile Edge Computing Networks with Energy Harvesting

Wang

Zhu³

et al. 2022

Wireless Communications and Mobile Computing

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This article studies a mobile edge computing (MEC) with one edge node (EN), where multiple unmanned aerial vehicles (UAVs) act as users which have some heavy tasks. As the users generally have limitations in both calculating and power supply, the EN can help calculate the tasks and meanwhile supply the power to the users through energy harvesting. We optimize the system by proposing a joint strategy to unpacking and energy harvesting. Specifically, a deep reinforcement learning (DRL) algorithm is implemented to provide a solution to the unpacking, while several analytical solutions are given to the power allocation of energy harvesting among multiple users. In particular, criterion I is the equivalent power allocation, criterion II is designed through equal data rate, while criterion III is based on the equivalent transmission delay. We finally give some results to verify the joint strategy for the UAV-aided multiuser MEC system with energy harvesting.

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“…As mentioned previously, the integration of NOMA technique in CRNs can improve the SE and the system throughput 16,17 . Accordingly, the researchers have extensively investigated the performance of NOMA‐HDCRNs, see References 49‐62 and references therein. In Reference 49, the authors have analyzed the outage probabilities of SUs in NOMA‐HDCRN.…”

Section: Introductionmentioning

confidence: 99%

Optimal power allocation for non‐orthogonal multiple access enabled full‐duplex underlay cognitive relay networks under partial relay selection

Aswathi

Babu

2022

Trans Emerging Tel Tech

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This article considers non-orthogonal multiple access (NOMA) enabled full-duplex (FD) underlay cognitive relay networks (ie, NOMA-FDCRNs) with partial relay selection scheme. The secondary network, where NOMA is used, consists of a secondary base station (SBS) sending messages to two secondary users (SUs), that is, a near SU (SU1) and a far SU (SU2), by utilizing a dedicated relay selected from a set of FD decode-and-forward nodes. We obtain analytical expressions for the outage probabilities (OPs) of the SUs and then deduce the asymptotic OP expressions as well. Further, expressions are obtained for the optimal power allocation (OPA) coefficients at the SBS and at the relay that separately maximizes the throughput of the secondary network in NOMA-FDCRN.Furthermore, the jointly optimal power allocation (JOPA) coefficients that maximize the throughput are also determined. The analyses consider (i) imperfect successive interference cancelation conditions, (ii) the tolerable interference limit of the primary receiver, (iii) the secondary nodes' maximum transmit power values, (iv) interference generated by the primary transmitter, and (v) the residual self-interference (RSI) at the FD relay. It is shown that the proposed OPA coefficients at the SBS and the JOPA can mitigate the impact of RSI and significantly improve the OP and throughput performance. The numerical results show that the proposed JOPA approach provides 32% and 106% improvement of throughput compared to random power allocation (RPA) and equal power allocation (EPA) strategies, respectively. The OP of SU1 and SU2 reduce by 68% and 73%, respectively, under the proposed JOPA compared to RPA, while compared to EPA, the OP of SU1 and SU2 reduce by 96% and 97%, respectively.

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UAV-Assisted Cooperative & Cognitive NOMA: Deployment, Clustering, and Resource Allocation

Cited by 38 publications

References 65 publications

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

Sensor-Assisted Rate Adaptation for UAV MU-MIMO Networks

UAV-Aided Multiuser Mobile Edge Computing Networks with Energy Harvesting

Optimal power allocation for non‐orthogonal multiple access enabled full‐duplex underlay cognitive relay networks under partial relay selection

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