Energy-Efficiency Computation Offloading Strategy in UAV Aided V2X Network With Integrated Sensing and Communication

Liu, Qian; Liang, Hairong; Luo, Rui; Liu, Qilie

doi:10.1109/ojcoms.2022.3195703

Cited by 24 publications

(10 citation statements)

References 27 publications

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“…Regarding possible future directions for our study, machine learning techniques can be utilized to estimate the channel parameters and minimize system overheads and feedback delays [32], [33]. Also, adopting and evaluating the integration of aerial communications [34], [35] on the performance of uplink and downlink NOMA deployments where unmanned aerial vehicles are provide additional degrees of freedom in network deployment represents another interesting research direction.…”

Section: Discussionmentioning

confidence: 99%

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On the Performance of Uplink Power-Domain NOMA with Imperfect CSI and SIC in 6G Networks

Ozduran,

Mohammadi,

Nomikos

et al. 2023

Preprint

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<p>Sixth generation (6G) networks must adopt spectral-efficient communication techniques to ensure massive connectivity for coexisting humans and machines. However, the impact of various practical issues must be analyzed and addressed, including imperfect channel state information (CSI), stemming by the channel estimation error (CEE) and feedback delay (F-D) with time-variant channels. This paper focuses on these issues in the context of uplink networks, relying on power-domain non-orthogonal multiple access (NOMA). Moreover, the degrading effect of imperfect successive interference cancellation (SIC), when randomly deployed multiple mobile terminals communicate with a single base station (BS) is considered. The system performance is measured by means of outage probability, error probability, ergodic rate, throughput, energy efficiency, and spectral efficiency. Analytical, asymptotic, and computer simulation results demonstrate that CEE causes system coding gain losses for low signal-to-noise ratio (SNR) while the disruptive effects of CEE become negligible in the high SNR. Results also show that F-D does not degrade the system performance in the low SNR but it causes system coding gain losses for high SNR. Also, imperfect SIC does not have any detrimental effect on the system performance for low SNR but results in reduced coding gain for high SNR. The theoretical and simulation findings of this study quantify the detrimental effect of practical limitations in NOMA-based uplink networks with coexisting users and devices, facilitating the adoption of optimized parameters to improve network performance.</p>

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Section: Discussionmentioning

confidence: 99%

“…Therefore, analytical expression before the Meijer's G function can be the final result for the asymptotic expression of the second part of the integral expression in (33). Utilizing (34) and newly obtained result, the final asymptotic ER expression for the j th user can be obtained as in (21).…”

Section: Proof Of Propositionmentioning

confidence: 99%

On the Performance of Uplink Power-Domain NOMA with Imperfect CSI and SIC in 6G Networks

Ozduran,

Mohammadi,

Nomikos

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…where 𝑃 is the power requirement for local computation and is proportional to the cubic power of the local frequency 𝑓 of the LT-UAV [23]. The resulting equation is as follows:…”

Section: E Local Computation Modelmentioning

confidence: 99%

JDACO: Joint Data Aggregation and Computation Offloading in UAV-Enabled Internet of Things for Post-Disaster Scenarios

Raivi,

Moh

2024

IEEE Internet Things J.

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Owing to high flexibility and rapid deployment, unmanned aerial vehicles (UAVs) can offer network coverage for Internet of things (IoT) devices in post-disaster scenarios. UAVaided mobile edge computing (MEC) provides computational support and facilitates optimal decision-making processes for ground-based IoT devices. However, existing literature has separately examined both data aggregation and computational offloading. In this paper, we introduce a Joint Data Aggregation and Computational Offloading (JDACO) scheme for UAVenabled IoT systems in post-disaster scenarios. JDACO's primary objective is to minimize the overall energy consumption and latency in the aggregation and computation processes. It achieves this by employing UAVs as MEC servers and deploying multiple UAVs. We initially design an objective function to assess the costs associated with the aggregation and offloading processes. Subsequently, we frame the optimization problem as a Markov model and employ a multi-agent deep reinforcement learning algorithm. This approach utilizes value decomposition with the double deep Q-Network algorithm to optimize data aggregation and enable a cost-effective offloading process through cooperative learning. Our experimental results demonstrate that our proposed JDACO scheme surpasses existing methods in terms of training time reduction, processed data volume, energy efficiency, and mission duration by 20%, 11.4%, 5.6%, and 11.2%, respectively, compared to the conventional schemes while serving up to 98% of IoT devices.

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“…The deep integration of UAVs, USVs, and UUVs can lead to optimal computing task allocation and real-time data collection, thus minimizing network latency. Efficient edge computing and caching algorithms should consider processing capabilities jointly with the trajectories of the unmanned vehicles, and their energy constraints in order to maintain high UAV availability in the network [158], [159].…”

Section: Security and Safetymentioning

confidence: 99%

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Νομικός

Gkonis

Bithas

et al. 2023

IEEE Open J. Commun. Soc.

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Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements. These future challenges include physical-layer aspects, non-orthogonal multiple access schemes, radical learning paradigms for swarms of UAVs and unmanned surface and underwater vehicles, as well as UAV-aided edge computing and caching.

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Energy-Efficiency Computation Offloading Strategy in UAV Aided V2X Network With Integrated Sensing and Communication

Cited by 24 publications

References 27 publications

On the Performance of Uplink Power-Domain NOMA with Imperfect CSI and SIC in 6G Networks

On the Performance of Uplink Power-Domain NOMA with Imperfect CSI and SIC in 6G Networks

JDACO: Joint Data Aggregation and Computation Offloading in UAV-Enabled Internet of Things for Post-Disaster Scenarios

A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

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