Impact of UAV Trajectory on NOMA-Assisted Cellular-Connected UAV Networks

Senadhira, Nilupuli; Durrani, Salman; Zhou, Xiangyun; Yang, Nan; Ding, Ming

doi:10.1109/icc40277.2020.9149407

Cited by 4 publications

(3 citation statements)

References 62 publications

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“…Moreover, [86] discovered that employing NOMA to intelligently integrate aBSs benefits terrestrial UEs by increasing the spectrum efficiency and system sum rate. The study of [87] focused on cellular-connected UAVs employed for surveillance, considering a trajectory-based movement in PD uplink aerial-terrestrial NOMA to enable simultaneous uplink transmissions. The work of [88] presents UAVs as aBS in the NOMA environment to enhance network performance, reduce transmission delays, and utilize deep reinforcement learning (DRL) to allow UAVs to interact with their environment.…”

Section: B Absmentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Mayarakaca,

Lee

2024

IEEE Access

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The rapid evolution of wireless communication has affected unmanned aerial vehicles (UAV), which are expected to be used in diverse applications in smart cities, military operations, and cellular networks. To address the significant impacts of rapid wireless communication advancements, along with the escalating demand for user equipment (UE), multiple access technique approaches, such as non-orthogonal multiple access (NOMA), have been proposed. NOMA has the key distinguishing feature of supporting more UE, particularly UAV-enabled communication networks. In these networks, to support more UE, multiple UE share the same frequency resources, which can be beneficial for UAV communication networks. To achieve optimal results, UAVs need to adaptively adjust to their environment, and this can be achieved through employing machine learning (ML). ML is a subfield of artificial intelligence (AI) that enables computers to learn from data and make predictions or decisions without being explicitly programmed. Employing ML can aid NOMA for UAV networks to analyze various parameters and rely on accurate predictions regarding various aspects of communication, such as channel conditions, user mobility, and traffic demands. The use of ML in NOMA-UAV networks can lead to significant improvements in wireless communication systems. In this paper, we present a survey on the potential of NOMA techniques applied to UAVs using ML methods to enhance UAVs in wireless communication networks. Specifically, a basic overview of UAV and NOMA will first be introduced. The role of NOMA in UAV networks is then divided into two categories: the principles and applications of NOMA in UAV networks. Finally, implement ML on NOMA for UAVs by representing the diverse applications of ML systems. In addition, we highlight several open research problems as possible directions for future research.INDEX TERMS Aerial networks, machine learning (ML), non-orthogonal multiple Access (NOMA), unmanned aerial vehicles (UAV).

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Section: B Absmentioning

confidence: 99%

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Mayarakaca,

Lee

2024

IEEE Access

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“…In their works, BER performances of the OFDM-HIQ-IM and LP-OFDM-IQ-IM systems have been investigated in comparison with the classical OFDM as well as existing OFDM-IM schemes. In [14], the authors considered employing cellular-linked aerial user equipment (AUE) for surveillance and monitoring. They enabled AUE for continuous uplink transmissions and also utilized powerdomain uplink aerial-terrestrial non-orthogonal multiple access (NOMA) enabling terrestrial user equipment (TUE).…”

Section: A Prior Workmentioning

confidence: 99%

Transceiver Design for Full-Duplex UAV Based Zero-Padded OFDM System With Physical Layer Security

et al. 2021

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In this paper, multi-antenna transceiver for zero-padded orthogonal frequency division multiplexing (OFDM) system is designed at mmWave by integrating full-duplex unmanned aerial vehicle (UAV) into the terrestrial cellular networks. Assuming that there exist no direct communication links between the ground base station (GBS) and the mobile users due to unexpected blockages from high storied buildings in urban area, the UAV applies decode-and-forward cooperative strategy on the received OFDM signals transmitted from GBS and re-transmits to the ground mobile users and passive eavesdropper. In this proposed system, intertwining logistic map (ILM)-cosine transform aided encryption algorithm combined with artificial noise enhancing physical layer security (PLS) is introduced. Also walsh-hadamard transform technique integrated with QR-decomposition based zero forcing (ZF) block diagonalization (QR-ZF-BD) precoding for multi-user interference reduction and non-iterative clipping and filtering technique for peak to average power ratio (PAPR) reduction are utilized. In addition, Low density parity check (LDPC) and repeat and accumulate (RA) channel coding with cholesky decomposition based ZF and minimum mean square error signal detection schemes for improved bit error rate (BER) are also introduced. Numerical results demonstrate the effectiveness of the proposed system in terms of PLS for color image transmission at high order digital modulation . At the complementary cumulative distribution function of probability level 1-6%, the estimated PAPR is found to have value of 6 dB.The three users achieve BER = 1 × 10 −4 at signal-to-noise ratio of 1.5 dB, 4 dB and 6 dB under RA channel coding and 16-QAM digital modulation.INDEX TERMS Bit error rate, channel coding, peak to average power ratio, physical layer security encryption, signal-to-noise ratio, zero-padded orthogonal frequency division multiplexing.

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“…Note that the distinction between air-to-ground and air-to-cellular lies in the non-negligible height of the terrestrial BS [29], [35], [36]. 1 Note that the AUE is capable of varying its height along the course of the trajectory. In Sections V C-D we assume that the AUE flies at a constant height along the entire trajectory and, in Sections V E-F, we consider a case where the AUE can vary its height at each trajectory point to achieve a certain quality of service.…”

Section: Channel Modelmentioning

confidence: 99%

Uplink NOMA for Cellular-Connected UAV: Impact of UAV Trajectories and Altitude

Senadhira

Durrani

Zhou

et al. 2019

Preprint

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This paper considers an emerging cellular-connected unmanned aerial vehicle (UAV) architecture for surveillance or monitoring applications. We consider a scenario where a cellular-connected aerial user equipment (AUE) periodically transmits in uplink, with a given data rate requirement, while moving along a given trajectory. For efficient spectrum usage, we enable the concurrent uplink transmission of the AUE and a terrestrial user equipment (TUE) by employing power-domain aerial-terrestrial nonorthogonal multiple access (NOMA), while accounting for the AUE's known trajectory. To characterize the system performance, we develop an analytical framework to compute the rate coverage probability, i.e., the probability that the achievable data rate of both the AUE and TUE exceeds the respective target rates. We use our analytical results to numerically determine the minimum height that the AUE needs to fly, at each transmission point along a given trajectory, in order to satisfy a certain quality of service (QoS) constraint for various AUE target data rates and different built-up environments. Specifically, the results show that the minimum height of the AUE depends on its distance from the BS as the AUE moves along the given trajectory. Our results highlight the importance of modeling AUE trajectory in cellular-connected UAV systems.

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Impact of UAV Trajectory on NOMA-Assisted Cellular-Connected UAV Networks

Cited by 4 publications

References 62 publications

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

A Survey on Non-Orthogonal Multiple Access for Unmanned Aerial Vehicle Networks: Machine Learning Approach

Transceiver Design for Full-Duplex UAV Based Zero-Padded OFDM System With Physical Layer Security

Uplink NOMA for Cellular-Connected UAV: Impact of UAV Trajectories and Altitude

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