Energy-Efficiency Power Allocation Design for UAV-Assisted Spatial NOMA

Jia, Min; Gao, Qiling; Guo, Qing; Gu, Xuemai

doi:10.1109/jiot.2020.3044090

Cited by 28 publications

(11 citation statements)

References 44 publications

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“…Then, a multi-UAV deployment algorithm based on spatial adaptive play was developed to tackle both power control and coverage maximization. An energy-efficient power allocation scheme for UAV-enabled spatial NOMA was proposed in [238]. The EE maximization problem comprising the power consumption, signal capacity, and spatial gain was formulated, after which a heuristic algorithm was developed to determine the optimal power allocation strategy.…”

Section: Conventional Approachesmentioning

confidence: 99%

“…Year Category Specific Algorithm CA ML [237] 2018 Heuristic [238] 2021 Heuristic [239] 2021 Heuristic [240] 2021 Heuristic [241] 2020 Heuristic [242] 2021 Heuristic [243] 2019 Heuristic [244] 2020 Heuristic [245] 2019 Heuristic [246] 2019 Heuristic [247] 2020 Heuristic [27] 2019 Heuristic [242] 2021 Heuristic [248] 2020 Heuristic Here, we consider studies that involve the scheduling UAV-BSs flights as well as their sequence of transmission in order to ensure that minimum energy is consumed in the process. We also examine research works that investigate how the fight of UAVs are coordinated and scheduled or how UAVs cooperate to achieve energy-efficient communication.…”

Section: Papermentioning

confidence: 99%

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A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

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Wireless communication networks have been witnessing unprecedented demand due to the increasing number of connected devices and emerging bandwidth-hungry applications. Although there are many competent technologies for capacity enhancement purposes, such as millimeter wave communications and network densification, there is still room and need for further capacity enhancement in wireless communication networks, especially for the cases of unusual people gatherings, such as sport competitions, musical concerts, etc. Unmanned aerial vehicles (UAVs) have been identified as one of the promising options to enhance capacity due to their easy implementation, pop-up fashion operation, and cost-effective nature. The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity where it is needed. However, UAVs mostly have limited energy storage, hence, their energy consumption must be optimized to increase flight time. In this survey, we investigate different energy optimization techniques with a top-level classification in terms of the optimization algorithm employed—conventional and machine learning (ML). Such classification helps understand the state-of-the-art and the current trend in terms of methodology. In this regard, various optimization techniques are identified from the related literature, and they are presented under the above-mentioned classes of employed optimization methods. In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs. Moreover, novel concepts, such as reflective intelligent surfaces and landing spot optimization, are also covered to capture the latest trends in the literature.

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Section: Conventional Approachesmentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Abubakar

Ahmad

Omeke

et al. 2023

Drones

View full text Add to dashboard Cite

show abstract

“…With this technique, the lesser wireless power transfer time will be needed from UAV to charge energy receivers when using NOMA. Another possibility to improve EE on UAV is by proposing a system spatial NOMA which is a combination method of NOMA and spatial modulation, yet it is important to consider the EE performance in a low power situation due to the energy-carrying capacity and rising energy consumption [119]. Conversely, throughput fairness being investigated in [120] with time-sharing NOMA able to maximize the EE and in [121] by applying fixed PA and random PA schemes framework of power resource.…”

Section: Unmanned Aerial Vehiclementioning

confidence: 99%

Energy-efficient non-orthogonal multiple access for wireless communication system

Darus

Idris

Hashim

2023

IJECE

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<p>Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed.</p>

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“…The UR as an aerial BS for providing the connectivity to terrestrial UEs in the downlink NOMA system was studied in [18], and the joint trajectory design and resource allocation algorithms were investigated to maximize the minimum achievable rate of UEs. To improve the EE by power allocation optimization in a 6G enabled UR network, a combination of NOMA and spatial modulation techniques were proposed in [19]. A power allocation scheme for NOMA-UR networks with circular trajectory was proposed in [20] which maximizes the SE of common UEs subject to increase the security for a specific UE.…”

Section: A Prior Workmentioning

confidence: 99%

Relay Selection and Power Allocation for Energy Efficiency Maximization in Hybrid Satellite-UAV Networks With CoMP-NOMA Transmission

Mirbolouk

Valizadeh

Amirani

et al. 2022

IEEE Trans. Veh. Technol.

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Non-orthogonal multiple access (NOMA) and coordinated multi-point (CoMP) are two fundamental techniques considered for the fifth generation (5G) of wireless communications. In this paper, a hybrid satellite-unmanned aerial vehicle (UAV) relay network (HSURN) is proposed where the UAV relays (URs) employ CoMP transmission to serve the terrestrial users (UEs). Furthermore, all UEs associated with the CoMP-URs form a single NOMA cluster. For this model, an optimization problem is formulated subject to the minimum quality of services (QoSs) requirements of the UEs, transmission power budgets and, successive interference cancellation (SIC), to select URs and allocate their transmission powers for the energy efficiency (EE) maximization. With this insight, first, a computationally efficient sub-optimal UR selection scheme is proposed. Then, the powers are allocated to the selected URs via the Lagrange multipliers optimization (LMO) method. Due to the non-convex nature of the considered problem, it is relatively difficult to be solved. Hence, a metaheuristic teaching-learning-based optimization (TLBO) algorithm is employed to achieve an efficient solution. Simulation results are provided to verify the effectiveness of the proposed sub-optimal relay selection scheme and the TLBO-based power allocation method compared to the LMO conventional method. Besides, the obtained results also reveal that the CoMP-NOMA transmission in the proposed scenario significantly improves the spectral efficiency (SE) and outage probability (OP) of the system compared to non-comp NOMA transmission case.Index Terms-Non-orthogonal multiple access (NOMA), Coordinated multi-point (CoMP) transmission, energy efficiency (EE), UAV relay selection, satellite terrestrial network, outage probability (OP). I. INTRODUCTIONThe 5G-satellite networks in the integrated architecture have emerged as a valuable infrastructure to meet the future radio access of smart devices. Combining satellite components into wireless systems is not only an indispensable way to provide seamless coverage and large capacity for users all over the world but also to ensure high QoS expectations [1]. Mobile satellite networks have been viewed as a promising technique for the smart grid, internet-of-thing (IoT), wireless sensor

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Energy-Efficiency Power Allocation Design for UAV-Assisted Spatial NOMA

Cited by 28 publications

References 44 publications

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches

Energy-efficient non-orthogonal multiple access for wireless communication system

Relay Selection and Power Allocation for Energy Efficiency Maximization in Hybrid Satellite-UAV Networks With CoMP-NOMA Transmission

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