Non-Orthogonal Multiple Access: Achieving Sustainable Future Radio Access

Yang, Kai; Yang, Nan; Ye, Neng; Jia, Min; Gao, Zhen; Fan, Rongfei

doi:10.1109/mcom.2018.1800179

Cited by 171 publications

(83 citation statements)

References 12 publications

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“…The EE and SE trade-off is revealed and the proposed scheme has been compared with the exhaustive search in terms of EE performance and computational complexity. In our future work, we will carry out the EE maximization problem for novel 5G platforms such as massive MIMO, non-orthogonal multiple access (NOMA) and ultra-dense heterogeneous networks [25] [26].…”

Section: Discussionmentioning

confidence: 99%

Energy Efficiency for MISO-OFDMA-Based User-Relay Assisted Cellular Networks

Baştürk

Chen

2020

IEEE Systems Journal

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Section: Discussionmentioning

confidence: 99%

Energy Efficiency for MISO-OFDMA-Based User-Relay Assisted Cellular Networks

Baştürk

Chen

2020

IEEE Systems Journal

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“…To simplify the design in the sequel, we assume that the channel is modeled by pure LoS links as commonly adopted in the literature, e.g., [14], [15], [21], [22]. As the UAV communication channel is dominated by the LoS links 4 , the CSI between each node and each UAV can be determined by its location [21], [22], [23], [25], [26]. Besides, the desired ground node users perform handshaking with the system regularly such that accurate location information is available for resource allocation design.…”

Section: B Signal Modelmentioning

confidence: 99%

“…3 We note that since the channel between the UAV and the ground terminals are LoS dominated [22], [23], [26], the UAVs would fly at the lowest allowable flight altitude to obtain a higher channel gain for maximizing the system energy efficiency. Thus, we consider a fixed UAVs' flight altitude of H = 100 m. 4 Based on field measurements [28], [29], the air-to-ground links between the UAVs and the ground terminals are LoS channels in rural areas when the flight altitude of a UAV is 100 meters and the length of side of the service area is 500 meters. Besides, the adopted LoS model can facilitate the design of resource allocation and trajectory in the sequel.…”

Section: B Signal Modelmentioning

confidence: 99%

Joint Trajectory and Resource Allocation Design for Energy-Efficient Secure UAV Communication Systems

Cai

Wei

et al. 2020

IEEE Trans. Commun.

199

125

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In this paper, we study the trajectory and resource allocation design for downlink energy-efficient secure unmanned aerial vehicle (UAV) communication systems, where an information UAV assisted by a multi-antenna jammer UAV serves multiple ground users in the existence of multiple ground eavesdroppers. The resource allocation strategy and the trajectory of the information UAV, and the jamming policy of the jammer UAV are jointly optimized for maximizing the system energy efficiency. The joint design is formulated as a non-convex optimization problem taking into account the quality of service (QoS) requirement, the security constraint, and the imperfect channel state information (CSI) of the eavesdroppers. The formulated problem is generally intractable. As a compromise approach, the problem is divided into two subproblems which facilitates the design of a low-complexity suboptimal algorithm based on alternating optimization approach. Simulation results illustrate that the proposed algorithm converges within a small number of iterations and demonstrate some interesting insights: (1) the introduction of a jammer UAV facilitates a highly flexible trajectory design of the information UAV which is critical to improving the system energy efficiency; (2) by exploiting the spatial degrees of freedom brought by the multi-antenna jammer UAV, our proposed design can focus the artificial noise on eavesdroppers offering a strong security mean to the system.

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“…Successive interference cancellation (SIC) is usually utilized to distinguish signals from multiple users [17]. Recently, many research studies combine grant-free transmission with NOMA scheme to simultaneously accomplish high throughput, large connectivity, and low energy cost [18][19][20]. FTN transmission is also considered in NOMA scenario to gain superior performance in spectral efficiency [21,22].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning-Aided Detection Method for FTN-Based NOMA

Pan

Wang

et al. 2020

Wireless Communications and Mobile Computing

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The rapid booming of future smart city applications and Internet of things (IoT) has raised higher demands on the next-generation radio access technologies with respect to connection density, spectral efficiency (SE), transmission accuracy, and detection latency. Recently, faster-than-Nyquist (FTN) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies to achieve higher SE and massive connections, respectively. In this paper, we aim to exploit the joint benefits of FTN and NOMA by superimposing multiple FTN-based transmission signals on the same physical recourses. Considering the complicated intra- and interuser interferences introduced by the proposed transmission scheme, the conventional detection methods suffer from high computational complexity. To this end, we develop a novel sliding-window detection method by incorporating the state-of-the-art deep learning (DL) technology. The data-driven offline training is first applied to derive a near-optimal receiver for FTN-based NOMA, which is deployed online to achieve high detection accuracy as well as low latency. Monte Carlo simulation results validate that the proposed detector achieves higher detection accuracy than minimum mean squared error-frequency domain equalization (MMSE-FDE) and can even approach the performance of the maximum likelihood-based receiver with greatly reduced computational complexity, which is suitable for IoT applications in smart city with low latency and high reliability requirements.

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Non-Orthogonal Multiple Access: Achieving Sustainable Future Radio Access

Cited by 171 publications

References 12 publications

Energy Efficiency for MISO-OFDMA-Based User-Relay Assisted Cellular Networks

Energy Efficiency for MISO-OFDMA-Based User-Relay Assisted Cellular Networks

Joint Trajectory and Resource Allocation Design for Energy-Efficient Secure UAV Communication Systems

A Deep Learning-Aided Detection Method for FTN-Based NOMA

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