Joint User Scheduling and Power Allocation Optimization for Energy-Efficient NOMA Systems With Imperfect CSI

Fang, Fang; Zhang, Haijun; Cheng, Julian; Roy, S.; Leung, Victor C. M.

doi:10.1109/jsac.2017.2777672

Cited by 263 publications

(172 citation statements)

References 31 publications

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“…Moreover, there are some other important research issues that could be done in future work based on the results of this work, such as cooperative NOMA systems, cognitive NOMA systems and NOMA systems with energy harvesting. Another interesting future topic is to analyze the impact of imperfect CSI on energy efficiency of secure NOMA systems, whereas the energy-efficient optimization problem for NOMA systems with imperfect CSI were solved in [56]. …”

Section: Discussionmentioning

confidence: 99%

Secrecy Outage of Max–Min TAS Scheme in MIMO-NOMA Systems

Lei

Zhang

Park

et al. 2018

IEEE Trans. Veh. Technol.

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Section: Discussionmentioning

confidence: 99%

Secrecy Outage of Max–Min TAS Scheme in MIMO-NOMA Systems

Lei

Zhang

Park

et al. 2018

IEEE Trans. Veh. Technol.

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“…In [160], the perfect knowledge of the CSI was assumed at the BS and the associated energy efficiency maximization problem was investigated in the NOMA downlink by jointly optimizing both the subchannel assignment and the power allocation. Since perfect CSI knowledge cannot be readily acquired, the authors of [161] considered the energy efficiency maximization problem of the NOMA downlink in the presence of realistic imperfect CSI. As a further development, the authors of [162] studied the energy efficiency optimization of MIMO-aided NOMA systems in the face of fading channels in conjunction with statistical CSI at the transmitter, where the energy efficiency was defined in terms of the ergodic capacity attained at unity power consumption.…”

Section: User Grouping and Resource Allocationmentioning

confidence: 99%

A Survey of Non-Orthogonal Multiple Access for 5G

Dai

Wang

Ding

et al. 2018

IEEE Commun. Surv. Tutorials

1,095

664

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Abstract-In the 5th generation (5G) of wireless communication systems, hitherto unprecedented requirements are expected to be satisfied. As one of the promising techniques of addressing these challenges, non-orthogonal multiple access (NOMA) has been actively investigated in recent years. In contrast to the family of conventional orthogonal multiple access (OMA) schemes, the key distinguishing feature of NOMA is to support a higher number of users than the number of orthogonal resource slots with the aid of non-orthogonal resource allocation. This may be realized by the sophisticated inter-user interference cancellation at the cost of an increased receiver complexity. In this article, we provide a comprehensive survey of the original birth, the most recent development, and the future research directions of NOMA. Specifically, the basic principle of NOMA will be introduced at first, with the comparison between NOMA and OMA especially from the perspective of information theory. Then, the prominent NOMA schemes are discussed by dividing them into two categories, namely, power-domain and code-domain NOMA. Their design principles and key features will be discussed in detail, and a systematic comparison of these NOMA schemes will be summarized in terms of their spectral efficiency, system performance, receiver complexity, etc. Finally, we will highlight a range of challenging open problems that should be solved for NOMA, along with corresponding opportunities and future research trends to address these challenges.

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“…It may originate from training based channel estimation [11], limited precision of feedback [12], partial CSI acquisition [13] and delays in CSI acquicition [14]. In this work, we adopt the additive error model [25], [26] of the channel imperfection, i.e.,…”

Section: Channel Uncertainty Modelmentioning

confidence: 99%

Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference

Yang

Shi

et al. 2020

IEEE Internet Things J.

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Edge machine learning can deliver low-latency and private artificial intelligent (AI) services for mobile devices by leveraging computation and storage resources at the network edge. This paper presents an energy-efficient edge processing framework to execute deep learning inference tasks at the edge computing nodes whose wireless connections to mobile devices are prone to channel uncertainties. Aimed at minimizing the sum of computation and transmission power consumption with probabilistic quality-of-service (QoS) constraints, we formulate a joint inference tasking and downlink beamforming problem that is characterized by a group sparse objective function. We provide a statistical learning based robust optimization approach to approximate the highly intractable probabilistic-QoS constraints by nonconvex quadratic constraints, which are further reformulated as matrix inequalities with a rank-one constraint via matrix lifting. We design a reweighted power minimization approach by iteratively reweighted 1 minimization with difference-of-convexfunctions (DC) regularization and updating weights, where the reweighted approach is adopted for enhancing group sparsity whereas the DC regularization is designed for inducing rankone solutions. Numerical results demonstrate that the proposed approach outperforms other state-of-the-art approaches.

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Joint User Scheduling and Power Allocation Optimization for Energy-Efficient NOMA Systems With Imperfect CSI

Cited by 263 publications

References 31 publications

Secrecy Outage of Max–Min TAS Scheme in MIMO-NOMA Systems

Secrecy Outage of Max–Min TAS Scheme in MIMO-NOMA Systems

A Survey of Non-Orthogonal Multiple Access for 5G

Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference

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