Clustered Millimeter-Wave Networks With Non-Orthogonal Multiple Access

Yi, Wenqiang; Liu, Yuanwei; Nallanathan, Arumugam; Elkashlan, Maged

doi:10.1109/tcomm.2019.2897632

Cited by 49 publications

(34 citation statements)

References 49 publications

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“…To comprehensively analyze the network's performance enhanced by RISs, a RISaided SISO-NOMA network is proposed. Motivated by the potential joint benefits of RISs and NOMA networks, whilst relying on analog beamforming [35], in this article we will analyse the performance of a RIS-aided NOMA DL scenario, where a priority-oriented design (POD) is proposed, which is also capable of enhancing the SE. In the proposed POD, we improve the performance of the user having the best channel gain, where all other users rely on RIS-aided beamforming.…”

Section: B Motivations and Contributionsmentioning

confidence: 99%

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Reconfigurable Intelligent Surface Aided NOMA Networks

Hou

Liu

Song

et al. 2020

IEEE J. Select. Areas Commun.

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341

192

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Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signalto-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart.

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Section: B Motivations and Contributionsmentioning

confidence: 99%

“…Since the elements in Φ are gleaned from random variables, and based on the insights from [35], the SINR of user v can be further approximated as:…”

Section: Ergodic Ratementioning

confidence: 99%

Reconfigurable Intelligent Surface Aided NOMA Networks

Hou

Liu

Song

et al. 2020

IEEE J. Select. Areas Commun.

Self Cite

341

192

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show abstract

“…8 shows that the coverage probability of all user selection schemes, namely RS and IN, tends to an error floor as discussed in Remark 5 and Remark 8. Compared with conventional wireless systems with random beamforming [48] and ground communications with BSs at 30 m [40], the proposed UAV network with aligned beamforming achieves higher coverage performance.…”

Section: Numerical Resultsmentioning

confidence: 99%

Clustered UAV Networks With Millimeter Wave Communications: A Stochastic Geometry View

Liu

Deng

et al. 2020

IEEE Trans. Commun.

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In order to satisfy the requirement of high throughput in most UAV applications, the potential of integrating millimeter wave (mmWave) communications with UAV networks is explored in this paper. A tractable three-dimensional (3D) spatial model is proposed for evaluating the average downlink performance of UAV networks at mmWave bands, where the locations of UAVs and users are randomly distributed with the aid of a Poisson cluster process. Moreover, an actual 3D antenna model with the uniform planar array is deployed at all UAVs to examine the impact of both azimuth and elevation angles. Based on this framework and two typical user selection schemes, closed-form approximation equations of the evaluated coverage probability and area spectral efficiency (ASE) are derived. In a noise-limited scenario, an exact expression is provided, which theoretically demonstrates that a large scale of antenna elements is able to enhance the coverage performance. Regarding the altitude of UAVs, there exists at least one optimal height for maximizing the coverage probability. Numerical results verify the proposed insight that non-line-of-sight transmission caused by obstacles have negligible effects on the proposed system. Another interesting result is that the ASE can be maximized by optimizing both the targeted data rate and the density of UAVs.

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“…For multicell scenarios, the impact of NOMA on large scale multicell IoT networks was investigated in [22]. To characterize the communication distances, the authors in [23] analysed the performance of large scale NOMA communications via stochastic geometry. It is worth noting that NOMA-IoT channels are time-varying in the real world.…”

Section: A Related Work and Motivations 1) Studies On Noma-iot Netwmentioning

confidence: 99%

Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach

Ahsan

Qin

et al. 2021

IEEE Trans. Wireless Commun.

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Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This paper develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput.

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Clustered Millimeter-Wave Networks With Non-Orthogonal Multiple Access

Cited by 49 publications

References 49 publications

Reconfigurable Intelligent Surface Aided NOMA Networks

Reconfigurable Intelligent Surface Aided NOMA Networks

Clustered UAV Networks With Millimeter Wave Communications: A Stochastic Geometry View

Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach

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