NOMA for 5G and beyond: literature review and novel trends

Abd‐Elnaby, Mohammed; Sedhom, Germien G.; El‐Rabaie, El‐Sayed M.; Elwekeil, Mohamed

doi:10.1007/s11276-022-03175-7

Cited by 10 publications

(2 citation statements)

References 177 publications

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“…It can meet the needs of power monitoring in key areas. It also provides methods to solve power system communication problems [5][6][7]. Although there is an increasing demand for star-earth collaborative network communication in the power system, star-earth collaborative network also suffers from resource constraints.…”

Section: Introductionmentioning

confidence: 99%

Star-earth cooperative network channel allocation algorithm based on power grid and deep learning

Song,

Xu,

Wang

et al. 2024

International Conference on Communication, Information, and Digital Technologies (CIDT2024)

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The star earth collaboration network private network can make up for the problems of poor signal coverage, limited bandwidth, low security and high cost that exist in the existing wireless public network. It can provide reliable and stable power system communications. However, the star-earth collaborative network suffers from resource constraints due to limited on-planet spectrum resources, power, computation and storage resources. Therefore, how to allocate these limited channel resources reasonably and efficiently, it is especially important to improve the resource utilization and system performance. Based on this, we establish a star-earth collaborative network architecture to meet the application of power scenarios. To improve the resource allocation efficiency of this network architecture, we model the channel allocation process by sensing the channel allocation state and beam user service request state in the environment through the satellite intelligences of the star-earth collaboration network. And then, we propose a dynamic channel allocation algorithm based on deep learning. We design state, action and reward functions for feedback optimization of channel resource allocation strategy based on deep learning, and a deep learning based channel resource allocation method is proposed to realize the optimal allocation of system channel resources. In the proposed method, the corresponding channel resources for users is allocated based on the channel allocation policy, and environment-based reward gain information is given to optimally update the channel allocation policy. The related simulation results show that our proposed channel resource allocation algorithm for star-terrestrial collaborative network has lower service blocking rate and higher channel utilization than other assistant allocation algorithms.

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Section: Introductionmentioning

confidence: 99%

Star-earth cooperative network channel allocation algorithm based on power grid and deep learning

Song,

Xu,

Wang

et al. 2024

International Conference on Communication, Information, and Digital Technologies (CIDT2024)

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“…The authors in References 12‐14 considered a downlink NOMA system with a set of users under the range of transmitting BS.In this work, the users under the range of a BS are paired to form multiple clusters. It is shown that when the worst channel user is paired with the best channel user, the Quality of Service (QoS) rate requirements can be met with lower transmission power.…”

Section: Introductionmentioning

confidence: 99%

User grouping and resource management in efficient NOMA‐OMA transmission scheme for infotainment message dissemination in VANETs

Sreelakshmi

Keerthi

Deepthi

2023

Trans Emerging Tel Tech

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The data distribution for infotainment services at vehicular ad‐hoc networks (VANETs) has become a research hotspot in recent years. Non‐orthogonal multiple access (NOMA) is accepted as an enabling technology for 5G and beyond due to its improved spectral efficiency. In this work, we consider a VANET communication scenario where users have common data demands such as high‐density maps, local entertainment/shopping options or other popular media content. For such a scenario, we propose an efficient clustering scheme together with a hybrid NOMA‐OMA scheme for improved spectral and power efficiency. For this setup, we also develop an optimal bandwidth allocation strategy to improve the power efficiency further. We formulate a one dimensional nonlinear power minimization problem such that the bandwidth allocation for the system can be optimized. The complexity and optimality analysis of optimization algorithms used for power minimization are performed. The improved performance of the proposed scheme in terms of power efficiency, spectral efficiency and latency performance is showcased in comparison to conventional NOMA and OMA approaches.

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Sum-rate maximization for a distributed space-time block code-aided cooperative NOMA with energy harvesting

Alamu,

Olwal,

Djouani

2024

Wireless Netw

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In this paper, we exploit the spatial and transmission diversities in cooperative non-orthogonal multiple access (C-NOMA) networks to improve the system sum-rate. To achieve this, we propose a user-pairing scheme where near-field user pairs serve as relays for user pairs in the far-field region. Based on this pairing scheme, we incorporate a space-time block code transmission technique at the near-field user pairs to maximize the transmission diversity in the cooperative phase. Moreover, we consider a non-linear energy harvesting model at the near-field user pair to alleviate the problem of energy consumption during the cooperative transmission phase. Further to this, we formulate a sum-rate maximization problem that is addressed from the viewpoint of joint power allocation factor and power splitting ratio optimization. We develop a low-computational iterative algorithm based on the concepts of the Stackelberg game and the Nash bargaining solution. We benchmark our findings with different scenarios, such as energy harvesting C-NOMA with a fixed power allocation factor and power splitting ratio, energy harvesting C-NOMA without STBC, non-cooperative NOMA, and orthogonal multiple access. The results obtained via simulations outperform the benchmark schemes.

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NOMA for 5G and beyond: literature review and novel trends

Cited by 10 publications

References 177 publications

Star-earth cooperative network channel allocation algorithm based on power grid and deep learning

Star-earth cooperative network channel allocation algorithm based on power grid and deep learning

User grouping and resource management in efficient NOMA‐OMA transmission scheme for infotainment message dissemination in VANETs

Sum-rate maximization for a distributed space-time block code-aided cooperative NOMA with energy harvesting

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