User selection and power allocation for guaranteed SIC detection in downlink beamforming Non-Orthogonal Multiple Access

Sayed-Ahmed, Abdelsalam; Elsabrouty, Maha

doi:10.1109/wd.2017.7918141

Cited by 12 publications

(17 citation statements)

References 10 publications

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“…As aforementioned, we have chosen the ZFBF as a precoding technique. 10,29,30 On the ZFBF approach, the BF vector from the cth cluster, w c , is generated based on the channel vector of the strongest user in the cth cluster, that is, H str ∈ C C×M s and can be written as…”

Section: User Grouping and Zero-forcing Beamformingmentioning

confidence: 99%

Antenna selection in nonorthogonal multiple access multiple‐input multiple‐output systems aided by machine learning

Souza

Alves

Abrão

2021

Trans Emerging Tel Tech

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This work proposes a transmitter antenna selection (TAS) method for multiple-input multiple-out (MIMO) nonorthogonal multiple access (NOMA) that is a promising multiple access technique for the fifth generation mobile communications systems. Specifically, we propose to activate the more suitable subset of base station antennas while allocating users into appropriate NOMA clusters such that the system operates in energy efficiency mode, selecting such appropriate antenna subset indexes that maximize the sum-rate (SR) of the NOMA-MIMO system. Once that the TAS based on exhaustive-search is very complex to be implemented in real communication systems, we propose an effective TAS method based on machine learning while keeping very promising performance. A convolutional neural network-based transmitter antenna selection (CNN-TAS) method is proposed for efficiently select antennas aiming at maximizing the system SR. Hence, extensive numerical results demonstrate that the CNN-TAS can suitably learn the problem, performing with very high accuracy choosing properly the antenna subset that maximizes the system spectral efficiency while reducing substantially the processing time of real-time operations. INTRODUCTIONNonorthogonal multiple access (NOMA) has been considered as a promising multiple access technology for the fifth generation (5G) cellular systems once that can significantly enhance system overall spectral efficiency (SE), accommodating two or more users on the same frequency and multiplexing the users in the power-domain, differently of others types of multiple-access techniques, for example, time division multiple access, frequency division multiple access, code division

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Section: User Grouping and Zero-forcing Beamformingmentioning

confidence: 99%

Antenna selection in nonorthogonal multiple access multiple‐input multiple‐output systems aided by machine learning

Souza

Alves

Abrão

2021

Trans Emerging Tel Tech

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“…In [11], the downlink beam design for sum-rate maximization was considered for one given cluster based on minorization-maximization. In [12]- [14], user scheduling and clustering is considered with the assumption that two users are assigned to each cluster. In [12], highly correlated users are chosen as candidates to be clustered, and two users having the largest channel gain difference are assigned to the same cluster.…”

Section: A Related Workmentioning

confidence: 99%

“…In [13], strong users are selected by the semi-orthogonal user selection (SUS) algorithm [15], and then weak users are selected using the matching user selection algorithm by considering inter-cluster interference. In [14], a fairness-oriented user selection algorithms was proposed by selecting two paired users based on their NOMA data rate. In all the works of [12]- [14], the beam design problem for MU-MISO NOMA was simplified by designing zero-forcing (ZF) beams based on strong users' channels and allocating the same beam to the weak user as the beam of the strong user in each cluster.…”

Section: A Related Workmentioning

confidence: 99%

“…In [14], a fairness-oriented user selection algorithms was proposed by selecting two paired users based on their NOMA data rate. In all the works of [12]- [14], the beam design problem for MU-MISO NOMA was simplified by designing zero-forcing (ZF) beams based on strong users' channels and allocating the same beam to the weak user as the beam of the strong user in each cluster. There also exists some study on multiple-input multipleoutput (MIMO) NOMA.…”

Section: A Related Workmentioning

confidence: 99%

“…• We proposed an effective user scheduling, beam design and power allocation method for MU-MISO NOMA downlink based on the above two-user Pareto-optimal design result by exploiting both the spatial domain provided by multiple transmit antennas and the power domain provided by SIC. The key advantages of the proposed method compared to the previous methods are that 1) the rates of strong users and weak users can be controlled arbitrarily under Pareto-optimality, which provides great operational flexibility to NOMA networks, and 2) beam design of the proposed method is generalized to include multi-dimensional subspace if available and to yield performance improvement, whereas the same onedimensional beam is always used by both strong and weak users in the same cluster in the previous methods [12]- [14].…”

Section: B Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Beam Design and User Scheduling for Nonorthogonal Multiple Access With Multiple Antennas Based on Pareto Optimality

Seo

Sung

2018

IEEE Trans. Signal Process.

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In this paper, an efficient transmit beam design and user scheduling method is proposed for multi-user (MU) multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) downlink, based on Pareto-optimality. The proposed beam design and user scheduling method groups simultaneously-served users into multiple clusters with practical two users in each cluster, and then applies spatical zeroforcing (ZF) across clusters to control inter-cluster interference (ICI) and Pareto-optimal beam design with successive interference cancellation (SIC) to two users in each cluster to remove interference to strong users and leverage signal-to-interference-plus-noise ratios (SINRs) of interference-experiencing weak users. The proposed method has flexibility to control the rates of strong and weak users and numerical results show that the proposed method yields good performance.

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User grouping and power allocation for energy efficiency maximization in mmWave-NOMA heterogeneous networks

2022

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User selection and power allocation for guaranteed SIC detection in downlink beamforming Non-Orthogonal Multiple Access

Cited by 12 publications

References 10 publications

Antenna selection in nonorthogonal multiple access multiple‐input multiple‐output systems aided by machine learning

Antenna selection in nonorthogonal multiple access multiple‐input multiple‐output systems aided by machine learning

Beam Design and User Scheduling for Nonorthogonal Multiple Access With Multiple Antennas Based on Pareto Optimality

User grouping and power allocation for energy efficiency maximization in mmWave-NOMA heterogeneous networks

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