XL-MIMO energy-efficient antenna selection under non-stationary channels

Ubiali, Gabriel Avanzi; Abrão, Taufik

doi:10.1016/j.phycom.2020.101189

Cited by 11 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For comparison purpose, the proposed CNN-based TAS method is compared with random-transmitter antenna selection (R-TAS) algorithm and HRPN. 33,34 First, we have investigated the EE relations at subsection 6.1, demonstrating the existence of an optimal point to explore higher EE in terms of M s . Afterward, we explore the CNN-based learning process in terms of training and test phases, that is, the loss function against epochs, its accuracy, and the real-time complexity in practical interest application, discussed in subsection 6.2 and 6.3, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Concerning Equation ( 11), one can note that there is just an inner product localized at denominator, and its complexity can be given by M s , that results from the h c, 1 w c term, where the square of the absolute value of a complex number require three real multiplications, while the multiplication of two complex numbers requires three real multiplications. 34 Hence, the complexity of |h c,1 w c | 2 term is given by 3M s + 3; the term ( P c c,1 ) 2 demands three real multiplications; the whole complexity of Equation ( 11) is given by 3M s + 6 real multiplications. Equation ( 13), can be obtained similarly to the former, hence the associated complexity is defined as 3M s + 3M s K + 4K + 8.…”

Section: Sinr's and Zf Precoding Complexitymentioning

confidence: 99%

“…The last step is to calculate the matrix-vector product between H H and mth column of the matrix (HH H ) −1 , which requires KM s complex multiplications plus K complex divisions to compute D −1 . Once that complex multiplications and complex divisions require three and seven flops, respectively, 34 the total complexity to obtain the whole combining matrix W ZFBF is given by:…”

Section: Sinr's and Zf Precoding Complexitymentioning

confidence: 99%

See 2 more Smart Citations

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

Souza

Alves

Abrão

2021

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Sinr's and Zf Precoding Complexitymentioning

confidence: 99%

Section: Sinr's and Zf Precoding Complexitymentioning

confidence: 99%

See 1 more Smart Citation

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

Souza

Alves

Abrão

2021

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…( 28)]. The default settings are as follow: the power of user pilot p Tr,k = 0.1W (20dBm) [29], the uplink noise power σ 2 Tr = −96dBm, the bandwidth W = 100MHz, P syn = 50mW, P CT = 48.2mW, P CR = 62.5mW, ς = 0.35 [25], the number of antennas in the BS M = 128, the length of VR L = 16, the number of users K = 4, the distance between the BS and users d k = 400m, the length of the channel coherence block is T = 200 symbols and the number of Monte-Carlo trials is 10 4 . For FDD systems, we set τ d = I + J to save pilot cost.…”

Section: Numerical Resultsmentioning

confidence: 99%

Average Spectral Efficiency for TDD-based non-Stationary XL-MIMO with VR Estimation

Zhang

Han

et al. 2022

2022 14th International Conference on Wireless Communications and Signal Processing (WCSP)

View full text Add to dashboard Cite

Extra large-scale multiple-input multiple-output (XL-MIMO) is a key technology for future wireless communication systems. This paper considers the effects of visibility region (VR) at the base station (BS) in a non-stationary multiuser XL-MIMO scenario, where only partial antennas can receive users' signal. In time division duplexing (TDD) mode, we first estimate the VR at the BS by detecting the energy of the received signal during uplink training phase. The probabilities of two detection errors are derived and the uplink channel on the detected VR is estimated. In downlink data transmission, to avoid cumbersome Monte-Carlo trials, we derive a deterministic approximate expression for ergodic average energy efficiency (EE) with the regularized zero-forcing (RZF) precoding. In frequency division duplexing (FDD) mode, the VR is estimated in uplink training and then the channel information of detected VR is acquired from the feedback channel. In downlink data transmission, the approximation of ergodic average EE is also derived with the RZF precoding. Invoking approximate results, we propose an alternate optimization algorithm to design the detection threshold and the pilot length in both TDD and FDD modes. The numerical results reveal the impacts of VR estimation error on ergodic average EE and demonstrate the effectiveness of our proposed algorithm.

show abstract

“…The conventional minimum mean-squared error (MMSE) channel estimation is rarely adopted in XL-MIMO due to its high complexity. Then, the conventional least-squares (LS) scheme, which requires no prior statistical information, was used to estimate the channel of XL-MIMO with low computational complexity in [81], [163]. However, the LS estimation scheme achieved a poor performance at the low signal-to-noise-ratio (SNR) scenarios.…”

Section: A Low-complexity Channel Estimationmentioning

confidence: 99%

Very large scale integration architectures of discrete transforms for digital signal processing applications

Wang¹

View full text Add to dashboard Cite

Luo Jianwen, Associate Professor Jong Ching Chuen, and Associate Professor Chang Chip Hong, for their friendship, help, discussions, suggestions and encouragement.I do also appreciate the support and help from my colleagues in the FTD Solutions Pte Ltd and in the Centre for Signal Processing, NTD. Especially, I would like to thank Qian Yue, Lu Dawei, Yang Fan, and Dr. Yu Zhuliang for their valuable comments, suggestions and criticisms.I have enjoyed the company and friendship of all of my friends in Singapore. I would like to thank them for making the past five years an unforgettable and invaluable experience in my life.Last, I wish to express my deepest gratitude to my dear parents, my dear brother, and my lovely four year old nephew. It is indeed their deep love and consistent encouragement that has kept me focused, determined, cheered up, and never to say give up.

show abstract

XL-MIMO energy-efficient antenna selection under non-stationary channels

Cited by 11 publications

References 24 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

Average Spectral Efficiency for TDD-based non-Stationary XL-MIMO with VR Estimation

Very large scale integration architectures of discrete transforms for digital signal processing applications

Contact Info

Product

Resources

About