Massive MIMO NOMA with wavelet pulse shaping to minimize undesired channel interference

Ahmad, Muneeb; Shin, Soo Young

doi:10.1016/j.icte.2022.06.005

Cited by 10 publications

(8 citation statements)

References 15 publications

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“…Intuitively, n 1 convolutions are applied to the image, and the kernel size for each convolution is c × f 1 × f 1 . The bias term in (10) represents an n 1dimensional vector, where each element is associated with the filter. A rectified linear unit is applied to the filter response, and can be expressed as…”

Section: B Srdnn Architecture and Trainingmentioning

confidence: 99%

“…Therefore, [7], [8] proposed non-orthogonal multiple access (NOMA) as a viable solution with the potential to increase the total capacity of future wireless communication networks. Moreover, the potential of a mMIMO-NOMA network was discussed in [9], [10], making mMIMO-NOMA a better approach for future wireless communications. A mMIMO-NOMA system can improve the performance of future wireless communication network in terms of both increased user connectivity and enhanced spectral efficiency [10].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the potential of a mMIMO-NOMA network was discussed in [9], [10], making mMIMO-NOMA a better approach for future wireless communications. A mMIMO-NOMA system can improve the performance of future wireless communication network in terms of both increased user connectivity and enhanced spectral efficiency [10].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, an imperfect CSI causes the network to execute an imperfect SIC, which results in the accumulation of undesired high power user's signal to the SIC performing user [16]. This unwanted signal creates significant interference in the mMIMO-NOMA network and impairs network performance by lowering SER [10], [16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Image super resolution based channel estimation for future wireless communication

Ahmad,

Shakeel,

Shin

2023

Computer Networks

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Section: B Srdnn Architecture and Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image super resolution based channel estimation for future wireless communication

Ahmad,

Shakeel,

Shin

2023

Computer Networks

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“…enhances the spectral efficiency (SE) of the network [8]. In [9], a DWT-based mMIMO-NOMA system was compared to a traditional mMIMO-NOMA system, and it was demonstrated that the DWT-based NOMA system provided higher spectrum efficiency and bit error rate (BER) for mass connectivity.…”

Section: R1a2b5b01001994)mentioning

confidence: 99%

Deep Learning aided SIC for Wavelet-based Massive MIMO-NOMA

Ahmad¹,

Shin²

2022

Preprint

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<p>In this study, a deep neural network (DNN) for a massive multiple-input-multiple-output (mMIMO) nonorthogonal multiple access (NOMA) system’s channel estimation and detection is presented. The proposed network applies deep learning (DL) to a discrete wavelet transform (DWT) based orthogonal frequency-division-multiplexing (OFDM) to reduce the noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. The transmission overhead is reduced because the presented network is trained to estimate and detect symbols with and without pilot signals.</p> <p>The results of this study demonstrate that our provided DL-based successive interference cancellation (SIC) method performs better than the conventional SIC-based signal detection in the mMIMONOMA network. The suggested wavelet-based mMIMO-NOMA is also compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA, particularly in terms of symbol error rate (SER).</p>

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Optimized deep learning‐based channel estimation for pilot contamination in a massive multiple‐input‐multiple‐output‐non‐orthogonal multiple access system

S.,

Singh,

P. N.

2024

Int J Communication

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SummaryOne of the advanced field in 5G cellular networks is the Massive Multiple‐Input‐Multiple‐Output (MIMO), which creates a massive antenna array by offering numerous antennas at the destination. This grows as a hot research topic in the wireless sectors as it enhances the volume and spectrum usage of the channel. The spectral efficiency (SE) is maximized using the abundant antennas employed by MIMO using spatial multiplexing of consumers, which needs precise channel state information (CSI). The SE is affected by both pilot overhead and pilot contamination. To mitigate the contamination and to estimate the suitable channel for communication, an efficient strategy is introduced using the proposed Namib Beetle Aquila optimization (NBAO)_Deep Q network (DQN). Here, the optimal pilot location is identified by employing NBAO, which is an integration of Namib beetle optimization (NBO) and Aquila optimizer (AO). Moreover, DQN is introduced to determine the suitable channel and metrics, such as bit error rate (BER) and normalized mean square error (MSE) is used for evaluation. The normalized MSE channel estimation is utilized to mitigate the effects of pilot contamination. Additionally, designed NBAO + DQN have attained a value of 0.0006 and 0.0005 for BER and normalized MSE.

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Massive MIMO NOMA with wavelet pulse shaping to minimize undesired channel interference

Cited by 10 publications

References 15 publications

Image super resolution based channel estimation for future wireless communication

Image super resolution based channel estimation for future wireless communication

Deep Learning aided SIC for Wavelet-based Massive MIMO-NOMA

Optimized deep learning‐based channel estimation for pilot contamination in a massive multiple‐input‐multiple‐output‐non‐orthogonal multiple access system

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