Examination of the Non-Orthogonal Multiple Access System Using Long Short Memory Based Deep Neural Network

Shankar, Ravi; Ramana, Venkata; Singh, Preeti; Gupta, Sandeep; Mehraj, Haider

doi:10.13052/jmm1550-4646.18214

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…After accounting for all plausible conditions except for the cyclic prefix (CP) and clipping distortion, we compare the performance of the DL detector to that of the simulated curves in terms of MMSE and the least square error (LSE) estimate. The simulation curves demonstrate that the detector's accuracy performs admirably when it reaches 1 when the SNR is higher than 15 dB, assuming that the DL technique is more robust to clipping distortion [17].…”

Section: Review Of Literaturementioning

confidence: 91%

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

Sudhanshu Tyagi

2023

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This study's goal is to provide an early analysis of deep learning (DL) for signal identification in wireless systems that use non-orthogonal multiple access (NOMA). The successive interference cancellation (SIC) approach is frequently used at the receiver in NOMA systems when several users are decoded successively. Without explicitly calculating channels, a DL-based NOMA receiver can decode messages for several users at once. To estimate the multiuser uplink channel (CE) and recognize the initial broadcast signal in this study, it is recommended that a deep neural network with bi-directional long short-term memory (Bi-LSTM) be utilized. The suggested Bi-LSTM model, in contrast to conventional CE techniques, may immediately retrieve transmission signals impacted by channel distortion. During the offline training phase, the Bi-LTSM model is trained using simulation data based on channel statistics. The trained model is then applied to retrieve the transmitted symbols in the stage of online deployment. According to the findings, the DL method could outperform a maximum probability detector that considers interference effects when inter-symbol interference is substantial.

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Section: Review Of Literaturementioning

confidence: 91%

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

Sudhanshu Tyagi

2023

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“…The authors employed the recurrent neural network (RNN) algorithm for identifying fading channel coefficients. In paper [18], the authors investigated an LSTM NOMA receiver under the frequency flat Rayleigh fading channel scenario. The LSTM algorithm was employed for obtaining the optimal receiver.…”

Section: Related Workmentioning

confidence: 99%

“…After performing the Fourier transform and removing the CP at the receiver, the resulting signal is [12]- [18]:…”

Section: Signal Modelmentioning

confidence: 99%

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Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions

Shankar¹,

Bangare²,

Kumar³

et al. 2022

JTIT

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This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established Rusing i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links.

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“…The NOMA scheme [13], [14] offers an optimistic answer to these challenges. MIMO, cognitive cooperative relaying, full-duplex relaying, millimeter-wave, and other technologies have been employed in conjunction with NOMA to increase throughput and guarantee user fairness in a wide range of fading channel distributions [15], [16]. Figure 2 shows a MIMO-NOMA network example [10], [17].…”

Section: /2022mentioning

confidence: 99%

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

Shankar¹,

Chaudhar²,

Mishra³

2022

JTIT

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In this paper, the performance of a deep learning based multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) system is investigated for 5G radio communication networks. We consider independent and identically distributed (i.i.d.) Nakagami-m fading links to prove that when using MIMO with the NOMA system, the outage probability (OP) and end-to-end symbol error rate (SER) improve, even in the presence of imperfect channel state information (CSI) and successive interference cancellation (SIC) errors. Further more, the stacked long short-term memory (S-LSTM) algorithm is employed to improve the system’s performance, even under time-selective channel conditions and in the presence of terminal’s mobility. For vehicular NOMA networks, OP, SER, and ergodic sum rate have been formulated. Simulations show that an S-LSTM-based DL-NOMA receiver outperforms least square (LS) and minimum mean square error (MMSE) receivers. Furthermore, it has been discovered that the performance of the end-to-end system degrades with the growing amount of node mobility, or if CSI knowledge remains poor. Simulated curves are in close agreement with the analytical results.

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Examination of the Non-Orthogonal Multiple Access System Using Long Short Memory Based Deep Neural Network

Cited by 5 publications

References 16 publications

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

Signal Identification in Non-Orthogonal Multiple Access Wireless Systems Using Bi-Directional Long Short-Term Memory Network

Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions

Investigation of Vehicular S-LSTM NOMA Over Time Selective Nakagami-m Fading with Imperfect CSI

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