Performance Evaluation of Multiplexed 5G-New Radio Network Services of Different Usage Scenarios

Chukwu, Emmanuel C.; Abdullahi, U. S.; Koyunlu, Gokhan; Sanusi, Jaafaru; Sani, Godwin; Gangfada, Ibrahim A.

doi:10.1109/icces48766.2020.9138021

Cited by 5 publications

(2 citation statements)

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“…FDD massive MIMO systems consider the spatial characteristics of the wireless channel, hence it is necessary to apply a new channel model to simulate the wireless environments in existing network. 3GPP R15 TR38.901 defines a new channel model named clustered delay line (CDL) for link evaluation, where the frequency range of the CDL model is 100 GHz with a maximum bandwidth of 2 GHz [41]. The CDL model is often implemented by phase initialization along four different polarizations and generating coefficients for each cluster [41].…”

Section: B Dtl Problem Of Csi Feedbackmentioning

confidence: 99%

“…3GPP R15 TR38.901 defines a new channel model named clustered delay line (CDL) for link evaluation, where the frequency range of the CDL model is 100 GHz with a maximum bandwidth of 2 GHz [41]. The CDL model is often implemented by phase initialization along four different polarizations and generating coefficients for each cluster [41]. The CDL model is divided into the CDL-A, CLD-B, CDL-C, CDL-D and CDL-E according to the simulated network environments.…”

Section: B Dtl Problem Of Csi Feedbackmentioning

confidence: 99%

See 1 more Smart Citation

Downlink CSI Feedback Algorithm With Deep Transfer Learning for FDD Massive MIMO Systems

Zeng

Sun

Gui

et al. 2021

IEEE Trans. Cogn. Commun. Netw.

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In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by finetuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a modelagnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).

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Section: B Dtl Problem Of Csi Feedbackmentioning

confidence: 99%

Section: B Dtl Problem Of Csi Feedbackmentioning

confidence: 99%