scholarly journals Performance Analysis of NOMA Downlink for Next- Generation 5G Network with Statistical Channel State Information

2021 ◽  
Vol 26 (4) ◽  
pp. 417-423
Author(s):  
Indrajeet Kumar ◽  
Manish Kumar Mishra ◽  
Ritesh Kumar Mishra
Keyword(s):  
Outage Probability ◽  
Channel State Information ◽  
Rayleigh Fading ◽  
High Reliability ◽  
Superior Performance ◽  
Channel State ◽  
State Information ◽  
Fifth Generation ◽  
Proposed Model ◽  
5G Network

Non-orthogonal multiple access (NOMA) is a compelling strategy that helps wireless networks of the fifth-generation (5G) to fulfill the diverse demands of increased fairness, high reliability, extensive connectivity, low delay and superior performance. Traditionally, NOMA network presumes perfect transmitter-side channel state information (CSI), which is almost impossible for a number of communication scenarios. Furthermore, this paper contemplates the realistic NOMA downlink method in the Rayleigh fading networks, where the statistical CSI linked with each user is known to the transmitter. We evaluate the outage probability of the proposed model and obtain a closed-form framework of the probability of outage for each user. Besides, the source can optimize the network’s achievable sum-rate for different users based on statistical CSI. The precision of our outage probability analysis and the optimum power allocation algorithm proposed are both verified by the analytical and simulation results.

scholarly journals Fusing RGB and Thermal Imagery with Channel State Information for Abnormal Activity Detection Using Multimodal Bidirectional LSTM

2021 ◽  
pp. 77-86
Author(s):  
Nikolaos Bakalos ◽  
Athanasios Voulodimos ◽  
Nikolaos Doulamis ◽  
Anastasios Doulamis ◽  
Kassiani Papasotiriou ◽  
...  
Keyword(s):  
Channel State Information ◽  
Short Term Memory ◽  
Bayesian Optimization ◽  
Superior Performance ◽  
Activity Detection ◽  
Real World Data ◽  
Channel State ◽  
State Information ◽  
Proposed Model ◽  
Bidirectional Lstm

AbstractIn this paper, we present a multimodal deep model for detection of abnormal activity, based on bidirectional Long Short-Term Memory neural networks (LSTM). The proposed model exploits three different input modalities: RGB imagery, thermographic imagery and Channel State Information from Wi-Fi signal reflectance to estimate human intrusion and suspicious activity. The fused multimodal information is used as input in a Bidirectional LSTM, which has the benefit of being able to capture temporal interdependencies in both past and future time instances, a significant aspect in the discussed unusual activity detection scenario. We also present a Bayesian optimization framework that fine-tunes the Bidirectional LSTM parameters in an optimal manner. The proposed framework is evaluated on real-world data from a critical water infrastructure protection and monitoring scenario and the results indicate a superior performance compared to other unimodal and multimodal approaches and classification models.

scholarly journals Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach

2021 ◽  
Vol 7 ◽  
pp. e682
Author(s):  
Mohamed Hassan Essai Ali ◽  
Ibrahim B.M. Taha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Channel State Information ◽  
Short Term Memory ◽  
Superior Performance ◽  
Short Term ◽  
Channel State ◽  
State Information ◽  
Term Memory ◽  
Long Short Term Memory

In this study, a deep learning bidirectional long short-term memory (BiLSTM) recurrent neural network-based channel state information estimator is proposed for 5G orthogonal frequency-division multiplexing systems. The proposed estimator is a pilot-dependent estimator and follows the online learning approach in the training phase and the offline approach in the practical implementation phase. The estimator does not deal with complete a priori certainty for channels’ statistics and attains superior performance in the presence of a limited number of pilots. A comparative study is conducted using three classification layers that use loss functions: mean absolute error, cross entropy function for kth mutually exclusive classes and sum of squared of the errors. The Adam, RMSProp, SGdm, and Adadelat optimisation algorithms are used to evaluate the performance of the proposed estimator using each classification layer. In terms of symbol error rate and accuracy metrics, the proposed estimator outperforms long short-term memory (LSTM) neural network-based channel state information, least squares and minimum mean square error estimators under different simulation conditions. The computational and training time complexities for deep learning BiLSTM- and LSTM-based estimators are provided. Given that the proposed estimator relies on the deep learning neural network approach, where it can analyse massive data, recognise statistical dependencies and characteristics, develop relationships between features and generalise the accrued knowledge for new datasets that it has not seen before, the approach is promising for any 5G and beyond communication system.

Sign in / Sign up

Export Citation Format

Share Document