Development of Neural Network-Based Spectrum Prediction Schemes for Cognitive Wireless Communication: A Case Study of Ilorin, North Central, Nigeria

The concept of spectrum prediction has become necessary as a result of the cost and time intensiveness of continuous spectrum measurements essential to the deployment of cognitive radio network riding on 5G and beyond. Spectrum prediction forecasts future channel states based on previously observed data from spectrum sensing. In this study, long-short term memory (LSTM), Artificial Neural Network-LSTM (ANN-LSTM), and ANN techniques are used for the prediction of spectrum channel duration metric in the 900/1800 spectrum bands, hitherto allocated for GSM services. Ilorin, an urban area in North central region of Nigeria was used as a case study. The root mean square errors (RMSEs) for the artificial neural networks (ANN), LSTM and ANN-LSTM for the GSM 900 spectrum were 3.126, 3.119 and 2.964 respectively with a regression coefficient of 0.9271 at a 60-minute observation time. The RMSEs for the ANN, LSTM, and ANN-LSTM for the GSM 1800 spectrum data were 3.577, 3.0428 and 2.791 respectively. The regression coefficient utilizing the ANN-LSTM with the real data set is 0.9147 at a 60-minute observation time. The results showed that ANN-LSTM technique performed better than either ANN or LSTM standing alone.

[1]  N. Surajudeen-Bakinde,et al.  Genetic Algorithm-Holt-Winters Based Minute Spectrum Occupancy Prediction: An Investigation , 2022, Journal of Engineering and Technological Sciences.

[2]  N. Surajudeen-Bakinde,et al.  A Hybrid Spectrum Opportunities Extraction Scheme for Cognitive Wireless Communication , 2022, Telematics and Informatics Reports.

[3]  Bilal Hassan,et al.  Deep learning-driven opportunistic spectrum access (OSA) framework for cognitive 5G and beyond 5G (B5G) networks , 2021, Ad Hoc Networks.

[4]  Zhigang Xu,et al.  Communication delay compensation for string stability of CACC system using LSTM prediction , 2021, Veh. Commun..

[5]  B Mishachandar,et al.  An underwater cognitive acoustic network strategy for efficient spectrum utilization , 2021 .

[6]  Ilesanmi Banjo Oluwafemi,et al.  Quantitative estimation of TV white space in Southwest Nigeria , 2021 .

[7]  Evelio M. G. Fernandez,et al.  Multi-Step-Ahead Spectrum Prediction for Cognitive Radio in Fading Scenarios , 2020 .

[8]  Yousaf Bin Zikria,et al.  Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks , 2020, Sensors.

[9]  Ahmed Chemori,et al.  A New Adaptive RISE Feedforward Approach based on Associative Memory Neural Networks for the Control of PKMs , 2020, Journal of Intelligent & Robotic Systems.

[10]  Zhang Jianbiao,et al.  Energy Detection Based Spectrum Sensing Strategy for CRN , 2020, 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS).

[11]  K. Adiat,et al.  Prediction of groundwater level in basement complex terrain using artificial neural network: a case of Ijebu-Jesa, southwestern Nigeria , 2019, Applied Water Science.

[12]  Aman Jantan,et al.  Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition , 2019, IEEE Access.

[13]  Martin A. Riedmiller,et al.  Adaptive long-term control of biological neural networks with Deep Reinforcement Learning , 2019, Neurocomputing.

[14]  Zhu Han,et al.  Stacked LSTM Deep Learning Model for Traffic Prediction in Vehicle-to-Vehicle Communication , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[15]  K. P. Soman,et al.  Stock price prediction using LSTM, RNN and CNN-sliding window model , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[16]  Nasir Faruk,et al.  Large scale spectrum survey in rural and urban environments within the 50 MHz–6 GHz bands , 2016 .

[17]  Dusit Niyato,et al.  A Neural Network Based Spectrum Prediction Scheme for Cognitive Radio , 2010, 2010 IEEE International Conference on Communications.

[18]  Mingyan Liu,et al.  Mining Spectrum Usage Data: A Large-Scale Spectrum Measurement Study , 2009, IEEE Transactions on Mobile Computing.

[19]  Miguel López-Benítez,et al.  Evaluation of Spectrum Occupancy in Spain for Cognitive Radio Applications , 2009, VTC Spring 2009 - IEEE 69th Vehicular Technology Conference.

[20]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[21]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[22]  F. Ehiagwina,et al.  DEVELOPMENT OF A SOLAR ENERGY TRACKING MECHANISM WITH ARTIFICIAL NEURAL NETWORK ENHANCEMENT , 2021 .

[23]  U. Ragavendran,et al.  Investigation of CRN Spectrum Sensing Techniques: A Scientific Survey , 2021 .

[24]  Shilpa Mayannavar,et al.  Performance Comparison of Markov Chain and LSTM Models for Spectrum Prediction in GSM Bands , 2020 .

[25]  Alagan Anpalagan,et al.  Efficient Energy Management for the Internet of Things in Smart Cities , 2017, IEEE Communications Magazine.