Abstract
A multi-cell Fog-Radio Access Network (F-RAN) architecture that takes into consideration the noisy interference from Internet of Things (IoT) devices and transmission takes place in the uplink with grant-free access. An edge node is used to connect the devices present in every cell and will hold a reasonable capacity in the central processor. The reading obtained from the IoT devices are used to determine the field of correlated Quality of Interests in every cell, transmitting using the Type-Based Multiple Access (TBMA) protocol. This is in contrast to the conventional protocols that are used for diagnostic purpose. In this proposed work, we have implemented the multi-cell F-RAN using cloud or edge detection in analysing the form of information-centric radio access. In a multi-cell system, cloud and edge detection are implemented and analysed. We have implemented model-based detectors and the probability of error for the asymptotic behavior in edge as well as cloud is determined. Similarly, cloud and edge detectors that are data driven are used when statistical models are not available.
References
- A. Zappone, M. Di Renzo, and M. Debbah, “Wireless networks design in the era of deep learning: Model-based, AI-based, or both?” arXiv preprint arXiv:1902.02647, 2019
- J. Zhang and O. Simeone, “Fundamental Limits of Cloud and CacheAided Interference Management with Multi-Antenna Edge Nodes,” IEEE Trans. on Information Theory, pp. 1–1, Mar. 2019
- Mugunthan, S. R. (2020). Decision Tree Based Interference Recognition for Fog Enabled IOT Architecture. Journal of trends in Computer Science and Smart technology (TCSST), 2(01), 15-25.
- P. Salvo Rossi, D. Ciuonzo, K. Kansanen, and T. Ekman, “Performance Analysis of Energy Detection for MIMO Decision Fusion in Wireless Sensor Networks Over Arbitrary Fading Channels,” IEEE Trans. on Wireless Commun., vol. 15, no. 11, pp. 7794–7806, Nov 2016.
- K. Stern, A. E. Kalør, B. Soret, and P. Popovski, “Massive Random Access with Common Alarm Messages,” IEEE Inter. Symp. on Inf. Theory (ISIT), 2019
- Y. Polyanskiy, “A perspective on massive random-access,” in IEEE International Symposium on Information Theory (ISIT), June 2017, pp. 2523–2527.
- Bhalaji, N. (2020). Efficient and secure data utilization in mobile edge computing by data replication. Journal of ISMAC, 2(01), 1-12.
- Zhao, Q., Tong, L., & Chen, Y. (2005, July). Energy-aware date-centric MAC for application-specific sensor networks. In IEEE/SP 13th Workshop on Statistical Signal Processing, 2005 (pp. 1238-1243). IEEE.
- Kassab, R., Simeone, O., & Popovski, P. (2020, May). Fog-Based Detection for Random-Access IoT Networks with Per-Measurement Preambles. In 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) (pp. 1-5). IEEE.
- Dommel, J., Utkovski, Z., Simeone, O., & Stanczak, S. (2021). Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks. arXiv preprint arXiv:2101.11309.
- Bellendorf, J., & Mann, Z. Á. (2020). Classification of optimization problems in fog computing. Future Generation Computer Systems, 107, 158-176.
- Shakya, S., Pulchowk, L. N., & Smys, S. (2020). Anomalies Detection in Fog Computing Architectures Using Deep Learning. Journal: Journal of Trends in Computer Science and Smart Technology March, 2020(1), 46-55.