Edge-CloudBlock-6G: Adopting AI Technology for Intelligent Spectrum Access and Secure Hybrid Beam forming in mmWave Massive MIMO Assisted Cognitive Radio based IoCV Environment
Keywords:
6G, Spectrum sensing, hybrid beamforming, Internet of Connected Vehicles (IoCV), Cognitive Radio Network (CRN), spectrum handoff, QoS, throughput, efficient‑CapsNet algorithm, red fox optimizationAbstract
Cognitive Radio Network (CRN), as a key technology to improve utilization of radio spectrum and spectrum sensing with Internet of Connected Vehicles (IoCV) which can significantly facilitate vehicle management and route planning to encounter the issues in reliable communication and spectrum access. Though, it faces issues such as high sensing delay, packet loss rate, latency and high throughput. In this proposed work, the existing issues are encountered through edge based Cognitive Radio Network (CRN) in Multi Input Multi Output (MIMO) using 6G (6GCRN-MIMO). Firstly, all Primary Users (PUs), Secondary Users (SUs)and pedestrians are authenticated using cloud based blockchain in order to increase security. The clustering is performed for management of network using modified K‐Means (MKM) algorithm and clustering is performed through entry of authenticated users with consideration of several metrics. The intersection safety information is provided over the proposed network for reduction of collision in intersection using Improved Naive Bayes(INB) algorithm. Spectrum sensing and spectrum handoff are performed using Efficient Capsule Network (E-CapsNet) algorithm when PU is inactive while SU is selecting optimal channel through various metrics for reduction of spectrum scarcity. The ERSU is used to collect and store the environment data to perform the secure routing process using Red Fox Optimization (RFO) algorithm. Hybrid beamforming is performed to enhance reliable communication with Asynchronous Advantage Actor Critic Learning (A3CL) method. The proposed approach is simulated by using SUMO and OMNET ++ and validated by comparing it with existing approaches in terms of spectrum efficiency, spectrum sensing and QoS. The results proved that the proposed approach achieved finest performance while compared to existing approaches.
Downloads
References
Khan, A.U., Abbas, G., Abbas, Z.H., Tanveer, M., Ullah, S., & Naushad, A. (2020). HBLP: A Hybrid Underlay-Interweave Mode CRN for the Future 5G-Based Internet of Things. IEEE Access, 8, 63403-63420.
Zhang, T., Zhang, D., Qiu, J., Zhang, X., Zhao, P., & Gong, C. (2019). A Kind of Novel Method of Power Allocation With Limited Cross-Tier Interference for CRN. IEEE Access, 7, 82571-82583.
Zonoozi, A., Kim, J., Li, X., & Cong, G. (2018). Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns. International Joint Conference on Artificial Intelligence.
Nandan, N., Majhi, S., & Wu, H. (2021). Beamforming and Power Optimization for Physical Layer Security of MIMO-NOMA Based CRN Over Imperfect CSI. IEEE Transactions on Vehicular Technology, 70, 5990-6001.
Pari, D., & Natarajan, J. (2022). Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments. Sensors (Basel, Switzerland), 22.
Xu, X., Zhang, X., Liu, X., Jiang, J., Qi, L., & Bhuiyan, M.Z. (2020). Adaptive Computation Offloading With Edge for 5G-Envisioned Internet of Connected Vehicles. IEEE Transactions on Intelligent Transportation Systems, 22, 5213-5222.
Deepanramkumar, P., & Jaisankar, N. (2022). BlockCRN-IoCV: Secure Spectrum Access and Beamforming for Defense Against Attacks in mmWave Massive MIMO CRN in 6G Internet of Connected Vehicles. IEEE Access, 10, 74220-74243.
Qureshi, K.N., Ahmed, M., Jeon, G., & Piccialli, F. (2021). An Enhanced Multi-Hop Intersection-Based Geographical Routing Protocol for the Internet of Connected Vehicles Network. IEEE Transactions on Intelligent Transportation Systems, 22, 3850-3858.
(2020). IEEE Standard for Spectrum Sensing Interfaces and Data Structures for Dynamic Spectrum Access and Other Advanced Radio Communication Systems. Dynamic Spectrum Access Decisions.
Cai, P., & Zhang, Y. (2020). Intelligent cognitive spectrum collaboration: Convergence of spectrum sensing, spectrum access, and coding technology. Intelligent and Converged Networks.
Soni, B., Patel, D.K., & López-Benítez, M. (2020). Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics. IEEE Access, 8, 97437-97451.
Koçkaya, K., & Develi, I. (2020). Spectrum sensing in cognitive radio networks: threshold optimization and analysis. EURASIP Journal on Wireless Communications and Networking, 2020, 1-19.
Priya M, K., Raja Kumar, R.V., Indumathi, P., & Satheeswaran, C. (2018). Spectrum and Traffic Aware Routing Protocol with cooperative information collection method for CR-VANET. 2018 International Conference on Recent Trends in Electrical, Control and Communication (RTECC), 35-40.
Liu, J., Ren, P., Xue, S., & Chen, H. (2012). Expected path duration maximized routing algorithm in CR-VANETs. 2012 1st IEEE International Conference on Communications in China (ICCC), 659-663.
Akter, S., & Mansoor, N. (2020). A Spectrum Aware Mobility Pattern Based Routing Protocol for CR-VANETs. 2020 IEEE Wireless Communications and Networking Conference (WCNC), 1-6.
Ghafoor, H., & Koo, I. (2016). Spectrum and connectivity aware anchor-based routing in cognitive vehicular ad hoc networks. 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), 679-684.
Nissel, R. (2022). Correctly Modeling TX and RX Chain in (Distributed) Massive MIMO—New Fundamental Insights on Coherency. IEEE Communications Letters, 26, 2465-2469.
Choi, H., & Baji'c, I.V. (2022). Scalable Video Coding for Humans and Machines. 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), 1-6.
Schwarz, S. (2020). Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification. IEEE Signal Processing Letters, 27, 1799-1803.
Liu, J., Mishra, K.V., & Saquib, M. (2022). Precoder Design for Joint In-Band Full-Duplex MIMO Communications and Widely-Distributed MIMO Radar. ICC 2022 - IEEE International Conference on Communications, 4679-4684.
Aujla, G.S., Singh, A., Singh, M., Sharma, S., Kumar, N., & Choo, K.R. (2020). BloCkEd: Blockchain-Based Secure Data Processing Framework in Edge Envisioned V2X Environment. IEEE Transactions on Vehicular Technology, 69, 5850-5863.
Khan, A.U., Abbas, G., Abbas, Z.H., Baker, T., & Waqas, M.M. (2020). Spectrum efficiency in CRNs using hybrid dynamic channel reservation and enhanced dynamic spectrum access. Ad Hoc Networks, 107, 102246.
Bagga, P., Sutrala, A.K., Das, A.K., & Vijayakumar, P. (2020). Blockchain-based batch authentication protocol for Internet of Vehicles. J. Syst. Archit., 113, 101877.
Kalra, M., Vohra, A., & Marriwala, N. (2022). LEACH based hybrid energy efficient routing algorithm for dynamic cognitive radio networks. Measurement: Sensors.
Xu, M., Yin, Z., Zhao, Y., & Wu, Z. (2022). Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network. Entropy, 24.
Indumathi, G., & Vaithianathan, V. (2021). Optimal relay and channel selection schemes for multiconstrained QoS multicast routing in cognitive radio ad hoc networks. International Journal of Communication Systems, 34.
Lee, W., & Song, H. (2021). Efficient Channel Feedback Scheme for Multi-User MIMO Hybrid Beamforming Systems. Sensors (Basel, Switzerland), 21.
Shen, S., & Clerckx, B. (2020). Joint Waveform and Beamforming Optimization for MIMO Wireless Power Transfer. IEEE Transactions on Communications, 69, 5441-5455.
Joon, R., & Tomar, P. (2022). Energy Aware Q-learning AODV (EAQ-AODV) routing for cognitive radio sensor networks. J. King Saud Univ. Comput. Inf. Sci., 34, 6989-7000.
Hojatian, H., Nadal, J., Frigon, J., & Leduc-Primeau, F. (2020). Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming. IEEE Transactions on Wireless Communications, 20, 7086-7099.
Li, Q., Su, W., Zhang, P., Cheng, X., Li, M., & Liu, Y. (2022). Blockchain-Based Method for Pre-Authentication and Handover Authentication of IoV Vehicles. Electronics.
Zhang, R., Wang, Y., & Feng, Y. (2022). A leakage‐based hybrid beamforming design for multi‐user mmWave massive MIMO systems. IET Communications.
Jiang, Q., Zhang, X., Zhang, N., Tian, Y., Ma, X., & Ma, J. (2021). Three-factor authentication protocol using physical unclonable function for IoV. Comput. Commun., 173, 45-55.
Ruan, H., Xiao, P., Xiao, L., & Kelly, J.R. (2021). Joint Iterative Optimization-Based Low-Complexity Adaptive Hybrid Beamforming for Massive MU-MIMO Systems. IEEE Transactions on Communications, 69, 1707-1722.
Wang, Y., Li, X., Wan, P., Chang, L., & Deng, X. (2021). Dueling deep Q-networks for social awareness-aided spectrum sharing. Complex & Intelligent Systems, 8, 1975 - 1986.
Hossain, M.A., Noor, R.M., Yau, K.A., Azzuhri, S.R., Z’Abar, M.R., Ahmedy, I.B., & Jabbarpour, M.R. (2021). Multi-Objective Harris Hawks Optimization Algorithm Based 2-Hop Routing Algorithm for CR-VANET. IEEE Access, 9, 58230-58242.
Srivastava, V., Singh, P., Malik, P.K., Singh, R., Tanwar, S., Alqahtani, F., Tolba, A.M., Marina, V., & Răboacă, M.S. (2023). Innovative Spectrum Handoff Process Using a Machine Learning-Based Metaheuristic Algorithm. Sensors (Basel, Switzerland), 23.
Jayakumar, L., & Janakiraman, S. (2019). A novel need based free channel selection scheme for cooperative CRN using EFAHP-TOPSIS. J. King Saud Univ. Comput. Inf. Sci., 34, 1326-1342.
Qi, C., Ci, W., Zhang, J., & You, X. (2022). Hybrid Beamforming for Millimeter Wave MIMO Integrated Sensing and Communications. IEEE Communications Letters, 26, 1136-1140.
Rajpoot, V., & Tripathi, V.S. (2019). A novel proactive handoff scheme with CR receiver based target channel selection for cognitive
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
