Next-Generation 6G Wireless Networks: Challenges and Opportunities in Terahertz (THz) Communication and AI-Driven Signal Processing

Abstract

The advent of 6G wireless networks promises to revolutionize communication by leveraging terahertz (THz) frequencies (0.1–10 THz) and artificial intelligence (AI) to achieve terabit-per-second (Tbps) speeds, ultra-low latency, and ubiquitous connectivity. However, THz communication faces critical challenges, including severe propagation losses, molecular absorption, and hardware limitations, which demand innovative solutions. This paper explores the synergistic role of AI-driven signal processing in overcoming these barriers, focusing on adaptive beamforming, channel estimation, and resource allocation. We present a comprehensive survey of THz channel characteristics and analyze state-of-the-art AI techniques—such as deep reinforcement learning (DRL) for beam alignment and federated learning for distributed optimization—that enhance the efficiency and reliability of THz networks. Furthermore, we identify open research challenges, including energy-efficient AI deployment, security vulnerabilities, and standardization gaps. By bridging theoretical models with practical implementations, this work provides a roadmap for realizing 6G’s potential, emphasizing the need for interdisciplinary collaboration across wireless engineering, AI, and materials science. Our findings underscore AI as a pivotal enabler for scalable and intelligent THz-based 6G networks, while highlighting future directions for industry and academia.

Introduction

The rapid growth in wireless data traffic driven by technologies like augmented reality, autonomous systems, and the Internet of Everything is pushing the development of sixth-generation (6G) networks, expected around 2030. 6G aims to deliver terabit-per-second speeds, ultra-low latency, and near-perfect reliability, far exceeding 5G capabilities. To achieve this, researchers focus on the terahertz (THz) frequency band (0.1–10 THz), which offers vast bandwidth but faces major challenges such as severe path loss, molecular absorption, hardware constraints, and blockage sensitivity.

Artificial intelligence (AI) is seen as a key enabler to overcome THz limitations by optimizing beamforming, channel estimation, and signal processing in real-time. AI techniques like deep learning, reinforcement learning, and federated learning are being developed for these tasks, although they introduce new challenges related to computational cost, energy efficiency, and security vulnerabilities.

The paper surveys these challenges and AI-driven solutions, reviews the evolution of wireless networks from 1G through 5G, and highlights the technological foundations and future roadmap for 6G. It emphasizes gaps such as real-world validation of THz-AI systems, energy sustainability, hardware inefficiencies, and lack of standardization.

In summary, 6G networks will integrate THz communication and advanced AI to enable revolutionary applications like smart cities, immersive realities, and brain-computer interfaces, but significant research and engineering challenges remain before practical deployment.

Conclusion

The integration of terahertz (THz) communication and AI-driven signal processing is poised to revolutionize 6G wireless networks, enabling unprecedented data rates (Tbps), ultra-low latency, and ubiquitous connectivity. However, this paper has identified critical challenges—propagation losses, hardware inefficiencies, mobility limitations, and security vulnerabilities—that must be addressed to realize this vision.

References

[1] IMT Traffic Estimates for the Years 2020 to 2030. International Telecommunication Union (ITU). Available online: https: //www.itu.int/pub/r-rep-m.2370 (accessed on 25 October 2023). [2] Bangerter, B.; Talwar, S.; Arefi, R.; Stewart, K. Networks and Devices for the 5G Era. IEEE Commun. Mag. 2014, 52, 90–96. [CrossRef] [3] Sinclair, M.; Maadi, S.; Zhao, Q.; Hong, J.; Ghermandi, A.; Bailey, N. Assessing the Socio-Demographic Representativeness of Mobile Phone Application Data. Appl. Geogr. 2023, 158, 102997. [CrossRef] [4] Huseien, G.F.; Shah, K.W. A Review on 5G Technology for Smart Energy Management and Smart Buildings in Singapore. Energy AI 2022, 7, 100116. [CrossRef] 26. Kaiser, M.S.; Zenia, N.; Tabassum, F.; Mamun, S.A.; Rahman, M.A.; Islam, M.d.S.; Mahmud, M. 6G Access Network for Intelligent Internet of Healthcare Things: Opportunity, Challenges, and Research Directions. Adv. Intell. Syst. Comput. 2020, 1309, 317–328. [CrossRef] 27. Baker, S.; Xiang, W. Artificial Intelligence of Things for Smarter Healthcare: A Survey of Advancements, Challenges, and Opportunities. IEEE Commun. Surv. Tutor. 2023, 25, 1261–1293. [CrossRef] 28. Kumar, R.; Gupta, S.K.; Wang, H.-C.; Kumari, C.S.; Korlam, S.S.V.P. From Efficiency to Sustainability: Exploring the Potential of 6G for a Greener Future. Sustainability 2023, 15, 16387. [CrossRef] 29. Nankya, M.; Chataut, R.; Akl, R. Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies. Sensors 2023, 23, 8840. [CrossRef] 30. Ray, P.P. A perspective on 6G: Requirement, technology, enablers, challenges and future road map. J. Syst. Archit. 2021, 118, 102180. [CrossRef] 31. Ismail, L.; Buyya, R. Artificial Intelligence Applications and Self-Learning 6G Networks for Smart Cities Digital Ecosystems: Taxonomy, Challenges, and Future Directions. Sensors 2022, 22, 5750. [CrossRef] 32. Kimachia, K.; Staff, T.; Clarke, M.; McQuarrie, K.; Millares, L.; Azhar, A.; Abbott, B. 5G vs 6G: What’s the Difference? TechRepublic, 23 Febeuary 2023. Available online: https://www.techrepublic.com/article/5g-vs-6g/ (accessed on 1 November 2023). 33. Adhikari, M.; Hazra, A. 6G-Enabled Ultra-Reliable Low-Latency Communication in Edge Networks. IEEE Commun. Stand. Mag. 2022, 6, 67–74. [CrossRef] 34. Hokazono, Y.; Kohara, H.; Kishiyama, Y.; Asai, T. Extreme Coverage Extension in 6G: Cooperative Non-terrestrial Network Architecture Integrating Terrestrial Networks. In Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 10–13 April 2022; pp. 138–143. [CrossRef] 35. Ruth, C. How 6G Can Transform the World and Technology. IEEE Standards Association. 2023. Available online: https: //standards.ieee.org/beyond-standards/how-6g-can-transform-the-world-and-technology/ (accessed on 14 March 2024). 36. Jain, P.; Gupta, A.; Kumar, N.; Guizani, M. Dynamic and Efficient Spectrum Utilization for 6G with THz, mmWave, and RF Band. IEEE Trans. Veh. Technol. 2023, 72, 3264–3273. [CrossRef] 37. 6G Flagship. Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence. Available online: https://www. 6gflagship.com/key-drivers-and-research-challenges-for-6g-ubiquitous-wireless-intelligence/ (accessed on 2 November 2023). 38. Ansere, J.A.; Kamal, M.; Khan, I.A.; Aman, M.N. Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems. Sensors 2023, 23, 4711. [CrossRef] 39. Zhang, L.; Du, Q.; Lu, L.; Zhang, S. Overview of the Integration of Communications, Sensing, Computing, and Storage as Enabling Technologies for the Metaverse over 6G Networks. Electronics 2023, 12, 3651. [CrossRef] 40. Shen, F.; Shi, H.; Yang, Y. A Comprehensive Study of 5G and 6G Networks. In Proceedings of the 2021 International Conference on Wireless Communications and Smart Grid (ICWCSG), Hangzhou, China, 13–15 August 2021; pp. 321–326. [CrossRef] 41. Techopedia. When 6G Met AI: How Next-Gen Mobile Networks Will Work. Available online: https://www.techopedia.com/6gand-ai-next-gen-mobile-networks-will-change-the-world (accessed on 8 November 2023). 42. Ashwin, M.; Alqahtani, A.S.; Mubarakali, A.; Sivakumar, B. Efficient Resource Management in 6G Communication Networks Using Hybrid Quantum Deep Learning Model. Comput. Electr. Eng. 2023, 106, 108565. [CrossRef] 43. Cloudait. Ai-Enabled Self-Healing Networks: A Crucial Pillar of 6G Reliability. AI Tools Practical Handbook. 1 August 2023. Available online: https://www.cloudaitech.net/ai-enabled-self-healing-networks-a-crucial-pillar-of-6g-reliability/ (accessed on 8 November 2023). [5] Baier, P.; Dürr, F.; Rothermel, K. TOMP: Opportunistic Traffic Offloading Using Movement Predictions. In Proceedings of the 37th Annual IEEE Conference on Local Computer Networks, Clearwater Beach, FL, USA, 22–25 October 2012; pp. 50–58. [CrossRef] [6] Gohar, A.; Nencioni, G. The Role of 5G Technologies in a Smart City: The Case for Intelligent Transportation System. Sustainability 2021, 13, 5188. [CrossRef] [7] Tataria, H.; Shafi, M.; Molisch, A.F.; Dohler, M.; Sjöland, H.; Tufvesson, F. 6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities. Proc. IEEE 2021, 109, 1166–1199. [CrossRef] [8] Murroni, M.; Anedda, M.; Fadda, M.; Ruiu, P.; Popescu, V.; Zaharia, C.; Giusto, D. 6G—Enabling the New Smart City: A Survey. Sensors 2023, 23, 7528. [CrossRef] [PubMed] [9] Singh, P.R.; Singh, V.K.; Yadav, R.; Chaurasia, S.N. 6G Networks for Artificial Intelligence-Enabled Smart Cities Applications: A Scoping Review. Telemat. Inform. Rep. 2023, 9, 100044. [CrossRef] [10] AI-Powered 6G Networks Will Reshape Digital Interactions. Available online: https://www.technologyreview.com/2023/10/26 /1082028/ai-powered-6g-networks-will-reshape-digital-interactions/ (accessed on 29 October 2023). [11] National Institute of Standards and Technology. Post-Quantum Cryptography Standardization. 2022. Available online: https://csrc.nist.gov/projects/post-quantum-cryptography/post-quantum-cryptography-standardization (accessed on 24 November 2024). [12] Kabir, H.D.; Abdar, M.; Khosravi, A.; Jalali, S.M.J.; Atiya, A.F.; Nahavandi, S.; Srinivasan, D. Spinalnet: Deep neural network with gradual input. IEEE Trans. Artif. Intell. 2022, 4, 1165–1177. [CrossRef] [13] Kabir, H.D.; Khanam, S.; Khozeimeh, F.; Khosravi, A.; Mondal, S.K.; Nahavandi, S.; Acharya, U.R. Aleatory-aware deep uncertainty quantification for transfer learning. Comput. Biol. Med. 2022, 143, 105246. [CrossRef] [14] Cashmore, M.; Collins, A.; Krarup, B.; Krivic, S.; Magazzeni, D.; Smith, D. Towards explainable AI planning as a service. arXiv 2019, arXiv:1908.05059. [15] Kabir, H. Reduction of class activation uncertainty with background information. arXiv 2023, arXiv:2305.03238. [16] Chen, L.; Chen, L.; Jordan, S.; Liu, Y.K.; Moody, D.; Peralta, R.; Perlner, R.A.; Smith-Tone, D. Report on Post-Quantum Cryptography; US Department of Commerce, National Institute of Standards and Technology: Washington, DC, USA, 2016; Volume 12. [17] Van Huynh, N.; Hoang, D.T.; Lu, X.; Niyato, D.; Wang, P.; Kim, D.I. Ambient backscatter communications: A contemporary survey. IEEE Commun. Surv. Tutor. 2018, 20, 2889–2922. [CrossRef] [18] Kabir, H.D.; Mondal, S.K.; Alam, S.B.; Acharya, U.R. Transfer learning with spinally shared layers. Appl. Soft Comput. 2024, 163, 111908. [CrossRef] [19] Kabir, H.D.; Mondal, S.K.; Khanam, S.; Khosravi, A.; Rahman, S.; Qazani, M.R.C.; Alizadehsani, R.; Asadi, H.; Mohamed, S.; Nahavandi, S.; et al. Uncertainty aware neural network from similarity and sensitivity. Appl. Soft Comput. 2023, 149, 111027. [CrossRef] [20] Mao, Q.; Hu, F.; Hao, Q. Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Commun. Surv. Tutor. 2018, 20, 2595–2621. [CrossRef] [21] Elayan, H.; Amin, O.; Shihada, B.; Shubair, R.M.; Alouini, M.S. Terahertz band: The last piece of RF spectrum puzzle for communication systems. IEEE Open J. Commun. Soc. 2019, 1, 1–32. [CrossRef] [22] ITU-R. Minimum requirements related to technical performance for IMT-2020 radio interface(s). Report 2017, 2410, 2410-2017. [23] Boulogeorgos, A.A.A.; Alexiou, A.; Merkle, T.; Schubert, C.; Elschner, R.; Katsiotis, A.; Stavrianos, P.; Kritharidis, D.; Chartsias, P.K.; Kokkoniemi, J.; et al. Terahertz technologies to deliver optical network quality of experience in wireless systems beyond 5G. IEEE Commun. Mag. 2018, 56, 144–151. [CrossRef] [24] Wang, C.X.; You, X.; Gao, X.; Zhu, X.; Li, Z.; Zhang, C.; Wang, H.; Huang, Y.; Chen, Y.; Haas, H.; et al. On the road to 6G: Visions, requirements, key technologies and testbeds. IEEE Commun. Surv. Tutor. 2023, 25, 905–974. [CrossRef] [25] Jiang, W.; Han, B.; Habibi, M.A.; Schotten, H.D. The road towards 6G: A comprehensive survey. IEEE Open J. Commun. Soc. 2021, 2, 334–366. [CrossRef] [26] Nasrallah, A.; Thyagaturu, A.S.; Alharbi, Z.; Wang, C.; Shao, X.; Reisslein, M.; ElBakoury, H. Ultra-low latency (ULL) networks: The IEEE TSN and IETF DetNet standards and related 5G ULL research. IEEE Commun. Surv. Tutor. 2018, 21, 88–145. [CrossRef] [27] VIVO. White Paper on 6G Vision, Requirement and Challenges. White Paper, 2020. Available online: http://www.vivo.com.cn/ 6g/CH/vivo6gvision.pdf (accessed on 10 September 2024). (In Chinese) [28] Zhong, M.; Yang, Y.; Yao, H.; Fu, X.; Dobre, O.A.; Postolache, O. 5G and IoT: Towards a new era of communications and measurements. IEEE Instrum. Meas. Mag. 2019, 22, 18–26. [CrossRef] [29] Barneto, C.B.; Turunen, M.; Liyanaarachchi, S.D.; Anttila, L.; Brihuega, A.; Riihonen, T.; Valkama, M. High-accuracy radio sensing in 5G new radio networks: Prospects and self-interference challenge. In Proceedings of the 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 3–6 November 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 1159–1163. [30] 6G Flagship. Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence. White Paper, 2019. Available online: https://www.mobilewirelesstesting.com/wp-content/uploads/2019/10/5G-evolution-on-the-path-to-6G-wp-en-36 08-3326-52-v0100.pdf (accessed on 10 September 2024). [31] NTT Docomo Inc. 5G Evolution and 6G. White Paper, 2020. Available online: https://www.nttdocomo.co.jp/english/binary/pdf/corporate/technology/whitepaper_6g/DOCOMO_6G_White_PaperEN_20200124.pdf (accessed on 10 September 2024). [32] Kimachia, K.; Staff, T.; Clarke, M.; McQuarrie, K.; Millares, L.; Azhar, A.; Abbott, B. 5G vs 6G: What’s the Difference? TechRepublic, 23 Febeuary 2023. Available online: https://www.techrepublic.com/article/5g-vs-6g/ (accessed on 1 November 2023). [33] Adhikari, M.; Hazra, A. 6G-Enabled Ultra-Reliable Low-Latency Communication in Edge Networks. IEEE Commun. Stand. Mag. 2022, 6, 67–74. [CrossRef] [34] Hokazono, Y.; Kohara, H.; Kishiyama, Y.; Asai, T. Extreme Coverage Extension in 6G: Cooperative Non-terrestrial Network Architecture Integrating Terrestrial Networks. In Proceedings of the 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 10–13 April 2022; pp. 138–143. [CrossRef] [35] Ruth, C. How 6G Can Transform the World and Technology. IEEE Standards Association. 2023. Available online: https: //standards.ieee.org/beyond-standards/how-6g-can-transform-the-world-and-technology/ (accessed on 14 March 2024). [36] Jain, P.; Gupta, A.; Kumar, N.; Guizani, M. Dynamic and Efficient Spectrum Utilization for 6G with THz, mmWave, and RF Band. IEEE Trans. Veh. Technol. 2023, 72, 3264–3273. [CrossRef] [37] 6G Flagship. Key Drivers and Research Challenges for 6G Ubiquitous Wireless Intelligence. Available online: https://www. 6gflagship.com/key-drivers-and-research-challenges-for-6g-ubiquitous-wireless-intelligence/ (accessed on 2 November 2023). [38] Ansere, J.A.; Kamal, M.; Khan, I.A.; Aman, M.N. Dynamic Resource Optimization for Energy-Efficient 6G-IoT Ecosystems. Sensors 2023, 23, 4711. [CrossRef] [39] Zhang, L.; Du, Q.; Lu, L.; Zhang, S. Overview of the Integration of Communications, Sensing, Computing, and Storage as Enabling Technologies for the Metaverse over 6G Networks. Electronics 2023, 12, 3651. [CrossRef] [40] Shen, F.; Shi, H.; Yang, Y. A Comprehensive Study of 5G and 6G Networks. In Proceedings of the 2021 International Conference on Wireless Communications and Smart Grid (ICWCSG), Hangzhou, China, 13–15 August 2021; pp. 321–326. [CrossRef] [41] Techopedia. When 6G Met AI: How Next-Gen Mobile Networks Will Work. Available online: https://www.techopedia.com/6gand-ai-next-gen-mobile-networks-will-change-the-world (accessed on 8 November 2023). [42] Ashwin, M.; Alqahtani, A.S.; Mubarakali, A.; Sivakumar, B. Efficient Resource Management in 6G Communication Networks Using Hybrid Quantum Deep Learning Model. Comput. Electr. Eng. 2023, 106, 108565. [CrossRef] [43] Cloudait. Ai-Enabled Self-Healing Networks: A Crucial Pillar of 6G Reliability. AI Tools Practical Handbook. 1 August 2023. Available online: https://www.cloudaitech.net/ai-enabled-self-healing-networks-a-crucial-pillar-of-6g-reliability/ (accessed on 8 November 2023). [44] Wang, C.; Jia, B.; Yu, H.; Chen, L.; Cheng, K.; Wang, X. Attention-aided Federated Learning for Dependency-Aware Collaborative Task Allocation in Edge-Assisted Smart Grid Scenarios. In Proceedings of the 2022 IEEE/CIC International Conference on Communications in China (ICCC), Foshan, China, 11–13 August 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 856–861. [45] Wang, X.; Kong, L.; Kong, F.; Qiu, F.; Xia, M.; Arnon, S.; Chen, G. Millimeter wave communication: A comprehensive survey. IEEE Commun. Surv. Tutor. 2018, 20, 1616–1653. [CrossRef] [46] Ippolito, L.J. Radio propagation for space communications systems. Proc. IEEE 1981, 69, 697–727. [CrossRef] [47] Ajorloo, H.; Manzuri-Shalmani, M.T. Modeling beacon period length of the UWB and 60-GHz mmWave WPANs based on ECMA-368 and ECMA-387 standards. IEEE Trans. Mob. Comput. 2012, 12, 1201–1213. [CrossRef] [48] Nitsche, T.; Cordeiro, C.; Flores, A.B.; Knightly, E.W.; Perahia, E.; Widmer, J.C. IEEE 802.11 ad: Directional 60 GHz communication for multi-Gigabit-per-second Wi-Fi. IEEE Commun. Mag. 2014, 52, 132–141. [CrossRef] [49] Han, B.; Wang, L.; Schotten, H.D. A 3D human body blockage model for outdoor millimeter-wave cellular communication. Phys. Commun. 2017, 25, 502–510. [CrossRef] [50] Al-samman, A.M.; Azmi, M.H.; Rahman, T.A. A survey of millimeter wave (mm-Wave) communications for 5G: Channel measurement below and above 6 GHz. In Recent Trends in Data Science and Soft Computing, Proceedings of the 3rd International Conference of Reliable Information and Communication Technology (IRICT 2018), Kuala Lumpur, Malaysia, 23–24 June 2018; Springer: Berlin/Heidelberg, Germany, 2019; pp. 451–463. [51] Huq, K.M.S.; Busari, S.A.; Rodriguez, J.; Frascolla, V.; Bazzi, W.; Sicker, D.C. Terahertz-enabled wireless system for beyond-5G ultra-fast networks: A brief survey. IEEE Netw. 2019, 33, 89–95. [CrossRef] [52] Rappaport, T.S.; Xing, Y.; Kanhere, O.; Ju, S.; Madanayake, A.; Mandal, S.; Alkhateeb, A.; Trichopoulos, G.C. Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond. IEEE Access 2019, 7, 78729–78757. [CrossRef] [53] Kabir, H.M.D. A frequency multiplier using three ambipolar graphene transistors. Microelectron. J. 2017, 70, 12–15. [CrossRef] [54] Chen, Z.; Ma, X.; Zhang, B.; Zhang, Y.; Niu, Z.; Kuang, N.; Chen, W.; Li, L.; Li, S. A survey on terahertz communications. China Commun. 2019, 16, 1–35. [CrossRef] [55] Hadi Sarieddeen, N.S.; Al-Naffouri, T.Y.; Alouini, M.S. Next Generation Terahertz Communications: A Rendezvous of Sensing, Imaging, and Localization. IEEE Commun. Mag. 2020, 58, 69–75. [CrossRef] [56] Zhang, J.; Zhu, M.; Hua, B.; Lei, M.; Cai, Y.; Zou, Y.; Tian, L.; Li, A.; Huang, Y.; Yu, J.; et al. 6G oriented 100 GbE real-time demonstration of fiber-THz-fiber seamless communication enabled by photonics. In Proceedings of the 2022 Optical Fiber Communications Conference and Exhibition (OFC), San Diego, CA, USA, 6–10 March 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 1–3. [57] Bariah, L.; Mohjazi, L.; Muhaidat, S.; Sofotasios, P.C.; Kurt, G.K.; Yanikomeroglu, H.; Dobre, O.A. A prospective look: Key enabling technologies, applications and open research topics in 6G networks. IEEE Access 2020, 8, 174792–174820. [CrossRef] [58] “Huawei Started Research on 6G ‘A Long Time Ago’, CEO Says”. Available online: https://www.rcrwireless.com/20190930/5g/ huawei-started-research-6g-long-time-ago-ceo-says (accessed on 2 November 2023). [59] Times, G. China Ramping up Research into 6G. Global Times. Available online: https://www.globaltimes.cn/content/1188617. shtml (accessed on 2 November 2023). [60] Tomás, J.P. LG to Focus on 6G Research with Partnership with Keysight Technologies. RCR Wireless News, 8 April 2021. Available online: https://www.rcrwireless.com/20210408/business/lg-to-focus-on-6g-research-with-partnership-with-keysighttechnologies (accessed on 2 November 2023). [61] Jiang, W.; Han, B.; Habibi, M.A.; Schotten, H.D. The Road towards 6G: A Comprehensive Survey. IEEE Open J. Commun. Soc. 2021, 2, 334–366. [CrossRef] [62] O’Hara, J.F.; Ekin, S.; Choi, W.; Song, I. A Perspective on Terahertz Next-Generation Wireless Communications. Technologies 2019, 7, 43. [CrossRef] [63] Writer, S. Japan Readies $2bn to Support Industry Research on 6G Tech. Nikkei Asia, 21 November 2019. Available online: https://asia.nikkei.com/Business/Technology/Japan-readies-2bn-to-support-industry-research-on-6G-tech (accessed on 2 November 2023). [64] 6G on the Horizon: A Global Overview of the Latest Developments in Wireless Technology: Market Research Blog. Market Research Reports® Inc., 5 April 2023. Available online: https://www.marketresearchreports.com/blog/2023/04/05/6g-horizonglobal-overview-latest-developments-wireless-technology (accessed on 2 November 2023). [65] Puspitasari, A.A.; An, T.T.; Alsharif, M.H.; Lee, B.M. Emerging Technologies for 6G Communication Networks: Machine Learning Approaches. Sensors 2023, 23, 7709. [CrossRef] [PubMed] [66] Banafaa, M.; Shayea, I.; Din, J.; Azmi, M.H.; Alashbi, A.; Daradkeh, Y.I.; Alhammadi, A. 6G Mobile Communication Technology: Requirements, Targets, Applications, Challenges, Advantages, and Opportunities. Alex. Eng. J. 2023, 64, 245–274. [CrossRef] [67] Alsabah, M.; Naser, M.A.; Mahmmod, B.M.; Abdulhussain, S.H.; Eissa, M.R.; Al-Baidhani, A.; Noordin, N.K.; Sait, S.M.; Al-Utaibi, K.A.; Hashim, F. 6G Wireless Communications Networks: A Comprehensive Survey. IEEE Access 2021, 9, 148191–148243. [CrossRef] [68] Ahmad, I.; Rodriguez, F.; Huusko, J.; Seppänen, K. On the Dependability of 6G Networks. Electronics 2023, 12, 1472. [CrossRef] [69] Kanellopoulos, D.; Sharma, V.K.; Panagiotakopoulos, T.; Kameas, A. Networking Architectures and Protocols for IoT Applications in Smart Cities: Recent Developments and Perspectives. Electronics 2023, 12, 2490. [CrossRef] [70] Hazarika, A.; Rahmati, M. Towards an Evolved Immersive Experience: Exploring 5G- and Beyond-Enabled Ultra-Low-Latency Communications for Augmented and Virtual Reality. Sensors 2023, 23, 3682. [CrossRef] [71] Yesilkaya, A.; Bian, R.; Tavakkolnia, I.; Haas, H. OFDM-based optical spatial modulation. IEEE J. Sel. Top. Signal Process. 2019, 13, 1433–1444. [CrossRef] [72] Eroglu, Y.S.; Anjinappa, C.K.; Guvenc, I.; Pala, N. Slow beam steering and NOMA for indoor multi-user visible light communications. IEEE Trans. Mob. Comput. 2019, 20, 1627–1641. [CrossRef] [73] Su, N.; Panayirci, E.; Koca, M.; Yesilkaya, A.; Poor, H.V.; Haas, H. Physical layer security for multi-user MIMO visible light communication systems with generalized space shift keying. IEEE Trans. Commun. 2021, 69, 2585–2598. [CrossRef] [74] Chowdhury, M.Z.; Hossan, M.T.; Islam, A.; Jang, Y.M. A comparative survey of optical wireless technologies: Architectures and applications. IEEE Access 2018, 6, 9819–9840. [CrossRef] [75] Al-Kinani, A.; Wang, C.X.; Zhou, L.; Zhang, W. Optical wireless communication channel measurements and models. IEEE Commun. Surv. Tutor. 2018, 20, 1939–1962. [CrossRef] [76] Marcus, M.; Burtle, J.; Franca, B.; Lahjouji, A.; McNeil, N. Federal Communications Commission Spectrum Policy Task Force; Report of the Unlicensed Devices and Experimental Licenses Working Group: Washington, DC, USA, 2002. [77] Kliks, A.; Kulacz, L.; Kryszkiewicz, P.; Bogucka, H.; Dryjanski, M.; Isaksson, M.; Koudouridis, G.P.; Tengkvist, P. Beyond 5G: Big data processing for better spectrum utilization. IEEE Veh. Technol. Mag. 2020, 15, 40–50. [CrossRef] [78] Liang, Y.C.; Zhang, Q.; Larsson, E.G.; Li, G.Y. Symbiotic radio: Cognitive backscattering communications for future wireless networks. IEEE Trans. Cogn. Commun. Netw. 2020, 6, 1242–1255. [CrossRef] [79] Bhattarai, S.; Park, J.M.J.; Gao, B.; Bian, K.; Lehr, W. An overview of dynamic spectrum sharing: Ongoing initiatives, challenges, and a roadmap for future research. IEEE Trans. Cogn. Commun. Netw. 2016, 2, 110–128. [CrossRef] [80] Wang, B.; Liu, K.R. Advances in cognitive radio networks: A survey. IEEE J. Sel. Top. Signal Process. 2010, 5, 5–23. [CrossRef] [81] Mitola, J. Cognitive radio for flexible mobile multimedia communications. In Proceedings of the 1999 IEEE International Workshop on Mobile Multimedia Communications (MoMuC’99) (Cat. No. 99EX384), San Diego, CA, USA, 15–17 November 1999; IEEE: Piscataway, NJ, USA, 1999; pp. 3–10. [82] Haykin, S. Cognitive radio: Brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 2005, 23, 201–220. [CrossRef] [83] Zhang, K.; Leng, S.; Peng, X.; Pan, L.; Maharjan, S.; Zhang, Y. Artificial intelligence inspired transmission scheduling in cognitive vehicular communications and networks. IEEE Internet Things J. 2018, 6, 1987–1997. [CrossRef] [84] Wang, C.; He, T.; Zhou, H.; Zhang, Z.; Lee, C. Artificial Intelligence Enhanced Sensors—Enabling Technologies to Next-Generation Healthcare and Biomedical Platform. Bioelectron. Med. 2023, 9, 17. [CrossRef] [PubMed] [85] Aliouche, H. What Is Remote Surgery/Telesurgery? 11 November 2021. Available online: https://www.news-medical.net/ health/What-is-Remote-SurgeryTelesurgery.aspx (accessed on 24 February 2024). [86] Kharche, S.; Kharche, J. 6G Intelligent Healthcare Framework: A Review on Role of Technologies, Challenges and Future Directions. J. Mob. Multimed. 2023, 19, 603–644. [CrossRef] [87] Alsharif, M.H.; Jahid, A.; Kannadasan, R.; Kim, M.-K. Unleashing the potential of sixth generation (6G) wireless networks in smart energy grid management: A comprehensive review. Energy Rep. 2024, 11, 1376–1398. [CrossRef]

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Copyright © 2025 Hamzah Abdullah Faraj, Omar Mohammed Abubaker Naser, Khamni Mohamed Elmbark. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.