[Paper Review] 6G Survey on Challenges, Requirements, Applications, Key Enabling Technologies, Use Cases, AI integration issues and Security aspects

Muhammad Sajjad Akbar, Zawar Hussain+3 more|arXiv (Cornell University)|Jun 2, 2022

Advanced Wireless Communication Technologies18 citations

TL;DR

This comprehensive survey synthesizes current research on 6G wireless networks, identifying key requirements, enabling technologies, AI integration challenges, and security issues. It proposes a roadmap for 6G deployment by analyzing terahertz spectrum use, advanced architectures like SDN/NFV, and AI-driven network optimization, with a focus on ultra-high throughput (up to 1 Tbps), sub-millisecond latency, and support for futuristic applications such as immersive VR, smart healthcare, and Industry 5.0.

ABSTRACT

Fifth-generation (5G) wireless networks will likely offer high data rates, increased reliability, and low delay for mobile, personal, and local area networks. Along with the rapid growth of smart wireless sensing and communication technologies, data traffic has increased significantly and existing 5G networks are not able to fully support future massive data traffic for services, storage, and processing. To meet the challenges ahead, research communities and industry are exploring the sixth generation (6G) Terahertz-based wireless network that is expected to be offered to industrial users in just ten years. Gaining knowledge and understanding of the different challenges and facets of 6G is crucial in meeting the requirements of future communication and addressing evolving quality of service (QoS) demands. This survey provides a comprehensive examination of specifications, requirements, applications, and enabling technologies related to 6G. It covers disruptive and innovative, integration of 6G with advanced architectures and networks such as software-defined networks (SDN), network functions virtualization (NFV), Cloud/Fog computing, and Artificial Intelligence (AI) oriented technologies. The survey also addresses privacy and security concerns and provides potential futuristic use cases such as virtual reality, smart healthcare, and Industry 5.0. Furthermore, it identifies the current challenges and outlines future research directions to facilitate the deployment of 6G networks.

Motivation & Objective

• To identify and analyze the core requirements and technical challenges for 6G wireless networks beyond 5G capabilities.
• To evaluate emerging enabling technologies such as terahertz communications, software-defined networking (SDN), network function virtualization (NFV), and cloud/fog computing for 6G.
• To examine the integration of artificial intelligence (AI) and machine learning (ML) in 6G networks, including optimization and intelligent communication frameworks.
• To assess security, privacy, and trust issues in 6G, especially in the context of massive connectivity and AI-driven systems.
• To explore futuristic use cases such as virtual reality, smart healthcare, and Industry 5.0, and their implications on network design.

Proposed method

• Conducted a systematic literature review across major databases (IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus) focusing on 6G-related works from the past decade.
• Selected and analyzed approximately 50 high-impact papers from top-tier journals and conferences, including IEEE Communications Surveys & Tutorials and IEEE Transactions on Wireless Communications.
• Categorized and synthesized findings into four core areas: use cases, enabling technologies, AI integration, and security/privacy challenges.
• Evaluated the role of terahertz (THz) spectrum (100 GHz–3 THz) in enabling ultra-high data rates and low latency in 6G networks.
• Explored architectural innovations such as SDN, NFV, and optical wireless backhaul to support massive connectivity and dynamic resource allocation.
• Analyzed AI/ML techniques for network optimization, including big data processing, adaptive beamforming, and distributed machine learning in 6G environments.

Experimental results

Research questions

• RQ1 What are the key performance requirements for 6G networks, particularly in terms of throughput, latency, reliability, and energy efficiency?
• RQ2 How can terahertz (THz) spectrum and advanced MIMO beamforming enable ultra-high data rates and low-latency communication in 6G?
• RQ3 What are the major challenges and opportunities in integrating artificial intelligence (AI) and machine learning (ML) into 6G network management and optimization?
• RQ4 What are the critical security and privacy threats in 6G networks, especially with massive IoT and AI-driven automation, and what mitigation strategies are proposed?
• RQ5 How can emerging architectures like SDN, NFV, fog/cloud computing, and optical wireless backhaul support the scalability and flexibility demands of 6G use cases?

Key findings

• 6G is expected to achieve peak data rates of up to 1 terabit per second (Tbps) using the terahertz (THz) spectrum, with one-microsecond transmission latency—1,000 times faster than 5G’s 1 ms.
• The integration of AI and ML in 6G networks enables autonomous, self-optimizing systems capable of real-time adaptation, spectrum sharing, and beam management in high-frequency bands.
• Enabling technologies such as software-defined networking (SDN), network function virtualization (NFV), and optical wireless backhaul are critical for achieving ultra-dense, scalable, and flexible 6G infrastructures.
• Security and privacy challenges in 6G are significantly amplified by massive connectivity and AI-driven automation, necessitating solutions like blockchain and zero-trust architectures.
• Futuristic applications such as immersive virtual reality, real-time remote surgery in smart healthcare, and autonomous industrial systems (Industry 5.0) are feasible only with 6G’s ultra-reliable, low-latency, and high-capacity connectivity.
• The deployment of 6G faces major technical hurdles, including the lack of commercial THz transceivers, beam management complexity in high-frequency bands, and efficient spectrum sharing in heterogeneous networks.