Low-Latency Communication

Background: As the demand for ultra-fast, low-latency communication continues to rise, Terahertz (THz) communication has emerged as a promising candidate for enabling next-generation 6G networks. However, environmental sensitivity and hardware challenges pose significant limitations. Objective: This study investigates the potential of THz communication to support ultra-high data transfer rates in 6G networks, with a focus on the impact of environmental conditions, hardware complexity, and modulation techniques. Method: Through simulation analysis under both optimal and adverse environmental conditions, the performance of THz communication was assessed. The study also explores emerging materials and adaptive technologies to mitigate performance degradation. Results: Under optimal conditions, THz communication demonstrated the ability to achieve data rates up to 8.5 Tbps with approximately 1 ms latency at 10 THz. However, in high humidity and non-line-of-sight (NLOS) scenarios, performance declined significantly, with the signal-to-noise ratio (SNR) dropping from 35 dB to 18 dB and the bit error rate (BER) increasing from 3×10⁻³ to 4×10⁻². Orthogonal Frequency Division Multiplexing (OFDM) outperformed Quadrature Amplitude Modulation (QAM) in BER under varying conditions. The integration of advanced materials such as graphene and photonic crystals, along with intelligent reflecting surfaces (IRS), showed promise in enhancing signal quality and thermal management. Conclusion: While THz communication exhibits strong potential for supporting the high-speed, low-latency demands of 6G, environmental vulnerabilities and hardware complexity remain key challenges. Future research should prioritize the development of cost-effective, scalable materials and adaptive technologies to improve performance and deployment feasibility in diverse conditions.

Background: Smart city evolution is fast-paced, and imposes severe demands on telecom infrastructures: it must be highly flexible and scalable for coping with bursty traffic loads and heterogeneous service needs. Legacy network systems are not well suited to handle the changing requirements of smart city environments with autonomous cars, IoT, and public safety systems. Objective : The study to offer an AI-native network slicing framework for 6G smart city networks in order to improve dynamic resource control and management. The framework aims to enhance the delay, energy, and resource performance metrics which are significant for smart city services. Method: To facilitate the real-time network resource orchestration depending on the changing traffic requirements and user preferences, the authors consider moving target defense adapted artificial intelligence with a Deep Reinforcement Learning (DRL) model. Simulations were carried out to compare the AI-native model to conventional and AI-supported slicing methods. Results : Simulation results validate that the AI-native network slicing framework outperforms current 5G solutions with 25% reduction in latency and 20% increase in energy efficiency. Furthermore, the model's online resource allocation scheme can enhance the utilization efficiency of the bandwidth and the energy by 15% compared with the traditional approaches. Such improvements especially in critical applications like traffic management, emergency response, and health care would be important. Conclusion: The presented results demonstrate that AI-native network slicing is a viable, flexible, and scalable solution for 6G smart city networks. The framework is designed to support the future sustainable and high-performance requirements of urban infrastructures, providing both energy-efficient real-time adaptability. This study provides an overarching front-to-end outlook to address the management issues of sophisticated resource systems, and puts AI-native network slicing at the base level of the emerging smart cities.