About this Research Topic
To achieve the ambitious performance goals of 6G networks, it is crucial to efficiently and intelligently manage three key resources: spectrum, energy, and big data. This requires the harmonious integration of three essential components: terahertz (THz) band communication, reconfigurable intelligent surfaces (RISs), and machine learning (ML). Together, these elements hold the potential to push the boundaries of 6G services and applications.
1. Unlocking Massive Wireless Speeds: The THz spectrum provides the opportunity for wireless connectivity at terabits per second (Tbps), offering an extensive and contiguous bandwidth resource.
2. Enhancing Connectivity Efficiency: RIS technology enables reliable and energy-efficient connectivity by manipulating phase-tuned incident-reflect paths, overcoming obstacles, and extending coverage to edge users.
3. Optimizing Network Performance: Machine learning leverages operational insights from network data to facilitate robust system optimization, addressing issues such as channel impairments and spectral resource utilization.
The interplay between THz, RIS, and ML addresses the challenges that hinder wireless communication performance and efficient use of spectral resources. The dynamic reconfiguration of RIS maximizes spectral and energy efficiency, increases reliability, and boosts overall system performance.
Regarding RIS technology, ongoing exploration spans novel antenna array designs across various architectural, material, and application dimensions, including unstructured, beyond-diagonal, metamaterial-based, and fluid antenna system prototypes. However, while the THz-RIS combination holds immense potential, several challenges persist:
1. Antenna Innovation: Prototyping advanced designs like beamforming metasurfaces and liquid antenna arrays for THz-RIS applications remains an active research area.
2. Network Optimization: Key challenges include real-time channel estimation, interference control with multiple RISs, and developing practical, low-complexity algorithms for efficient THz-RIS network operation.
3. Channel Modeling: Practical THz-RIS channel models that account for dual mobility, multi-connectivity, arbitrary RIS element configurations, and cascaded RIS networks are subjects of ongoing investigation.
4. Cross-Layer Frameworks: Novel frameworks and low-complexity algorithms for joint transmitter, receiver, and RIS phase shift beamforming vectors/codebooks are being explored to enhance real-time optimization and network capacity.
5. Energy Efficiency: Integrating energy efficiency optimization into system design and optimization frameworks is essential for sustainability, but it remains an open research challenge.
To address these challenges, we invite contributions from academia and industry in various areas, including:
• Innovative antenna array design and prototyping for THz-RIS.
• Field trials and experimentation in THz-RIS scenarios.
• Channel modeling and estimation techniques for THz-RIS networks.
• Development of energy-efficient and low-complexity algorithms.
• AI/ML/DRL-driven optimization strategies.
• Joint precoding and optimization techniques.
• System-level performance evaluations.
• Practical deployment use cases and standardization efforts.
• Mobility and multi-connectivity approaches.
• Radio resource management strategies.
• Integration of aerial-terrestrial networks featuring THz-RIS.
• Exploration of THz-RIS interaction with other wireless technologies (e.g., SWIPT, NOMA, D2D, V2X, UAVs, cell-free MIMO, backscatter communication, etc.).
1. Unlocking Massive Wireless Speeds: The THz spectrum provides the opportunity for wireless connectivity at terabits per second (Tbps), offering an extensive and contiguous bandwidth resource.
2. Enhancing Connectivity Efficiency: RIS technology enables reliable and energy-efficient connectivity by manipulating phase-tuned incident-reflect paths, overcoming obstacles, and extending coverage to edge users.
3. Optimizing Network Performance: Machine learning leverages operational insights from network data to facilitate robust system optimization, addressing issues such as channel impairments and spectral resource utilization.
The interplay between THz, RIS, and ML addresses the challenges that hinder wireless communication performance and efficient use of spectral resources. The dynamic reconfiguration of RIS maximizes spectral and energy efficiency, increases reliability, and boosts overall system performance.
Regarding RIS technology, ongoing exploration spans novel antenna array designs across various architectural, material, and application dimensions, including unstructured, beyond-diagonal, metamaterial-based, and fluid antenna system prototypes. However, while the THz-RIS combination holds immense potential, several challenges persist:
1. Antenna Innovation: Prototyping advanced designs like beamforming metasurfaces and liquid antenna arrays for THz-RIS applications remains an active research area.
2. Network Optimization: Key challenges include real-time channel estimation, interference control with multiple RISs, and developing practical, low-complexity algorithms for efficient THz-RIS network operation.
3. Channel Modeling: Practical THz-RIS channel models that account for dual mobility, multi-connectivity, arbitrary RIS element configurations, and cascaded RIS networks are subjects of ongoing investigation.
4. Cross-Layer Frameworks: Novel frameworks and low-complexity algorithms for joint transmitter, receiver, and RIS phase shift beamforming vectors/codebooks are being explored to enhance real-time optimization and network capacity.
5. Energy Efficiency: Integrating energy efficiency optimization into system design and optimization frameworks is essential for sustainability, but it remains an open research challenge.
To address these challenges, we invite contributions from academia and industry in various areas, including:
• Innovative antenna array design and prototyping for THz-RIS.
• Field trials and experimentation in THz-RIS scenarios.
• Channel modeling and estimation techniques for THz-RIS networks.
• Development of energy-efficient and low-complexity algorithms.
• AI/ML/DRL-driven optimization strategies.
• Joint precoding and optimization techniques.
• System-level performance evaluations.
• Practical deployment use cases and standardization efforts.
• Mobility and multi-connectivity approaches.
• Radio resource management strategies.
• Integration of aerial-terrestrial networks featuring THz-RIS.
• Exploration of THz-RIS interaction with other wireless technologies (e.g., SWIPT, NOMA, D2D, V2X, UAVs, cell-free MIMO, backscatter communication, etc.).
Keywords: Reconfigurable Intelligent Surfaces, Antennas Array, Terahertz, Machine Learning, Massive MIMO, Beamforming, 6G
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
