Traditional spectrum allocation is no longer sustainable with the growing demands of 5G and 6G. The primary technical challenges include real-time interference management, latency constraints, and fragmented spectrum availability, while rigid licensing structures and lack of international coordination pose significant policy hurdles. A more adaptive approach is required, where AI-driven dynamic spectrum allocation, predictive interference mitigation, and blockchain-based leasing models enable real-time optimization and fair resource distribution. To bridge AI techniques with regulatory reforms, a collaborative framework involving telecom operators, policymakers, and AI researchers is essential to balance efficiency, fairness, and compliance. My publication, Deleted research item The research item mentioned here has been deleted

Thank Sir Chandrakanth Lekkala , for your insightful response! Building on the excellent points raised, I'm curious to explore further how these frameworks could be practically implemented. For example:

• Real-Time Regulatory Adaptation: How can regulatory frameworks be designed to adapt in real time to AI-driven decisions without compromising safety?
• Explainable AI: What role does explainable AI play in ensuring transparency and trust in spectrum allocation decisions?
• Blockchain Integration: Can blockchain or distributed ledger technologies enhance the security and transparency of dynamic spectrum leasing models?
• Interoperability: How might we ensure seamless integration of AI-driven systems with existing legacy networks under current regulatory regimes?

A recent paper on the topic can be found here Preprint Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges

1. Primary Technical and Policy Challenges

A. Technical Challenges

• Dynamic Spectrum Sharing & Interference Management: FR3 (7-24 GHz) is being considered for 6G, but coexistence with incumbent services (satellites, radio astronomy, defense) presents significant interference risks. AI-driven spectrum sensing and allocation need to operate in real-time to avoid disrupting existing services while maximizing spectrum utilization.
• Beam Management & Adaptive Transmission: The paper highlights beam alignment challenges due to varying angular spreads in FR3. Dynamic AI-driven beamforming is needed to optimize SINR and ensure reliable ISAC performance under mobility and blockage conditions.
• Multi-Band Coordination & Handover Efficiency: With FR3 integrating mid-band and mmWave, AI-powered spectrum agility is required to dynamically hop between frequencies based on congestion, propagation conditions, and user requirements. Efficient handover policies must be developed to minimize disruptions in dynamic environments.
• Energy-Efficient AI Algorithms: AI-driven spectrum optimization must balance computation overhead vs. real-time decision-making, particularly for edge-based inference in O-RAN architectures.

B. Policy Challenges

• Regulatory Approval for AI-Managed Dynamic Spectrum Access (DSA)Traditional fixed spectrum licenses may restrict AI-based spectrum sharing. Policies must evolve to support Dynamic Spectrum Access (DSA) frameworks that allow AI to manage spectrum in real-time.
• Incumbent Coexistence & Spectrum RefarmingRegulators must ensure protection for existing services while allowing new deployments. Interference nulling and AI-driven co-existence policies could be crucial for regulatory approval.
• Standardization & Global Alignment3GPP & ITU must align on AI-powered spectrum management frameworks for FR3. The paper highlights O-RAN’s role in spectral intelligence—future policies should ensure interoperability of AI-driven spectrum controllers across vendors and regions.

2. Innovative Strategies to Address These Issues

A. AI-Enhanced Spectrum Management (O-RAN rApps/xApps)

• The paper discusses how AI-driven RIC (rApps/xApps) in O-RAN can dynamically allocate spectrum based on real-time interference and traffic conditions.
• Key innovations include:AI-powered dynamic carrier aggregation to optimize coverage vs. capacity trade-offs in FR3. Interference-aware AI models to predict congestion hotspots and dynamically adjust transmission power, beamforming, and frequency allocation. Hybrid AI-ML models integrating historical data & real-time sensing to improve spectrum agility.

B. Multi-Band Sensing & Intelligent Frequency Hopping

• The paper highlights multi-band sensing in FR3, suggesting:AI-optimized frequency hopping to dynamically switch between FR3 bands based on interference & blockage conditions. AI-driven multi-band pilot design to minimize spectral redundancy and improve signal acquisition speed in dynamic conditions.

C. Regulatory Sandboxing & Policy Evolution

• AI-managed spectrum trials (Regulatory Sandboxes) to test AI-based dynamic allocation models in real-world FR3 environments before full-scale deployment.
• Development of Machine-Learning-Based Spectrum Sharing Policies that allow adaptive spectrum leasing & trading for AI-powered DSA.

AI-Enabled Spectrum Coordination and Enforcement

AI models must comply with existing frameworks while optimizing spectrum use through:

• Real-Time Spectrum Sensing & Decision-Making: AI-based DSA should adapt in milliseconds to avoid interference (e.g., reinforcement learning algorithms for spectrum efficiency).
• Dynamic Policy Implementation: AI should automatically adjust to regulatory changes and compliance requirements (e.g., region-specific spectrum rules).
• Inter-Operator Spectrum Coordination: AI-driven systems should facilitate multi-operator spectrum sharing to maximize efficiency.

Ahmad Bazzi

Thank you, Sir Ahmad Bazzi , for sharing such a comprehensive and insightful analysis, along with the reference to the preprint on Upper Mid-Band Spectrum for 6G. The breakdown of both technical and policy challenges—from AI-driven dynamic spectrum sharing to multi-band sensing and intelligent frequency hopping—is incredibly valuable.

Building on your points, I find the idea of AI-powered spectrum agility in FR3 particularly intriguing. Given the increasing complexity of spectrum environments, do you think reinforcement learning-based approaches could complement traditional optimization techniques for real-time handover decisions? Additionally, as O-RAN continues evolving, how can standardization efforts ensure that AI-driven spectrum controllers maintain interoperability across different regions and vendors?

On the policy front, your mention of Regulatory Sandboxes is a crucial point. Testing AI-based spectrum allocation in controlled environments could help bridge the gap between regulatory constraints and real-world deployments. However, one challenge remains: How can regulators define and enforce fail-safe mechanisms to prevent unintended interference, especially in critical applications (e.g., defense, aviation, and emergency communications)?

Sirajo MUHAMMAD Sani Gidanmadi

Thank you, Sir Sirajo MUHAMMAD Sani Gidanmadi , for your valuable insights on AI-enabled spectrum coordination and enforcement. Your points on real-time spectrum sensing, inter-operator spectrum coordination, and dynamic policy implementation highlight some of the most pressing challenges in AI-driven spectrum management.

I completely agree that millisecond-level spectrum adaptation is key to avoiding interference. Given that reinforcement learning is often proposed for spectrum decision-making, how do you see the trade-off between model complexity and real-time performance, particularly when deploying AI-based spectrum sensing on edge devices with limited computational resources?

Your mention of inter-operator spectrum coordination is also crucial. Do you see a potential role for federated AI models in enabling multiple operators to collaboratively optimize spectrum allocation while preserving data privacy?