The First Annual Wireless Innovation Forum Workshop on AI/ML and Agentic AI in Advanced Wireless Communications and Spectrum Management (WinnAI 2026)

The integration of artificial intelligence, machine learning, and agentic AI systems into wireless communications and spectrum management represents one of the most significant technological opportunities of our time. This workshop provides a unique forum for advancing the state-of-the-art in this critical area while bringing together the diverse expertise of the Wireless Innovation Forum's technical committees.

We invite the global wireless community to contribute to this important dialogue by submitting high-quality research papers, participating in workshop discussions, and engaging in collaborative efforts to advance AI/ML applications in wireless systems.

Workshop Overview

The Wireless Innovation Forum, a leading international organization driving the future of radio communications and spectrum sharing technologies since 1996, invites researchers, engineers, industry professionals, and academics to submit original research papers for a groundbreaking multi-committee workshop on Artificial Intelligence, Machine Learning, and Agentic AI Systems in Advanced Wireless Communications and Spectrum Management.

This workshop represents a unique convergence of the Forum's four major technical committees: the Citizens Broadband Radio Service (CBRS) Committee, the Software Defined Systems (SDS) Committee, the 6GHz Committee, and the Wireless Innovation Committee (WInnCmte). Together, these committees encompass the full spectrum of modern wireless innovation, from dynamic spectrum sharing and software-defined radio architectures to next-generation 6G technologies and emerging applications.

As the wireless industry stands at the threshold of technological transformation, the integration of artificial intelligence and machine learning technologies into wireless communications systems has emerged as a critical enabler for addressing the growing complexity of spectrum management, network optimization, and service delivery.

The advent of agentic AI systems—autonomous agents capable of independent decision-making, learning, and adaptation—promises to revolutionize how wireless networks operate, manage resources, and respond to dynamic conditions.

This workshop will bring together the global wireless community to explore, discuss, and advance the state-of-the-art in AI-enabled wireless technologies across all domains of the Forum's technical expertise. We seek contributions that demonstrate innovative applications of AI/ML and agentic AI systems in spectrum sharing, software-defined radio architectures, automated frequency coordination, dynamic network management, and emerging wireless technologies.

Technical Scope and Topics of Interest

This workshop encompasses a broad range of technical topics that span the expertise areas of all four Forum committees. We encourage submissions that address AI/ML and agentic AI applications in any of the following areas, as well as interdisciplinary work that bridges multiple domains.

CBRS Committee Focus Areas: AI/ML in Dynamic Spectrum Sharing

The Citizens Broadband Radio Service represents one of the most successful implementations of dynamic spectrum sharing, with its three-tier architecture enabling coexistence between Navy radar systems, Priority Access License holders, and General Authorized Access users in the 3.5 GHz band. The integration of AI and ML technologies into CBRS systems offers significant opportunities for enhancing spectrum efficiency, improving interference management, and enabling more sophisticated coordination mechanisms.

Spectrum Access System (SAS) Intelligence and Automation: We seek papers that explore the application of AI and ML technologies to enhance the capabilities of Spectrum Access Systems. Topics of particular interest include machine learning algorithms for interference prediction and mitigation, reinforcement learning approaches for dynamic spectrum allocation, and agentic AI systems that can autonomously negotiate spectrum access between different tiers of users. Research on predictive analytics for spectrum demand forecasting, intelligent algorithms for protecting incumbent Navy radar operations, and AI-driven optimization of spectrum grants and coordination procedures is especially welcome.

Environmental Sensing Capability (ESC) Enhancement through AI: The Environmental Sensing Capability plays a critical role in detecting incumbent activity and triggering protection mechanisms. We invite submissions on AI-powered spectrum sensing techniques, including deep learning approaches for signal detection and classification, anomaly detection algorithms for identifying unusual spectrum usage patterns, and machine learning methods for improving the accuracy and reliability of ESC measurements. Research on distributed sensing networks with AI coordination, predictive maintenance of ESC sensors using machine learning, and intelligent fusion of multiple sensing modalities is particularly relevant.

Multi-Agent Systems for CBSD Coordination: Citizens Broadband Radio Service Devices operate in a complex environment where coordination between multiple devices is essential for efficient spectrum utilization. We encourage papers on multi-agent reinforcement learning for CBSD coordination, distributed optimization algorithms for interference management, and agentic AI systems that enable CBSDs to autonomously negotiate and coordinate their spectrum usage. Research on swarm intelligence approaches for large-scale CBSD deployments, game-theoretic models for spectrum sharing with AI agents, and hierarchical coordination architectures is of significant interest.

Intelligent Protection of Incumbent Users: The protection of incumbent users, particularly Navy radar systems, is a fundamental requirement of the CBRS framework. We seek contributions on AI-driven approaches for real-time monitoring of incumbent activity, machine learning algorithms for predicting incumbent usage patterns, and intelligent systems for dynamic protection area adjustment. Research on AI-enhanced propagation modeling for incumbent protection, automated interference analysis and mitigation, and adaptive protection mechanisms that learn from historical data is particularly valuable.

Software Defined Systems (SDS) Committee Focus Areas: AI/ML in Software-Defined Radio

The Software Defined Systems Committee focuses on advancing software communications architectures and software-defined radio technologies that enable flexible, reconfigurable, and interoperable radio systems. The integration of AI and ML technologies into software-defined radio architectures offers transformative opportunities for creating truly cognitive radio systems that can adapt to changing conditions, optimize their performance, and provide enhanced capabilities.

Cognitive Radio Architectures and AI-Enhanced Waveform Adaptation: We invite papers on cognitive radio architectures that incorporate AI and ML technologies for intelligent spectrum sensing, dynamic spectrum access, and adaptive waveform selection. Topics of interest include reinforcement learning algorithms for cognitive radio decision-making, deep learning approaches for spectrum environment characterization, and agentic AI systems that can autonomously select and configure radio waveforms based on mission requirements and environmental conditions. Research on AI-driven parameter optimization for software-defined radios, machine learning approaches for waveform design and adaptation, and intelligent resource allocation in SDR systems is particularly welcome.

Software Communications Architecture (SCA) Evolution with AI Integration: The Software Communications Architecture provides a framework for developing portable, interoperable software-defined radio applications. We seek contributions on integrating AI and ML capabilities into SCA-compliant systems, including AI-enhanced middleware for dynamic service composition, machine learning approaches for automated software component discovery and integration, and intelligent frameworks for SCA application lifecycle management. Research on AI-driven code generation for SCA applications, automated testing and validation of AI-enabled SCA systems, and semantic approaches to SCA component interoperability is of significant interest.

Intelligent Resource Management and Optimization: Software-defined radio systems must efficiently manage computational, memory, and communication resources to deliver optimal performance. We encourage submissions on AI and ML approaches for resource management in SDR systems, including reinforcement learning algorithms for dynamic resource allocation, predictive analytics for resource demand forecasting, and intelligent load balancing across distributed SDR platforms. Research on AI-driven power management for battery-operated SDR devices, machine learning approaches for thermal management in high-performance SDR systems, and adaptive quality-of-service management is particularly relevant.

Automated Testing, Certification, and Compliance: The complexity of modern software-defined radio systems makes traditional testing and certification approaches increasingly challenging. We invite papers on AI-driven approaches for automated test generation, machine learning methods for fault detection and diagnosis in SDR systems, and intelligent frameworks for compliance verification. Research on AI-enhanced certification processes for SCA applications, automated performance benchmarking using machine learning, and intelligent approaches to interoperability testing is especially valuable.

6GHz Committee Focus Areas: AI/ML in Automated Frequency Coordination

The 6GHz Committee has developed innovative automated frequency coordination systems that enable unlicensed devices to operate in the 6 GHz band while protecting incumbent microwave services. The application of AI and ML technologies to AFC systems offers significant opportunities for improving coordination efficiency, enhancing incumbent protection, and enabling more sophisticated sharing mechanisms.

Intelligent Automated Frequency Coordination (AFC) Systems: We seek papers that explore the integration of AI and ML technologies into AFC systems to enhance their decision-making capabilities and coordination efficiency. Topics of particular interest include machine learning algorithms for interference analysis and prediction, agentic AI systems that can negotiate frequency assignments between different stakeholders, and reinforcement learning approaches for optimizing AFC decisions over time. Research on AI-enhanced propagation modeling for AFC systems, intelligent algorithms for dynamic protection criteria adjustment, and machine learning approaches for spectrum availability prediction is especially welcome.

AI-Powered Spectrum Sensing and Database Management: Accurate spectrum sensing and database management are critical components of effective AFC systems. We invite submissions on AI-powered spectrum sensing techniques for the 6 GHz band, including deep learning approaches for incumbent signal detection, machine learning methods for spectrum occupancy prediction, and intelligent algorithms for database accuracy improvement. Research on AI-driven approaches for crowdsourced spectrum sensing, machine learning techniques for handling uncertainty in spectrum databases, and intelligent fusion of multiple data sources for AFC decision-making is particularly relevant.

Machine Learning for Propagation Modeling and Interference Analysis: Accurate propagation modeling is essential for effective incumbent protection in AFC systems. We encourage papers on machine learning approaches for improving propagation prediction accuracy, AI-enhanced models that can adapt to local environmental conditions, and intelligent algorithms for complex interference scenario analysis. Research on deep learning techniques for terrain-aware propagation modeling, AI-driven approaches for handling propagation uncertainty, and machine learning methods for real-time interference assessment is of significant interest.

Security and Trust in AI-Enabled AFC Systems: The security and trustworthiness of AFC systems are paramount for ensuring reliable incumbent protection and system integrity. We seek contributions on AI-based security monitoring for AFC systems, machine learning approaches for detecting malicious or anomalous behavior, and intelligent frameworks for ensuring the trustworthiness of AI-driven AFC decisions. Research on adversarial machine learning in the context of AFC systems, AI-enhanced authentication and authorization mechanisms, and explainable AI approaches for AFC decision transparency is particularly valuable.

Wireless Innovation Committee (WInnCmte) Focus Areas: AI/ML in Emerging Wireless Technologies

The Wireless Innovation Committee serves as the Forum's incubator for exploring cutting-edge wireless technologies and emerging applications. The committee's current focus areas include highly dynamic spectrum sharing, 6G wireless technologies, unmanned vehicle communications, and advanced spectrum sharing frameworks, all of which present significant opportunities for AI and ML integration.

Highly Dynamic Spectrum Sharing with Agentic AI: The committee's highly dynamic spectrum sharing initiative aims to extend and optimize spectrum sharing practices for future applications, including scenarios with airborne incumbents and rapidly changing spectrum conditions. We invite papers on agentic AI systems for highly dynamic spectrum sharing, including multi-agent reinforcement learning approaches for real-time spectrum negotiation, AI-driven algorithms for managing spectrum sharing with mobile incumbents, and intelligent frameworks for coordinating spectrum access across multiple domains. Research on predictive analytics for dynamic spectrum availability, machine learning approaches for handling uncertainty in highly dynamic environments, and AI-enhanced coordination protocols for time-critical spectrum sharing is particularly welcome.

AI-Native 6G Wireless Technologies: The evolution toward 6G wireless systems presents unprecedented opportunities for integrating AI as a fundamental architectural component rather than an add-on capability. We seek contributions on AI-native 6G network architectures, including intelligent network slicing with AI-driven resource allocation, autonomous network operations with self-organizing and self-healing capabilities, and agentic AI systems for end-to-end network orchestration. Research on AI-enhanced massive MIMO systems, machine learning approaches for ultra-reliable low-latency communications, and intelligent frameworks for 6G network digital twins is of significant interest.

Intelligent Unmanned Vehicle Communications: The proliferation of unmanned aerial vehicles, autonomous ground vehicles, and other robotic systems creates new challenges and opportunities for wireless communications. We encourage submissions on AI and ML approaches for unmanned vehicle communications, including swarm intelligence algorithms for coordinating communications in drone fleets, machine learning techniques for predictive mobility management, and agentic AI systems for vehicle-to-everything (V2X) communications. Research on AI-driven approaches for maintaining connectivity in highly mobile scenarios, intelligent algorithms for managing communications in beyond-visual-line-of-sight operations, and machine learning methods for optimizing spectrum usage in unmanned vehicle networks is particularly relevant.

Advanced Spectrum Sharing Frameworks with AI Integration: The committee's work on advanced spectrum sharing frameworks, including collaboration with ETSI on temporary and flexible spectrum access, provides opportunities for exploring novel AI applications. We invite papers on AI-enhanced frameworks for on-demand spectrum access, machine learning approaches for temporary spectrum sharing coordination, and intelligent algorithms for scalable private network deployment. Research on AI-driven spectrum trading and brokerage systems, machine learning techniques for quality-of-service guarantee in shared spectrum, and agentic AI approaches for automated spectrum negotiation is especially valuable.

Cross-Committee and Interdisciplinary Topics

Federated Learning and Distributed AI in Wireless Systems: The distributed nature of wireless networks makes federated learning and distributed AI particularly relevant for wireless applications. We seek papers on federated learning approaches for spectrum management across multiple domains, distributed AI algorithms for coordinated network optimization, and privacy-preserving machine learning techniques for wireless systems. Research on federated learning for spectrum sensing and database improvement, distributed reinforcement learning for network-wide optimization, and blockchain-enabled AI coordination in wireless networks is of significant interest.

Explainable AI and Trustworthy Systems for Wireless Communications: The deployment of AI systems in critical wireless infrastructure requires high levels of transparency, explainability, and trustworthiness. We invite submissions on explainable AI approaches for wireless network management, trustworthy AI frameworks for spectrum sharing decisions, and interpretable machine learning methods for wireless system optimization. Research on AI safety and robustness in wireless applications, ethical AI frameworks for spectrum allocation, and human-AI collaboration in wireless network operations is particularly welcome.

Edge AI and Real-Time Processing for Wireless Applications: The need for low-latency decision-making in wireless systems makes edge AI and real-time processing critical capabilities. We encourage papers on edge AI architectures for wireless networks, real-time machine learning algorithms for spectrum management, and distributed AI processing frameworks for wireless applications. Research on AI acceleration techniques for wireless edge computing, energy-efficient AI processing in wireless devices, and real-time optimization algorithms for dynamic wireless environments is especially relevant.

Quantum-Enhanced AI for Wireless Communications: The intersection of quantum computing, artificial intelligence, and wireless communications represents an emerging frontier with significant potential. We seek contributions on quantum machine learning approaches for wireless optimization, quantum-enhanced algorithms for spectrum management, and quantum AI frameworks for next-generation wireless systems. Research on quantum sensing for spectrum applications, quantum-secured AI communications, and hybrid classical-quantum algorithms for wireless network optimization is of particular interest.

Integrated Sensing and Communications (ISAC): The convergence of wireless communications and radar sensing into unified systems, is creating opportunities for cross-disciplinary innovation spanning telecommunications, signal processing, and artificial intelligence. This approach enables wireless networks to simultaneously provide communications services while performing sophisticated sensing functions such as target detection, localization, and environmental monitoring. The cross-disciplinary nature of ISAC requires expertise from RF engineering for dual-function waveform design, machine learning for intelligent signal processing and target classification, and network optimization for managing trade-offs between sensing and communication performance. Applications include autonomous vehicles requiring simultaneous V2X communication and environmental sensing, smart cities integrating communication infrastructure with traffic monitoring, industrial IoT systems providing connectivity and predictive maintenance sensing, and next-generation cellular networks offering location services and environmental awareness as native capabilities, creating intelligent sensing platforms that understand and interact with their physical environment.

Submission Categories and Guidelines

We welcome submissions in multiple categories to accommodate different types of contributions and stages of research development. All submissions should clearly articulate their relevance to AI/ML applications in wireless communications and spectrum management, with particular emphasis on how the work advances the state-of-the-art in one or more of the Forum's technical domains.

Full Research Papers (8-12 pages): Full research papers should present complete, original research contributions with comprehensive experimental evaluation, theoretical analysis, or system implementation. These papers should include detailed related work analysis, clear problem formulation, thorough methodology description, comprehensive results and evaluation, and discussion of implications and future work. Full papers will undergo rigorous peer review and, if accepted, will be allocated extended presentation time during the workshop.

Short Papers and Work-in-Progress (4-6 pages): Short papers are intended for presenting preliminary results, novel ideas in early stages of development, position papers on emerging topics, or focused contributions that address specific aspects of larger problems. These submissions should clearly articulate the research problem, present initial results or conceptual frameworks, and discuss planned future work. Short papers will be reviewed for technical soundness and potential impact.

Industry and Application Papers (6-8 pages): Industry papers should focus on practical applications, deployment experiences, commercial implementations, or industry perspectives on AI/ML in wireless systems. These papers should emphasize real-world applicability, practical challenges and solutions, business implications, and lessons learned from actual deployments. Industry papers will be evaluated based on practical relevance, implementation quality, and contribution to industry understanding.

Tutorial and Survey Papers (10-15 pages): Tutorial and survey papers should provide comprehensive overviews of specific AI/ML application areas in wireless communications, synthesize existing research, identify gaps and opportunities, and provide guidance for future research directions. These papers should demonstrate deep understanding of the field, comprehensive literature coverage, and clear identification of research challenges and opportunities.

Submission Requirements and Format

All submissions must be original work that has not been previously published or is currently under review elsewhere. Papers should be written in English and formatted according to the IEEE conference paper template. Submissions must include clear abstracts that summarize the contribution, comprehensive references to related work, and appropriate acknowledgment of funding sources and collaborations.

Technical Requirements Papers should include sufficient technical detail to enable reproducibility of results, clear description of experimental methodology and evaluation metrics, appropriate statistical analysis of results where applicable, and discussion of limitations and potential negative results. For papers involving human subjects or proprietary data, appropriate ethical considerations and privacy protections should be addressed.

Reproducibility and Open Science: We strongly encourage authors to make their research reproducible by providing access to code, datasets, and experimental configurations where possible. Authors should consider submitting supplementary materials that support reproducibility, including source code, datasets (where permissible), detailed experimental configurations, and additional results or analysis not included in the main paper.

Review Process and Evaluation Criteria

All submissions will undergo a peer review process conducted by experts from academia, industry, and government organizations with relevant expertise. The review process will evaluate submissions based on technical quality, novelty and significance, relevance to workshop themes, clarity of presentation, and potential impact on the field.

Key Dates

Register Paper: 16 February, 2026 
Submission Deadline: 2 March, 2026
Notification Date: 13 April, 2026
Camera Ready Manuscript: 4 May, 2026
PowerPoint Slides: 11 May, 2026
Workshop: 14 May, 2026 Submission Portal Papers should be submitted through the workshop's online submission system at https://edas.info/N34626.

Workshop Organizing Committee

The workshop is organized collaboratively by representatives from all four Wireless Innovation Forum committees.

General Chairs: Andy Clegg and John Glossner

Technical Program Committee Chairs

• CBRS Committee: Richard Bernhardt
• SDS Committee: Sarvpreet Singh
• 6GHz Committee: Peter Young
• WinnCmte: Naotaka Sato and Richard Bernhardt

About the Wireless Innovation Forum

The Wireless Innovation Forum is an international, non-profit organization dedicated to driving the future of radio communications and spectrum sharing technologies. Established in 1996, the Forum brings together equipment vendors, software developers, service providers, government users, and academic institutions to advance technologies supporting innovative spectrum utilization and wireless communications systems development. The Forum's work spans critical areas including Citizens Broadband Radio Service (CBRS), software-defined radio architectures, 6 GHz automated frequency coordination, and emerging wireless technologies. Through its collaborative approach and proven track record of successful standards development, the Forum continues to play a leading role in shaping the future of wireless communications worldwide.

For more information about the Wireless Innovation Forum and its technical committees, visit www.wirelessinnovation.org.

Questions?: Email John Glossner