Intelligent Spectrum Management

• Intelligent spectrum management is a dynamic approach that allocates, shares, and optimizes radio-frequency resources using AI, fuzzy logic, and contextual awareness.
• It integrates advanced AI/ML methods, such as deep reinforcement learning and foundation models, to enable real-time, adaptive decision-making and improved spectral efficiency.
• Practical applications include 6G industrial networks, UAV and satellite communications, and dynamic spectrum marketplaces, ensuring robust, scalable, and efficient wireless services.

Intelligent spectrum management is the systematic, context-aware allocation, sharing, and orchestration of radio-frequency spectrum resources using advanced computational methodologies, including AI, dynamic policy frameworks, hierarchical optimization, foundation models, semantic signal processing, and adaptive marketplace mechanisms. This multifaceted field addresses the increasing scarcity, heterogeneity, and real-time dynamics of spectrum use across terrestrial, non-terrestrial, and integrated network infrastructures. By combining context-rich environmental awareness, predictive analytics, dynamic resource allocation, and machine reasoning, intelligent spectrum management aims to maximize spectral efficiency, minimize interference, and provide robust, scalable support for present and next-generation wireless applications.

1. Evolution from Fixed to Context-Aware Dynamic Spectrum Control

Traditional spectrum management systems relied on static, inflexible assignments with rigid administrative boundaries, leading to substantial underutilization and limited adaptability. Intelligent spectrum management fundamentally departs from this mode by leveraging real-time measurements, environmental awareness, and adaptive control, thereby supporting opportunistic, cooperative, and marketplace-based sharing schemes. The paradigm incorporates cognitive radio (CR), cognitive satellite (CogSat) principles, and hierarchical control to operate flexibly across licensed, unlicensed, and shared regimes.

A representative early intelligent spectrum management system (Bhattacharya et al., 2011) uses fuzzy logic to allow cognitive radios to share unused spectrum adaptively by quantifying key situational parameters—signal strength, node mobility (velocity), spectrum efficiency, and spatial distance to primary users—through fuzzified linguistic variables and a rule base of 81 IF–THEN expressions. The decision metric for spectrum access is computed using a Mamdani-type fuzzy inference system:

$$\text{Spectrum access possibility} = w \times \min\{\mu_{\text{signal}},\; \mu_{\text{velocity}},\; \mu_{\text{efficiency}},\; \mu_{\text{distance}}\},$$

where each $\mu$ denotes the membership value of a corresponding parameter. This quantitative, context-driven approach is foundational to dynamic, situation-aware spectrum decision-making.

2. AI/ML-Driven Sensing, Interpretation, and Decision Architectures

Recent advances are characterized by the integration of discriminative and generative AI models, deep reinforcement learning (DRL), foundation models, and semantic cognitive frameworks for predictive analysis, resource optimization, and knowledge discovery (Rasti et al., 19 Feb 2025, Cheng et al., 2020, Zhou et al., 2 May 2025, Zhang et al., 31 Aug 2025). For example, in O-RAN-based spectrum marketplaces, discriminative AI (e.g., DDQN) directly predicts actions such as spectrum acquisition or relinquishment based on current states, while generative AI (GANs, VAEs, diffusion models) simulates possible traffic and spectral demand scenarios, thereby improving both robustness and forecast accuracy (Rasti et al., 19 Feb 2025).

SpectrumFM, a large foundation model architecture for spectrum management, combines convolutional layers (for spectral locality) and multi-head self-attention modules (MHSA) (for capturing long-range dependencies), trained via self-supervised objectives—masked signal reconstruction and next-slot prediction—on large-scale in-phase and quadrature (IQ) data (Zhou et al., 2 May 2025). The hybrid latent representation learned by SpectrumFM provides transferable features across tasks: automatic modulation classification, technology identification, anomaly detection, and spectrum sensing. This yields improvements such as up to 12.1% AMC accuracy increase and spectrum sensing AUC of 0.97 at –4 dB SNR, establishing new performance baselines.

Semantic cognition frameworks introduce multi-layered analysis, progressing from basic signal/device understanding to relational topology/interference mapping and finally to intentional/behavioral semantics, embedding “why” and “what next” considerations directly into spectrum allocation decisions (Zhang et al., 31 Aug 2025). For instance, semantic-enhanced classifiers outperform traditional IQ signal-only approaches with test accuracy improvements from 61.05% to 99.35% on standardized datasets using additional feature embeddings.

3. Multi-Timescale and Hierarchical Optimization Frameworks

Modern spectrum management separates control into hierarchical tiers and multiple timescales to address the latency, scale, and heterogeneity of future networks.

• Dual-Timescale Frameworks: Moderate timescale (seconds–minutes) centralized optimization determines long-term resource/frequency partitioning among cells or sub-networks. The fast timescale (milliseconds) leverages distributed, opportunistic scheduling to rapidly adapt transmission to traffic and interference fluctuations (Teng et al., 2016). Resource allocation variables (e.g., $y^\mathcal{F}$ for frequency patterns) are iteratively updated using convex optimization and fixed-point iterations, reducing average packet delays by as much as 30% compared to static allocations.
• Hierarchical Deep Reinforcement Learning (HDRL): In integrated satellite–terrestrial (TN–NTN) networks, HDRL decomposes spectrum sharing decisions into global (satellite-level), regional (sub-region/HAP-level), and local (UAV/base station-level) policies, each with its own state-action-reward model and temporal abstraction (Umer et al., 9 Mar 2025). Policies are optimized using PPO, with higher tiers setting subgoals/constraints for lower tiers. The system improves spectral efficiency and throughput while maintaining computational tractability and responsiveness to dynamic network conditions.
• Digital Twin-Driven Optimization: Time-varying DTs, incorporating 3D maps and predicted user/satellite trajectories, enable anticipatory resource planning for bandwidth, power, and assignment optimization via mixed-integer nonlinear programming and iterative SCA/compressed sensing methods (Nguyen-Kha et al., 28 Jul 2025).

4. Marketplace and Policy-Based Mechanisms

Contemporary frameworks embed spectrum trading and policy evaluation as core mechanisms to orchestrate multi-operator, multi-application collaboration.

• Marketplace Model: The O-RAN-based, brokered spectrum marketplace supports dynamic, multi-granular spectrum trading among mobile, virtual, and micro-operators using real-time and predictive analytics (Rasti et al., 19 Feb 2025). Operators issue buy/sell requests across spatio-temporal domains, with the broker optimizing trades and allocations via cost-utility functions such as:

$R = -(\alpha D + \beta S + \gamma P + T),$

where $D$ and $S$ represent resource deficit and surplus, $P$ the monetary cost, $T$ transaction cost, and $\alpha, \beta, \gamma$ are weightings reflecting user satisfaction and expenditure.

• Semantic Policy Frameworks: Machine-readable policy ontologies (OWL, PROV-O, GeoSPARQL) enable automated reasoning over spectrum access requests (Santos et al., 2020). Requests are classified, prioritized, and evaluated against a knowledge graph of policies using a reasoning pipeline that explains permit/deny decisions, supporting live field deployments with rapid (<10 s) evaluation times and robust policy composability.
• Liberalized Usage Rights and Fluid Trading: The vision for an ideal spectrum regime centers on Spectrum Usage Rights (SURs), where explicit aggregate interference limits replace rigid transmit power constraints and licenses become commoditized, tradable, and shareable dynamically across usage types (e.g., repurposing broadcasting to mobile broadband) (Webb et al., 8 Mar 2025). This model underpins both exclusive and database-driven dynamic shared access (e.g., CBRS, LSA), paving the way for decentralized, AI-assisted spectrum management.

5. Practical Applications and Technology Agnosticism

Intelligent spectrum management frameworks are applied across multiple domains and deployment scenarios:

• Industrial 6G NiN Scenarios: Dynamic Spectrum Management (DSM) coordinates spectrum allocations for static and nomadic sub-networks with mission-critical and best-effort QoS (e.g., CNC control, sensor streaming) (Lindenschmitt et al., 27 Aug 2024, Lindenschmitt et al., 26 Aug 2025). Centralized managers, coordinated via KIRA self-organizing routing, ensure responsive, zero-touch reconfiguration and seamless connectivity, allowing industrial systems to dynamically accommodate mobile AGVs or process monitoring demands.
• UAV and Disaster Relief Networks: Hierarchical and reinforcement learning-based spectrums sharing leverages UAV task assignment and relocation strategies to maximize both spectrum utility and network lifetime in emergency contexts (Shamsoshoara et al., 2019, Shamsoshoara et al., 2019).
• Satellite and Integrated Systems: Dynamic spectrum management techniques underpin cognitive satellite networks, supporting both opportunistic and concurrent spectrum access with real-time, distributed AI-driven resource adaptation (Silva et al., 30 Aug 2025). DRL and federated training address the computational and architectural constraints of satellite deployment.
• Technology Agnostic Detection/Event Streaming: Spectrum streamer systems use sliding-window, distribution-based anomaly detection (Chi-squared tests on per-frequency time series) and modular, pipeline architectures to provide real-time, queryable event detection across any wireless protocol or signal (Fortuna et al., 2018), enabling spectrum occupancy analysis, interference mapping, and policy verification in both human and automated contexts.

6. Spectrum Awareness, Environmental Sensing, and Semantic Interpretation

Advances in environmental awareness focus on multi-dimensional spectrum hyperspace sensing; ASHA frameworks extend beyond binary occupancy checks to multi-parameter, context-rich awareness by extracting modulation, burst, and waveform features and autonomously adapting sensing parameters (filter bandwidths, thresholds) (Gorcin et al., 2015). Noise floor and parameter estimation modules (e.g., NFSPEM) utilize power statistics, cumulative sum (CUSUM) change-point detection, and empirical Bayesian inference for robust signal identification.

Spectrum cognition frameworks elevate this further, integrating multi-domain (time, frequency, space, time-frequency) and multi-layer (signal, object, network) analysis with a semantic situation layer that fuses device characterization, network interaction, and intent inference (Zhang et al., 31 Aug 2025). This hierarchical approach enables next-generation networks to transition from spectrum occupancy detection to actionable, context-driven resource management, with proven improvements in both accuracy and operational resilience.

7. Open Challenges and Future Directions

The surveyed literature converges on several open research challenges for intelligent spectrum management:

• Allocation Granularity and Spatio-Temporal Dynamics: Determining optimal granularity in spectrum slices (temporal, spectral, spatial) remains non-trivial; fine-grained control enhances adaptability but increases complexity and computational overhead (Rasti et al., 19 Feb 2025).
• AI Model Efficiency and Resilience: Robustness of AI-driven approaches under adversarial attacks, data scarcity, low SNR, and heterogeneous real-world conditions requires further investigation (Zhou et al., 2 May 2025, Zhang et al., 31 Aug 2025).
• Policy Standardization and Regulatory Harmonization: Achieving global, interoperable standards that facilitate dynamic trading, shared access, and cross-domain security is still an unresolved challenge, particularly for satellite, non-terrestrial, and integrated networks (Silva et al., 30 Aug 2025, Patil et al., 2022).
• Scalability and Real-Time Guarantees: Scalable, decentralized spectrum management leveraging federated learning, edge-based intelligence, and efficient policy dissemination will be required to meet the low-latency demands of 6G and beyond (Lindenschmitt et al., 26 Aug 2025, Liu et al., 2 Apr 2025).
• Economic Models and Incentivization: Dynamic pricing, micro-licensing, and tokenized spectrum management must align technical allocation with market-driven incentives and user behavior (Webb et al., 8 Mar 2025, Rasti et al., 19 Feb 2025).

The field continues to advance toward highly adaptive, context-aware, and market-driven spectrum management—a necessary evolution given the proliferation of interactive, heterogeneous, and mission-critical wireless services anticipated in future generations of global communications infrastructure.