Dynamic Spectrum Sharing Overview

• Dynamic Spectrum Sharing is a framework that allows flexible and efficient use of radio frequency bands through real-time sensing, policy enforcement, and adaptive algorithms.
• It integrates diverse access mechanisms (time, frequency, spatial) and optimization techniques to enhance throughput and spectral efficiency by up to 30%.
• Blockchain and decentralized architectures ensure secure, verifiable spectrum leasing and enforcement, supporting scalable deployments in 5G/6G networks.

Dynamic spectrum sharing is a class of mechanisms and architectures that enable multiple independent users, systems, or operators to flexibly and efficiently access radio frequency (RF) spectrum bands whose availability, allocation, and interference environment may vary across time, frequency, and space. Unlike static, preassigned spectrum divisions, dynamic approaches employ sensing, real-time policy, algorithmic control, and enforcement to continuously adapt spectrum use, maximizing utilization and service performance while respecting regulatory and technical constraints. This paradigm has evolved to encompass a broad range of techniques—from cognitive radio and machine learning-driven resource allocation to auction-based trading and decentralized ledger enforcement—empowering spectrum-as-a-service models at the heart of next-generation wireless networks.

1. Dynamic Spectrum Sharing Models and Access Taxonomy

Dynamic spectrum sharing frameworks are distinguished by the level and method of adaptation, the granularity of resource division, and the explicitness of user rights. Key dimensions include:

• Sharing Types
  • Static sharing: Pre-assigned, fixed spectrum slices according to long-term service-level agreements (SLAs).
  • Dynamic (pre-agreed ratios): Allocation ratios vary over time or frequency but respect SLAs in the long run.
  • Opportunistic dynamic (cognitive/SaaS): Secondary users exploit instantaneous white spaces, typically based on real-time occupancy sensing and with strict primary-user protection (Moubayed et al., 2020).
• Access Mechanisms
  • Time-domain (TDMA): Exclusive or adaptive time slots per user or operator.
  • Frequency-domain (FDMA/OFDM): Allocation of contiguous or non-contiguous resource blocks; can be static or dynamic.
  • Spatial-domain (SDMA/Beamforming): Directional reuse and spatial multiplexing.
  • Code-division (CDMA): Orthogonal/near-orthogonal spreading codes for user separation.
• Technology Level
  • Intra-technology: Within a single radio access technology (RAT), such as LTE/LTE-A.
  • Inter-technology: Spanning multiple RATs (e.g., LTE-WiFi co-existence).
• User Rights Hierarchy
  • Equal rights: Symmetric sharing among users.
  • Hierarchical/cognitive: Strict primary user protection (overlay: idle slot exploitation; underlay: interference temperature compliance).
• Cell/Tier Level
  • Multi-tier reuse: Macro, micro, pico, and femto cells share spectrum with power-aware interference management (Moubayed et al., 2020).

This taxonomy underpins the diversity of architectural, algorithmic, and regulatory approaches deployed in contemporary dynamic spectrum sharing systems.

2. Optimization and Algorithmic Formulation

At the core of dynamic spectrum sharing is the assignment of frequency, time, and/or spatial resources $x_{i,f,t} \in \{0,1\}$ and transmit powers $p_{i,f,t} \geq 0$ over users $i$, resources $f$, and times $t$. Typical objectives include maximizing sum-rate or spectral efficiency, or minimizing power/interference, subject to assignment, power, service-level, and regulatory constraints:

• Primal optimization examples

$$\max_{\{x,p\}} \sum_t \sum_i \sum_f x_{i,f,t}\;R_{i,f,t}(p_{i,f,t}, h_{i,f,t})$$

subject to: \begin{align*} &\sum_{i} x_{i,f,t} \leq 1, \;\; \forall f,t \ &\frac{\sum_{t,f} x_{i,f,t}}{\sum_{t,f} 1} \geq S_i,\;\; \sum_i S_i=1 \ &0 \leq p_{i,f,t} \leq P_{i}^{\max}, \;\; \forall i,f,t \ &\sum_{f} x_{i,f,t} R_{i,f,t}(p_{i,f,t}) \geq R_{i}^{\min},\;\; \forall i,t \ &\sum_{i} x_{i,f,t} p_{i,f,t} g_{i\to PU, f} \leq I_{f,t}^{th},\;\; \forall f,t \end{align*} where $S_i$ is the SLA share, $R_i^{\min}$ the minimum rate, and $I_{f,t}^{th}$ the interference-temperature limit (Moubayed et al., 2020).

Mixed-integer and convex relaxation techniques are commonly used for tractability, but their complexity typically motivates machine learning-driven approximations in real-time deployments.

3. Machine Learning and Algorithmic Enablers

Dynamic spectrum sharing increasingly leverages machine learning across several core functions:

• Traffic and Channel Prediction (Supervised Learning): Time-series of spectrum occupancy, device metrics, and interference are used as features in deep networks or SVR models, minimizing MSE of channel/load forecasts.
• Allocation and Control (Reinforcement Learning): States include current allocations and interference maps; actions assign resource/power vectors; rewards combine throughput and penalties (e.g., for exceeding interference limits). Q-learning and deep Q-networks (DQN) are prominent, with the application of deep echo state networks (DEQN) yielding rapid convergence and adaptability in highly dynamic and non-Markovian environments (Chang et al., 2020).
• Interference Detection (Deep Learning): CNNs classify high-dimensional occupancy patterns or spectrum-sensing "waterfall" plots for interference/anomaly events.
• Federated Learning: Distributed base stations (BSs) or edge devices train local models on privacy-sensitive spectrum data, aggregating only model weights via FedAvg to produce global predictors without raw data exchange, providing privacy guarantees and data locality (Moubayed et al., 2020, Vo et al., 18 Jun 2024).

A representative RL reward function is: $$r_t = \sum_{i,f} x_{i,f,t} R_{i,f,t} - \lambda\sum_{f} \max\bigl(0, I_{f,t} - I_{f,t}^{th}\bigr)$$ with policy updates via classic (tabular) or deep Q-learning.

4. Decentralized and Blockchain-Enabled Sharing

DSS implementation in decentralized trustless environments has leveraged blockchain and cryptographic protocols:

• Spectrum as NFT (ERC-4907/NFST): Each atomic spectrum slice is tokenized as a Non-Fungible Spectrum Token, supporting autonomous on-chain auction/leasing and automatic expiry. Leasing auctions are implemented via smart contracts, and all metadata, bids, and allocations are enforceable and auditable on-chain, with no trusted third party (Ye et al., 21 May 2025).
• Sharded, Multi-Tier Architectures for Satellite-Terrestrial DSS: PSC-DSS introduces tiered regional regulators, intra/inter-region consensus (PBFT), and single-chain ledgering, ensuring scalable, secure, and region-customizable spectrum allocation across national and global domains (Wang et al., 4 Aug 2024).
• Secure Spectrum Auctions (Paillier/VCG): Multi-tier auctions employ homomorphic encryption to prevent auction fraud and collusion, achieving incentive compatibility and spatial reuse via combinatorial optimization and conflict-graph encoding (Abdelhadi et al., 2015).

These approaches ensure spectrum-access rights, lease duration, and spatial validity are cryptographically verifiable, with automated enforcement and dispute resolution.

5. System Architecture and Practical Deployment

The prototypical SaaS (Spectrum-as-a-Service) architecture integrates:

• Northbound API: Spectrum/service providers request allocations with specified bandwidth, QoS, and duration parameters.
• Cognitive Sensing/Feature Aggregation: Continuous monitoring of occupancy, interference, and channel state; time series analysis.
• ML Engine: Encapsulates supervised predictors and RL-based decision models.
• Broker/Orchestrator: Central enforcement point for SLA, regulatory, and coexistence policies; arbitration and grant issuance.
• Southbound Controller: Real-time programming of radio/BS parameters (frequency/time/power) to enforce allocations.
• Online Feedback Loop: Model weights and policy parameters updated continuously to reflect environment and traffic non-stationarity (Moubayed et al., 2020).

Open RAN (O-RAN) integration leverages RIC (RAN Intelligent Controller) principles, placing ML-driven control loops as xApps at the near-real-time RIC (10 ms–1 s) or non-RT RIC (>1 s), enabling sub-second adaptation and intent-driven spectrum control (e.g., via AdapShare) (Gopal et al., 29 Aug 2024, Baldesi et al., 2022).

6. Performance, Benchmarking, and Empirical Insights

Evaluations of dynamic sharing highlight:

• Throughput Gain: +20–30% system-wide vs. static allocation; spectral efficiency up to 2× with opportunistic SU access.
• Fairness: Jain's index >0.9 maintained across operators under RL- or SLA-aware scheduling.
• Convergence: DQN agents reach near-optimal solution in ~2,000 episodes (<5 s in real-time emulation); DEQN achieves similar performance 20–40× faster than LSTM-based DRQN, with better SU throughput and PU protection (Chang et al., 2020).
• Power and Delay: Aggregate transmit power reduced by ~25% (vs. exclusive), and mean packet latency by 25% under multi-tier sharing (Moubayed et al., 2020).
• Blockchain Latency and Throughput: ERC-4907 spectrum auctions achieve average confirmation in 12–15 s, 0.002–0.005 ETH per lease, and linear scaling with transaction count (Ye et al., 21 May 2025); PSC-DSS supports >100 TPS with sub-50 ms consensus at scale (Wang et al., 4 Aug 2024).

Empirical studies on REM-driven dynamic sharing in 5G/6G confirm large gains for secondary systems (>8 Mbps mean rates) with negligible impact (<10%) on primary user QoS; optimality is robust even under mobility or parameter drift (Kryszkiewicz et al., 2018).

7. Challenges, Security, and Research Directions

Key open challenges and corresponding guidelines include:

• Computational Complexity vs. Real-Time: Mixed-integer/convex programs are NP-hard; ML agents (especially RL/DNN) can yield near-optimal allocations in milliseconds, supporting at-scale real-time operations (Moubayed et al., 2020).
• Security and Privacy: Byzantine users, federated learning attacks (gradient inversion, poisoning), and adversarial jammers are acute threats, requiring robust aggregation, differential privacy, and secure aggregation/SGX protocols (Vo et al., 18 Jun 2024, Moubayed et al., 2020). Blockchain protocols provide non-repudiation and verifiability, but consensus latency and resource limits (PBFT $O(N^2)$ messages) impose scaling boundaries (Wang et al., 4 Aug 2024).
• SLA Enforcement/Policy Compliance: Integration of SLA constraints into RL-reward or Lagrangian structures is required for enforceable long-term fairness and compliance.
• Generalization and Cold-Start: Transfer learning and federated retraining speed up adaptation to new cells/bands/users.
• Regulation and Standardization: API, data schema, and coexistence framework standardization (e.g., O-RAN, 3GPP, ETSI LSA) are prerequisites for multi-operator, cross-technology, cross-layer deployments.
• Scalability: Hierarchical ML (edge↔cloud) and sharded blockchain mitigate central bottlenecks and support hyper-dense, global footprints.

• (Moubayed et al., 2020) Machine Learning Towards Enabling Spectrum-as-a-Service Dynamic Sharing
• (Ye et al., 21 May 2025) Dynamic Spectrum Sharing Based on the Rentable NFT Standard ERC4907
• (Chang et al., 2020) Deep Echo State Q-Network (DEQN) and Its Application in Dynamic Spectrum Sharing for 5G and Beyond
• (Vo et al., 18 Jun 2024) Security and Privacy of 6G Federated Learning-enabled Dynamic Spectrum Sharing
• (Kryszkiewicz et al., 2018) Context-based spectrum sharing in 5G wireless networks based on Radio Environment Maps
• (Wang et al., 4 Aug 2024) Blockchain-Enabled Dynamic Spectrum Sharing for Satellite and Terrestrial Communication Networks
• (Abdelhadi et al., 2015) A Multi-Tier Wireless Spectrum Sharing System Leveraging Secure Spectrum Auctions
• (Gopal et al., 29 Aug 2024) AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN
• (Baldesi et al., 2022) ChARM: NextG Spectrum Sharing Through Data-Driven Real-Time O-RAN Dynamic Control
• (Walishetti et al., 25 Sep 2024) Evaluation of Spectrum Sharing Algorithms for Networks with Heterogeneous Wireless Devices

Dynamic spectrum sharing, realized with these algorithmic and architectural advances, is the technical cornerstone for spectrum-as-a-service systems, 5G/6G multi-tenant networks, and secure, efficient spectrum markets. The state-of-the-art integrates real-time analytics, machine learning, and distributed ledger technologies to provide scalable, fair, and regulation-compliant spectrum utilization.