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\begin{document}

\title{Cognitive Radio in the 6G Era: Spectrum Sensing, AI-Driven Resource Allocation, and Coexistence Challenges}

\author{
Mohammad Kazem Moghimi$^{1}$,
M. Khosravi$^{2}$,
Zakaria Che Muda$^{3}$,
Yousef Sharrab$^{4}$\\[1ex]

\small $^{1}$\textit{Department of Communication Engineering, University of Sistan and Baluchestan, Zahedan, Iran. Email: k.moghimies@pgs.usb.ac.ir}\\
\small $^{2}$\textit{Shiraz University of Medical Sciences, Shiraz, Iran. Email: dr\_mkhosravi@sums.ac.ir}\\
\small $^{3}$\textit{Faculty of Engineering and Quantity Surveying, INTI International University, Nilai, Malaysia. Email: zakaria.chemuda@newinti.edu.my}\\
\small $^{4}$\textit{Data Science and Artificial Intelligence Department, Isra University, Amman, Jordan. Email: yousef.sharrab@iu.edu.jo}\\
}

\maketitle


\begin{abstract}
The advent of sixth-generation (6G) mobile networks, marked by an exponential proliferation of wireless devices and novel applications, has dramatically intensified the need for intelligent and adaptive spectrum management. Cognitive radio (CR), initially conceived for dynamic spectrum access, is now being fundamentally reshaped for the 6G era through its convergence with artificial intelligence (AI), edge computing, and reconfigurable intelligent environments.
This survey presents a comprehensive overview of this transformation, structured around three foundational pillars: the evolution of spectrum sensing, the rise of AI-native resource allocation, and the challenges of multi-domain coexistence.
We begin by charting the progression of spectrum sensing from classical techniques like energy detection to advanced, data-driven approaches that harness deep learning, federated intelligence, and generative AI, tailored for the high-frequency regimes of THz and mmWave bands.
We then explore the shift in resource allocation from conventional optimization frameworks to end-to-end AI-native paradigms, encompassing deep reinforcement learning, multi-agent collaboration, and the use of digital twins for predictive modeling.
The survey further investigates the intricate coexistence of terrestrial, non-terrestrial (NTN), and RIS-assisted networks, tackling emergent issues such as cross-technology interference, security vulnerabilities, and dynamic regulatory landscapes.
Concluding with a forward-looking perspective, we pinpoint critical open challenges from semantic-aware spectrum sharing and privacy-preserving learning to quantum-resistant security and "green sensing" that are pivotal for building sustainable 6G ecosystems.
By integrating concepts from diverse fields, this work serves as both a definitive reference and a strategic roadmap for researchers and engineers pioneering the next generation of autonomous wireless systems.
\end{abstract}

\begin{IEEEkeywords}
Cognitive radio, 6G, spectrum sensing, AI-driven, resource allocation, coexistence, federated learning.
\end{IEEEkeywords}

\section{Introduction}
Cognitive radio (CR), a concept pioneered by Joseph Mitola in 1999~\cite{MitolaCR}, is an intelligent wireless communication paradigm that enhances spectrum utilization by dynamically adapting to its environment. Its key function is to permit secondary users (SUs) to access underutilized licensed spectrum bands owned by primary users (PUs), operating under the strict constraint of avoiding any harmful interference to the licensed transmissions.
The CR cycle typically involves four essential functions:
\begin{itemize}
    \item \textbf{Spectrum sensing}: Detecting the presence or absence of PUs in a given frequency band.
    \item \textbf{Spectrum decision}: Selecting the optimal spectrum band based on channel conditions and quality-of-service (QoS) requirements.
    \item \textbf{Spectrum sharing}: Coordinating access among multiple SUs to prevent collisions and ensure fairness.
    \item \textbf{Spectrum mobility}: Seamlessly switching to alternative bands when the current spectrum becomes occupied by PUs.
\end{itemize}


The performance of cognitive radio systems is quantified by interdependent Key performance metrics (KPIs), including probability of detection, probability of false alarm, achievable throughput, end-to-end latency, energy efficiency, and interference temperature. These metrics are governed by a complex interplay of sensing duration, channel dynamics, hardware limitations, and regulatory boundaries.
Despite their conceptual origins in the 4G era, CR systems saw limited practical deployment in both 4G and early 5G networks. The traditional methodologies, dependent on hand-crafted heuristics and model-based optimization, lacked the flexibility required for increasingly dynamic and heterogeneous environments. This critical shortcoming has driven the convergence of CR with artificial intelligence (AI), enabling a shift toward truly autonomous operation. The evolutionary path of these capabilities is detailed in Table~\ref{tab:evolution}.

%%%%%%%%%%%%%%%%%%%%%%%%%% Table 1
\begin{table}[t]
\centering
\renewcommand{\arraystretch}{0.9}
\setlength{\tabcolsep}{3.5pt}
\caption{Evolution of cognitive radio capabilities from 4G to 6G.}
\label{tab:evolution}
\begin{tabular}{|c|p{2.2cm}|p{2.2cm}|p{2.4cm}|}
\hline
\textbf{Gen.} & \textbf{CR Role} & \textbf{Key Features} & \textbf{Limitations} \\ \hline
4G & Dynamic access & OFDMA, centralized control & Static, policy-driven access \\ \hline
5G & Dynamic sharing \& coexistence & Slicing, URLLC, edge compute & Limited intelligence, weak cross-domain coordination \\ \hline
6G & AI-native cognitive networking & THz, RIS, NTN, semantic links & High complexity, scalability, security challenges \\ \hline
\end{tabular}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

In \textbf{4G}  networks, CR was primarily regarded as a promising mechanism for opportunistic spectrum access, aimed at mitigating the chronic under utilization of licensed bands (most notably TV white spaces). Although foundational techniques such as energy detection, feature-based cyclostationary detection, and cooperative sensing were extensively studied, practical deployment remained limited. Key barriers included constrained on-device computational capability, dependence on centralized geo-location databases, and inflexible, static regulatory policies.
The arrival of \textbf{5G} marked a significant shift, expanding the role of CR toward dynamic resource sharing and multi-technology coexistence in densely heterogeneous environments. Innovations such as network slicing, millimeter-wave (mmWave) bands, licensed shared access (LSA), and frameworks like CBRS opened new avenues for more agile spectrum management. Nevertheless, most 5G-era CR implementations remained essentially reactive, relying on semi-centralized control planes and conventional model-based optimization rather than genuine learning or proactive intelligence.
In sharp contrast, the emerging \textbf{6G} vision redefines cognitive radio as a fully AI-native, network-intrinsic capability ~\cite{Aslam6GCRN,Jawad6GSurvey,6GIntelligentNetwork,MLCRSurvey}. Cognition is no longer an optional overlay but a core architectural principle, deeply infused throughout the protocol stack and enabled by distributed machine learning, digital twins, semantic communications, and context-aware reasoning, and predictive world models. 6G CR systems are expected to autonomously perceive, learn, predict, and adapt in real time across extreme scenarios, including ultra-high-mobility vehicular networks, terahertz (THz) propagation, visible-light communication, and fully integrated space–air–ground–sea ecosystems.
Fig.~\ref{fig:overview} depicts this evolutionary trajectory, illustrating how 6G CR seamlessly fuses pervasive spectrum sensing, AI-driven resource orchestration, and proactive coexistence management across all relevant frequency domains (sub-6 GHz, mmWave, THz, optical wireless) and network strata (terrestrial, non-terrestrial, and reconfigurable intelligent surfaces).
This profound transformation from 5G to 6G is propelled by several breakthrough technological enablers that collectively overcome historical limitations and unlock unprecedented levels of intelligence and adaptability in spectrum management:
\begin{itemize}
    \item \textbf{AI and ML}: Advanced AI/ML techniques empower cognitive radios with proactive, data-driven intelligence for spectrum sensing, decision-making, and autonomous policy adaptation. Deep reinforcement learning (DRL), multi-agent systems, graph neural networks (GNNs), and generative models have emerged as key enablers of distributed, scalable cognition~\cite{DLCRN,DLFrameworkCR}.

    \item \textbf{Reconfigurable Intelligent Surfaces (RIS)}: RIS technology transforms the wireless environment into a programmable medium, allowing CR systems to intelligently manipulate signal propagation. This expands spectrum awareness beyond the frequency and time domains into spatial and angular domains, effectively creating new “spatial spectrum opportunities.”

    \item \textbf{Integrated Sensing and Communication (ISAC)}: By jointly supporting radar-like sensing and data transmission, ISAC enables 6G CR networks to achieve high-resolution environmental perception while maintaining communication efficiency. Each transmitted waveform simultaneously acts as a sensing probe, facilitating continuous spectrum-opportunity discovery.

    \item \textbf{Edge and Federated Intelligence}: Edge computing provides ultra-low-latency inference and real-time learning close to end devices, while federated learning (FL) allows large numbers of distributed CR nodes to collaboratively train models without sharing raw sensory data. This mitigates privacy concerns and reduces communication overhead~\cite{YangFL6G,QamarFLmmWave,AhmedIoMT}.

    \item \textbf{Semantic and Goal-Oriented Communications}: Moving beyond the conventional bit-centric paradigm, semantic communication focuses on transmitting meaning rather than raw data, significantly reducing bandwidth requirements. Combined with goal-oriented communication principles, it enables CR systems to prioritize task-relevant information and support context-aware resource allocation aligned with high-level application objectives.
\end{itemize}
Taken together, these technological enablers elevate CR from a reactive, opportunistic overlay to a proactive, AI-native, and deeply context-aware pillar of 6G networks, one fully equipped to meet the era’s stringent demands for terabit-per-second data rates, ultra-reliable low-latency communication (URLLC), massive connectivity, near-instantaneous sensing, and truly seamless global coverage.

The rest of the paper is organized as follows. Section~II describes spectrum sensing in the 6G context. Section~III presents AI-driven resource allocation and spectrum management. Section~IV discusses coexistence and spectrum sharing challenges. Open research directions are identified in Section~V, and conclusions are drawn in Section~VI.

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}[t!]
\centering
\includegraphics[clip,trim=2 1 6 3, scale=0.17]{F1.png}
\caption{Overview of cognitive radio in the 6G era. (a) Evolution of cognitive radio from classical sensing to AI-driven resource management, federated learning, and RIS-assisted cognition. (b) AI-driven CR architecture, showing the interaction between spectrum sensing, resource allocation, and environmental dynamics. (c) Coexistence in 6G networks, highlighting CR operation alongside Wi-Fi~7, non-terrestrial networks (NTN), RIS, and radar systems. (d) Graphical abstract summarizing the four key pillars: spectrum sensing, AI-driven resource allocation, coexistence challenges, and AI-enabled cognition.}
\label{fig:overview}
\end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section{Spectrum Sensing in the 6G Context}
Spectrum sensing is the cornerstone of CR operation, enabling SUs to identify vacant spectrum portions without causing harmful interference to PUs. In the 6G era, spectrum sensing extends beyond traditional detection mechanisms to include data-driven, context-aware, and multi-domain intelligence that spans the frequency, spatial, temporal, and even semantic dimensions.

The unique characteristics of 6G, such as THz operation, dense device connectivity, and reconfigurable wireless environments, necessitate new sensing paradigms that integrate AI, edge processing, and cooperative intelligence~\cite{AbdelbasetDLSS,DeepWidebandSS}. Conventional spectrum sensing techniques form the theoretical foundation of CR operation. The four main classical approaches are: Energy Detection (ED), Matched Filter Detection (MFD), Cyclostationary Feature Detection (CFD), and Cooperative Spectrum Sensing (CSS).
ED is the simplest and most widely used sensing method due to its low computational complexity and its independence from prior knowledge of the PU signal. It measures the received signal energy over a specific observation window and compares it with a predefined threshold. While effective in additive white Gaussian noise (AWGN) environments, it suffers from noise uncertainty, which significantly degrades its detection accuracy at low signal-to-noise ratios (SNRs).
MFD requires precise knowledge of the PU signal characteristics (e.g., modulation type, pulse shape, and bandwidth). It provides optimal detection performance under known signal conditions but demands complex synchronization and prior information, making it impractical for the highly dynamic 6G environments with multiple heterogeneous signals.
CFD exploits the periodic statistical properties of modulated signals to differentiate between noise and legitimate transmissions. CFD performs better than ED under low SNRs and in non-stationary environments but involves high computational complexity and long sensing times, limiting its applicability in real-time 6G scenarios.
CSS enhances detection reliability by leveraging the collective observations of multiple CR nodes. It mitigates the effects of fading, shadowing, and the hidden-node problem. CSS employs data-fusion strategies (soft or hard combining) to improve overall detection accuracy. However, it introduces significant signaling overhead, latency, and security concerns, which become increasingly critical in dense 6G deployments.
AI-driven spectrum sensing represents a paradigm shift from static, threshold-based decisions to adaptive, data-driven intelligence~\cite{AISpectrumMeta,AIOptSS}. By learning patterns of spectrum occupancy and signal characteristics, AI-based sensing enables proactive spectrum awareness and real-time adaptation.
Deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers have been employed to automatically extract spectral features from raw data~\cite{RoopaDLSS,VijayCNNTransformer,OptimizingCRN}. CNNs are effective in identifying frequency-domain patterns, while RNNs and their variants (such as LSTM or GRU) excel at modeling temporal dependencies. More recently, Transformer architectures and GNNs have demonstrated strong performance in learning spectral correlations across distributed nodes, making them particularly suitable for large-scale 6G networks.

Reinforcement learning frameworks enable CR agents to learn optimal sensing policies through interaction with the environment. Using DRL methods such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), CR nodes can dynamically adjust sensing intervals, thresholds, and energy levels to maximize long-term spectrum utilization while minimizing interference. DRL is particularly advantageous in 6G systems where environmental dynamics, user mobility, and channel conditions vary rapidly.
Meta-learning allows CR systems to adapt quickly to new environments with limited data by leveraging prior learning experiences. Similarly, transfer learning enables the reuse of pre-trained models across different frequency bands or geographical regions, accelerating convergence and reducing training costs. These techniques are expected to be instrumental in deploying AI-based CR models across diverse 6G scenarios with minimal retraining.
In conventional AI-based sensing, centralized model training often raises privacy concerns, incurs excessive backhaul communication, and faces scalability limitations. FL has emerged as a promising alternative, allowing distributed CR nodes to collaboratively train a global model without sharing raw data~\cite{YangFL6G,QamarFLmmWave}. Each node locally updates model parameters based on its observed spectrum data and transmits only the weight updates to an aggregator. Federated sensing architectures in 6G are supported by edge intelligence, where local edge servers perform aggregation and inference to reduce latency. However, several challenges persist, including model synchronization over unreliable links, heterogeneous (non-IID) data distributions, and robustness against malicious or corrupted updates~\cite{Saeed6GSec}.
Advanced schemes, such as blockchain-assisted FL and differential-privacy-based learning, are being explored to enhance the reliability and trustworthiness of federated sensing~\cite{AlMatariBlockchain}.

6G introduces operation in THz (0.1–10 THz) and mmWave (30–300 GHz) bands, where sensing faces distinct challenges and opportunities~\cite{Aslam6GCRN,EnergyEff6GCRN}. High-frequency circuits suffer from increased thermal noise, phase noise, and non-linearity, which complicate signal detection. Furthermore, the highly directional beams in mmWave/THz systems necessitate beam-aligned sensing, requiring CR nodes to scan spatial domains in addition to frequency bands.
6G also promotes the integration of sensing and communication functions, enabling CR to reuse communication signals for environmental perception and spectrum awareness. RIS can enhance sensing resolution by controlling reflection coefficients and improving signal visibility in non-line-of-sight (NLOS) scenarios. AI-driven techniques, such as DRL-based beam selection and CNN-based signal classification, are essential to handle the massive data volume and dynamic propagation environments in these high-frequency regimes.
The effectiveness of spectrum sensing methods is commonly assessed using the following metrics:
\begin{itemize}
    \item \textbf{Probability of detection ($P_\mathrm{d}$)}: Probability of correctly identifying a PU signal.
    \item \textbf{Probability of false alarm ($P_\mathrm{f}$)}: Probability of incorrectly detecting a PU when the channel is idle.
    \item \textbf{Sensing time ($T_\mathrm{s}$)}: Duration of observation required to achieve a reliable decision.
    \item \textbf{Energy efficiency ($\eta$)}: Ratio of successful detections to consumed sensing energy.
    \item \textbf{Computational complexity ($C$)}: Processing and memory demands of the sensing algorithm.
\end{itemize}
Table~\ref{tab:sensing} compares representative sensing approaches, highlighting their accuracy, complexity, adaptivity, and qualitative remarks.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}[t]
\centering
\renewcommand{\arraystretch}{0.85}
\setlength{\tabcolsep}{3.5pt}
\caption{Comparison of spectrum sensing techniques.}
\label{tab:sensing}
\begin{tabular}{|c|c|c|c|c|p{2cm}|}
\hline
\textbf{Category} & \textbf{Technique} & \textbf{Acc.} & \textbf{Comp.} & \textbf{Adapt.} & \textbf{Remarks} \\ \hline
Classical & ED & Med. & Low & Low & Simple; noise‐sensitive \\ \hline
Classical & CFD & High & High & Low & Reliable; costly \\ \hline
Cooperative & CSS & High & Med. & Med. & Robust; coordination needed \\ \hline
AI-Based & CNN/RNN & V.High & High & High & Learns spectral features \\ \hline
AI-Based & DRL & High & Med. & V.High & Strong adaptation \\ \hline
Federated & FL Sensing & High & Med. & High & Privacy; low data sharing \\ \hline
\end{tabular}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
Spectrum sensing in 6G is thus transitioning from signal-driven to intelligence-driven paradigms. While classical techniques remain relevant for baseline and low-complexity applications, AI-enabled and federated sensing approaches are indispensable for large-scale, adaptive, and privacy-aware operation. Future CR systems will likely adopt multi-domain sensing frameworks that jointly exploit time, frequency, spatial, and semantic information to achieve holistic spectrum awareness in increasingly heterogeneous 6G ecosystems.

\section{AI-Driven Resource Allocation and Spectrum Management}
Efficient resource allocation is essential for maximizing the performance of CR networks, particularly in the 6G era where vast numbers of heterogeneous devices operate across sub-6~GHz, mmWave, and THz bands. Traditional spectrum management approaches rooted in analytical optimization and rule-based policies, struggle to cope with the extreme scale, dynamism, and uncertainty of 6G environments.
AI, especially ML, has emerged as a transformative paradigm enabling real-time learning, prediction, and adaptation~\cite{MLResourceAlloc}. This section reviews the evolution of resource allocation techniques from classical optimization to AI-native, distributed, and multi-agent frameworks tailored for 6G CR systems.
Before the widespread adoption of AI, CR resource allocation relied primarily on mathematical optimization, heuristic algorithms, and game-theoretic models. Convex optimization methods were used for power control, channel assignment, and user scheduling under quality-of-service (QoS) and interference constraints. Typical examples include:
\begin{itemize}
    \item water-filling power allocation,
    \item linear and quadratic programming for channel assignment,
    \item Lagrangian dual decomposition.
\end{itemize}
While analytically tractable and provably optimal under idealized assumptions, these methods depend heavily on accurate channel state information (CSI), predictable user behavior, and precise interference models, conditions rarely satisfied in highly dynamic 6G scenarios.
Game theoretic models were extensively explored to characterize interactions among SUs. Both non-cooperative and cooperative formulations were considered, but they suffer from:
\begin{itemize}
    \item lack of guaranteed equilibrium convergence,
    \item suboptimal operating points when equilibria do exist,
    \item poor scalability as network size grows.
\end{itemize}

Metaheuristic algorithms such as genetic algorithms, particle swarm optimization, and simulated annealing offer flexibility and near-optimal performance in complex conditions. However, their iterative nature renders them unsuitable for applications with stringent latency requirements.
In summary, traditional methods face the following limitations:
\begin{itemize}
    \item limited scalability in ultra-dense 6G CR networks,
    \item strong dependency on accurate environmental modeling,
    \item weak generalization to new operating conditions,
    \item slow convergence in rapidly changing environments.
\end{itemize}
These limitations have accelerated the transition toward learning-based resource allocation. DRL formulates resource allocation as a sequential decision-making problem, enabling CR agents to derive optimal policies through direct interaction with the radio environment~\cite{DLCRN,AIOptimSpectrum}. Commonly used DRL algorithms include:
\begin{itemize}
    \item DQN,
    \item double and dueling DQN variants,
    \item Deep Deterministic Policy Gradient (DDPG),
    \item PPO.
\end{itemize}

Representative 6G CR applications comprise:
\begin{itemize}
    \item dynamic channel selection,
    \item transmit power control,
    \item sub-band allocation in mmWave/THz systems,
    \item joint spectrum sensing and access optimization.
\end{itemize}

DRL-based approaches offer several key advantages:
\begin{itemize}
    \item no need for explicit environmental or interference models,
    \item ability to capture complex nonlinear relationships,
    \item adaptation to time-varying traffic and channel conditions.
\end{itemize}
However, challenges remain, including training instability, high sample complexity, and careful management of exploration, exploitation trade-offs.
Given the inherently distributed architecture of 6G-encompassing IoT devices, RIS elements, UAVs, and satellite components, multi-agent reinforcement learning (MARL) has become a natural and powerful tool for CR resource management~\cite{MLCRSurvey}.
Independent learning, where each SU trains its own policy (e.g., independent DQN), provides excellent scalability and avoids central coordination but often suffers from non-stationarity and slow convergence. A more effective paradigm is centralized training with decentralized execution (CTDE), implemented in algorithms such as MADDPG,
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{table}[t]
\centering
\scriptsize
\renewcommand{\arraystretch}{0.85}
\setlength{\tabcolsep}{1.2pt}
\caption{Comparison of optimization and learning approaches for 6G cognitive radio systems.}
\label{tab:approaches}
\begin{tabular}{|l|c|c|c|c|p{1.2cm}|}
\hline
\textbf{Approach} & \textbf{Adaptivity} & \textbf{Scalability} & \textbf{Complexity} & \textbf{Suitability} & \textbf{Notes} \\ \hline
Classical Optimization & Low & Medium & High & Medium &
Accurate \newline models \\ \hline
Game Theory & Medium & Medium & Medium & Low &
Often \newline suboptimal \\ \hline
Metaheuristics & Medium & Low & High & Low &
Slow, \newline iterative \\ \hline
DRL & High & High & Med--High & High &
Learns \newline long-term \\ \hline
MARL & Very High & Very High & High & Medium &
Distributed \newline learning \\ \hline
FL & High & Very High & Medium & High &
Privacy- \newline preserving \\ \hline
Edge Intelligence & Very High & High & Low & Very High &
Low-latency \newline tasks \\ \hline
\end{tabular}
\end{table}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
AI-native methods, especially DRL, MARL, and FL, thus represent the future of CR resource allocation in 6G. They provide the adaptability, predictive capability, and autonomy required for complex multi-domain spectrum environments. The integration of digital twins, edge intelligence, and RIS-assisted optimization further enhances efficiency and scalability, but open issues remain in terms of training stability, data heterogeneity, adversarial robustness, and computation overhead for ultra-low-power devices.
\section{Coexistence and Spectrum Sharing in 6G Cognitive Radio Networks}

The coexistence landscape in 6G CR networks has grown dramatically more complex as diverse wireless ecosystems converge in shared and adjacent spectrum. Wi-Fi 7/8, beyond-5G/6G cellular systems, radar networks, massive IoT deployments, RISs, and NTNs increasingly compete for the same resources, resulting in severe congestion and heightened risk of cross-technology interference~\cite{Aslam6GCRN,6GSecuritySurvey, Refme1, Refme2}.
Unlike previous generations, 6G coexistence must be proactive and multi-dimensional, spanning spectral, spatial, temporal, and regulatory domains. Effective spectrum sharing demands advanced sensing, intelligent interference mitigation, and adaptive resource allocation capable of handling:
\begin{itemize}
  \item Hidden-terminal problems and unreliable/incomplete sensing observations,
  \item Heterogeneous QoS requirements across coexisting technologies,
  \item Cross-domain conflicts between terrestrial and non-terrestrial components,
  \item Complex propagation environments induced by RIS-assisted reflections.
\end{itemize}

Fig.~\ref{fig:overview}(c) illustrates these challenging coexistence scenarios, in which CR nodes must harmoniously coordinate with Wi-Fi stations, radar systems, NTN links, and RIS-controlled paths while strictly protecting PUs and meeting diverse service-level constraints.
Beyond technical hurdles, 6G coexistence introduces critical trust, security, and regulatory challenges. Cooperative sensing is inherently vulnerable to falsified reports, compromised IoT endpoints, and malicious manipulation of RIS phase profiles. Mitigating these threats requires:
\begin{itemize}
  \item Robust anomaly detection and attack identification mechanisms,
  \item Secure, resilient spectrum-sharing protocols,
  \item Privacy-preserving collaborative learning (e.g., differential privacy, secure multiparty computation).
\end{itemize}
Blockchain-assisted solutions are gaining traction for providing immutable audit trails, decentralized trust anchors, and incentive-compatible spectrum trading in CR-enabled 6G IoT ecosystems~\cite{AlMatariBlockchain}.
Large-scale spectrum sharing in 6G also hinges on regulatory evolution. Legacy approaches fixed licensing, duty-cycle restrictions, and listen-before-talk etiquette are inadequate for the scale and dynamism of future networks. New paradigms such as spectrum-as-a-service, on-demand brokerage, AI-enforced dynamic policies, and blockchain-backed incentive mechanisms are expected to become cornerstone enablers of safe, fair, and efficient coexistence.
In summary, unlocking the full potential of 6G demands deeply integrated CR architectures that seamlessly combine distributed intelligence, federated learning, digital twins, and cross-domain sensing to orchestrate harmonious, secure, and high-performance coexistence in ultra-dense, multi-technology radio environments.

\section{Open Research Directions}

Despite remarkable progress in CR for 6G, several fundamental challenges continue to impede the realization of fully autonomous, scalable, and trustworthy spectrum-sharing systems. The stringent requirements of emerging 6G technologies, THz/sub-THz communications, ultra-low-latency services, and RIS-dominated propagation reveal critical gaps in current solutions.
Ultra-wideband spectrum sensing at THz frequencies remains severely constrained by hardware impairments, molecular absorption, high path loss, and extremely narrow beamwidths. Addressing these limitations will require ultra-lightweight algorithms capable of jointly exploiting frequency, spatial, and temporal correlations while operating under strict power and computation budgets~\cite{DeepWidebandSS}.
Ultra-low-latency cognitive decision-making (sub-millisecond response) is vital for applications such as holographic telepresence, high-speed vehicular networks, and XR. Achieving this necessitates breakthroughs in accelerated DRL, neuromorphic CR hardware, edge-native continual learning, and real-time interpretable AI.
Federated and distributed intelligence, while conceptually aligned with 6G-scale CR, continues to suffer from non-IID spectrum occupancy, high communication overhead, client drift, and susceptibility to poisoned or Byzantine updates. New robust, communication-efficient, and heterogeneity-aware federated frameworks are urgently needed~\cite{QamarFLmmWave,YangFL6G,Saeed6GSec}.
The synergy between RIS and distributed learning remains largely unexplored. Open challenges include federated or multi-agent training of RIS phase profiles, large-scale cooperative control of multi-RIS environments, and fully autonomous reflection optimization in dynamic, mobility-intensive settings~\cite{RISFLSpectrum}.
Semantic- and goal-oriented CR represents a transformative paradigm for context-driven and bandwidth-efficient spectrum access. However, transitioning to fully AI-native CR significantly expands the attack surface, exposing systems to adversarial sensing spoofing, model inversion, federated poisoning, and RIS manipulation~\cite{6GSecuritySurvey}. Countermeasures based on blockchain-anchored trust, provably robust learning models, differential privacy, secure enclaves, and certified AI guarantees are essential research priorities.
Finally, large-scale validation demands open, standardized 6G CR testbeds, high-fidelity digital-twin platforms, and multi-domain field trials that incorporate terrestrial, non-terrestrial, Wi-Fi, radar, and RIS elements. Equally crucial are harmonized global regulatory frameworks that support flexible, AI-driven, and secure dynamic spectrum access.

\section{Conclusion}

Cognitive radio is expected to become a core technology in 6G as wireless systems grow more heterogeneous, spectrum-limited, and intelligence-driven. This survey reviewed key advances in 6G-enabled CR across spectrum sensing, resource allocation, and multi-technology coexistence. Spectrum sensing has evolved from classical detection to advanced AI-driven, multi-domain methods that operate reliably in mmWave and THz bands. Resource allocation has shifted from rigid analytical models to adaptive learning-based frameworks, including DRL, MARL, federated intelligence, and digital-twin-assisted training to meet the demands of ultra-dense 6G networks.
Coexistence in 6G brings new complexity due to the integration of terrestrial and non-terrestrial networks, Wi-Fi 7/8, radar systems, massive IoT, and RIS-assisted environments. Achieving seamless operation across these technologies requires holistic CR architectures with distributed intelligence, secure spectrum sharing, cooperative sensing, and cross-domain coordination.
Ultimately, building fully intelligent 6G spectrum ecosystems depends on developing trustworthy, highly efficient, and tightly integrated cognitive radio systems capable of navigating an increasingly dynamic wireless landscape. As a future work, the proposed idea can be developed towards by enabling more energy-efficient, resilient, and inclusive wireless connectivity through intelligent spectrum management in 6G networks \cite{Srinivasan2023, Attar2021 }.  

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\end{document}
