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arxiv: 2602.09615 · v2 · submitted 2026-02-10 · 📡 eess.SP

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Collaborative Spectrum Sensing in Cognitive and Intelligent Wireless Networks: An Artificial Intelligence Perspective

Peng Yi , Ying-Chang Liang
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Pith reviewed 2026-05-16 03:08 UTC · model grok-4.3

classification 📡 eess.SP
keywords collaborative spectrum sensingcognitive radio networksartificial intelligencesemantic communicationdeep learningreinforcement learningwireless communications
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The pith

AI transforms collaborative spectrum sensing into an efficient joint communication and computation framework via semantic methods.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper surveys recent advances in using artificial intelligence for collaborative spectrum sensing in cognitive wireless networks. It starts with the basics of CSS including detectors and fusion, then categorizes AI approaches into discriminative deep learning, generative models, and deep reinforcement learning. Building on these, it explores semantic communication as a way to upgrade CSS by transmitting only relevant features, making it a combined communication and computation system for single and multi-user cases. This matters because it offers a path to handle complex wireless environments more efficiently than traditional methods.

Core claim

By extracting and transmitting task-relevant features, AI-empowered semantic communication upgrades collaborative spectrum sensing from a computation-centric approach to a highly efficient joint communication and computation framework for both single-user and multi-user scenarios.

What carries the argument

The semantic communication paradigm that extracts and transmits only task-relevant features instead of full data.

Load-bearing premise

The AI techniques reviewed, including semantic communication, are assumed to be mature enough for practical deployment in real wireless environments without significant additional challenges.

What would settle it

A field experiment showing that semantic feature transmission does not improve sensing accuracy or reduce overhead compared to standard fusion methods in multi-user cognitive radio networks.

Figures

Figures reproduced from arXiv: 2602.09615 by Peng Yi, Ying-Chang Liang.

Figure 1
Figure 1. Figure 1: Illustration of the collaborative spectrum sensing sce [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Organization and structure of this paper. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: System-level diagram of the collaborative spectrum sensing framework, illustrating the end-to-end signal processing [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Timing structure of the collaborative spectrum sensing [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of architectures between conventional communication and SemCom. [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: System model of single-user SemCom for remote [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: System models of multi-user SemCom. (a) Traditional OMA-based reporting, utilizing orthogonal resources (e.g., time [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance comparison. TABLE IV: Comparison in one sensing period, when K = 6, M = 28, N = 100, SNR ^= −15 dB, SNR \= 0 dB. HDF (Majority Rule) SDF (Equal Gain Combining) SemCom-CNN Metrics / Methods ED MED MMED CAV ED MED MMED CAV Pd (Pfa = 10−3 ) 0.260 0.698 0.129 0.042 0.002 0.005 0.002 0.003 0.995 Number of utilized subchannels 6 (K) 6 (K) 6 (K) 6 (K) 48 (8 × K) 48 (8 × K) 48 (8 × K) 48 (8 × K) 8 Infe… view at source ↗
read the original abstract

Artificial intelligence (AI) has become a key enabler for next-generation wireless communication systems, offering powerful tools to cope with the increasing complexity, dynamics, and heterogeneity of modern wireless environments. To illustrate the role and impact of AI in wireless communications, this paper takes collaborative spectrum sensing (CSS) in cognitive and intelligent wireless networks as a representative application and surveys recent advances from an AI perspective. We first introduce the fundamentals of CSS, including the general framework, classical detector design, fusion strategies and evaluation metrics. Then, we present an overview of the state-of-the-art research on AI-driven CSS, classified into three categories according to learning paradigms: discriminative deep learning (DL), generative DL models, and deep reinforcement learning (DRL). Building on this, we explore AI-empowered semantic communication (SemCom) as a paradigm-shifting solution for CSS. By extracting and transmitting task-relevant features, SemCom upgrades CSS from a computation-centric approach to a highly efficient joint communication and computation framework. Both single-user and multi-user SemCom scenarios are elaborated in detail. Finally, we discuss limitations, open challenges, and future research directions at the intersection of AI and wireless communication.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript surveys AI techniques for collaborative spectrum sensing (CSS) in cognitive and intelligent wireless networks. It first outlines CSS fundamentals (framework, classical detectors, fusion strategies, and metrics), then categorizes state-of-the-art AI-driven CSS research into discriminative deep learning, generative DL models, and deep reinforcement learning. Building on this, it examines AI-empowered semantic communication (SemCom) as a paradigm that upgrades CSS to a joint communication-computation framework, detailing single-user and multi-user scenarios, before discussing limitations and future directions.

Significance. As a structured literature survey, the paper synthesizes recent advances at the intersection of AI and spectrum sensing and offers a forward-looking perspective on semantic communication. If the categorization is representative and the SemCom discussion accurately reflects cited works, it provides a useful roadmap for researchers developing intelligent wireless systems, highlighting the shift toward joint communication and computation paradigms.

major comments (2)
  1. [AI-empowered semantic communication section] The central perspective that AI-empowered SemCom upgrades CSS to a highly efficient joint communication and computation framework (detailed in the SemCom section) is load-bearing for the paper's contribution; however, the manuscript should explicitly cite and summarize quantitative results from the surveyed literature (e.g., spectrum efficiency or detection accuracy gains) to substantiate the efficiency claims rather than presenting them at a high level.
  2. [Overview of state-of-the-art research on AI-driven CSS] In the overview of AI-driven CSS, the three-category classification (discriminative DL, generative DL, DRL) is clear, but the paper does not address potential overlaps or transitions between categories (e.g., how DRL builds on discriminative models in dynamic CSS environments), which weakens the framework's utility for readers seeking to navigate the literature.
minor comments (3)
  1. Ensure consistent acronym usage and first-use definitions throughout (e.g., CSS, SemCom, DRL) to improve readability for a broad audience.
  2. [Fundamentals of CSS] The discussion of evaluation metrics in the fundamentals section could be more explicitly linked to how they are applied or adapted in the AI-specific subsections.
  3. [Limitations, open challenges, and future research directions] Future research directions would benefit from more concrete examples, such as specific open problems in multi-user SemCom coordination or integration with emerging standards.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the positive assessment and constructive feedback on our manuscript. We have addressed the major comments by planning specific revisions to strengthen the paper.

read point-by-point responses

  1. Referee: [AI-empowered semantic communication section] The central perspective that AI-empowered SemCom upgrades CSS to a highly efficient joint communication and computation framework (detailed in the SemCom section) is load-bearing for the paper's contribution; however, the manuscript should explicitly cite and summarize quantitative results from the surveyed literature (e.g., spectrum efficiency or detection accuracy gains) to substantiate the efficiency claims rather than presenting them at a high level.

    Authors: We agree that substantiating the efficiency claims with quantitative results from the literature would strengthen the SemCom section. In the revised manuscript, we will explicitly cite and summarize key quantitative findings, such as reported gains in spectrum efficiency and improvements in detection accuracy from the surveyed works on AI-empowered SemCom for CSS. revision: yes

  2. Referee: [Overview of state-of-the-art research on AI-driven CSS] In the overview of AI-driven CSS, the three-category classification (discriminative DL, generative DL, DRL) is clear, but the paper does not address potential overlaps or transitions between categories (e.g., how DRL builds on discriminative models in dynamic CSS environments), which weakens the framework's utility for readers seeking to navigate the literature.

    Authors: We acknowledge that highlighting overlaps and transitions between the categories could improve the utility of our classification framework. We will revise the overview section to include a discussion on potential overlaps, for example, how DRL approaches often build upon discriminative DL models for state representation in dynamic spectrum sensing environments. revision: yes

Circularity Check

0 steps flagged

No significant circularity; literature survey with no internal derivations

full rationale

This is a survey paper that reviews external literature on collaborative spectrum sensing (CSS) and AI techniques without advancing new derivations, equations, fitted parameters, or proofs. The abstract and structure explicitly frame the content as an overview of state-of-the-art research classified by learning paradigms, followed by a discussion of semantic communication as an existing paradigm. No load-bearing steps reduce by construction to self-citations, self-definitions, or renamed inputs; all claims reference prior work by other authors. The central perspective on upgrading CSS via semantic communication is presented as interpretive discussion rather than a testable or derived result internal to the paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey paper, the central claims rest on the completeness and accuracy of the reviewed literature; no new free parameters, axioms, or invented entities are introduced by the authors themselves.

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