Machine Learning for Spectrum Sharing: A Survey

Carlo Fischione; Charles Casimiro Cavalcante; Francisco R. V. Guimar\~aes; Gabor Fodor; Jos\'e Mairton B. da Silva Jr.; Mats Bengtsson

REVIEW 4 major objections 5 minor 296 references

Machine Learning for Spectrum Sharing: A Survey

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This survey argues that the state of the art of machine learning for spectrum sharing can be organized into four mechanisms—sensing, allocation, access, handoff—plus beamforming and security, and that the work in each area clusters into…

desk verdict Competent, well-organized survey whose 'complete overview' claim outruns the undocumented reference selection; useful as an entry point, not as a definitive audit. read the letter →

arxiv 2411.19032 v1 pith:MBDPTSWE submitted 2024-11-28 eess.SP cs.NI

Francisco R. V. Guimarães , José Mairton B. da Silva Jr. , Charles Casimiro Cavalcante , Gabor Fodor , Mats Bengtsson , Carlo Fischione This is my paper · ORCID

classification eess.SPcs.NI

keywords spectrumsharingmachinelearningsensingallocationaccesshandoffreinforcementdynamic

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish a complete map of how machine learning is being used to share wireless spectrum in 5G and 6G networks. It organizes the field into four mechanisms—sensing, allocation, access, and handoff—and adds beamforming and security, then sorts the literature by learning paradigm. A sympathetic reader would take the central claim to be that this taxonomy captures the state of the art and reveals which combinations of ML method and spectrum-sharing task are mature and which are open. If right, it gives researchers a structured way to place new results and spot gaps.

What carries the argument

The organizing object is the four-mechanism pipeline—spectrum sensing, spectrum allocation, spectrum access, and spectrum handoff—that connects the radio environment to the user. The survey uses this pipeline together with the division of ML into supervised, unsupervised, and reinforcement learning; each chapter maps one mechanism onto the learning families, with tables summarizing the algorithm, network type, and contribution of each cited work.

What would settle it

A systematic literature review of ML for spectrum sharing using explicit inclusion criteria from the same databases and time window would settle the claim: if a substantial fraction of qualifying papers fall outside the paper's four mechanisms, beamforming, security, or its ML taxonomy, or if the table summaries mischaracterize several cited works, the central organizational claim is falsified.

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Extended reading notes

Core claim

The discovery on offer is not a new algorithm but a comprehensive classification: recent ML-based spectrum sharing can be seen as the application of three learning families to four coordinated mechanisms. The paper provides mathematical formulations of the ML methods in the spectrum sharing context, surveys and tabulates supervised, unsupervised, semi-supervised, and reinforcement-learning solutions for sensing; Q-learning and deep Q-learning plus other RL for allocation and access; and identifies handoff, beamforming, and security as further aspects with emerging ML contributions. It claims to cover what prior surveys miss: ML fundamentals, all four sharing mechanisms, beamforming, and security in one place.

Load-bearing premise

The survey's value depends on the selected references being a faithful picture of the whole field, since the authors describe no systematic literature search or inclusion criteria; if the selection is skewed, the map looks complete when it is not.

Editorial extensions

If this is right

A new spectrum-sharing solution can be located in the taxonomy, making systematic comparison with neighboring methods straightforward.
The mathematical formulations reveal a common structure: classification for sensing, and Markov decision processes or reinforcement learning for allocation and access.
If the coverage is accurate, spectrum sensing is the most mature ML area, while handoff, beamforming, and security are comparatively thinner and are candidate directions for future work.
The tables offer a quick reference for which RL algorithms—Q-learning, deep Q-network, double DQN, DDPG, PPO—have been applied to which network types.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Beyond the paper: the 'complete overview' claim rests on an unsystematic reference selection, so the map is best read as representative rather than exhaustive.
Beyond the paper: because most surveyed studies use simulation with different metrics, the taxonomy says little about which ML solution performs best in deployment; a shared benchmark suite would test the field's practical progress.
Beyond the paper: the gap pattern suggests that the least explored combinations—such as unsupervised learning for spectrum access or ML for handoff—are the likely sites of the field's next experiments.

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Signed reviews

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Dear colleague,

This is a competent, clearly organized survey of ML for spectrum sharing, and it is probably useful as an entry point for a grad student or engineer new to the area. What it is not, despite the abstract's promise, is a demonstrably complete overview of the state of the art.

What is actually good: the paper does more than the four-part taxonomy (sensing, allocation, access, handoff). It adds a self-contained ML primer with mathematical formulations that are often skipped (empirical risk, SGD, neural network layers, Q-learning), and it extends coverage to beamforming and security, which prior surveys like [36] did not include. The summary tables in Chapters 4 and 5 are genuinely useful for comparing RL-based allocation and access methods. The authors also engaged honestly with 17 prior surveys, explaining where each falls short.

Where it is soft: the completeness claim rests on an undocumented reference selection. There is no description of databases, query strings, inclusion/exclusion criteria, or a time window. The keyword density map in Fig. 1.3 is described as showing keywords occurring in at least five references, but there is no protocol for how the reference set was assembled. So a reader cannot audit whether 2023-2024 work (transformer-based sensing, generative AI allocation, digital-twin sharing) was missed. That is the load-bearing weakness, and it is fixable by moderating the language or adding a search methodology. The one-sentence table summaries are second-hand interpretations; if any mischaracterize a cited work, the comparison inherits the error. There are also several typos (Eq. (3.2) lists H0 and H0 instead of H0 and H1; Eq. (2.5) uses m1 in both terms). These are minor but suggest a final proofread pass.

The ML exposition is standard and accurate; there are no derivations to validate or fitted parameters, so the circularity burden is zero.

Who this is for: newcomers to ML-based spectrum sharing who want a structured map and quick comparison tables. It will not settle any open research problem, and it should not be cited as evidence of completeness. I would still send it to peer review because the tables and cross-survey comparison give it enough substance that a referee can meaningfully check coverage and recommend revisions. I would ask the authors to tone down the complete claim and either document the search or reframe it as a representative selection.

Recommendation: engage with it, but treat the completeness claim as a hypothesis the authors need to justify, not a fact.

Referee Report

4 major / 5 minor

Summary. This manuscript is a survey of machine learning (ML) methods applied to spectrum sharing. It opens with an introduction to supervised learning, unsupervised learning, and reinforcement learning, then reviews ML-based solutions for spectrum sensing, spectrum allocation, spectrum access, spectrum handoff, beamforming, and security. The paper compares its scope with earlier surveys and presents summary tables of the surveyed works, a keyword-density map, and a discussion of open challenges. The abstract and Section 1.1 claim that the paper provides a complete overview of the state of the art in ML for spectrum sharing.

Significance. If the coverage is representative, the survey would be a useful reference map for researchers entering this area. The paper has several strengths: it includes a self-contained mathematical tutorial on the main ML families, it covers all four spectrum-sharing mechanisms plus beamforming and security, it provides comparison tables that organize the literature, and it closes with a concrete list of open problems. These are valuable features for a survey in a journal-style monograph series. However, the central claim of completeness is not supported by an auditable literature-selection procedure, and the ML tutorial contains several mathematical errors in equations that are later used as conceptual foundations for the surveyed applications. These issues do not require redoing the survey, but they do require revision before the paper can serve as a reliable reference.

major comments (4)

[Section 1.1, Tables 1.1-1.2, Fig. 1.3] The abstract and Section 1.1 claim a 'complete overview of the state-of-the-art of machine learning for spectrum sharing,' but the paper never describes how the reference set was assembled. No databases, query strings, time window, inclusion/exclusion criteria, or screening process are reported, and Fig. 1.3 refers to a 'considered database' without defining it. As a result, a reader cannot audit whether the claimed coverage is complete or biased. I recommend adding a reproducible search protocol and, ideally, a bibliometric recall check against 2020-2024 literature, or softening the completeness claim to reflect the actual selection procedure.
[Section 2.1.3, Eq. (2.5)] The between-class covariance matrix is written as S_B = (m1 - m1)(m1 - m1)^T, which is identically zero. It should be (m1 - m0)(m1 - m0)^T. As written, the Rayleigh quotient in Eq. (2.7) is degenerate and the Fisher discriminant analysis tutorial is incorrect. This is a load-bearing error because the paper explicitly advertises a mathematical description of ML methods as one of its contributions.
[Sections 2.3.2 and 2.3.3, Eqs. (2.57) and (2.61)] The Q-learning update in Eq. (2.57) uses 'arg max_a q_pi(s_{t+1}, a)' where the update target should involve 'max_a q_pi(s_{t+1}, a)', i.e., the value, not the maximizing action. Similarly, the deep Q-learning update in Eq. (2.61) uses the next action a_{t+1} in the target term, which is the SARSA target rather than the DQN target; the correct target is r_{t+1} + gamma max_a q(s_{t+1}, a; w_t). Since Q-learning and DQN are the central algorithmic themes of Chapters 4 and 5, these errors weaken the tutorial foundation of the survey.
[Table 4.2, row [151]-[154]] The table summarizes references [151]-[154] as 'Satellite IoT' and as decreasing DQN instability via experience replay and target networks. The body text, however, describes [151] as a NOMA channel-assignment problem, [152] as a vehicle-to-vehicle network, and only [153] and [154] as satellite systems. This concrete mismatch shows that the one-sentence table summaries can mischaracterize the cited papers. I recommend checking every table entry against its source, because the comparative value of the survey depends on the fidelity of these second-hand summaries.

minor comments (5)

[Chapter 2 title] The chapter heading reads 'Introduction to Machine Leaning' and should read 'Machine Learning'.
[Section 3.2, Eq. (3.2)] The text says the thresholds are for 'class H0 and H0 respectively'; this should be H0 and H1. It would also help to verify the direction of the inequalities, since the sentence 'if channel is free' and 'if channel is busy' can be read in either order depending on the chosen encoding.
[Table 1.2, row [38]] The entry ends with '6G systes' and should read '6G systems'.
[Table 5.2, row [120]] The comment column reads 'Proposed a The DDQN algorithm' and should be corrected to 'Proposed a DDQN algorithm'.
[Table 6.1] Several entries contain the typo 'handoof' instead of 'handoff', and Section 6.1 spells 'Nave Bayes' instead of 'Naive Bayes'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey contains no derivations, fitted parameters, or self-citation-dependent claims; its coverage claims are external assertions, not results derived from inputs.

full rationale

This is a survey paper. Its content is a map of external literature: Section 2 recounts textbook ML fundamentals (e.g., Sutton and Barto for RL, Hastie et al. for supervised/unsupervised learning), and Sections 3-6 summarize cited works on sensing, allocation, access, handoff, beamforming, and security. There is no equation in the paper whose output is defined in terms of the paper's own conclusions, no parameter fitted to a dataset and then renamed as a prediction, and no uniqueness theorem or ansatz imported from the authors' own prior work to force a choice. The abstract's 'complete overview' claim and Section 1.1's comparison with prior surveys [24]-[40] are coverage assertions: they can be checked against the external literature, but they are not derived from the paper's own equations. The keyword density map (Fig. 1.3) is a descriptive statistic of the cited reference set, not a load-bearing derivation. The tables' one-sentence summaries are second-hand interpretations; if inaccurate, that is a correctness risk, not circular reasoning. Because no step reduces to its own inputs by construction, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The survey introduces no new mathematical objects or fitted parameters. Its content is a synthesis of prior literature. The central claim of completeness rests on two domain assumptions: representativeness of the surveyed selection and adequacy of the four-mechanism taxonomy.

assumptions (2)

domain assumption The surveyed reference set is representative of the state of the art in ML for spectrum sharing.
The survey claims completeness (Abstract) but provides no systematic search or inclusion criteria; the validity of the overview rests on this assumption.
domain assumption The four-mechanism decomposition (sensing, allocation, access, handoff) plus beamforming/security covers the spectrum sharing problem space.
Section 1.1 introduces this taxonomy and the entire survey is organized around it; if the taxonomy mis-partitions the field, the survey's organizational value is compromised.

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Cite this review

Pith. "Pith review of Machine Learning for Spectrum Sharing: A Survey." pith.science (2026). https://pith.science/paper/MBDPTSWE

@misc{pith2026241119032,
  author       = {Pith},
  title        = {Pith review of: Machine Learning for Spectrum Sharing: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MBDPTSWE}},
  note         = {Machine review of arXiv:2411.19032}
}

read the original abstract

The 5th generation (5G) of wireless systems is being deployed with the aim to provide many sets of wireless communication services, such as low data rates for a massive amount of devices, broadband, low latency, and industrial wireless access. Such an aim is even more complex in the next generation wireless systems (6G) where wireless connectivity is expected to serve any connected intelligent unit, such as software robots and humans interacting in the metaverse, autonomous vehicles, drones, trains, or smart sensors monitoring cities, buildings, and the environment. Because of the wireless devices will be orders of magnitude denser than in 5G cellular systems, and because of their complex quality of service requirements, the access to the wireless spectrum will have to be appropriately shared to avoid congestion, poor quality of service, or unsatisfactory communication delays. Spectrum sharing methods have been the objective of intense study through model-based approaches, such as optimization or game theories. However, these methods may fail when facing the complexity of the communication environments in 5G, 6G, and beyond. Recently, there has been significant interest in the application and development of data-driven methods, namely machine learning methods, to handle the complex operation of spectrum sharing. In this survey, we provide a complete overview of the state-of-theart of machine learning for spectrum sharing. First, we map the most prominent methods that we encounter in spectrum sharing. Then, we show how these machine learning methods are applied to the numerous dimensions and sub-problems of spectrum sharing, such as spectrum sensing, spectrum allocation, spectrum access, and spectrum handoff. We also highlight several open questions and future trends.

Figures

Figures reproduced from arXiv: 2411.19032 by the authors.

**Figure 1.1.** Coexistence of different technologies in a spectrum sharing scenario. systems are supported by four mechanisms: 1. Spectrum sensing: in this mechanism, signal features are extracted from the environment to determine the radio frequency occupancy condition, i.e., which channels are in use and which ones are free. 2. Spectrum allocation: receives the channel characterization from sensing mechanism or directly from the… view at source ↗

**Figure 1.2.** Relationship among spectrum sharing mechanisms. References [24]–[27] cover the state-of-the-art of spectrum sensing for cognitive radio (CR). The main focus of [24] is the classification and review of different sensing techniques using traditional and ML schemes, while [25] provides a deep learning (DL) detailed survey for spectrum sensing. Reference [26] discusses recent spectrum sensing and dynamic spectrum access… view at source ↗

**Figure 1.3.** Density of the keywords presented in the cited references of this survey [PITH_FULL_IMAGE:figures/full_fig_p014_1_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 2.1.** Figure 2.1: Examples of supervised, unsupervised, and reinforcement learning problems. First, we have a classification problem solved using supervised and unsupervised learning, respectively. Then, we show an example of the reinforcement learning process between an agent with t…

**Figure 3.1.** Figure 3.1: ML approaches for spectrum sensing covered in this survey. from the environment and surroundings as well their ability to adapt to environment changes [61]. Regardless of which bandwidth or cooperative paradigm is used, the main goal of spectrum sensing is to detect …

**Figure 3.2.** Figure 3.2: Illustration of ML usage for cooperative spectrum sensing. The SUs sense the desired channel and the classifier decides which class the collected signal belong to. of the feature vectors. For k = 1, the classification problem relies on calculate the Euclidean distanc…

**Figure 4.1.** Figure 4.1: Illustration of spectrum allocation. In this example K = M = 3 and each user is allocated to a single channel. be allocated or removed to/from a specified channel. For example, if a user is allocated in a channel with other users or suffers from deep fading, this act…

**Figure 5.1.** Figure 5.1: Illustration of spectrum access. based on some performance criterion which usually is related to packet transmission success. Based on this general idea, many works in the literature considered RL solutions to address DSA problems. 5.2 Q-learning Based Methods Q-lear…

**Figure 6.1.** Figure 6.1: Illustration of a spectrum handoff process. also effective to reduce handoffs when the SUs have prior knowledge of the environment [231]. In [232] the mobile users of a CRN were modeled as particles which move to the optimal solution, i.e., home locator register or v…

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