Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities

Chenfeng Meng; Chi Zhang; Christos Masouros; Murat Temiz; Orhan Kaplan; Yanwei Fu; Yongwei Zhang

REVIEW 3 major objections 6 minor 234 references

Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read This survey argues that offline-trained neural networks, used as learned surrogates for non-convex optimization, are the practical path to real-time integrated sensing and communication, replacing iterative solvers whose execution times rea

desk verdict A competent, current survey of DL-for-ISAC that delivers a useful taxonomy; the computational complexity claims in Section 7 go beyond what the cited evidence supports and need a rewrite. read the letter →

arxiv 2509.06968 v1 pith:NSR4N5NJ submitted 2025-08-23 eess.SP cs.AI

Murat Temiz , Yongwei Zhang , Yanwei Fu , Chi Zhang , Chenfeng Meng , Orhan Kaplan , Christos Masouros This is my paper · ORCID

classification eess.SPcs.AI

keywords deeplearningintegratedsensingandcommunication6Gnetworkswaveformdesignchannelestimationbeamformingcomputationalcomplexityreal-timesignalprocessing

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

Integrated sensing and communication (ISAC) lets one waveform and one hardware platform both deliver data and sense the environment, a feature 6G networks are expected to rely on; its obstacle is the heavy iterative and optimization-based signal processing needed to design waveforms, beamformers, channel estimates, and receivers. This survey tries to establish that deep learning can remove that obstacle: trained networks can produce near-optimal solutions across all these ISAC modules while running in microseconds to milliseconds, because the expensive training happens offline and inference is only matrix multiplication. The payoff, if the survey is right, is that ISAC becomes deployable on low-power, latency-constrained devices, and the same hardware can handle sensing and communication without the computational burden that currently forces a trade-off between the two. The survey supports this by categorizing dozens of recent studies and comparing their reported runtimes and performance against conventional baselines.

What carries the argument

The load-bearing mechanism is the trained neural network as a learned surrogate for a hard optimization problem. In the surveyed systems, a network maps raw inputs (CSI, pilot symbols, received waveforms, echo vectors) directly to the desired solution (a waveform matrix, precoder, channel estimate, demodulated bits, or target range/velocity/angle). The property carrying the argument is the separation of training from inference: training cost is paid offline on powerful hardware, while inference only performs matrix and vector multiplications that scale gently with the number of antennas and can run in parallel on low-power accelerators. The architectures reviewed include FCDNN/MLP, CNN, LSTM

What would settle it

Run a controlled benchmark on identical hardware where an iterative ISAC solver (for example, the 0.233-second augmented-Lagrangian method of [181]) and a DL surrogate are tested on the same antenna count, channel model, and SNR range; if the DL model's inference time exceeds the iterative solver's, or its sum rate and detection probability fall materially below the advertised near-optimal values, the survey's general complexity-reduction claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that DL-based techniques are a viable, efficient alternative to conventional iterative or optimization-based methods for ISAC system design. It asserts that trained neural networks supply near-optimal solutions for waveform optimization, symbol-level precoding, channel estimation, data demodulation, and target parameter estimation, with inference times in the microsecond-to-millisecond range versus 0.1–30 seconds for the surveyed optimization-based methods. The evidence is organized by system module: transmitter-side waveform and precoder design, channel estimation, and receiver-side demodulation and sensing processing. Across these categories the survey highligh

Load-bearing premise

The survey assumes that the runtime and performance numbers reported by different papers, produced on different simulators, hardware platforms, and problem sizes, can be compared directly to conclude that deep learning is generally fast and near-optimal for ISAC.

Editorial extensions

If this is right

ISAC transmitters could update precoders and waveforms within a few hundred milliseconds, tracking changing channel conditions that multi-second iterative solvers cannot keep up with.
Channel estimators trained offline can beat LS and LMMSE in ISAC-specific interference scenarios, and extreme learning machines can train in a fraction of the time of gradient-based networks while keeping comparable accuracy.
Receiver-side DL can jointly demodulate communication data and estimate sensing parameters, in some cases removing the need for separate interference-cancellation stages.
Weight quantization and pruning can shrink a DL-based ISAC precoder's memory and compute to about one-sixth while keeping most of its performance, making edge-device deployment plausible.
Federated and transfer learning can reduce training time and data requirements, enabling multi-cell ISAC networks to cooperate without sharing raw sensing data.

Reading between the lines

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

The paper compares runtimes across different simulators and hardware rather than through a controlled benchmark; if such a benchmark reproduced the reported gap, the central complexity claim would be confirmed on firmer ground.
The learned-surrogate pattern likely extends beyond the surveyed modules to joint localization-and-communication and semantic ISAC, since those tasks share the same structure of optimizing under a dual objective—the paper only hints at these as future directions.
A testable consequence is that a quantized CNN or LSTM trained for a fixed antenna count should need retraining when the antenna count changes; the paper names scalability as a gap, implying that fixed-size architectures will not gracefully generalize.
The field would benefit from standard ISAC datasets and hardware baselines; absent those, the near-optimality claim remains a collection of per-paper demonstrations rather than a verified universal property.

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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

Short version: this is a competent and current survey of deep learning for ISAC, and someone new to the area would come away with a good map of the literature. The one part I would not take at face value is Section 7's computational complexity discussion.

What it does well: the paper organizes a lot of material. Table 3 gives a taxonomy by application area (waveform design, beamforming, channel estimation, receiver processing), and the per-section tables summarize each cited method's learning strategy, architecture, inputs, outputs, pros, and caveats. It covers recent 2024-2025 papers that earlier surveys miss, and the background on learning strategies and DL architectures is accurate and readable. For a survey, that is real value.

Where it is soft: Section 7 argues that DL-based methods have much lower inference complexity than iterative or optimization-based methods. The paper itself says "it is difficult to provide a direct computational complexity comparison," then goes ahead and generalizes that DL runs in microseconds to milliseconds, that its execution time "slightly increases" with antennas, and that iterative methods "may exponentially increase." The only concrete evidence is one SLP example: 9.91 s and 0.233 s for two iterative algorithms versus 0.01-0.02 s for a DL approach. Those are wall-clock numbers from different papers, almost certainly on different hardware, simulators, and problem sizes. No scaling analysis backs the antenna-dependence claim. The qualitative direction is probably right—DL inference does tend to be faster than iterative optimization at runtime—but the survey overstates it as a settled quantitative fact. This matters because the abstract's claim about "reduced computational complexity" leans on that section. A revised version should either add a controlled comparison, cite one if it exists, or hedge the generalization more carefully.

Minor issues: the survey does not disclose how papers were selected for inclusion, which is common but worth noting; and there are a few typos and sloppy equations (e.g., the discount factor in Eq. (4)).

Overall: the paper is a legitimate survey, not a new result, and the central tension in Section 7 is a moderate flaw rather than a fatal one. The taxonomy and coverage make it worth a serious referee; I would send it out but ask for Section 7 to be rewritten with proper caveats.

Referee Report

3 major / 6 minor

Summary. The paper is a survey of deep learning (DL) techniques for integrated sensing and communication (ISAC) systems. It reviews DL learning strategies and architectures, ISAC fundamentals and performance metrics, and then categorizes recent work on DL-based waveform design, beamforming/precoding, predictive beamforming, channel estimation, and receiver processing (demodulation, target estimation, gesture recognition). Section 7 addresses computational complexity, arguing that DL inference is markedly faster than iterative/optimization methods and scales more favorably with antenna count. Section 8 outlines challenges and future directions, including model-based DL, scalability, lightweight models, and networking. The abstract's central claim is that DL-based techniques provide near-optimal solutions with reduced computational complexity, making them suitable for real-time, resource-limited ISAC systems.

Significance. If the survey is reliable, it provides a useful structured reference for the ISAC and machine-learning communities. Its strengths include a clear taxonomy of DL architectures and their ISAC use cases, comparative tables across many recent works (including 2023–2025 papers), and a dedicated treatment of training and inference complexity. The paper also identifies open challenges that are generally well chosen. However, the load-bearing complexity claim in Section 7 rests on non-comparable wall-clock times and is internally inconsistent with statements elsewhere in the manuscript. These issues must be addressed before the survey's central thesis can be considered supported.

major comments (3)

[Section 7.2, Table 11] The manuscript acknowledges 'it is difficult to provide a direct computational complexity comparison between the DL-based techniques and optimization-based or iterative methods' (paragraph after Table 10), but then makes broad quantitative assertions: iterative methods 'can take up to 10 s or more' and DL techniques 'can be executed in a few hundred milliseconds,' and that iterative methods 'may exponentially increase' with antennas while DL 'slightly increases.' The only concrete evidence is one SLP example ([181] vs. [182]) with wall-clock times from different studies, almost certainly on different hardware and problem sizes. Table 11 lists generic ranges (0.1–30 s vs. microseconds–milliseconds) with no sources or experimental conditions. Because the abstract's central claim of 'reduced computational complexity' is supported primarily by these assertions, they need to be either rigorou
[Table 9, Section 7.2] There is a direct internal contradiction. Table 9, row [43], lists 'Exponentially increasing complexity with the number of antennas' as a note for a DNN-transformer receiver. Section 7.2, however, states that 'the execution time of DL-based techniques slightly increases with the number of antennas.' These two claims cannot both be true for the surveyed literature. Moreover, Section 8.1 and Section 8.3 state that DL models are trained for a fixed number of inputs/outputs and that changing the number of antennas may require retraining, which is also inconsistent with a general claim of benign scaling with antenna count. The authors should either restrict the scaling claim to specific architectures (e.g., fully connected networks with fixed input dimension) or explicitly acknowledge counterexamples such as [43].
[Section 7, Table 10] The methodological basis for the complexity comparison is thin. Table 10 gives per-layer real-multiplication counts for generic NN architectures, but the text then jumps to categorical execution-time comparisons without explaining how per-layer counts translate to end-to-end ISAC task complexity, training cost, memory usage, or parallelization benefits. The table also omits definitions for all symbols (NN, NI, NF, NK, NO, NS, NH—the duplicate 'NI' is confusing; presumably one is NH). Since the section is the paper's principal evidence for its central claim, the authors should either connect the per-layer complexity model to the cited ISAC applications or clearly frame the Section 7.2 statements as qualitative observations from individual papers rather than general laws.

minor comments (6)

[Equation (4)] The first term inside the expectation should be R(i0), not i0; as written it is dimensionally inconsistent with the reward notation. Please check the transcription.
[Equation (6)] The notation 'γPT_{t=1} ψ_t(W)' appears to be a typesetting error for a summation over t (likely ∑_{t=1}^T ψ_t(W)). Please correct and define T.
[Section 1] In the introduction, 'Table 3 1 presents the summary' appears to be a typo; it should likely read 'Table 1' or 'Table 3.' Check the cross-reference.
[Section 5.2] The sentence 'when the number of training samples is limited, it is performance is degraded' contains a grammar error; also the DNN channel-estimator claim about outperforming LMMSE only with high training samples is stated a bit ambiguously.
[Table 7, row [39]] The Notes column says 'Extremely training compared to CNN-based estimators,' which is ungrammatical. It presumably means 'Extremely fast training' or 'Extremely low training time.'
[Section 8.4] Typo: 'such as ash mobile devices' should be 'such as mobile devices' (or 'smartphones'). Also, Section 8.9 contains a duplicate sentence about semantic communication in ISAC systems; please remove the repetition.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's claims are literature synthesis, not derived from fitted inputs or self-citation chains.

full rationale

This is a survey/review paper, not a derivation. It contains no fitted parameters, no predictions extracted from a subset of data, and no theorem whose conclusion is equivalent to its premise by construction. The central assertion that DL-based techniques provide near-optimal solutions with reduced computational complexity is presented as a synthesis of the cited literature (e.g., [22], [24], [28], [36]–[39], [40], [181], [182]) rather than as a result derived from self-defined quantities. The paper explicitly acknowledges in Section 7 that 'it is difficult to provide a direct computational complexity comparison between the DL-based techniques and optimization-based or iterative methods,' and that DL complexity depends on the architecture—this admission undercuts any claim that the survey forces its own conclusion. Self-citations such as [27], [129], [139], and [196] appear as examples of the surveyed research area; they are normal expert self-reference and are not load-bearing in a logical sense. Even where the authors' own prior work (e.g., [27]) is cited for pruning/quantization complexity results, those are empirical reported numbers, not premises that define the survey's conclusions. Potential concerns about comparing wall-clock execution times across different hardware and problem sizes (Section 7.2 and Table 11) are evidence-quality or correctness issues, not circularity. No equation in the paper is shown to be equivalent to another by definition, and no 'prediction' reduces to an input fit. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No new entities or fitted parameters are introduced. The survey's central conclusions depend on the accuracy of the prior art it cites and on standard DL/ISAC mathematics.

assumptions (3)

standard math The standard formulations of supervised, unsupervised, and reinforcement learning losses (Eqs. 1-4) and ISAC performance metrics (Eqs. 7-10) are correct as stated.
These equations are used in Sections 2 and 3 without derivation, relying on textbook knowledge and prior literature.
domain assumption The cited papers' experimental results are accurately represented in the summary tables and text.
The survey's conclusions, e.g., that DL-based channel estimators outperform LS, rest on faithful summary of [36]-[39] and others.
domain assumption The taxonomy of ISAC into communication-centric, radar-centric, and dual-function designs covers the field.
This categorization in Section 3 is presented as comprehensive; if it omits important classes, the survey's coverage claim weakens.

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Cite this review

Pith. "Pith review of Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities." pith.science (2026). https://pith.science/paper/NSR4N5NJ

@misc{pith2026250906968,
  author       = {Pith},
  title        = {Pith review of: Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NSR4N5NJ}},
  note         = {Machine review of arXiv:2509.06968}
}

read the original abstract

This article comprehensively reviews recent developments and research on deep learning-based (DL-based) techniques for integrated sensing and communication (ISAC) systems. ISAC, which combines sensing and communication functionalities, is regarded as a key enabler for 6G and beyond networks, as many emerging applications, such as vehicular networks and industrial robotics, necessitate both sensing and communication capabilities for effective operation. A unified platform that provides both functions can reduce hardware complexity, alleviate frequency spectrum congestion, and improve energy efficiency. However, integrating these functionalities on the same hardware requires highly optimized signal processing and system design, introducing significant computational complexity when relying on conventional iterative or optimization-based techniques. As an alternative to conventional techniques, DL-based techniques offer efficient and near-optimal solutions with reduced computational complexity. Hence, such techniques are well-suited for operating under limited computational resources and low latency requirements in real-time systems. DL-based techniques can swiftly and effectively yield near-optimal solutions for a wide range of sophisticated ISAC-related tasks, including waveform design, channel estimation, sensing signal processing, data demodulation, and interference mitigation. Therefore, motivated by these advantages, recent studies have proposed various DL-based approaches for ISAC system design. After briefly introducing DL architectures and ISAC fundamentals, this survey presents a comprehensive and categorized review of state-of-the-art DL-based techniques for ISAC, highlights their key advantages and major challenges, and outlines potential directions for future research.

Figures

Figures reproduced from arXiv: 2509.06968 by the authors.

**Figure 1.** Learning strategies. tor machines (SVM), logistic regression, deep neural networks (DNN), k-nearest neighbors (KNN), random forests, decision trees, boosted trees, can be trained via supervised learning [52, 53]. This learning type requires a large amount of labeled data for training. It strives to learn the relationship between the input and output data depending on the learning parameters during training. For in… view at source ↗

**Figure 2.** DL architectures. of data during a learning procedure [49]. More extensive neural networks can attain higher accuracy on more complex and sophisticated tasks that also require massive amounts of data for learning [85]. Moreover, recent advances in CPUs, GPUs, and distributed computing have enabled training more extensive neural network (NN) models with vast amounts of data [86] [PITH_FULL_IMAGE:figures/full_fig_p0… view at source ↗

**Figure 3.** A communication-centric ISAC system architecture. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

Figures from the paper (10 more)

**Figure 4.** Figure 4: A radar-centric ISAC system architecture. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

**Figure 5.** Figure 5: Design and optimization of dual-functional wave [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

**Figure 6.** Figure 6: DL-based ISAC transmitter architecture. significantly reduce the computational complexity of ISAC signal design. The ISAC transmitter mainly deals with modulating the communication data, generating and optimizing the ISAC waveform, and designing beamforming or precod…

**Figure 7.** Figure 7: ISAC waveforms design via DL. 4.1 Waveform Design and Optimization [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

**Figure 8.** Figure 8: Comparison of DL-based predictive beamforming [PITH_FULL_IMAGE:figures/full_fig_p013_8.png]

**Figure 9.** Figure 9: An example of DL-based channel estimator. [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

**Figure 10.** Figure 10: Comparison of DL-based channel estimation [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]

**Figure 11.** Figure 11: DL-based ISAC receiver architecture [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]

**Figure 12.** Figure 12: Demodulation performance of various DL-based [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]

**Figure 13.** Figure 13: Radar target classification and recognition using CNNs. [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]

Discussion (0). Continue with ORCID to comment.

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