Near-Field ISAC: Synergy of Dual-Purpose Codebooks and Space-Time Adaptive Processing

Abdulkadir Celik; Ahmed Hussain; Ahmed M. Eltawil; Asmaa Abdallah

REVIEW 4 major objections 5 minor 1 cited by

Near-Field ISAC: Synergy of Dual-Purpose Codebooks and Space-Time Adaptive Processing

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Near-field ISAC can cut space-time adaptive processing cost by three orders of magnitude by feeding it codebook-based position estimates.

desk verdict Plausible framework for NF-ISAC beam training plus STAP, but the headline 1000x complexity reduction rests on an unspecified reduced-dimension STAP and a training-sample inconsistency. read the letter →

arxiv 2501.08776 v2 pith:RQVZ44NI submitted 2025-01-15 eess.SP

Ahmed Hussain , Asmaa Abdallah , Abdulkadir Celik , Ahmed M. Eltawil This is my paper · ORCID

classification eess.SP

keywords near-fieldintegratedsensingandcommunicationspace-timeadaptiveprocessingpolarcodebookDFTbeamtrainingcluttersuppressionultra-massiveMIMO

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

This paper argues that near-field integrated sensing and communication can be made practical by letting the communication codebook feed the radar processor: a DFT codebook finds a coarse position, a polar codebook refines it, and the refined candidates shrink a space-time adaptive processor's search so much that its cost drops by roughly three orders of magnitude. This matters because conventional STAP is computationally prohibitive for ultra-massive MIMO arrays, and because slow-moving targets are otherwise buried in clutter. On the paper's terms, the complexity falls from $O(L(MN)^3)$ to $O(l_c(M n_c)^3)$, with a reduction factor of at least 1000. The paper also demonstrates a 15 dB SINR gain over Doppler filtering and rate performance close to perfect-CSI bounds, so the claimed gain is not only computational.

What carries the argument

The load-bearing mechanism is the dual-purpose codebook chain. A DFT codebook, already used for initial access, measures angular spread and matches it to a lookup table to get coarse range and angle. A polar codebook, sampled in range by beam-depth inside the effective beam-focused Rayleigh distance (EBRD), refines this to $l_c$ range and $n_c$ angle candidates. These candidates define the search space of a reduced-dimension NF-STAP whose steering vector is the Hadamard product of a Doppler vector and a spherical-wavefront spatial steering vector. The reduction in search space, not any new radar hardware, is what converts the cubic cost in $MN$ into a cubic cost in the much smaller $M n_c$.

What would settle it

Place a target just outside the polar-codebook candidate window that the DFT stage selects, run the proposed NF-STAP, and compare detection with a full-dimension STAP; a miss in the reduced-dimension processor would show the search-space confinement premise is false. Alternatively, measure the wall-clock time of both processors on the same 256-element, 128-pulse CPI; if the realized speedup is far below 1000x, the complexity claim fails.

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Extended reading notes

Core claim

The central claim is that near-field STAP, normally too expensive for ultra-massive MIMO because its covariance matrix is $MN \times MN$, can be made practical by preceding it with codebook-based beam training. The DFT codebook's angular spread gives a coarse range-angle estimate; the polar codebook then restricts the target to $l_c$ candidate range bins and $n_c$ candidate angle samples; and the STAP processor needs to evaluate only those candidates. On the paper's terms this lowers the complexity from $O(L(MN)^3)$ to $O(l_c(M n_c)^3)$, a factor $LN^3/(l_c n_c^3) \ge 1000$, while a case study shows a 15 dB SINR gain over Doppler filtering and a rate close to perfect-CSI performance.

Load-bearing premise

The whole thousandfold speedup rests on the premise that the codebook estimates shrink the target to a handful of candidate range-angle bins, and that the cheaper adaptive filter used on those bins is actually the same kind of STAP as the full one; the paper assumes this without specifying the reduced-dimension filter.

Editorial extensions

If this is right

With $N=256$, $M=128$, and typical $l_c$, $n_c$ on the order of tens, NF-STAP becomes feasible on a per-CPI basis rather than a batch offline computation.
Initial access sweeps can double as sensing: the DFT stage needs no extra reference signals, so the communication overhead stays at 5G levels.
Slow-moving targets whose Doppler is buried in mainlobe clutter are recoverable, with about 15 dB SINR gain over plain Doppler filtering.
After refinement with only a handful of extra beams, achievable rate approaches the perfect-CSI upper bound, so sensing does not come at a communication cost.
The same angular-spread lookup table can be reused for beam tracking, so the framework covers both initial access and mobility scenarios.

Reading between the lines

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

A testable extension is to make the polar-codebook window size adaptive: since the reduction factor is $LN^3/(l_c n_c^3)$, shrinking $l_c$ and $n_c$ as the DFT estimate becomes more reliable would buy even more speedup, at the cost of a higher miss probability when the target sits outside the window.
The DFT lookup table encodes angular spread as a function of range, angle, and carrier frequency; the same construction could be regenerated for other bands or array geometries, and its range resolution could be improved by using multiple frequencies jointly.
The architecture suggests a general recipe for ISAC: any coarse localizer, not only a DFT codebook, can feed a constrained STAP search; camera or LiDAR cues would plug into the same slot and inherit the complexity reduction, a direction the paper lists as future work.

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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

Here's my read on 2501.08776. The genuinely new thing is the system integration: DFT angular-spread mapping for coarse range-angle estimates, polar codebook refinement, and a NF-STAP whose search space is narrowed by the codebook outputs. That combination, with the 28 GHz slow-target case study, is not in the prior work. The paper also does a decent job explaining why NF beam focusing changes the STAP problem, and the self-citations to the authors' own DFT mapping and polar boundary work are disclosed and relevant rather than hidden.

The soft spot is exactly where the reader puts it. The central complexity reduction, from O(L(MN)^3) to O(lc(M nc)^3) with a claimed 1000x factor, is asserted rather than derived. The paper never defines the 'reduced-dimension NF-STAP' it uses in the case study—no preprocessor, no reduced covariance estimator, no training-cell selection, no inversion procedure. Without that, O(lc(M nc)^3) is an assumed model, not a result. The stress-test note adds a real inconsistency: the case study says 4M nc = 4096 training samples approach optimal STAP, but if L is on the order of hundreds—as the complexity argument assumes—the data cube cannot supply 4096 independent training snapshots. If L is large enough to supply them, the cost of forming those snapshots belongs in the complexity budget. Neither accounting appears. The paper actually lists 'reduced-dimension STAP' as a future direction in the conclusion while using it in the simulations, which is a tell.

The simulation curves are suggestive but lack error bars, and no code or detailed parameter settings are provided, so the performance claims are hard to check. These are all addressable, not fatal. The framework is plausible and worth a serious referee, but it needs major revision: specify the reduced-dimension STAP algorithm, derive the complexity with sample-support constraints, add sensitivity analysis, and release enough detail to reproduce the 15 dB SINR gain and rate curves.

I'd take it to review, not desk reject, but I would not cite it in its current form.

Referee Report

4 major / 5 minor

Summary. The paper proposes a unified near-field ISAC framework in which a DFT codebook provides coarse range/angle estimates from the angular spread of NF users, a polar codebook refines those estimates within a candidate polar region, and a customized NF-STAP detector uses the refined range/angle candidates to reduce computational complexity. The central quantitative claim is that the STAP complexity falls from O(L(MN)^3) to O(lc(M nc)^3), yielding a reduction factor LN^3/(lc nc^3) of at least 1000, while preserving detection and communication performance. Simulation results report a 15 dB SINR gain over conventional Doppler filtering, near-optimal STAP performance with 4M nc training samples, and communication rates close to the perfect-CSI benchmark.

Significance. If the claimed complexity reduction and performance are substantiated, the paper would make a useful contribution to NF-ISAC by connecting beam-training codebooks with adaptive radar processing. The strength of the paper is its system-level vision: reusing DFT beam-training signals for coarse sensing and polar codebook candidates to narrow the STAP search is a sensible architectural idea, and the SINR/rate results are suggestive. The paper also explicitly identifies practical issues such as heterogeneous training cells and hardware impairments. However, the central complexity reduction rests on a reduced-dimension NF-STAP that is never specified, the sample-support argument is not reconciled with the stated complexity model, and the simulation evidence is presented without error bars or statistical detail. These gaps must be addressed before the quantitative claims can be accepted.

major comments (4)

[Section IV, Step 3 (Low-Complexity NF-STAP) and Section V (NF-STAP Case Study)] The reduction from O(L(MN)^3) to O(lc(M nc)^3) is asserted rather than derived. The 'reduced-dimension NF-STAP' is never defined: the text does not specify the preprocessor that maps the full MN-dimensional snapshot to dimension M nc, the reduced covariance estimator, the training-cell selection, or the inversion procedure. Without these details, O(lc(M nc)^3) is an assumed complexity model, not a result. Please provide the explicit reduced-dimension algorithm and a step-by-step complexity count, including the cost of forming and inverting the reduced covariance matrix.
[Section V, NF-STAP Case Study and Section IV, Step 3] The claimed complexity reduction is inconsistent with the stated training-sample requirement. For M=128 and nc=8, the reduced covariance matrix is 1024x1024, and the text says that 4M nc = 4096 training samples approach optimal performance. If L is on the order of hundreds, as the complexity argument assumes, the radar data cube cannot supply 4096 independent range-bin snapshots. If L is large enough to supply them, then the cost of forming and exploiting those L snapshots must be included in the complexity budget. This tension undermines the 1000x reduction claim and the simulation setup; please clarify how the training samples are obtained and how their cost is accounted for.
[Section IV, Step 3, complexity-reduction factor statement] The statement that 'the reduction factor is surely LN^3/(lc nc^3) ≥ 1000, given that N and L are on the order of hundreds, while lc and nc are on the order of tens' is not justified by those order-of-magnitude assumptions. For example, with L=100, N=100, lc=90, and nc=90, the ratio is approximately 1.5, far below 1000. Please replace the 'surely' claim with explicit ranges for L, N, lc, and nc under which the 1000x reduction holds, and state the actual parameters used in the simulation.
[Section V, Figure 5 and accompanying text] The quantitative performance claims, including the 15 dB SINR gain over Doppler filtering and the statement that 4M nc training samples 'closely approaches the optimal STAP performance,' are based on simulation curves without error bars or confidence intervals. Since the central evidence for the framework is these curves, please provide multiple independent trials or an analytical variance estimate, and report the number of Monte Carlo runs used to generate each panel.

minor comments (5)

[Section IV, Step 1] The DFT angular-spread mapping and the lookup-table construction rely on reference [13], which is the authors' own arXiv preprint and is not summarized or validated in the paper. A brief derivation or a reproduction of the key mapping would help the reader assess the coarse-estimation step.
[Section II and reference [11]] The EBRD concept and the polar codebook sampling are taken from reference [11], which is listed as 'Submitted to IEEE Trans. Wireless Commun.' and is not yet peer-reviewed. Please either provide the relevant definitions in the paper or cite a peer-reviewed source.
[Section II, 'Lateral vs. Axial Resolution'] The statement 'beamwidth is constant in both NF and FF' should be qualified; the beamwidth is constant for a given array and steering direction in the FF, but the NF beam characteristics vary with focus distance and angle. Please clarify to avoid ambiguity.
[Section V, Case study parameters] The simulation setup omits several specific parameters such as the noise and clutter statistics, the number of Monte Carlo runs, the exact polar codebook sizes, and the construction of the reduced-dimension STAP. These should be included for reproducibility.
[Conclusion and Future Directions] The phrase 'ookup tables' on page 7 should be 'lookup tables'. There is also a missing space in 'Mutli-modal' in the futures section heading; these typos should be corrected.

Circularity Check

2 steps flagged · score 4.0 of 10

DFT and polar-codebook foundations are imported from same-author prior work, making the coarse-to-refined chain load-bearing on self-citations; the NF-STAP complexity reduction is an assumed model rather than a circular prediction.

uniqueness imported from authors [Section III, Step 1 'UE Discovery & Coarse Sensing using DFT Codebooks' (around Fig. 4)]
"where the angular spread is uniquely mapped to UE position for a given carrier frequency [13]. It increases as the user moves closer to the BS, reflecting the pronounced NF effects. The range-, angle-, and frequency-dependent characteristics of angular spread can be leveraged to estimate the position of user. To this aim, a lookup table can be precomputed to encapsulate the angular spread for each polar point at a given frequency."

The paper presents the DFT angular-spread-to-position mapping as an established fact and builds the entire two-stage coarse-to-refined estimation chain on it. The cited source [13] is a same-author arXiv preprint (Hussain, Abdallah, Celik, Eltawil), and the current paper gives no derivation, independent measurement, or external benchmark for the uniqueness. The lookup-table-based position estimate is therefore a restatement of the cited prior work rather than a result derived here. Since the coarse range/angle estimates bootstrap the polar codebook and the low-complexity NF-STAP search-space reduction, the central framework's first step reduces to an unverified self-citation.
self citation load bearing [Section II, 'FF Codebooks vs. NF Codebooks', Polar Codebooks paragraph; carried into Section III, Step 2]
"Polar codebooks are designed based on the Rayleigh distance, which often overestimates the NF region in terms of beam-focusing and multiplexing gains [8], [11]. This overestimation leads to excessive sampling and unnecessarily large codebook sizes. As a remedy, recent research has explored NF codebooks that are optimized based on beam-depth and sampled within the EBRD region [11], yielding lower spatial correlation between codewords and superior performance while significantly reducing beam-training overhead."

The polar codebook's candidate range samples (lc) and the EBRD boundary that defines the NF region are not derived in this paper; they are imported from [11], a same-author submitted manuscript. The paper then uses these lc candidate range samples in the complexity expression O(lc(M nc)^3) and in the claimed reduction factor LN^3/(lc nc^3) >= 1000. Thus the search-space size that produces the headline complexity reduction is inherited from a self-citation, not independently established by the present derivation.

full rationale

The paper's central synergy claim has a two-stage derivation chain. Stage 1 (DFT codebook coarse range/angle estimation) depends on a 'unique' angular-spread-to-position mapping that is cited to [13], an arXiv preprint by the same four authors; no proof or independent validation is provided in this manuscript. Stage 2 (polar refinement) depends on EBRD and beam-depth-based codebook sampling from [11], also a same-author submitted work. These are load-bearing because the polar candidate range/angle samples (lc, nc) are exactly what narrows the NF-STAP search and yields the claimed O(lc(M nc)^3) complexity. However, the NF-STAP case study itself (NF steering vector, covariance estimation, CFAR, SINR gain, rate results) is presented as a simulation with enough detail to be independently reproduced, and the 15 dB SINR gain and rate curves are not fitted quantities renamed as predictions. The O(lc(M nc)^3) complexity claim is better characterized as an unsupported algorithmic assumption than as a circular result: the reduced-dimension NF-STAP preprocessor and covariance estimator are never specified, so the complexity expression is asserted rather than derived. That missing proof is a correctness risk, not a circular equivalence. Because the framework's bootstrap step leans heavily on same-author prior work while the STAP results retain independent content, the score is 4 rather than 6 or higher.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The framework relies on standard near-field spherical wavefront propagation, a uniqueness assumption for the angular-spread lookup table, homogeneous training cells for covariance estimation, and a specific complexity model for reduced-dimension STAP. No new physical entities are introduced; the dual-purpose codebook is a system design, not a new entity.

free parameters (2)

lc (candidate range samples in polar refinement) = order of tens (not explicitly stated in the text)
Chosen by hand in the framework; it directly appears in the complexity term O(lc(M nc)^3) and controls the trade-off between accuracy and overhead.
nc (candidate angle samples in polar refinement) = 8 used in the case study
Selected for the simulation; the paper states performance approaches optimal STAP with 4M nc training samples and nc = 8, but no sensitivity analysis over nc is provided.

assumptions (4)

domain assumption Angular spread observed by a user maps uniquely to a range-angle location via a precomputed lookup table, assuming a dominant single path and no significant multipath or calibration error.
This mapping is the basis of DFT-based coarse estimation; the paper itself acknowledges calibration and multipath could break it and suggests deep learning as a replacement in the future directions section.
domain assumption STAP cost is dominated by the cubic inversion of an (M x nc)-dimensional covariance matrix after reduced-dimension processing, and this reduced-dimension processing preserves target SINR.
Used to justify the jump from O(L(MN)^3) to O(lc(M nc)^3); no reduced-dimension STAP architecture is specified in the paper.
domain assumption Training cells used for covariance estimation are homogeneous and free of secondary targets.
The paper explicitly notes this assumption can be violated and suggests Doppler compensation, diagonal loading, and use of data structure as remedies.
domain assumption Static ground clutter appears across all range and angle bins at zero Doppler, so a Doppler null suffices for clutter suppression.
This is the clutter model in the case study; it limits the generality of the claims to stationary, distributed clutter.

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Cite this review

Pith. "Pith review of Near-Field ISAC: Synergy of Dual-Purpose Codebooks and Space-Time Adaptive Processing." pith.science (2026). https://pith.science/paper/RQVZ44NI

@misc{pith2026250108776,
  author       = {Pith},
  title        = {Pith review of: Near-Field ISAC: Synergy of Dual-Purpose Codebooks and Space-Time Adaptive Processing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RQVZ44NI}},
  note         = {Machine review of arXiv:2501.08776}
}

read the original abstract

Integrated sensing and communication (ISAC) has emerged as a transformative paradigm, enabling situationally aware and perceptive next-generation wireless networks through the co-design of shared network resources. With the adoption of millimeter-wave (mmWave) and terahertz (THz) frequency bands, ultra-massive MIMO (UM-MIMO) systems and holographic surfaces unlock the potential of near-field (NF) propagation, characterized by spherical wavefronts that facilitate beam manipulation in both angular and range domains. This paper presents a unified approach to near-field beam-training and sensing, introducing a dual-purpose codebook design that employs discrete Fourier transform (DFT)-based codebooks for coarse estimation of sensing parameters and polar codebooks for parameter refinement. Leveraging these range and angle estimates, a customized low-complexity space-time adaptive processing (STAP) technique is proposed for NF-ISAC to detect slow-moving targets and efficiently mitigate clutter. The interplay between codebooks and NF-STAP framework offers three key advantages: reduced communication beam training overhead, improved estimation accuracy, and minimal STAP computational complexity. Simulation results show that the proposed framework can reduce STAP complexity by three orders of magnitude, validating efficacy, and highlighting the potential of the proposed approach to seamlessly integrate NF communication and sensing functionalities in future wireless networks.

Figures

Figures reproduced from arXiv: 2501.08776 by the authors.

**Figure 2.** Illustration of radar data cube and flow diagram of the proposed NF-STAP algorithm. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Demonstration of NF-Sensing complexity reduction through the interplay of DFT codebooks, polar codebooks, and NF-STAP. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Illustration of DFT angular spread in the NF region and polar codebooks for an ULA operating at [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: The target illumination scenario is shown in the top-left diagram. After STAP processing, the range-Doppler and angle-Doppler maps are presented [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

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Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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M. Golzadeh, E. Tiirola et al. , “Downlink sensing in 5G-advanced and 6G: SIB1-assisted SSB approach,” in 97th V ehicular Technology Conference (VTC2023-Spring). IEEE, Apr. 2023, pp. 1–7

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