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Sensing-Enhanced Handover Criterion for Low-Altitude Wireless Network (LAWNs)

T0 review · 3 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A joint handover criterion that combines RSRP with ISAC distance estimates reduces the handover region length by 75.20% and improves activation probability by 76.31% in drone networks.

desk verdict A sensible OR-rule handover idea with clean derivations, but the headline improvements rest on an independence assumption that the paper's own model contradicts, plus a factor-of-two CRLB error. read the letter →

arxiv 2505.16350 v2 pith:ZB3Y3C7L submitted 2025-05-22 cs.IT math.IT

Jingli Li , Yiyan Ma , Bo Ai , Weijie Yuan , Qingqing Cheng , Guoyu Ma , Mi Yang , Yunlong Lu
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Wenwei Yue Zhangdui Zhong
This is my paper · ORCID
classification cs.ITmath.IT
keywords low-altitudewirelessnetworkshandoverintegratedsensingandcommunicationRSRPCramér–RaolowerboundOFDMdronecellularactivationprobability
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 asks whether a cellular drone network can make handovers more reliable by letting the network's own sensing measurements, not just the received signal strength, decide when to switch base stations. It models an ISAC base station that estimates the drone's distance from its echo signal, derives the Cramér–Rao lower bound for that distance estimate, and proposes a joint handover criterion: trigger a handover if either the RSRP of the target base station exceeds the serving base station by a hysteresis margin, or the estimated distance to the serving base station exceeds the distance to the target by a threshold. In simulation over the paper's ground-to-air geometry, the joint criterion reduces the average handover region length by 75.20% and improves the handover activation probability by 76.31% relative to RSRP-only operation at $\mathrm{SNR}\ge 0$ dB with a 20% sensing pilot ratio. The reason to care is that low-altitude drones move in three dimensions and suffer frequent redundant or failed handovers under RSRP-only rules, and this paper gives a concrete mechanism, with a parameter-derived error model, for why sensing distance should fix part of that problem.

What carries the argument

The load-bearing object is the joint handover criterion itself: an OR-combination of a signal-strength event and a sensing-distance event. The RSRP event captures the traditional A3 rule through the $Q$-function of a shadow-fading contrast; the distance event captures the spatial geometry of the drone relative to the two base stations through ISAC range estimates. What carries the argument is the Cramér–Rao lower bound $\mathrm{CRLB}(d) = \frac{3c^2}{8\pi^2\gamma \Delta f^2 M \rho N (\rho N - 1)(2\rho N - 1)}$, which turns the sensing pilot ratio, bandwidth, SNR, and OFDM resource count into a concrete error variance for the distance trigger, and the independence assumption that lets the two events combine as $P(A\cup B)=P(A)+P(B)-P(A)P(B)$.

What would settle it

Run the paper's geometry in a Monte Carlo simulation where the shadow-fading term and the distance-estimation error are drawn from a joint Gaussian with a positive correlation coefficient, and compare the empirical handover activation probability with the formula $P_{\mathrm{RSRP}} + P_{\mathrm{dist}} - P_{\mathrm{RSRP}} P_{\mathrm{dist}}$. A correlation of even 0.3 across the handover region would reduce the joint probability enough to erase a substantial part of the claimed 76.31% improvement; a field test with a real ISAC base station and a drone at 100–300 m altitude, using measured distance-error statistics instead of the CRLB, would settle which regime holds.

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Extended reading notes

Core claim

The central claim is that a handover trigger based on the union of two events outperforms the conventional RSRP-only trigger in the low-altitude drone setting. The first event is the standard A3 condition, $P_T - P_S > \Gamma$, whose activation probability under shadow fading is $P_{\mathrm{HO}}^{\mathrm{RSRP}} = Q\!\left(\frac{\Gamma + L(d_T) - L(d_S)}{\sqrt{2}\,\sigma_{\mathrm{SF}}}\right)$. The second event uses ISAC distance estimates $\hat d_S$, $\hat d_T$ modeled as Gaussian with variance equal to the Cramér–Rao lower bound of Eq. (7), giving $P_{\mathrm{HO}}^{\mathrm{dist}} = Q\!\left(\frac{d_{\mathrm{th}} + d_T - d_S}{\sqrt{\mathrm{CRLB}(d_T) + \mathrm{CRLB}(d_S)}}\right)$. The joint criterion fires when either condition holds, so its activation probability is $P_{\mathrm{HO}}^{\mathrm{joint}} = P_{\mathrm{HO}}^{\mathrm{RSRP}} + P_{\mathrm{HO}}^{\mathrm{dist}} - P_{\mathrm{HO}}^{\mathrm{RSRP}} P_{\mathrm{HO}}^{\mathrm{dist}}$, under the paper's stated independence assumption. The paper reports that this union rule shortens the spatial handover region by 75.20% on average and raises activation probability by 76.31% at $\mathrm{SNR}\ge 0$ dB with sensing pilot ratio 20%, while retaining most of the gain at lower SNR and lower pilot ratios.

Load-bearing premise

The load-bearing premise is that the signal-strength measurement and the sensing-distance estimate fail independently, so the probability that either one triggers a handover is the simple sum minus the product; if the two errors are positively correlated, the reported activation-probability gain shrinks, and the paper's Gaussian, CRLB-achieving error model also makes the distance estimate look more accurate than real estimators usually are.

Editorial extensions

If this is right

  • At $\mathrm{SNR}\ge 0$ dB and a 20% sensing pilot ratio, the joint criterion reduces the average handover region length by 75.20% and raises activation probability by 76.31% compared with RSRP-only.
  • The joint criterion keeps its advantage at lower SNR because the RSRP branch remains active when sensing accuracy degrades, so the OR rule degrades more slowly than sensing alone.
  • The lower boundary of the handover region stays near $x = -13$ m as the distance threshold $d_{\mathrm{th}}$ ranges from 0 to 100 m, making the criterion less sensitive to threshold tuning than the RSRP-only rule.
  • Inside the handover region the joint criterion improves the effective data rate by up to 2.67 Mbps, supporting latency-sensitive drone services such as real-time video and control signaling.
  • The added complexity of distance estimation, based on a joint angle-range-velocity estimation method, is $O[(N_t + \log(MN) + g)MN]$, which the paper argues is comparable to conventional OFDM transceiver operations.

Reading between the lines

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

  • The same union-of-triggers logic could be applied to other ISAC outputs, such as Doppler or angle estimates, which would matter for non-line-of-sight or maneuvering flight where distance alone is less informative.
  • If real estimators do not achieve the CRLB, the distance branch contributes less than the paper's model says, so the 76.31% figure is best read as an upper bound for the activation-probability gain.
  • The paper's fixed-straight-line trajectory leaves ping-pong handovers out of scope; adding a time-to-trigger or hysteresis to the distance event would be a direct test of whether the joint criterion also reduces unnecessary back-and-forth handovers.
  • A field measurement of the correlation between RSRP shadowing and ISAC distance errors would tell whether the independence assumption is safe or whether the joint formula needs a covariance term.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 6 minor

Summary. The manuscript proposes a sensing-enhanced handover (HO) activation criterion for cellular-connected drones, fusing the standard RSRP-based A3 event with an ISAC-based distance comparison. The authors derive a Cramér-Rao lower bound (CRLB) for round-trip distance estimation in an OFDM ISAC link, define the joint HO probability as the union of the two triggering events under an independence assumption, and evaluate the resulting HO region length and activation probability through numerical evaluation of the analytic formulas. For SNR ≥ 0 dB and 20% sensing pilot ratio, they report a 75.20% reduction in average HO region length and a 76.31% improvement in activation probability relative to the RSRP-only baseline.

Significance. The topic is timely given the growing interest in low-altitude wireless networks and ISAC, and the paper provides a transparent analytic framework with closed-form performance metrics and 3GPP-aligned parameter settings. The main strength is the explicit connection between sensing accuracy (CRLB) and handover decision statistics, together with a complexity assessment for the additional distance estimation. However, the headline quantitative claims are not robust: the activation-probability improvement is structurally guaranteed by defining the joint criterion as a union, and the reported magnitude rests on an independence assumption that is inconsistent with the paper's own shadow-fading model and on a CRLB formula that appears to contain a factor-of-two error. If these derivation issues are corrected, the framework could be a useful starting point for sensing-assisted HO design, but the current numerical claims are not supported as stated.

major comments (3)
  1. [Section III, Eq. (10) and Footnote 3] The factorization P_joint = P_RSRP + P_dist - P_RSRP*P_dist in Eq. (10) requires the RSRP-triggering event and the distance-triggering event to be independent. In the model of Section II.B, the sensing path gain √β_S shares the same shadow-fading term ε as the communication gain, and γ in Eq. (7) depends on β_S. Consequently, the CRLB, and therefore the distribution of the distance estimate in Eq. (9), depends on the same random shadowing that drives the RSRP difference in Eq. (8). The two events are statistically dependent, so the footnote's appeal to 'distinct physical origins' is not supported by the model. For positive correlation the true union probability is smaller than Eq. (10), and the reported 76.31% improvement is not a consequence of the stated model. Please re-derive the joint probability from the full model (averaging over ε) or provide an explicitly justified independence model.
  2. [Section II.C, Eqs. (5)-(7)] The likelihood in Eq. (5) is written for a real Gaussian observation: the exponent lacks the complex modulus and the normalization corresponds to a scalar real Gaussian. The received signal r_{m,n} is complex, and for circularly symmetric complex Gaussian noise the Fisher information for a deterministic complex signal contains a factor of 2 that is missing in Eq. (6). As a result, the CRLB in Eq. (7) is a factor of two larger than the correct bound. Since Eq. (9) uses CRLB(d_S)+CRLB(d_T) as the distance-error variance, this error directly inflates P_dist and distorts all reported improvements and HO-region lengths. Please correct the likelihood and Fisher information derivation and recompute the numerical results.
  3. [Section IV.B.5 / Fig. 6 and Abstract] The statement that the joint criterion improves the HO activation probability by 76.31% is, by construction, a consequence of taking the union of two events: P_joint ≥ P_RSRP whenever P_dist > 0, regardless of any physical mechanism. The existence of an improvement is therefore not a simulation finding but a mathematical identity. The manuscript should not present it as a surprising result; instead, the paper should clearly state that the contribution lies in the magnitude and parameter sensitivity of the improvement, and should focus the analysis on the assumptions (independence, CRLB-achieving estimator) that control that magnitude.
minor comments (6)
  1. [Eq. (5)] The exponent in the likelihood should use the squared modulus |r_{m,n} - α a_{m,n} e^{-jA_n τ_S}|^2, and the normalization should be that of a complex Gaussian (e.g., (πσ^2)^{-1}); the current expression is not valid for complex data.
  2. [Section IV.B.1 / Fig. 2] The reported D_HO ranges in Fig. 2 are not accompanied by the y-coordinate or the averaging procedure; please specify whether these are for y=0 or averaged over y.
  3. [References] Reference [8] appears before [15] in the introduction, and the bibliography ordering is scrambled (for example, [15] is placed after [7] and before [8]). Please reorder the references so that citations appear in numerical order.
  4. [Section II.B] The statement that the LoS probability approaches 100% at 100-300 m altitude is asserted without a specific reference; if TR 36.777 is the source, please cite the relevant section or annex.
  5. [Section IV.B.6 / Eq. (11)] The effective data rate R_eff is defined using P_HO as a function of position, but the spatial map in Fig. 7 is not accompanied by an explanation of how the 'maximum data rate difference' is computed or whether it is averaged over a drone trajectory or user distribution; please clarify.
  6. [General] The paper uses the term 'simulation' to describe what appears to be numerical evaluation of the closed-form expressions (8)-(10); if no Monte Carlo simulation is performed, please state explicitly that the curves are analytic evaluations.

Circularity Check

2 steps flagged · score 6.0 of 10

Activation-probability improvement is a tautological consequence of the union definition in Eq. (10); the 76.31% figure further depends on an independence assumption that the paper's own shadow-fading model contradicts.

  1. self definitional [Section I, second contribution bullet; Section III, Eq. (10)]
    "A joint HO criterion is proposed by integrating the sensing distance parameter and RSRP, which triggers an HO whenever either the distance condition or the RSRP condition is met. By incorporating distance information, the proposed approach improves the HO success rate compared to RSRP-based methods. ... P^joint_HO = P[(P_T - P_S > Γ) ∪ (d̂_S - d̂_T > d_th)] = P^RSRP_HO + P^dist_HO - P^RSRP_HO · P^dist_HO."

    The joint criterion is defined as the union of the RSRP event A and the distance event B. By set theory, P(A∪B) ≥ P(A) for any B, and Eq. (10) is exactly this union. Therefore the claimed improvement in activation probability over RSRP-only is not a prediction derived from the ISAC physics but a restatement of the definition of the joint criterion. The headline 76.31% is obtained by evaluating this union under the assumed independence and selected parameters, so the sign of the improvement is forced by construction; only the magnitude carries additional model content.

  2. other [Section II.B-C, Eq. (7), Eq. (8), Eq. (10) and Footnote 3]
    "Given the distinct physical origins and statistical independence between these two uncertainty sources, it is reasonable to assume independence between the RSRP-based and distance-based activation events as modeled in (10). ... the average per-subcarrier SNR is defined as γ = β_S N_t^2 P_sum/(N σ^2) ... √β_S = 10^{−(L_S+ε)/20}."

    The factorization in Eq. (10) requires the RSRP activation event and the distance activation event to be independent. But in the paper's own model the same shadow-fading term ε enters the RSRP difference in Eq. (8) and the per-subcarrier SNR γ that sets CRLB(d) in Eq. (7). The distance-estimation variance is therefore a function of the same ε that drives the RSRP event, so the two events are statistically dependent. The product form P_RSRP·P_dist is an additional input assumption, not a consequence of the model; the 76.31% improvement is computed from this assumption, making the headline number an artifact of the assumed independence rather than a derived result.

full rationale

The paper is a modeling-and-simulation study with no fitted parameters and no load-bearing self-citations; the Cramér-Rao bound derivation, the RSRP Q-function, and the distance-event Q-function are standard and self-contained. The significant circularity is in the joint-criterion claim: because the joint criterion is defined as the union of the RSRP and distance conditions, the activation-probability improvement over RSRP-only is guaranteed by definition, and Eq. (10) merely encodes that union. The numeric 76.31% therefore does not test the mechanism; it evaluates the assumed union and the assumed independence. That independence assumption is contradicted by the model's shared shadow-fading term, which affects both the RSRP difference and the CRLB-based distance variance, so the quantitative improvement is not derived from the stated premises. The HO-region-length reduction (75.20%) is not purely tautological, which is why the score is 6 rather than 8-10. Separately, the CRLB expression appears to omit the factor of 2 for complex-Gaussian noise; that is a correctness issue, not circularity.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

The paper introduces no new physical entities. The joint HO criterion is a decision rule, not an entity. All quantities (CRLB, Q-functions, probabilities) are standard analytic constructs.

free parameters (3)
  • Distance threshold d_th = 50 m
    Hand-chosen threshold for the distance-based trigger; directly controls P_dist and the reported HO region length and activation probability improvements.
  • Sensing pilot ratio rho = 20% (nominal configuration)
    Design choice balancing sensing and communication bandwidth; headline results use rho=20%, and performance degrades at rho=6%.
  • RSRP hysteresis Gamma = 2 dB
    Taken from 3GPP [11], but it is a user-set threshold that defines the RSRP baseline and affects the relative improvement of the joint criterion.
assumptions (6)
  • domain assumption LoS-only propagation using the 3GPP UMa-AV LoS model with shadowing log-normal.
    Section II.B states LoS probability approaches 100% at 100-300 m altitude; NLoS is not modeled in the central derivation, though a footnote mentions an NLoS model below 100 m.
  • domain assumption Perfect beam alignment, w = v, giving beamforming gain G = N_t^2.
    Section II.B assumes the transmit beam is aligned with the drone's array response; beam misalignment would reduce sensing SNR and worsen the reported gains.
  • ad hoc to paper Distance estimation achieves the CRLB and the estimation error is zero-mean Gaussian with variance CRLB(d).
    Section II.C states 'Assuming that the BS's distance estimation could achieve the CRLB...' with no specific estimator or achievability argument; this is an optimistic idealization.
  • ad hoc to paper Statistical independence between RSRP-based and distance-based activation events.
    Footnote 3 asserts independence because the physical origins differ; no correlation analysis or sensitivity study is provided, and Eq. (10) depends on it.
  • domain assumption Drone velocity and mobility during the HO decision are negligible.
    Footnote 1 says velocity effects are negligible compared to distance in the short HO period; this removes Doppler and trajectory dynamics from the model.
  • domain assumption RSRP handover event follows the shadowing-only A3 model from [13].
    Eq. (8) uses the A3 event with log-normal shadowing and no fast fading; multipath fading is ignored even though the communication bandwidth is 10 MHz.

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Cite this review

Pith. "Pith review of Sensing-Enhanced Handover Criterion for Low-Altitude Wireless Network (LAWNs)." pith.science (2026). https://pith.science/paper/ZB3Y3C7L

@misc{pith2026250516350,
  author       = {Pith},
  title        = {Pith review of: Sensing-Enhanced Handover Criterion for Low-Altitude Wireless Network (LAWNs)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZB3Y3C7L}},
  note         = {Machine review of arXiv:2505.16350}
}
read the original abstract

With the rapid growth of the low-altitude economy, the demand for cellular-enabled low-altitude wireless networks (LAWN) is rising significantly. The three-dimensional mobility of drones will lead to frequent handovers (HOs) in cellular networks, while traditional reference signal received power (RSRP)-based criteria may fail to capture the dynamic environment, causing redundant HOs or HO failures. To address this issue and motivated by the underutilization of sensing information in conventional HO mechanisms, we propose a novel HO activation criterion for drone systems that integrates both sensing parameters provided by integrated sensing and communication (ISAC) signals and RSRP. First, we construct an ISAC signal model tailored for low-altitude scenarios and derive the Cram\'er--Rao lower bound for sensing distance estimation. Subsequently, we propose a novel joint HO criterion that extends the conventional RSRP-based method by integrating sensing information from ISAC signals, enabling more reliable HOs in dynamic drone environments. Simulation results show that the joint HO criterion outperforms the baseline RSRP-based criterion under different signal-to-noise ratio (SNR) and sensing pilot ratio conditions. Particularly, when the SNR exceeds 0dB and the sensing pilot ratio is 20%, the proposed joint HO criterion reduces the average HO region length by 75.20% and improves the activation probability by 76.31%.

Figures

Figures reproduced from arXiv: 2505.16350 by the authors.

Figure 1
Figure 1. G2A geometric model of the ISAC-enabled drone HO process. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. HO activation probability distributions for various HO criteria at [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. HO activation probability under varying triggering thresholds for the [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: Average improvement in HO activation probability versus SNR for [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Distribution of the effective data rate difference between the joint HO [PITH_FULL_IMAGE:figures/full_fig_p005_7.png]

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