Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs

Nicola Marchetti; Ramy Amer; Walid Saad

REVIEW 2 major objections 4 minor 44 references

Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs

T0 review · 2 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Coordinated multi-point transmission can lift a hovering drone's coverage from 28% with nearest-BS association to about 60%, yet down-tilted antennas keep aerial coverage below ground users'.

desk verdict The static CoMP coverage analysis is solid stochastic geometry, but the mobile 3D analysis is built on an unnormalized transition-length density and the mobility results do not hold up. read the letter →

arxiv 1908.07774 v1 pith:H4RK7TFV submitted 2019-08-21 cs.IT eess.SPmath.IT

Ramy Amer , Walid Saad , Nicola Marchetti This is my paper · ORCID

classification cs.ITeess.SPmath.IT

keywords cellular-connectedUAVscoordinatedmulti-pointtransmissioncoverageprobability3DrandomwaypointmobilityhandoverratestochasticgeometryPoissonpointprocessmaximumratio

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 asks whether clusters of ground base stations that transmit together can give drones—flying taxis, delivery drones—the reliable cellular link that ordinary single-cell handoffs cannot. It models base stations grouped into disjoint clusters that serve a hovering or moving drone jointly under maximum ratio transmission, and derives upper and lower bounds on the drone's coverage probability. The central quantitative claim is that cooperative transmission raises drone coverage from 28% under nearest-base-station association to about 60% at low SIR thresholds, for a collaboration distance of 200 m. The paper also argues that because base-station antennas are tilted downward, high-altitude drones are served by antenna side-lobes and face line-of-sight interference, so their coverage remains below ground users' no matter how many base stations cooperate. For mobile drones, it introduces a 3D random-waypoint mobility model and derives handover rate and handover probability as functions of speed, altitude range, and network density.

What carries the argument

The load-bearing machinery is the CoMP cluster: a ball of collaboration radius $R_c$ whose base stations serve one UAV-UE with maximum ratio transmission, each precoder aligned to the channel phase. The coverage analysis approximates the desired-signal power by a Gamma random variable (second-order moment match) and evaluates inter-cluster interference through its Laplace transform; derivatives of that transform fill a lower-triangular Toeplitz matrix $T_K$, and the conditional coverage probability is the induced $\ell^1$ norm of $\mathbf{e}^\top T_K$. For mobility, the 3D random-waypoint model defines a step $U=\sqrt{\rho^2+(Z_n-Z_{n-1})^2}$ with claimed density $f_U(u)=2\pi\mu u e^{-\pi\mu u^2}\Omega(\mu,\hbar)$, which reduces to the 2D Rayleigh density when the altitude range $\hbar$ vanishes. The same model produces the steady-state altitude distribution $f_{Z_\infty}(z)$ used to average mobile coverage over vertical motion.

What would settle it

Numerically integrate the claimed density $f_U(u)=2\pi\mu u e^{-\pi\mu u^2}\Omega(\mu,\hbar)$ over $u\ge 0$ for finite $\mu$ and $\hbar$; the integral is not 1, which contradicts normalization and therefore removes the ground from the derived handover rate, handover probability, and mobile coverage theorems. A simulation of 3D random-waypoint step lengths would show the same mismatch.

Watch

Extended reading notes

Core claim

The paper's central claim is that clustered cooperative transmission makes aerial coverage analytically tractable and quantitatively much better: at an SIR threshold near $-5$ dB, the coverage probability of a hovering UAV rises from about 28% with nearest-base-station association to about 60% with a 200 m collaboration distance (an average of 2.5 cooperating base stations). It further claims a structural ceiling: because base-station antennas are down-tilted, a high-altitude UAV is served by antenna side-lobes and faces LoS-dominated interference, so its coverage is always bounded above by a ground user's regardless of the transmission scheme. For mobile UAVs, the paper introduces a 3D random-waypoint model and derives closed-form handover rate and handover probability: handover falls as the altitude range grows and rises with speed, while vertical fluctuations around a fixed mean altitude leave coverage nearly unchanged. These results are obtained by bounding the Nakagami-faded desired signal with Cauchy-Schwarz, matching the sum of Gammas, and carrying the interference Laplace transform through a Toeplitz matrix.

Load-bearing premise

The mobile half of the paper rests on Lemma 1's formula for the distance between two consecutive 3D waypoints; that formula must keep the vertical change no larger than the total distance, but as printed it omits this constraint and does not integrate to one, so the handover and mobile-coverage results built on its mean are not grounded.

Editorial extensions

If this is right

If the model holds, operators can raise drone coverage from about 28% to 60% at low SIR thresholds by serving each drone from a cluster of base stations within roughly 200 m.
The down-tilt ceiling means aerial users cannot overtake ground users in coverage under conventional antennas, so CoMP narrows but does not remove the aerial-coverage penalty.
Drones that change altitude frequently have lower handover rates and handover probabilities than drones flying horizontally at the same speed.
Drone speed degrades the coverage probability when handovers have a failure cost, but vertical motion around a fixed mean altitude has only a marginal effect.
The analytic bounds allow system designers to trade collaboration distance, base-station density, and SIR threshold without running full network simulations.

Reading between the lines

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

Editorial inference: If the 28%-to-60% gain survives field conditions, cooperative transmission offers a densification-free path to drone coverage, because the gain comes from turning interferers into servers rather than adding base stations.
Editorial inference: The down-tilt ceiling implies that closing the aerial-terrestrial gap entirely requires changing the antenna pattern itself, such as upward-tilted or steerable beams for aerial users, not merely enlarging cooperation clusters.
Editorial inference: Because the printed 3D step-length density is not normalized, the paper's mobility formulas should be treated as a framework to re-derive with the constraint $|z_n-z_{n-1}|\le u$, not as final numerical predictions.
Editorial inference: A testable consequence of the mobility model is that, at equal ground speed, a drone with vertical excursions should experience fewer handovers than a ground vehicle, which could be checked with drone measurement campaigns alongside drive tests.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

Quick take: the static half of this paper is a competent piece of work; the mobile half rests on a density that is not a PDF, so the handover and mobile-coverage claims are currently unsupported.

The static CoMP analysis uses the standard toolkit — Cauchy–Schwarz to bound a weighted sum of Nakagami variables, Gamma moment matching, and the Toeplitz–Laplace representation from Yu et al. The upper and lower bounds track simulation in Fig. 2, and the comparison with nearest-association is useful. The setup is clear, and the overlap with the authors' own WCNC 2019 paper is disclosed. That part is worth a read.

The problem is Lemma 1. The stated density f_U(u)=2πµu e^{−πµu²} Ω(µ,ℏ) drops the constraint that the vertical difference |z_n−z_{n−1}| cannot exceed u. Conditioned on that difference d, the horizontal length must be at least |d|, and averaging over the triangular density of d gives a u-dependent factor C(u)=∫_{−min(u,ℏ)}^{min(u,ℏ)} (ℏ−|d|)/ℏ² e^{πµd²} dd. Replacing C(u) by Ω(µ,ℏ)=C(ℏ) assigns probability mass to unphysical pairs and makes the density integrate to Ω, which is strictly above 1. So E[U]=Ω/(2√µ) is not the mean transition length, and the handover rate in (11), the handover probability in (14), and Theorems 2 and 3 all inherit the error. The proof is omitted, which makes the issue worse; the claimed simulation match for (11) is hard to credit unless the simulator used the same incorrect density.

The "GUE always beats UAV" message is also more assumed than proved. With Gs≪Gm and UAVs restricted to side-lobes, the ordering is baked into the model. That is a reasonable modeling choice for down-tilted antennas, but the abstract states it as a finding rather than a consequence.

Minor issues: there are indexing and notation typos in the integral limits (e.g., Theorem 1), and no code or data are provided.

Bottom line: the static analysis is solid enough to be refereed; the mobile half needs a corrected Lemma 1 and a full re-derivation before the mobility claims can be taken seriously. I would send this to review with a clear major-revision message, and I would tell the authors to separate the static contribution from the mobile one.

Referee Report

2 major / 4 minor

Summary. The paper studies coordinated multi-point (CoMP) transmission for cellular-connected UAV user equipments (UAV-UEs) in both static hovering and mobile 3D scenarios. Using stochastic geometry, the authors model BSs as a PPP with cluster-centric cooperation, derive upper and lower bounds on UAV-UE coverage probability via Cauchy-Schwarz and Gamma moment matching, and propose a 3D random waypoint mobility model to analyze handover rate, handover probability, and mobile coverage. The central conclusions are that CoMP substantially improves UAV coverage (e.g., from 28% to 60% at low SIR thresholds for static UAVs) and that UAV coverage is always upper bounded by that of ground users due to down-tilted antennas and LoS-dominated interference.

Significance. If correct, the static analysis is a valuable contribution: Theorem 1 and Corollary 1 provide tractable bounds that match simulations, and the down-tilt ceiling result has clear practical relevance. The paper also addresses an important and timely problem. However, the mobile half of the paper is compromised by a fundamental error in Lemma 1, so the claimed first rigorous 3D mobility analysis is not established. The static results and the qualitative CoMP benefit may survive a correction, but the numerical handover-rate and mobile-coverage claims require substantial rework.

major comments (2)

[Section IV, Lemma 1] The stated density f_U(u)=2πµu e^{−πµu²} Ω(µ,ℏ) is not a probability density. Let D=Z_n−Z_{n−1}, which has the triangular density (ℏ−|d|)/ℏ² on [−ℏ,ℏ]. Conditionally on D=d, U=√(ρ²+d²) has density 2πµu e^{−πµ(u²−d²)} for u≥|d| and zero otherwise. Averaging over d gives a u-dependent factor ∫_{−min(u,ℏ)}^{min(u,ℏ)} (ℏ−|d|)/ℏ² e^{πµ d²} dd, not the constant Ω(µ,ℏ). Consequently ∫_0^∞ f_U(u) du = Ω(µ,ℏ) > 1 for every ℏ>0, and the stated mean E[U]=Ω(µ,ℏ)/(2√µ) is incorrect. Because the handover rate in (11), the inter-CoMP handover rate in (16), and the associated conclusions about vertical motion lowering handover rate all rely on this E[U], the mobile handover-rate analysis is not grounded. The proof of Lemma 1 is omitted, and the simulation match in Fig. 7(a) does not repair a false PDF.
[Section V, Theorems 2 and 3] Theorems 2 and 3 use the handover probabilities from (14) and (20) as exact quantities in (26) and (27). Lemma 2, however, is explicitly an upper bound obtained via Jensen's inequality, so 1−P(H|r0) is a lower bound on the no-handover probability; the joint term P(Υ≥ϑ, H|r0) is then replaced by a product of the SIR coverage and the handover probability without any stated independence or conditioning justification. The resulting expressions are therefore approximations or bounds, not the claimed coverage probabilities, and the direction of the error is not analyzed. This affects the mobile coverage results in Figs. 7(c) and 8(c).

minor comments (4)

[Theorem 1, Eq. (8)] The integral notation ∫_{rκ=Rc}^{∞} with a vector rκ is ill-defined; the joint PDF ∏ 2ri/Rc² is supported on [0,Rc]^κ, so the integral should be over [0,Rc]^κ or written more explicitly.
[Theorem 1 and Appendix A] The notation Pl_c|r = ‖e_TK‖_1 is used without defining e_TK or the induced ℓ1 norm in context; please define e_1^T T_K or an equivalent vector-matrix product.
[Section III-B] The claim that the Gamma approximation 'can be easily verified via numerical simulations that are omitted due to space limitations' leaves the approximation unverified; please include the verification or a concrete reference.
[General] There are typographical issues, including 'and and' in the author affiliation footnote and inconsistent spacing in 'UA V-UEs' throughout; these should be corrected in a revision.

Circularity Check

1 steps flagged · score 3.0 of 10

The coverage and handover derivations are largely self-contained; the headline claim that UAV-UE coverage is always below GUE coverage is, however, largely encoded in the side-lobe/main-lobe gain comparison, and Lemma 1's 3D transition-length density has an unnormalized form that is a correctness issue rather than a circularity.

self definitional [Section II-A after Eq. (2); Section III-B comparison setup before Fig. 2]
"Given this setup, it is reasonable to assume that UA V-UEs are always served from the antennas’ side-lobes while the GUEs are served from the antennas’ main-lobes with antenna gains Gs and Gm, respectively, where Gs≪Gm. ... We assume that the BSs’ antennas are ideally down-tilted accounting for the GUEs, i.e., the antenna gains for desired and interfering signals are Gm and Gs, respectively. Under such a setup, we observe that the coverage probability of GUEs substantially outperforms that of UA V-UEs."

The paper's conclusion that the coverage probability of a UAV-UE is 'always upper bounded by that of a GUE' is built into the comparison setup rather than emerging from an independent derivation. In Eq. (2), every UAV link is assigned the small side-lobe gain Gs, while the GUE comparison assigns the desired signal the main-lobe gain Gm, with Gs≪Gm imposed in the model. With all other network ingredients held fixed, the GUE desired-signal term is larger by construction, so the qualitative ordering is a restatement of the antenna-gain assumption. This is a structural, partial circularity: the quantitative coverage curves still require the stochastic-geometry computation, but the headline upper-bound claim is not an independent prediction.

full rationale

Most of the derivation chain is non-circular. The static coverage bounds in Theorem 1 and Corollaries 1–2 are derived in Appendices A–B from Cauchy–Schwarz, Gamma moment matching [33], and the Toeplitz Laplace representation [34], with parameters taken from external channel models; no quantity is fitted to the coverage output. The mobile handover analysis follows the standard handover-count [38]–[40] and handover-cost [40]–[42] models, with β as a model knob rather than a fitted parameter. The self-citations [1] and [14] are background or conference pointers, and the journal proofs are largely self-contained, so they are not load-bearing. The one step that is close to circularity is the qualitative 'UAV always worse than GUE' claim, which is installed by the Gs≪Gm side-lobe/main-lobe comparison and then reported as a result. Separately, Lemma 1 has a serious non-circular technical flaw: the proof is omitted ('details omitted due to space limitations'), and the stated density f_U(u)=2πµu e^{−πµu²}Ω(µ,ℏ) drops the constraint u≥|z_n−z_{n−1}|, making Ω a constant instead of a u-dependent factor and giving a density that integrates to Ω>1. That invalidates E[U], Eq. (11), Eq. (14), and the mobile parts of Theorems 2–3, but it is a correctness problem, not a circularity. Therefore the circularity score is 3: one structural self-definitional element, while the main coverage and handover computations retain independent content.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The paper's quantitative claims rest on the standard stochastic-geometry assumptions listed above, plus the 3D RWP distributional assumptions. No genuinely new physical entity is introduced. The key unverified input is the transition-length PDF in Lemma 1, which this review finds non-normalized, and the hand-tuned mobility parameter mu and handover cost beta that drive the mobile numerical results.

free parameters (2)

Mobility parameter mu = not fitted; 100-300 km^-2 in simulations
Controls the Rayleigh distribution of horizontal transition lengths in the 3D RWP model; handover rate and probability depend on mu through Omega(mu, hbar).
Handover failure probability beta = not fitted; 0.5 or 1 in simulations
Penalty in the linear handover cost model (21); mobile coverage results are directly sensitive to this arbitrarily chosen knob.

assumptions (6)

domain assumption BS locations form a homogeneous PPP and the hexagonal cluster is approximated by an equal-area disk of radius Rc; the typical UAV-UE is placed at the cluster center.
Section II. The cluster-center assumption is explicitly stated to be for tractability and is treated as an upper bound on coverage for random locations [31], but all coverage formulas depend on it.
domain assumption High-altitude UAV-UEs are served exclusively through BS antenna side-lobes with gain Gs, while GUEs are served through main-lobes with gain Gm, with Gs much smaller than Gm.
Section II. This encodes the down-tilt conclusion in the model; the claim that GUE coverage always bounds UAV coverage is not a derived theorem but a consequence of choosing Gs much smaller than Gm.
domain assumption The desired signal is LoS-dominated and modeled with Nakagami-ml; interference includes both LoS and NLoS components with integer Nakagami parameters.
Section III-B. Justified by high altitude, but the LoS-dominance of the desired signal is assumed rather than derived from the channel model.
domain assumption 3D waypoints are i.i.d., horizontal transition lengths are Rayleigh with parameter mu, vertical altitude is uniform on [h1, h2], speed is constant, and horizontal and vertical displacements are independent.
Section IV. Adopted from RWP literature [36], [38], [23] for tractability; the handover formulas are sensitive to these distributions, and the displacement PDF in Lemma 1 is not a valid consequence of them.
domain assumption The 1D random waypoint steady-state altitude distribution from [35] describes the UAV-UE altitude during long flights.
Used in Theorems 2 and 3 to average coverage over altitude; taken from prior work and applied without validation for UAV trajectories.
domain assumption The linear handover cost model in (21) maps handover probability to coverage probability through a single failure parameter beta.
Adopted from [40]-[42]; not justified for UAV links but used to turn handover probability into the mobile coverage expressions.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs." pith.science (2026). https://pith.science/paper/H4RK7TFV

@misc{pith2026190807774,
  author       = {Pith},
  title        = {Pith review of: Mobility in the Sky: Performance and Mobility Analysis for Cellular-Connected UAVs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/H4RK7TFV}},
  note         = {Machine review of arXiv:1908.07774}
}

read the original abstract

Providing connectivity to unmanned aerial vehicle-user equipments such as drones or flying taxis is a major challenge for tomorrow cellular systems. In this paper, the use of coordinated multi-point transmission for providing seamless connectivity to UAV user equipments is investigated. In particular, a network of clustered ground base stations that cooperatively serve a number of UAVUEs is considered. Two scenarios are studied: scenarios with static, hovering UAV user equipments and scenarios with mobile UAV-UEs. Under a maximum ratio transmission, a novel framework is developed and leveraged to derive upper and lower bounds on the UAV-UE coverage probability for both scenarios. Using the derived results, the effects of various system parameters such as collaboration distance, UAVUE altitude, and UAV-UE velocity on the achievable performance are studied. Results reveal that, for both static and mobile UAV user equipments, when the BS antennas are tilted downwards, the coverage probability of a high-altitude UAV-UE is upper bounded by that of ground users regardless of the transmission scheme. Moreover, for low signal-to-interference-ratio thresholds, it is shown that CoMP transmission can improve the coverage probability of UAV user equipments, e.g., from 28% under the nearest association scheme to 60% for a collaboration distance of 200m.

Figures

Figures reproduced from arXiv: 1908.07774 by the authors.

**Figure 1.** Illustration of the proposed system model where BSs cooperatively serve high-altitude UAV-UEs via CoMP transmission. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

**Figure 2.** The derived upper and lower bounds on the coverage probability of UAV-UEs are plotted versus the SIR threshold [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** The proposed 3D mobility model for UAV-UEs which incorporates the typical 2D spatial RWP and 1D RWP for the [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Figures from the paper (5 more)

**Figure 4.** Figure 4: The probability of handover is computed based on the network geometry. [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

**Figure 5.** Figure 5: The probability of handover is plotted versus network parameters for nearest association and CoMP transmission schemes [PITH_FULL_IMAGE:figures/full_fig_p018_5.png]

**Figure 6.** Figure 6: The derived upper and lower bounds on the static UAV-UE coverage probability are plotted versus the UAV-UE altitude [PITH_FULL_IMAGE:figures/full_fig_p023_6.png]

**Figure 7.** Figure 7: Effect of the 3D mobility on the performance of aerial and UEs when they are associated with their nearest BSs. In (c), [PITH_FULL_IMAGE:figures/full_fig_p023_7.png]

**Figure 8.** Figure 8: Effect of the 3D mobility on the performance of aerial and ground UEs when they are served via CoMP transmission [PITH_FULL_IMAGE:figures/full_fig_p025_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

44 extracted references · 41 canonical work pages

[1]

Caching to the sky: Performance analysis of cache-assisted CoMP for cellular-connected UA Vs,

R. Amer, W. Saad, H. ElSawy, M. Butt, and N. Marchetti, “Caching to the sky: Performance analysis of cache-assisted CoMP for cellular-connected UA Vs,” inProc. of the IEEE Wireless Communications and Networking Conference (WCNC) , Marrakech, Morocco, April. 2019

work page 2019
[2]

Integration of satellite and aerial communications for heterogeneous ﬂying vehicles,

M. V ondra, M. Ozger, D. Schupke, and C. Cavdar, “Integration of satellite and aerial communications for heterogeneous ﬂying vehicles,” IEEE Network, vol. 32, no. 5, pp. 62–69, September 2018

work page 2018
[3]

A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Network, to appear , 2019. August 22, 2019 DRAFT 29

work page 2019
[4]

Capacity and coverage enhancement using long-endurance tethered airborne base stations,

M. A. Kishk, A. Bader, and M.-S. Alouini, “Capacity and coverage enhancement using long-endurance tethered airborne base stations,” arXiv preprint arXiv:1906.11559 , 2019

arXiv 1906
[5]

On the 3-D Placement of Airborne Base Stations Using Tethered UAVs

——, “On the 3-D placement of airborne base stations using tethered uavs,” arXiv preprint arXiv:1907.04299 , 2019

work page Pith review arXiv 1907
[6]

Drones in distress: A game-theoretic countermeasure for protecting uavs against gps spooﬁng,

A. Eldosouky, A. Ferdowsi, and W. Saad, “Drones in distress: A game-theoretic countermeasure for protecting uavs against gps spooﬁng,” arXiv preprint arXiv:1904.11568 , 2019

arXiv 1904
[7]

A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,

M. Mozaffari, W. Saad, M. Bennis, Y . Nam, and M. Debbah, “A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,” IEEE Communications Surveys Tutorials , pp. 1–1, 2019

work page 2019
[8]

Beyond 5G with UA Vs: Foundations of a 3D wireless cellular network,

M. Mozaffari, A. Taleb Zadeh Kasgari, W. Saad, M. Bennis, and M. Debbah, “Beyond 5G with UA Vs: Foundations of a 3D wireless cellular network,” IEEE Transactions on Wireless Communications , vol. 18, no. 1, pp. 357–372, Jan 2019

work page 2019

Show all 44 references

[9]

Cellular-connected UA V: Potential, challenges, and promising technologies,

Y . Zeng, J. Lyu, and R. Zhang, “Cellular-connected UA V: Potential, challenges, and promising technologies,”IEEE Wireless Communications, vol. 26, no. 1, pp. 120–127, February 2019

2019
[10]

Unmanned aircraft systems’ trial report,

L. Qualcomm, “Unmanned aircraft systems’ trial report,” 2017

2017
[11]

The sky is not the limit: LTE for unmanned aerial vehicles,

X. Lin, V . Yajnanarayana, S. D. Muruganathan, S. Gao, H. Asplund, H.-L. Maattanen, M. Bergstrom, S. Euler, and Y .-P. E. Wang, “The sky is not the limit: LTE for unmanned aerial vehicles,” IEEE Communications Magazine , vol. 56, no. 4, pp. 204–210, April 2018

2018
[12]

LTE in the sky: trading off propagation beneﬁts with interference costs for aerial nodes,

B. Van der Bergh, A. Chiumento, and S. Pollin, “LTE in the sky: trading off propagation beneﬁts with interference costs for aerial nodes,” IEEE Communications Magazine , vol. 54, no. 5, pp. 44–50, May 2016

2016
[13]

Coexistence of terrestrial and aerial users in cellular networks,

M. M. Azari, F. Rosas, A. Chiumento, and S. Pollin, “Coexistence of terrestrial and aerial users in cellular networks,” in Proc. of IEEE Globecom Workshops (GC Wkshps) , Singapore, Dec 2017, pp. 1–6

2017
[14]

Towards a connected sky: Performance of beamforming with down-tilted antennas for ground and UA V user co-existence,

R. Amer, W. Saad, and N. Marchetti, “Towards a connected sky: Performance of beamforming with down-tilted antennas for ground and UA V user co-existence,” IEEE Communications Letters , pp. 1–1, 2019

2019
[15]

Cell-free massive MIMO for UA V communications,

C. D’Andrea, A. Garcia-Rodriguez, G. Geraci, L. G. Giordano, and S. Buzzi, “Cell-free massive MIMO for UA V communications,” arXiv preprint arXiv:1902.03578 , 2019

1902 arXiv
[16]

Energy efﬁciency of RSMA and NOMA in cellular-connected mmwave UA V networks,

A. Rahmati, Y . Yapıcı, N. Rupasinghe, I. Guvenc, H. Dai, and A. Bhuyany, “Energy efﬁciency of RSMA and NOMA in cellular-connected mmwave UA V networks,”arXiv preprint arXiv:1902.04721 , 2019

1902 arXiv
[17]

Downlink coverage and rate analysis of an aerial user in integrated aerial and terrestrial networks,

N. Cherif, M. Alzenad, H. Yanikomeroglu, and A. Yongacoglu, “Downlink coverage and rate analysis of an aerial user in integrated aerial and terrestrial networks,” arXiv preprint arXiv:1905.11934 , 2019

1905 arXiv
[18]

Multi-beam UA V communication in cellular uplink: Cooperative interference cancellation and sum-rate maximization,

L. Liu, S. Zhang, and R. Zhang, “Multi-beam UA V communication in cellular uplink: Cooperative interference cancellation and sum-rate maximization,” arXiv preprint arXiv:1808.00189 , 2018

2018 arXiv
[19]

Cellular-enabled UA V communication: A connectivity-constrained trajectory optimiza- tion perspective,

S. Zhang, Y . Zeng, and R. Zhang, “Cellular-enabled UA V communication: A connectivity-constrained trajectory optimiza- tion perspective,” IEEE Transactions on Communications , vol. 67, no. 3, pp. 2580–2604, March 2019

2019
[20]

Trajectory optimization for cellular-connected UA V under outage duration constraint,

S. Zhang and R. Zhang, “Trajectory optimization for cellular-connected UA V under outage duration constraint,” arXiv preprint arXiv:1901.04286, 2019

1901 arXiv
[21]

Interference management for cellular-connected UA Vs: A deep reinforcement learning approach,

U. Challita, W. Saad, and C. Bettstetter, “Interference management for cellular-connected UA Vs: A deep reinforcement learning approach,” IEEE Transactions on Wireless Communications , vol. 18, no. 4, pp. 2125–2140, April 2019

2019
[22]

Coverage probability of 3-D mobile UA V networks,

P. K. Sharma and D. I. Kim, “Coverage probability of 3-D mobile UA V networks,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 97–100, Feb 2019

2019
[23]

Random 3D mobile UA V networks: Mobility modeling and coverage probability,

——, “Random 3D mobile UA V networks: Mobility modeling and coverage probability,” IEEE Transactions on Wireless Communications, vol. 18, no. 5, pp. 2527–2538, May 2019

2019
[24]

Moving aerial base station networks: A stochastic geometry analysis and design perspective,

S. Enayati, H. Saeedi, H. Pishro-Nik, and H. Yanikomeroglu, “Moving aerial base station networks: A stochastic geometry analysis and design perspective,” IEEE Transactions on Wireless Communications , vol. 18, no. 6, pp. 2977–2988, June 2019

2019
[25]

Cellular connectivity for UA Vs: Network modeling, performance analysis and design guidelines,

M. M. Azari, F. Rosas, and S. Pollin, “Cellular connectivity for UA Vs: Network modeling, performance analysis and design guidelines,” IEEE Transactions on Wireless Communications , pp. 1–1, 2019

2019
[26]

Mobility support for cellular connected unmanned aerial vehicles: Performance and analysis,

S. Euler, H. Maattanen, X. Lin, Z. Zou, M. Bergström, and J. Sedin, “Mobility support for cellular connected unmanned aerial vehicles: Performance and analysis,” 2018. [Online]. Available: http://arxiv.org/abs/1804.04523 August 22, 2019 DRAFT 30

2018 arXiv
[27]

Handover challenges for cellular-connected drones,

A. Fakhreddine, C. Bettstetter, S. Hayat, R. Muzaffar, and D. Emini, “Handover challenges for cellular-connected drones,” in Proc. of ACM Workshop on Micro Aerial Vehicle Networks, Systems, and Applications , NY , USA, 2019, pp. 9–14

2019
[28]

Performance impact of LoS and NLoS transmissions in dense cellular networks,

M. Ding, P. Wang, D. López-Pérez, G. Mao, and Z. Lin, “Performance impact of LoS and NLoS transmissions in dense cellular networks,” IEEE Transactions on Wireless Communications , vol. 15, no. 3, pp. 2365–2380, March 2016

2016
[29]

A stochastic model for UA V networks positioned above demand hotspots in urban environments,

B. Galkin, J. Kibilda, and L. Da Silva, “A stochastic model for UA V networks positioned above demand hotspots in urban environments,” IEEE Transactions on Vehicular Technology, pp. 1–1, 2019

2019
[30]

Austin, Unmanned aircraft systems: UAVS design, development and deployment

R. Austin, Unmanned aircraft systems: UAVS design, development and deployment . John Wiley & Sons, 2011, vol. 54

2011
[31]

Cooperative caching and transmission design in cluster-centric small cell networks,

Z. Chen, J. Lee, T. Q. S. Quek, and M. Kountouris, “Cooperative caching and transmission design in cluster-centric small cell networks,” IEEE Transactions on Wireless Communications , vol. 16, no. 5, pp. 3401–3415, May 2017

2017
[32]

Haenggi, Stochastic geometry for wireless networks

M. Haenggi, Stochastic geometry for wireless networks . Cambridge University Press , 2012

2012
[33]

Multiuser MIMO in distributed antenna systems with out-of-cell interference,

R. W. Heath Jr, T. Wu, Y . H. Kwon, and A. C. Soong, “Multiuser MIMO in distributed antenna systems with out-of-cell interference,” IEEE Transactions on Signal Processing , vol. 59, no. 10, pp. 4885–4899, Oct 2011

2011
[34]

A uniﬁed framework for the tractable analysis of multi-antenna wireless networks,

X. Yu, C. Li, J. Zhang, M. Haenggi, and K. B. Letaief, “A uniﬁed framework for the tractable analysis of multi-antenna wireless networks,” IEEE Transactions on Wireless Communications , vol. 17, no. 12, pp. 7965–7980, Dec 2018

2018
[35]

The node distribution of the random waypoint mobility model for wireless ad hoc networks,

C. Bettstetter, G. Resta, and P. Santi, “The node distribution of the random waypoint mobility model for wireless ad hoc networks,” IEEE Transactions on Mobile Computing , vol. 2, no. 3, pp. 257–269, July 2003

2003
[36]

Stochastic properties of the random waypoint mobility model,

C. Bettstetter, H. Hartenstein, and X. Pérez-Costa, “Stochastic properties of the random waypoint mobility model,” Wireless Networks, vol. 10, no. 5, pp. 555–567, 2004

2004
[37]

Spatial node distribution of the random waypoint mobility model with applications,

E. Hyytia, P. Lassila, and J. Virtamo, “Spatial node distribution of the random waypoint mobility model with applications,” IEEE Transactions on Mobile Computing , vol. 5, no. 6, pp. 680–694, June 2006

2006
[38]

Towards understanding the fundamentals of mobility in cellular networks,

X. Lin, R. K. Ganti, P. J. Fleming, and J. G. Andrews, “Towards understanding the fundamentals of mobility in cellular networks,” IEEE Transactions on Wireless Communications , vol. 12, no. 4, pp. 1686–1698, April 2013

2013
[39]

Modeling and analyzing the cross-tier handover in heterogeneous networks,

X. Xu, Z. Sun, X. Dai, T. Svensson, and X. Tao, “Modeling and analyzing the cross-tier handover in heterogeneous networks,” IEEE Transactions on Wireless Communications , vol. 16, no. 12, pp. 7859–7869, Dec 2017

2017
[40]

Mobility-aware analysis of 5G and B5G cellular networks: A tutorial,

H. Tabassum, M. Salehi, and E. Hossain, “Mobility-aware analysis of 5G and B5G cellular networks: A tutorial,” arXiv preprint arXiv:1805.02719, 2018

2018 arXiv
[41]

Handoff rate and coverage analysis in multi-tier heterogeneous networks,

S. Sadr and R. S. Adve, “Handoff rate and coverage analysis in multi-tier heterogeneous networks,” IEEE Transactions on Wireless Communications, vol. 14, no. 5, pp. 2626–2638, May 2015

2015
[42]

Velocity-aware handover management in two-tier cellular networks,

R. Arshad, H. ElSawy, S. Sorour, T. Y . Al-Naffouri, and M. Alouini, “Velocity-aware handover management in two-tier cellular networks,” IEEE Transactions on Wireless Communications , vol. 16, no. 3, pp. 1851–1867, March 2017

2017
[43]

A tractable approach to coverage and rate in cellular networks,

J. G. Andrews, F. Baccelli, and R. K. Ganti, “A tractable approach to coverage and rate in cellular networks,” IEEE Transactions on Communications , vol. 59, no. 11, pp. 3122–3134, November 2011

2011
[44]

I. S. Gradshteyn and I. M. Ryzhik, Table of integrals, series, and products . Academic press, 2014. August 22, 2019 DRAFT

2014

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [1]

Caching to the sky: Performance analysis of cache-assisted CoMP for cellular-connected UA Vs,

R. Amer, W. Saad, H. ElSawy, M. Butt, and N. Marchetti, “Caching to the sky: Performance analysis of cache-assisted CoMP for cellular-connected UA Vs,” inProc. of the IEEE Wireless Communications and Networking Conference (WCNC) , Marrakech, Morocco, April. 2019

work page 2019

[2] [2]

Integration of satellite and aerial communications for heterogeneous ﬂying vehicles,

M. V ondra, M. Ozger, D. Schupke, and C. Cavdar, “Integration of satellite and aerial communications for heterogeneous ﬂying vehicles,” IEEE Network, vol. 32, no. 5, pp. 62–69, September 2018

work page 2018

[3] [3]

A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,

W. Saad, M. Bennis, and M. Chen, “A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,” IEEE Network, to appear , 2019. August 22, 2019 DRAFT 29

work page 2019

[4] [4]

Capacity and coverage enhancement using long-endurance tethered airborne base stations,

M. A. Kishk, A. Bader, and M.-S. Alouini, “Capacity and coverage enhancement using long-endurance tethered airborne base stations,” arXiv preprint arXiv:1906.11559 , 2019

arXiv 1906

[5] [5]

On the 3-D Placement of Airborne Base Stations Using Tethered UAVs

——, “On the 3-D placement of airborne base stations using tethered uavs,” arXiv preprint arXiv:1907.04299 , 2019

work page Pith review arXiv 1907

[6] [6]

Drones in distress: A game-theoretic countermeasure for protecting uavs against gps spooﬁng,

A. Eldosouky, A. Ferdowsi, and W. Saad, “Drones in distress: A game-theoretic countermeasure for protecting uavs against gps spooﬁng,” arXiv preprint arXiv:1904.11568 , 2019

arXiv 1904

[7] [7]

A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,

M. Mozaffari, W. Saad, M. Bennis, Y . Nam, and M. Debbah, “A tutorial on UA Vs for wireless networks: Applications, challenges, and open problems,” IEEE Communications Surveys Tutorials , pp. 1–1, 2019

work page 2019

[8] [8]

Beyond 5G with UA Vs: Foundations of a 3D wireless cellular network,

M. Mozaffari, A. Taleb Zadeh Kasgari, W. Saad, M. Bennis, and M. Debbah, “Beyond 5G with UA Vs: Foundations of a 3D wireless cellular network,” IEEE Transactions on Wireless Communications , vol. 18, no. 1, pp. 357–372, Jan 2019

work page 2019