Fluid Aerial Networks: UAV Rotation for Inter-Cell Interference Mitigation

Enzhi Zhou; George K. Karagiannidis; Panagiotis D. Diamantoulakis; Sotiris A. Tegos; Yue Xiao; Ziyue Liu

REVIEW 4 major objections 6 minor 40 references

Fluid Aerial Networks: UAV Rotation for Inter-Cell Interference Mitigation

T0 review · 4 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that rotating the antenna array of a UAV base station, while keeping the same maximum-ratio beamforming, reduces interference to neighboring cells and improves the multi-cell sum rate by about 10 percent in line-of-sight…

desk verdict Plausible idea with a broken printed sum-rate formula; the 10% gain cannot be assessed as written, but the mechanism is worth a major revision. read the letter →

arxiv 2507.01289 v1 pith:QYCNAHTS submitted 2025-07-02 cs.NI eess.SP

Enzhi Zhou , Yue Xiao , Ziyue Liu , Sotiris A. Tegos , Panagiotis D. Diamantoulakis , George K. Karagiannidis This is my paper · ORCID

classification cs.NIeess.SP

keywords UAVrotationinter-cellinterferenceaerialbasestationbeamforminggainmaximumratiotransmissionmmWaveMIMOsum-rateoptimizationline-of-sightchannel

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 argues that physically rotating a UAV-mounted antenna array provides a nearly free degree of freedom for interference management in multi-cell aerial networks. Under line-of-sight channels, the beamforming weights of a maximum-ratio transmitter are determined purely by the relative angles between the UAV and its users, so rotating the array changes the gain pattern toward users in neighboring cells while leaving the intended user's beamforming gain untouched. The authors derive a closed-form interference gain that separates into horizontal and vertical factors and use it to formulate a sum-rate maximization over rotation angles, solved by a low-complexity alternating scheme. Simulations show that in interference-limited regimes, rotating the three UAV base stations improves the average multi-cell user rate by roughly 10 percent over fixed orientations, with performance close to exhaustive search. The significance is that interference mitigation is achieved without extra RF chains, CSI feedback, or joint beamforming optimization.

What carries the argument

The object that carries the argument is the interference gain $g_{\{k_c,k_u\}}(\omega)$, derived in Proposition 1 as a product of two Fejér-kernel-type factors: $g_h(\Phi) g_v(\Phi)$, where each factor is $\sin^2(M\Delta\Omega/2)/(M\sin^2(\Delta\Omega/2))$ with $\Delta\Omega$ the difference of directional cosines between the intended and interfered user. Under MRT beamforming the gain is exactly the squared channel correlation $|h_{u,c,k_c} h_{u,u,k_u}^H|^2$, so rotation enters because a counterclockwise rotation $\omega$ maps $(\alpha_i,\beta_i)$ to $(\alpha_i+\omega,\beta_i-\omega)$ for every user while keeping the pitch $\gamma_i$ fixed. This mapping makes the interference gain a one-dimensional function of $\omega$ for each UAV, lets the authors restrict the search to $[0,\pi/2)$ via Lemma 2, and yields the alternating AUR algorithm whose per-UAV update is a one-dimensional search over $W$ discrete angles.

What would settle it

A field experiment or ray-tracing simulation in a moderately scattering environment (for example, an urban setting with Rician K-factor near 0 dB) that compares a fixed-orientation ABS with a rotated ABS under identical MRT beamforming: if the measured interference at neighboring-cell users does not drop by roughly the predicted amount, or the intended user's gain degrades noticeably, the central claim fails. A simpler laboratory check would measure the two-user array factor of a square patch array as a function of mechanical rotation and compare the null depths and side-lobe levels against Proposition 1.

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Extended reading notes

Core claim

The central claim is that rotating a UAV base station's downward-facing square antenna array, while recomputing the same position-based MRT beamformer, can reshape the array's interference pattern on the ground and thereby reduce inter-cell interference without sacrificing the beamforming gain toward the intended ground user. The paper establishes this through an interference-gain analysis: under a pure line-of-sight channel, the correlation between the serving and interfering channel steering vectors factors into a product of two Dirichlet-like gains, one horizontal and one vertical, each a function of the directional cosines of the UAV-user angles. Because the azimuth angles of all users transform by the same rotation angle while the pitch angle stays fixed, the interference gain becomes a controllable function of the rotation angle, and a $\pi/2$ rotational symmetry of the square array limits the search interval. The proposed alternating UAV rotation algorithm greedily updates one rotation angle at a time and is shown in simulation to capture most of the exhaustive-search gain, improving the average per-user rate by about 10 percent in the interference-limited high-SNR regime.

Load-bearing premise

The analysis assumes every UAV-to-ground link is a pure line-of-sight channel with negligible scattering, so the channel is fully described by the array steering vector; if multipath or blockage is significant, position-based MRT is no longer optimal and rotating the array may not produce the predicted interference reduction.

Editorial extensions

If this is right

In interference-limited line-of-sight deployments, rotating UAV base stations can yield roughly 10 percent average per-user rate gains without additional hardware or CSI feedback.
The $\pi/2$ symmetry of square arrays cuts the search space, making rotation optimization practical with linear complexity $O(LNW)$ in the number of UAVs.
The rotation gain grows with antenna array size because narrower beams allow finer interference steering, and shrinks as user density rises because one rotation angle must serve many interfered users.
The scheme remains robust to positioning errors up to about 20 meters RMS, retaining nearly full gain, which supports low-rate X2-based coordination between UAVs.
UAV rotation can be integrated with trajectory planning and user scheduling as an additional spatial control dimension for future aerial networks.

Reading between the lines

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

The same angular-rotation mechanism could transfer to terrestrial base stations with mechanically steerable arrays or to reconfigurable intelligent surfaces, where the phase profile rotates instead of the physical array.
The factorization of the interference gain suggests an analytic shortcut: choose the rotation angle that nulls the dominant interferer's directional-cosine difference, potentially avoiding the discrete search altogether.
Because rotation preserves the intended user's gain while reshuffling side-lobe exposure, deployments with clustered users should see larger gains, a testable prediction beyond the paper's random-user simulations.
Under Rician fading with significant multipath, the predicted gain would likely shrink as the K-factor drops; the same correlation framework could quantify that degradation.

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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 the short version: the core idea—rotating a UAV's planar array and recomputing MRT to steer sidelobes away from neighboring cells—is sound and worth pursuing. But as written, the paper's own sum-rate formula (25) predicts the opposite of the claimed 10% gain, so the headline result is not supported by the equations. I'd send it back for major revision.

What's new: this extends the 6DMA rotation idea to UAV-mounted arrays with a simple alternating search, benchmarked against exhaustive search in a three-cell LoS deployment. The physical picture is credible: with MRT recomputed after rotation, the main lobe stays on the intended user while sidelobes rotate, giving a few dB of SINR and roughly 10% rate gain in interference-limited regimes. That's a legitimate contribution, though incremental—standard array-factor math, no new hardware.

The soft spots are not minor. Equation (25) defines the sum rate as log2(1 + P/(tilde_eta + L sigma^2)), where tilde_eta is the dimensionless SINR from (24). That's dimensionally wrong (P is watts, tilde_eta is unitless) and, worse, it decreases as tilde_eta rises—so reducing interference would lower the rate. Unless the simulations used the correct log2(1+tilde_eta), the formula contradicts the abstract's 10% claim. The authors need to correct (25) and state which expression generated Figs. 8-10. Second, the rotation transformation in (18) uses tilde_beta_i = beta_i - omega, while Lemma 2's proof uses tilde_beta_i = beta_i + pi/2 and assumes alpha_i + beta_i = pi/2, a constraint that doesn't hold for arbitrary GU positions. So the claimed restriction to [0, pi/2) is not established. There are also no error bars on the 10% figure, and the pure-LoS assumption, while acknowledged, is load-bearing.

None of this kills the idea; a corrected version could well confirm the trend. The analytical core just isn't reliable in this version.

Who's this for: researchers in UAV placement, 6DMA, or interference management in aerial networks. It deserves a serious referee—the idea is timely—but I'd recommend major revision: fix (25), reconcile (18) with Lemma 2, justify the symmetry restriction, and share code or at least standard deviations for the headline number.

Referee Report

4 major / 6 minor

Summary. The paper studies a downlink multi-cell network in which each cell is served by a UAV acting as an aerial base station with a two-dimensional planar antenna array. Under a line-of-sight position-based channel model, the authors analyze how rotating the UAV's array changes the beamforming gain toward users in neighboring cells while preserving the gain toward the intended user, formulate a sum-rate maximization problem over the rotation angles, and propose a low-complexity alternating optimization algorithm (AUR) benchmarked against exhaustive search. Simulation results are reported for three-cell deployments with random user positions, claiming an average sum-rate improvement of about 10% over a fixed orientation in interference-limited scenarios.

Significance. The idea of exploiting array orientation as an additional controllable degree of freedom for inter-cell interference mitigation is timely and potentially useful, as it requires only position information and no additional RF chains or joint beamforming. The paper provides an explicit analytical framework, an alternating optimization algorithm with complexity analysis, an exhaustive-search benchmark, and a robustness evaluation under user-position errors. However, several load-bearing formulas in the analytical core are incorrect as printed, and the numerical evidence for the headline 10% gain is presented without error bars. The qualitative concept is plausible, but the quantitative claims are not yet supported by the manuscript in its current form.

major comments (4)

[Section IV-A, Eq. (25)] The sum-rate expression in Eq. (25) is inconsistent with the SINR definition in Eq. (24). Eq. (24) defines $\tilde{\eta}_{c,k_c}$ as the SINR, so the Shannon rate must be $\log_2(1+\tilde{\eta}_{c,k_c})$. Instead, Eq. (25) writes $\log_2\!\left(1 + P/(\tilde{\eta}_{c,k_c} + L_{c,c,k_c}\sigma_n^2)\right)$. This expression mixes a power quantity $P$ with a dimensionless SINR plus a noise-power term, and it is decreasing in $\tilde{\eta}_{c,k_c}$: as interference decreases, $\tilde{\eta}$ increases, the argument $P/(\tilde{\eta}+L\sigma^2)$ decreases, and the rate decreases. Since the proposed rotation is specifically intended to reduce interference, Eq. (25) as printed predicts that rotation degrades the sum rate, the opposite of the effect claimed in the abstract and in Fig. 8. Please correct Eq. (25) to $\log_2(1+\tilde{\eta}_{c,k_c})$ and confirm that Algorithm 1 and all simulations were computed with the corrected expression. If the simulations used Eq. (25) literally, the reported 10% gain is an artifact and must be recomputed.
[Section III-A, Proposition 1 and Eq. (16)] The interference-gain formula in Eq. (16) and Proposition 1 is incorrect for the normalized steering vectors defined in Eq. (1). With $\psi_h(\theta)=M^{-1/2}[1,e^{j\pi\cos\theta},\ldots,e^{j(M-1)\pi\cos\theta}]$, the squared inner product is $|\psi_h(\theta_{k_c})^H \psi_h(\theta_{k_u})|^2 = (1/M^2)\sin^2(M\Delta\Omega/2)/\sin^2(\Delta\Omega/2)$. The printed Eq. (16) has $(1/M)\sin^2(\cdot)/\sin^2(\cdot)$, and the intermediate expression $(1/M)\sum_{k=1}^M |e^{j(k-1)\Delta\Omega}|$ is not equal to a squared modulus; it evaluates to 1 independently of $\Delta\Omega$. This overestimates the interference gain by a factor of $M$ in each one-dimensional factor, hence by $M^2$ in the two-dimensional product $g_h g_v$. At $\Delta\Omega=0$ the printed formula gives $M$ rather than the correct value 1, which is inconsistent with the unit-norm channel vectors used elsewhere in the paper. Please correct Proposition 1 and clarify whether the simulation code used the corrected normalization; if not, the numerical rates and the 10% improvement need to be recomputed with the correct expression.
[Section III-B, Lemma 2 and Eq. (18)] The proof of Lemma 2 is internally inconsistent with Eq. (18) and relies on an invalid geometric constraint. Eq. (18) states that a counterclockwise rotation by $\omega$ gives $\tilde{\alpha}_i=\alpha_i+\omega$ and $\tilde{\beta}_i=\beta_i-\omega$, but the proof of Lemma 2 sets $\tilde{\beta}_i=\beta_i+\pi/2$ for $\omega=\pi/2$. In addition, the proof assumes $\alpha_i+\beta_i=\pi/2$ for all GUs; this identity holds only for GUs in the first quadrant of the UAV-local coordinate system and is not satisfied by the random circular user distributions used in Section V. The identity $\cos(\pi-\beta)=\cos\beta$ used in Eq. (20) is also false; the correct identity is $\cos(\pi-\beta)=-\cos\beta$. The $\pi/2$-periodicity claim may be salvageable by the symmetry of the square array (the horizontal and vertical factors swap), but the argument as written does not establish it. Since the search-range restriction $[0,\pi/2)$ in Eq. (26) and the complexity reduction in Remark 2 rely on this lemma, the proof must be corrected or the algorithm must be applied on $[0,\pi)$ unless periodicity is rigorously established.
[Section V, Fig. 8] The headline improvement of approximately 10% is presented in Figs. 8a–8c without any confidence intervals, error bars, or statistical significance tests, even though Table I reports only 50 Monte Carlo trials with randomly dropped users in each trial. Given that the average-rate curves are close (e.g., 4.55 vs. 5.05 bps/Hz for $M=8$), the reported gain could be within the trial-to-trial variation. Please report the standard error or confidence intervals of the average rate, and preferably also box plots or a paired comparison, so that the existence and magnitude of a nonzero gain can be assessed.

minor comments (6)

[Throughout] The spacing in 'UA V' is inconsistent (sometimes 'UAV' appears, e.g., in the references); please standardize the notation.
[Section II, X2 interface] The X2 interface is described as part of 3GPP New Radio; X2 is the LTE inter-base-station interface, while NR uses the Xn interface. Please correct this technical detail.
[Algorithm 1] The discrete search set is written as $\omega_u \in \{0, \pi/(2W), \ldots, \pi/2\}$. With the claimed $\pi/2$ periodicity, $\pi/2$ duplicates 0, so the set should be $\{0, \pi/(2W), \ldots, \pi/2 - \pi/(2W)\}$ or the endpoints should be explicitly identified as equivalent.
[Fig. 7 caption] The caption says 'Black dashed lines indicate the boundaries ... while black dashed lines represent the user distribution regions'; the second phrase should refer to different line types or markers, as the current wording describes both elements identically.
[General] There are several typographical errors, including 'beaforming' in Section III-A and 'thea' in Section III-B; a careful proofread is needed.
[Abstract and Section V] The abstract and conclusion state 'about 10%' improvement, while Fig. 8 shows values from 9.1% to 11.1%; please either state the range or use a consistent phrasing.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: the interference-gain analysis follows algebraically from the stated LoS channel model and the ~10% gain is a simulation result benchmarked against exhaustive search; the authors' self-citations are confined to the literature review and are not load-bearing.

full rationale

This paper's central chain is self-contained and contains no circular reduction. The interference gain in Lemma 1 and Proposition 1 follows algebraically (Kronecker-product structure and Dirichlet-kernel array factors) from the LoS steering-vector channel model in Eq. (2), with no parameter fitted to any output of the paper; the rotation effect enters only through the angle substitution (18) into that derived formula. The headline ~10% sum-rate gain is a Monte Carlo outcome of Algorithm 1 evaluated against a fixed-orientation baseline and, crucially, benchmarked against exhaustive search in Fig. 8, an independent optimality reference rather than a self-referential input. The authors' self-citations ([1], [2], [3], [24], [32], [37]) appear only in the literature review or motivation (relaying, data collection, edge computing, RL-based placement, directional-antenna modeling, and RIS-based interference mitigation, respectively), and none supplies a load-bearing premise for the rotation-interference claim; no uniqueness theorem is imported from prior work, and the pi/2 search-range restriction is proven in Lemma 2 rather than cited. What the manuscript does contain are internal-consistency (correctness) flaws that are distinct from circularity: Eq. (25) inserts the noise term L_{c,c,k_c}*sigma_n^2 a second time after Eq. (24) already includes it, and since Eq. (25) is decreasing in the SINR tilde_eta, it would predict that reducing interference lowers the rate, the opposite of the claimed gain; and Lemma 2 applies beta_i + pi/2 while Eq. (18) defines tilde_beta_i = beta_i - omega, so the [0, pi/2) restriction proof is suspect. These issues make the printed formulas unreliable as written but do not make the derivation equivalent to its inputs, so the circularity score remains minimal.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The derivation of the interference gain relies on the standard UPA array factor and pure LoS channel model. The only paper-specific modeling assumption is the rotation angle transformation (18), which appears internally inconsistent. No new physical entities are introduced; the rotation angle is an optimization variable. The quantitative 10% gain is scenario-dependent, determined by the chosen simulation geometry and parameters.

free parameters (3)

UAV cell geometry (positions, radius, min separation) = 3 cells at (500,500,200), (500,1500,200), (1000,1500,200); radius 500 m; min separation 200 m
The reported ~10% sum-rate gain is simulated for this specific geometry; other geometries would yield different gains.
Number of antenna elements M = 8, 16, 32
The gain from rotation increases with M; the paper reports results for these values chosen by hand.
Angle discretization W = 4, 8, 16, 32
Algorithm parameter controlling the trade-off between complexity and solution accuracy; chosen by hand in simulations.

assumptions (5)

domain assumption Each UAV-GU link is dominated by a direct LoS channel with negligible scattering, so the channel is fully characterized by the steering vector h_k = a_k ψ_h(θ_k) ⊗ ψ_v(φ_k).
Section II states the system is considered in suburban or rural environments with negligible scattering; the entire interference gain analysis (Lemma 1, Proposition 1) relies on this.
standard math The antenna array is a downward-facing planar array with M by M half-wavelength spaced elements, and the steering vectors follow the Kronecker product model in (1)-(2).
Standard array factor model; cited without proof in Section II.
domain assumption MRT beamforming with normalized channel norm, so |h_{c,c,k_c} f_{c,k_c}|^2 = 1, and the desired user's gain is unaffected by rotation.
Assumed in (22) and used to simplify the SINR; valid because the beamformer is recomputed after rotation based on the rotated channel.
ad hoc to paper Rotating the UAV by ω transforms the user angles as ᾶ_i = α_i + ω and β̃_i = β_i − ω while γ_i remains invariant.
Eq. (18) is a modeling assumption specific to this paper; its sign convention is inconsistent with Lemma 2 and with a standard coordinate rotation, which is a source of concern.
domain assumption Each UAV has one RF chain, GUs are scheduled on different time resources, and each neighboring UAV schedules one of its users per time slot.
Section II states this scheduling assumption, used in the SINR expression (22).

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Cite this review

Pith. "Pith review of Fluid Aerial Networks: UAV Rotation for Inter-Cell Interference Mitigation." pith.science (2026). https://pith.science/paper/QYCNAHTS

@misc{pith2026250701289,
  author       = {Pith},
  title        = {Pith review of: Fluid Aerial Networks: UAV Rotation for Inter-Cell Interference Mitigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QYCNAHTS}},
  note         = {Machine review of arXiv:2507.01289}
}

read the original abstract

With the rapid development of aerial infrastructure, unmanned aerial vehicles (UAVs) that function as aerial base stations (ABSs) extend terrestrial network services into the sky, enabling on-demand connectivity and enhancing emergency communication capabilities in cellular networks by leveraging the flexibility and mobility of UAVs. In such a UAV-assisted network, this paper investigates position-based beamforming between ABSs and ground users (GUs). To mitigate inter-cell interference, we propose a novel fluid aerial network that leverages ABS rotation to increase multi-cell capacity and overall network efficiency. Specifically, considering the line-of-sight channel model, the spatial beamforming weights are determined by the orientation angles of the GUs. In this direction, we examine the beamforming gain of a two-dimensional multiple-input multiple-output (MIMO) array at various ground positions, revealing that ABS rotation significantly affects multi-user channel correlation and inter-cell interference. Based on these findings, we propose an alternative low-complexity algorithm to design the optimal rotation angle for ABSs, aiming to reduce inter-cell interference and thus maximize the sum rate of multi-cell systems. In simulations, exhaustive search serves as a benchmark to validate the optimization performance of the proposed sequential ABS rotation scheme. Moreover, simulation results demonstrate that, in interference-limited regions, the proposed ABS rotation paradigm can significantly reduce inter-cell interference in terrestrial networks and improve the multi-cell sum rate by approximately 10\% compared to fixed-direction ABSs without rotation.

Figures

Figures reproduced from arXiv: 2507.01289 by the authors.

**Figure 3.** Evolution of the 3D beam pattern with UAV rotation. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** The effects of UAV rotation on projected beam gain. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

Figures from the paper (4 more)

**Figure 5.** Figure 5: The effect of UAV rotation on the interference gain [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 7.** Figure 7: The inter-cell interference with UAV rotation, [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

**Figure 8.** Figure 8: Average GU rate with the AUR algorithm under different numbers of UAV antennas. [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

**Figure 9.** Figure 9: Average GU rate and convergence of AUR algorithm. [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]

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Y . Liu, B. Zhang, D. Guo, H. Wang, and G. Ding, “Joint Precoding Design and Location Optimization in Joint Communication, Sensing and Computing of UA V Systems,” IEEE Trans. Cognit. Commun. Networking, vol. 10, no. 2, pp. 541–552, 2024

2024
[22]

CoMP in the Sky: UA V Placement and Movement Optimization for Multi-User Communications,

L. Liu, S. Zhang, and R. Zhang, “CoMP in the Sky: UA V Placement and Movement Optimization for Multi-User Communications,” IEEE Trans. Commun., vol. 67, no. 8, pp. 5645–5658, 2019

2019
[23]

Deployment Optimization of UA V-Aided Networks Through a Dynamic Tunable Model,

J. Liu, H. Zhang, and Y . He, “Deployment Optimization of UA V-Aided Networks Through a Dynamic Tunable Model,” IEEE Commun. Lett. , vol. 25, no. 7, pp. 2348–2352, 2021

2021
[24]

Learning-Aided UA V 3D Placement and Power Allocation for Sum-Capacity Enhancement Under Varying Altitudes,

Z. Kaleem, W. Khalid, A. Muqaibel, A. A. Nasir, C. Yuen, and G. K. Karagiannidis, “Learning-Aided UA V 3D Placement and Power Allocation for Sum-Capacity Enhancement Under Varying Altitudes,” IEEE Commun. Lett. , vol. 26, no. 7, pp. 1633–1637, 2022

2022
[25]

Efficient Local Map Search Algorithms for the Placement of Flying Relays,

J. Chen and D. Gesbert, “Efficient Local Map Search Algorithms for the Placement of Flying Relays,” IEEE Trans. Wireless Commun. , vol. 19, no. 2, pp. 1305–1319, 2020

2020
[26]

Geometric Analysis-Based 3D Anti-Block UA V Deployment for mmWave Communications,

F. Li, C. He, X. Li, J. Peng, and K. Yang, “Geometric Analysis-Based 3D Anti-Block UA V Deployment for mmWave Communications,”IEEE Commun. Lett., vol. 26, no. 11, pp. 2799–2803, 2022

2022
[27]

3D UA V Deployment in Multi-UA V Networks With Statistical User Position Information,

L. Wang, H. Zhang, S. Guo, and D. Yuan, “3D UA V Deployment in Multi-UA V Networks With Statistical User Position Information,”IEEE Commun. Lett., vol. 26, no. 6, pp. 1363–1367, 2022

2022
[28]

Physical- Environment-Map-Aided 3-D Deployment Optimization for UA V- Assisted Integrated Localization and Communication in Urban Areas,

S. Bi, Z. Zhuo, X.-H. Lin, Y . Wu, and Y .-J. A. Zhang, “Physical- Environment-Map-Aided 3-D Deployment Optimization for UA V- Assisted Integrated Localization and Communication in Urban Areas,” IEEE Internet Things J. , vol. 11, no. 9, pp. 15 490–15 503, 2024

2024
[29]

3-D Positioning and Resource Allocation for Multi-UA V Base Stations Under Blockage-Aware Channel Model,

P. Yi, L. Zhu, Z. Xiao, R. Zhang, Z. Han, and X.-G. Xia, “3-D Positioning and Resource Allocation for Multi-UA V Base Stations Under Blockage-Aware Channel Model,” IEEE Trans. Wireless Commun. , vol. 23, no. 3, pp. 2453–2468, 2024

2024
[30]

Learning to Deployment: Data-Driven On-Demand UA V Placement for Throughput Maximization,

L. Wang, H. Zhang, S. Guo, D. Li, and D. Yuan, “Learning to Deployment: Data-Driven On-Demand UA V Placement for Throughput Maximization,” IEEE Trans. Veh. Technol. , vol. 73, no. 6, pp. 8007– 8012, 2024

2024
[31]

Lightweight 3-D Beamform- ing Design in 5G UA V Broadcasting Communications,

W. Miao, C. Luo, G. Min, and Z. Zhao, “Lightweight 3-D Beamform- ing Design in 5G UA V Broadcasting Communications,” IEEE Trans. Broadcast., vol. 66, no. 2, pp. 515–524, 2020. 13

2020
[32]

Position and Orientation Planning of the UA V With Rectangle Coverage Area,

E. Zhou, Z. Liu, W. Zhou, P. Lan, and Z. Dong, “Position and Orientation Planning of the UA V With Rectangle Coverage Area,”IEEE Trans. Veh. Technol., vol. 74, no. 1, pp. 1719–1724, 2025

2025
[33]

6DMA Enhanced Wireless Network with Flexible Antenna Position and Rotation: Opportunities and Challenges,

X. Shao and R. Zhang, “6DMA Enhanced Wireless Network with Flexible Antenna Position and Rotation: Opportunities and Challenges,” IEEE Commun. Mag. , vol. 63, no. 4, pp. 121–128, 2025

2025
[34]

6D Movable Antenna Enhanced Wireless Network via Discrete Position and Rotation Opti- mization,

X. Shao, R. Zhang, Q. Jiang, and R. Schober, “6D Movable Antenna Enhanced Wireless Network via Discrete Position and Rotation Opti- mization,” IEEE J. Sel. Areas Commun. , vol. 43, no. 3, pp. 674–687, 2025

2025
[35]

Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity,

X. Shao, R. Zhang, Q. Jiang, J. Park, T. Q. S. Quek, and R. Schober, “Distributed Channel Estimation and Optimization for 6D Movable Antenna: Unveiling Directional Sparsity,” IEEE J. Sel. Top. Signal Process., vol. 19, no. 2, pp. 349–365, 2025

2025
[36]

Dynamic Beam Coverage for Satellite Communications Aided by Movable-Antenna Array,

L. Zhu, X. Pi, W. Ma, Z. Xiao, and R. Zhang, “Dynamic Beam Coverage for Satellite Communications Aided by Movable-Antenna Array,” IEEE Trans. Wireless Commun., vol. 24, no. 3, pp. 1916–1933, 2025

1916
[37]

Ris-assisted multi-cell over- the-air computation,

Y . Xiao, S. A. Tegos, S. Huang, P. D. Diamantoulakis, D. Tyrovolas, Z. Ma, G. K. Karagiannidis, and P. Fan, “Ris-assisted multi-cell over- the-air computation,” IEEE Trans. Wireless Commun. , pp. 1–1, 2025

2025
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A contemporary survey on fluid antenna systems: Fundamentals and networking perspectives,

H. Hong, K.-K. Wong, H. Xu, X. Guo, F. R. Ghadi, Y . Chen, Y . Xu, C.-B. Chae, B. Liu, K.-F. Tong, and Y . Zhang, “A contemporary survey on fluid antenna systems: Fundamentals and networking perspectives,”
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Evolved Universal Terrestrial Radio Access Network (E- UTRAN); X2 Application Protocol (X2AP),

3GPP, “Evolved Universal Terrestrial Radio Access Network (E- UTRAN); X2 Application Protocol (X2AP),” 3GPP, Technical Speci- fication 36.423, dec 2020

2020
[2025]

Available: https://arxiv.org/abs/2506.13317

[Online]. Available: https://arxiv.org/abs/2506.13317

arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

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Energy-aware trajectory optimization for uav-mounted RIS and full-duplex relay,

D. Tyrovolas, N. A. Mitsiou, T. G. Boufikos, P.-V . Mekikis, S. A. Tegos, P. D. Diamantoulakis, S. Ioannidis, C. K. Liaskos, and G. K. Karagiannidis, “Energy-aware trajectory optimization for uav-mounted RIS and full-duplex relay,” IEEE Internet Things J. , vol. 11, no. 13, pp. 24 259–24 272, 2024

work page 2024

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Energy-aware design of uav-mounted RIS networks for IoT data collection,

D. Tyrovolas, P.-V . Mekikis, S. A. Tegos, P. D. Diamantoulakis, C. K. Liaskos, and G. K. Karagiannidis, “Energy-aware design of uav-mounted RIS networks for IoT data collection,” IEEE Trans. Commun. , vol. 71, no. 2, pp. 1168–1178, 2023

work page 2023

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Multiple access in the era of distributed computing and edge intelligence,

N. G. Evgenidis, N. A. Mitsiou, V . I. Koutsioumpa, S. A. Tegos, P. D. Diamantoulakis, and G. K. Karagiannidis, “Multiple access in the era of distributed computing and edge intelligence,” Proc. IEEE, vol. 112, no. 9, pp. 1497–1526, 2024

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Toward autonomous multi-UA V wireless network: A survey of reinforcement learning-based approaches,

Y . Bai, H. Zhao, X. Zhang, Z. Chang, R. J ¨antti, and K. Yang, “Toward autonomous multi-UA V wireless network: A survey of reinforcement learning-based approaches,” IEEE Commun. Surveys & Tuts. , vol. 25, no. 4, pp. 3038–3067, 2023

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Accessing From the Sky: A Tutorial on UA V Communications for 5G and Beyond,

Y . Zeng, Q. Wu, and R. Zhang, “Accessing From the Sky: A Tutorial on UA V Communications for 5G and Beyond,” Proc. IEEE, vol. 107, no. 12, pp. 2327–2375, 2019

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What Will the Future of UA V Cellular Communications Be? A Flight From 5G to 6G,

G. Geraci, A. Garcia-Rodriguez, M. M. Azari, A. Lozano, M. Mezzav- illa, S. Chatzinotas, Y . Chen, S. Rangan, and M. D. Renzo, “What Will the Future of UA V Cellular Communications Be? A Flight From 5G to 6G,” IEEE Commun. Surveys & Tuts. , vol. 24, no. 3, pp. 1304–1335, 2022

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Hybrid Offline-Online Design for UA V-Enabled Data Harvesting in Probabilistic LoS Channels,

C. You and R. Zhang, “Hybrid Offline-Online Design for UA V-Enabled Data Harvesting in Probabilistic LoS Channels,” IEEE Trans. Wireless Commun., vol. 19, no. 6, pp. 3753–3768, 2020

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A Survey on Millimeter-Wave Beamforming Enabled UA V Communica- tions and Networking,

Z. Xiao, L. Zhu, Y . Liu, P. Yi, R. Zhang, X.-G. Xia, and R. Schober, “A Survey on Millimeter-Wave Beamforming Enabled UA V Communica- tions and Networking,” IEEE Commun. Surveys & Tuts. , vol. 24, no. 1, pp. 557–610, 2022

work page 2022