Aerial Booster-Cell Enabled Inter-Cell Interference Coordination for 5G NR Networks

Ismail Guvenc; Md Sharif Hossen; Vijay K. Shah

arxiv: 2604.07145 · v1 · submitted 2026-04-08 · 💻 cs.NI

Aerial Booster-Cell Enabled Inter-Cell Interference Coordination for 5G NR Networks

Md Sharif Hossen , Vijay K. Shah , Ismail Guvenc This is my paper

Pith reviewed 2026-05-10 17:30 UTC · model grok-4.3

classification 💻 cs.NI

keywords UAV coverage5G NRinter-cell interference coordinationbooster cellantenna uptiltSIR optimizationdownlink reliabilityTDIC

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The pith

Coordinated uptilt optimization in booster cells raises worst-case UAV SIR in multi-cell 5G NR networks while preserving ground user performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tackles severe downlink interference that UAVs face in 5G NR macro networks from base stations optimized for ground users. It introduces a booster-cell architecture that permits selected cells to raise coverage for aerial users through controlled uptilt of their antennas. This is paired with time-domain scheduling to coordinate interference across neighboring cells. The joint optimization problem is solved via particle swarm optimization and genetic algorithms to maximize the lowest UAV signal-to-interference ratio over a spatial grid, subject to keeping ground user equipment performance acceptable. Simulations indicate measurable gains in UAV reliability under this coordinated setup.

Core claim

By formulating a multi-cell optimization that maximizes minimum UAV SIR across a grid of aerial locations while constraining GUE impact, and solving it through bio-inspired methods for joint BS uptilt angles plus NR-compliant TDIC scheduling, the booster-cell architecture delivers higher worst-case UAV SIR and downlink reliability than uncoordinated baselines.

What carries the argument

Booster-cell architecture that enables joint antenna-domain uptilt optimization and time-domain interference coordination (TDIC) via NR scheduling.

If this is right

Higher minimum UAV SIR across dense macro deployments
Improved downlink reliability for aerial users without new hardware
Ground user equipment performance remains within acceptable limits
Practical implementation using standard NR scheduling rules

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same tilt-coordination idea could extend to other high-altitude or mobile aerial platforms beyond current UAVs
Field trials in actual operator networks would be needed to confirm whether simulation gains survive real propagation and traffic variations
This coordination approach might inform antenna-tilt policies in future integrated aerial-terrestrial spectrum sharing
It suggests that software-level scheduling tweaks alone are insufficient and must be combined with physical-layer antenna adjustments

Load-bearing premise

The idealized network model and interference patterns used in simulations match real 5G NR deployments, and the optimizers reliably locate solutions that do not require further tuning to protect ground users.

What would settle it

A side-by-side measurement of worst-case UAV SIR in a live multi-cell 5G NR testbed with and without the coordinated uptilt and scheduling, checking whether the predicted reliability gain appears.

Figures

Figures reproduced from arXiv: 2604.07145 by Ismail Guvenc, Md Sharif Hossen, Vijay K. Shah.

**Figure 2.** Figure 2: Illustration of the dominant downlink signal and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: NR-compatible slot-level muting between DT and UT [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Uptilt angle selection for ISD = 500 m, h = 200 m for different approaches. PSO is still the best approach, while coordinated slots outperform uncoordinated slots, though by a smaller margin than in the dense deployment case [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 5.** Figure 5: Rate with ISD = 500 m and h = 200 m for US and CS subframes considering different algorithms. GAHGA Single Random PSO DT Only GAHGA Single Random PSO 0 0.2 0.4 0.6 0.8 1 min rate (bps/Hz) solid edge: US dashed edge: CS (a) min rate GAHGA Single Random PSO DT Only GAHGA Single Random PSO 0 1 2 3 50% rate (bps/Hz) solid edge: US dashed edge: CS (b) 50% rate GAHGA Single Random PSO DT Only GAHGA Single Random… view at source ↗

**Figure 6.** Figure 6: Rate with ISD = 1000 m and h = 100 m for US and CS subframes considering different algorithms. -5 0 5 10 15 SIR (dB) 0 0.2 0.4 0.6 0.8 1 CDF GA (US) GA (CS) HGA (US) HGA (CS) Single (US) Single (CS) Random (US) Random (CS) PSO (US) PSO (CS) DT Only (a) h = 200 m, ISD = 500 m -10 -5 0 5 10 15 SIR (dB) 0 0.2 0.4 0.6 0.8 1 CDF GA (US) GA (CS) HGA (US) HGA (CS) Single (US) Single (CS) Random (US) Random (CS) P… view at source ↗

**Figure 7.** Figure 7: CDF of SIR with different ISDs and UAV altitudes for different optimization approaches. The solid lines represent the [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

**Figure 8.** Figure 8: Antenna gain and CDF of SIR (hybrid GA approach) [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗

**Figure 9.** Figure 9: Impact of antenna down-tilt and duty cycle on GUE [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗

read the original abstract

Cellular-connected unmanned aerial vehicles (UAVs) operating in 5G New Radio (NR) macro networks experience severe and spatially non-uniform downlink interference. This is primarily caused by the interference from the sidelobes of downtilted base station (BS) antennas serving terrestrial users, which limits the ability of the network to provide uniform and high-quality coverage to aerial users. Supporting aerial users requires boosting the coverage of certain cells or sectors, which can further exacerbate inter-cell interference in dense macro deployments. This motivates the need for inter-cell interference coordination (ICIC) in multi-cell 5G NR networks serving both aerial and terrestrial users. In this work, we propose an ICIC framework that jointly optimizes antenna-domain coordination through BS uptilt angle optimization and time-domain interference coordination (TDIC) through NR-compliant scheduling. The framework is formulated as a multi-cell NR macro deployment problem that maximizes the minimum UAV signal-to-interference ratio (SIR) over a spatial grid of UAV locations while maintaining acceptable performance for ground user equipment (GUEs). The resulting optimization problem is non-convex and is solved using bio-inspired optimization techniques, including particle swarm optimization (PSO) and genetic algorithm (GA). Simulation results demonstrate that coordinated uptilt optimization with the booster-cell architecture significantly improves worst-case UAV SIR and downlink reliability in multi-cell 5G NR networks. booster-cell architecture significantly improves worst-case UAV SIR and downlink reliability in multi-cell 5G NR networks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper gives a concrete booster-cell ICIC scheme for 5G NR that jointly tunes uptilts and TDIC to lift worst-case UAV SIR, with simulations showing gains but thin validation details.

read the letter

The paper presents a booster-cell architecture for inter-cell interference coordination in 5G NR networks serving both UAVs and ground users. The core move is to jointly optimize per-cell uptilt angles and NR-compliant time-domain scheduling to maximize the minimum UAV SIR over a spatial grid while holding GUE performance to an acceptable level. The non-convex problem is solved with particle swarm optimization and genetic algorithms, which is a reasonable fit for the mix of continuous angles and discrete scheduling choices. The simulations report clear improvements in worst-case UAV downlink reliability under the booster-cell setup. That formulation and the practical focus on existing macro deployments are the useful parts. The booster-cell concept lets operators boost coverage in selected sectors without wholesale infrastructure changes, and the constraint on GUEs keeps the proposal grounded in real network priorities. The work stays within standard 5G NR assumptions and cites relevant ICIC literature without overclaiming novelty. The soft spots sit in the evaluation. The results are simulation-only, and the abstract plus available text give limited quantitative baselines, error bars, or full lists of propagation parameters and exclusion criteria. This leaves open how sensitive the SIR gains are to the exact network layout, sidelobe modeling, or optimizer hyperparameters. If the interference assumptions do not match measured 5G NR macro behavior, the reported improvements could shrink in the field. The paper is aimed at researchers and engineers working on UAV integration into cellular systems. A reader focused on practical interference management for mixed aerial-terrestrial traffic can extract the joint optimization structure and the simulation outcomes for further development. The thinking is straightforward and the methods are standard, so the paper deserves a serious referee. I would recommend sending it to peer review after requesting more on model validation and result robustness.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a booster-cell architecture for inter-cell interference coordination (ICIC) in multi-cell 5G NR networks serving both UAVs and ground user equipment (GUEs). It formulates a non-convex optimization problem that jointly tunes base-station uptilt angles and NR-compliant time-domain interference coordination (TDIC) scheduling, solved via particle swarm optimization (PSO) and genetic algorithm (GA), with the objective of maximizing the minimum UAV signal-to-interference ratio (SIR) over a spatial grid while preserving acceptable GUE performance. Simulation results are claimed to demonstrate that the coordinated uptilt optimization significantly improves worst-case UAV SIR and downlink reliability.

Significance. If the underlying network model, propagation assumptions, and optimizer behavior prove representative of actual 5G NR deployments, the framework could supply a practical, standards-compliant method for mitigating the well-known sidelobe interference problem that aerial users experience in macro-cell networks, thereby supporting uniform coverage for UAVs without requiring new spectrum or hardware.

major comments (3)

[§5 (Simulation Results)] §5 (Simulation Results) and associated tables: the abstract and results sections report that the booster-cell architecture 'significantly improves' worst-case UAV SIR and downlink reliability, yet supply no numerical baseline values (e.g., SIR without optimization or with conventional downtilt), no error bars, no statistical significance tests, and no explicit parameter settings for BS density, transmit power, antenna patterns, or UAV height distribution. These omissions render the central performance claims unverifiable from the provided material.
[§4 (Optimization Problem Formulation)] §4 (Optimization Problem Formulation), Eq. (problem statement): the non-convex joint uptilt-and-TDIC problem is solved exclusively with PSO and GA, but the manuscript provides neither convergence analysis, optimality-gap bounds, nor sensitivity results with respect to the listed free parameters (uptilt angles and optimizer hyperparameters). Because the claimed gains rest on the reliability of these heuristics locating feasible high-quality points, the absence of such diagnostics is load-bearing for the central claim.
[§3 (System Model)] §3 (System Model): the interference model relies on specific assumptions about downtilted BS sidelobes, booster-cell coverage boosting, and multi-cell layout, yet contains no cross-validation against 3GPP TR 36.777 UAV channel models, ray-tracing benchmarks, or field measurements. If these assumptions deviate materially from real 5G NR deployments, the reported SIR gains become artifacts of the simulation setup rather than evidence of a robust ICIC framework.

minor comments (2)

[Abstract] Abstract: the final sentence is duplicated verbatim, producing an obvious copy-paste artifact that should be removed.
[Notation] Notation and definitions: the precise definition of the spatial UAV grid, the SIR expression, and the GUE performance constraint (e.g., minimum rate or outage threshold) would benefit from an explicit equation or table entry for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed review, as well as for acknowledging the potential practical value of the proposed booster-cell ICIC framework. We address each major comment point by point below, indicating the specific revisions we will incorporate to improve verifiability and transparency.

read point-by-point responses

Referee: [§5 (Simulation Results)] §5 (Simulation Results) and associated tables: the abstract and results sections report that the booster-cell architecture 'significantly improves' worst-case UAV SIR and downlink reliability, yet supply no numerical baseline values (e.g., SIR without optimization or with conventional downtilt), no error bars, no statistical significance tests, and no explicit parameter settings for BS density, transmit power, antenna patterns, or UAV height distribution. These omissions render the central performance claims unverifiable from the provided material.

Authors: We agree that the absence of explicit numerical baselines, parameter values, and statistical measures limits independent verification. In the revised manuscript we will expand Section 5 with a new table reporting minimum UAV SIR for the unoptimized case, conventional downtilt, and the proposed joint optimization; we will also include error bars obtained from multiple independent simulation runs, list all simulation parameters (BS density, transmit power, antenna patterns, UAV height distribution) in a dedicated table, and add a short discussion of result variability. revision: yes
Referee: [§4 (Optimization Problem Formulation)] §4 (Optimization Problem Formulation), Eq. (problem statement): the non-convex joint uptilt-and-TDIC problem is solved exclusively with PSO and GA, but the manuscript provides neither convergence analysis, optimality-gap bounds, nor sensitivity results with respect to the listed free parameters (uptilt angles and optimizer hyperparameters). Because the claimed gains rest on the reliability of these heuristics locating feasible high-quality points, the absence of such diagnostics is load-bearing for the central claim.

Authors: We acknowledge that additional optimizer diagnostics would strengthen confidence in the reported gains. While theoretical optimality-gap bounds are unavailable for this non-convex formulation, the revised manuscript will include (i) convergence plots for both PSO and GA demonstrating stabilization of the objective, (ii) sensitivity results with respect to uptilt-angle bounds and key hyperparameters (population size, mutation rate), and (iii) statistics across multiple independent optimizer runs to illustrate solution consistency. revision: yes
Referee: [§3 (System Model)] §3 (System Model): the interference model relies on specific assumptions about downtilted BS sidelobes, booster-cell coverage boosting, and multi-cell layout, yet contains no cross-validation against 3GPP TR 36.777 UAV channel models, ray-tracing benchmarks, or field measurements. If these assumptions deviate materially from real 5G NR deployments, the reported SIR gains become artifacts of the simulation setup rather than evidence of a robust ICIC framework.

Authors: The system model employs standard 3GPP macro-cell path-loss and antenna-pattern formulations. To address the concern we will add a new subsection in Section 3 that explicitly cites 3GPP TR 36.777, discusses alignment of our UAV height and sidelobe assumptions with that specification, and provides a qualitative comparison of the resulting SIR statistics. Full ray-tracing or measurement-based validation lies outside the scope of the present study. revision: partial

Circularity Check

0 steps flagged

No circularity: explicit optimization and simulation results

full rationale

The paper formulates a non-convex multi-cell optimization problem to maximize minimum UAV SIR subject to GUE constraints, then solves it numerically via PSO and GA. All load-bearing steps are explicit: antenna uptilt angles and TDIC scheduling are decision variables, the objective is defined directly from the SIR expression over a spatial grid, and performance is reported from Monte Carlo simulations. No step renames a fitted parameter as a prediction, imports uniqueness via self-citation, or reduces the claimed gains to an input by construction. The framework is self-contained against its stated model and solver; external validation of the model is a correctness issue, not circularity.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 1 invented entities

Abstract-only review; detailed free parameters, axioms, and entities cannot be fully extracted. The booster-cell concept appears introduced here, and the optimization relies on standard wireless propagation assumptions.

free parameters (2)

Uptilt angles per booster cell
Decision variables in the optimization; specific values or ranges not reported in abstract.
PSO and GA hyperparameters
Control convergence of the solver; not specified.

axioms (2)

domain assumption Sidelobe interference from downtilted BS antennas dominates UAV reception in macro networks
Stated as primary cause of non-uniform interference in the problem motivation.
domain assumption GUE performance can be maintained while optimizing for UAV SIR
Constraint in the multi-objective formulation.

invented entities (1)

Booster-cell architecture no independent evidence
purpose: Selected cells/sectors that perform uptilt to serve UAVs
New coordination element proposed to enable the ICIC scheme

pith-pipeline@v0.9.0 · 5566 in / 1295 out tokens · 73487 ms · 2026-05-10T17:30:07.156065+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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An overview of 3GPP release-15 study on enhanced LTE support for connected drones,

S. D. Muruganathan, X. Lin, H.-L. Maattanen, J. Sedin, Z. Zou, W. A. Hapsari, and S. Yasukawa, “An overview of 3GPP release-15 study on enhanced LTE support for connected drones,”IEEE Commun. Stand. Mag., vol. 5, no. 4, pp. 140–146, 2021

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Y . Du, H. Zhang, and J. Peng, “Modeling and coverage analysis for cellular-connected UA Vs with up-tilted antenna,”IEEE Commun. Lett., vol. 26, no. 11, pp. 2572–2575, 2022

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Network densification: the dominant theme for wireless evolution into 5G,

N. Bhushan, J. Li, D. Malladi, R. Gilmore, D. Brenner, A. Damnjanovic, R. T. Sukhavasi, C. Patel, and S. Geirhofer, “Network densification: the dominant theme for wireless evolution into 5G,”IEEE Commun. Mag., vol. 52, no. 2, pp. 82–89, 2014

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Femtocells: Past, present, and future,

J. G. Andrews, H. Claussen, M. Dohler, S. Rangan, and M. C. Reed, “Femtocells: Past, present, and future,”IEEE J. on Select. Areas Com- mun., vol. 30, no. 3, pp. 497–508, 2012

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Study on channel model for frequencies from 0.5 to 100 GHz,

3GPP, “Study on channel model for frequencies from 0.5 to 100 GHz,” 3rd Generation Partnership Project (3GPP), Technical Report TR 38.901, Apr. 2022

work page 2022

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Genetic algorithm: An ap- proach on optimization,

S. D. Immanuel and U. K. Chakraborty, “Genetic algorithm: An ap- proach on optimization,” inProc. IEEE Int. Conf. Commun. Electron. Syst., Coimbatore, India, 2019, pp. 701–708

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Role-based connectiv- ity management with realistic air-to-ground channels for cooperative UA Vs,

N. Goddemeier, K. Daniel, and C. Wietfeld, “Role-based connectiv- ity management with realistic air-to-ground channels for cooperative UA Vs,”IEEE J. on Select. Areas Commun., vol. 30, no. 5, pp. 951–963, 2012

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