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Joint Communication and Sensing in Aerial Corridors: A Novel Stochastic Geometry Framework

Harris K. Armeniakos, Petros S. Bithas, Athanasios G. Kanatas, Harpreet S. Dhillon

arXiv:2610.01359v1cs.ITmath.IT

TL;DR

JCAS research in UAV networks has limited coverage of aerial corridors, where interference and 3D user geometry matter. This paper models a finite cylindrical corridor with a 3D BPP and derives exact-form coverage expressions for a UAV-BS tracking a user that communicates uplink. More directional beams improve JCAS performance, particularly for shorter corridors.

  • Problem

    JCAS in UAV networks remains largely unexplored, and corridor analysis with a 3D antenna pattern requires characterizing both Euclidean and angular user distances.

    Source: §I · p. 2

  • Method

    The paper models a fixed number of UAV-UEs as a 3D BPP in a finite cylindrical corridor and derives exact-form JCAS coverage expressions for a 3GPP-pattern UAV-BS tracking a user that communicates uplink.

    Source: §I · p. 2, A. Spatial and Network Modeling · p. 2

  • Results

    More directional UAV-BS beams notably improve JCAS performance for shorter corridors, while for longer corridors the improvement is more significant at larger corridor radii.

    Source: §IV · p. 5

  • Takeaways & Limitations

    Directional beams are useful for suppressing interference, and corridor radius and length are relevant design parameters for JCAS performance.

    Source: §IV · p. 5, §V · p. 6

    The model assumes sensing and communication use different time-frequency resources and leaves other scheduling mechanisms for future work.

    Source: A. Spatial and Network Modeling · p. 2

Abstract

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In unmanned aerial vehicle (UAVs) networks, joint communication and sensing (JCAS) is emerging as a key enabler to support extended and continuous communication and sensing capabilities for sixth-generation (6G) services among UAVs operating in swarms. Within this framework, aerial corridors provide a structured environment for supporting coordinated and reliable operations. In this paper, a comprehensive frame- work is presented to investigate the JCAS coverage probability (CP) of a UAV-base station (BS) in an aerial corridor populated by UAV-user equipments (UEs). The corridor is modeled as a finite cylinder, within which a fixed number of UAV-UEs are spatially distributed according to a three-dimensional (3D) binomial point process (BPP). The UAV-BS is assumed to be equipped with a realistic 3D third generation partnership project (3GPP) antenna pattern and exploits radar sensing capabilities to track a known UAV-UE. Subsequently, the tracked UAV-UE is assumed to perform uplink communication with the UAV-BS. Accordingly, the JCAS CP at the UAV-BS is analyzed under the presence of clutter and uplink communication interference, and exact-form analytical expressions are derived. To the best of our knowledge, this is the first work to develop a stochastic geometry framework for the rigorous analysis of JCAS performance in aerial corridors, with UAV-UE locations modeled as a 3D BPP. Among several insights, results show that increasing the directivity of the UAV-BS antenna beams leads to notable JCAS performance gains, particularly for shorter UAV corridors.

I. Introduction

UAV corridors focus network design on reliable connectivity in defined airspace, while JCAS can let UAV-BSs track UAVs and support their uplink communication. This work addresses the limited analysis of JCAS in aerial networks by modeling corridor users as a 3D BPP and deriving exact-form coverage expressions.

I. Introduction

  • UAV corridors concentrate traffic in designated airspace, motivating reliable connectivity within those areas rather than across the entire sky.Corridors are also intended to minimize collision risks and avoid disruption to existing air traffic.Source: §I · p. 1
  • JCAS lets a UAV-BS monitor a UAV-UE’s position and trajectory while supporting that UE’s uplink communication.The collected JCAS data can also be forwarded to terrestrial base stations.Source: §I · p. 1
  • JCAS for UAV networks remains largely unexplored, despite interference from other UAVs creating clutter and communication interference that makes antenna configuration and beam design important.The cited work also notes that UAV-UE locations in corridors call for a 3D model that captures angular as well as Euclidean distances.Source: §I · p. 2
  • The framework models a fixed number of isotropic UAV-UEs as a 3D BPP in a finite cylindrical corridor, with a 3D 3GPP-pattern UAV-BS tracking one UE as it communicates uplink.It derives the joint spatial-location PDF needed to characterize angular coordinates and obtains exact-form JCAS coverage-probability expressions.Source: §I · p. 2, A. Spatial and Network Modeling · p. 2

B. Beamforming and Antenna Modeling

The UAV-BS uses a realistic 3GPP antenna pattern and steers its beam toward the tracked UAV-UE, giving that UE maximum gain while other users contribute off-boresight clutter.

B. Beamforming and Antenna Modeling

  • The model adopts a realistic 3GPP antenna pattern whose gain varies within the 3 dB beamwidth range.The pattern specifies vertical and horizontal beamwidths and a 30 dB front-to-back ratio.Source: B. Beamforming and Antenna Modeling · pp. 2–3
  • Sensing data from the tracked UAV-UE informs the UAV-BS’s instantaneous position or trajectory estimate and beam-steering direction.The model assumes the UAV-BS steers its beam toward the tracked UAV-UE using angular estimation.Source: B. Beamforming and Antenna Modeling · p. 3
  • The tracked UAV-UE receives maximum antenna gain, whereas other UAV-UEs are seen through off-boresight gains that act as clutter interference.Source: B. Beamforming and Antenna Modeling · p. 3

C. Path Loss and Channel Models

The channel model uses power-law path loss for line-of-sight links and Nakagami-m fading, with independent communication and sensing channels.

C. Path Loss and Channel Models

D. Association Policy and SIR Definition

A UAV-UE is selected uniformly at random for tracking and uplink communication; sensing and communication SIRs account for interference from other users.

D. Association Policy and SIR Definition

  • The tracked UAV-UE is selected uniformly at random from the N users and then performs uplink communication with the UAV-BS.Source: 1) UAV-UE Association Policy · p. 3
  • The sensing SIR compares the tracked UAV-UE’s reflected echo with clutter from the other N − 1 corridor users.The radar model uses the tracked UE’s radar cross-section, while aggregate clutter depends on the interfering users’ antenna gains.Source: 2) Communication and Sensing SIR… · p. 3
  • The communication SIR accounts for uplink interference from the other active UAV-UEs in the corridor.The passage characterizes this as a worst-case interference scenario.Source: 2) Communication and Sensing SIR… · p. 3

III. JCAS Performance Analysis

JCAS coverage probability is the probability that the UAV-BS simultaneously meets the communication and sensing SIR thresholds.

III. JCAS Performance Analysis

  • JCAS CP is the probability that communication and sensing SIRs at the receiving UAV-BS simultaneously exceed their respective thresholds.The communication and tracking thresholds are predefined as θc and θs, respectively.Source: §III · p. 3
  • The communication threshold θc specifies the SIR required for successful communication at the UAV-BS.Source: §III · p. 3
  • The sensing threshold θs specifies the SIR required for successful tracking at the UAV-BS.Source: §III · p. 3

A. Sensing Coverage Probability

Sensing coverage analysis characterizes UAV-UE locations in spherical coordinates and derives conditional clutter interference and sensing coverage expressions for a tracked UAV-UE.

A. Sensing Coverage Probability

  • The joint sensing and communication coverage characterization depends on UAV-UE distances and angular coordinates.Source: A. Sensing Coverage Probability · p. 3
  • The UAV-UE location PDF is expressed in spherical coordinates within the cylindrical corridor, with a geometry-dependent radial limit.The coordinates span r ∈ [0, rmax(θ, ϕ)], θ ∈ [0, π], and ϕ ∈ [−π, π].Source: A. Sensing Coverage Probability · p. 4
  • Conditioned on the tracked UAV-UE location, the aggregate clutter interference distribution is characterized by a conditional Laplace transform.The transform uses the location PDF from Lemma 1.Source: A. Sensing Coverage Probability · p. 4
  • The conditional sensing coverage probability is given for a tracked UAV-UE at distance r0 from the UAV-BS.Its expression uses s0 = mθs σ0g2maxl2(r0).Source: A. Sensing Coverage Probability · p. 4

B. Communication Coverage Probability

Communication coverage analysis characterizes aggregate interference and gives a conditional communication coverage expression for a tracked UAV-UE.

B. Communication Coverage Probability

C. JCAS Coverage Probability

Theorem 1 gives the UAV-BS's JCAS coverage probability by combining the conditional sensing and communication coverage results and averaging over the tracked UAV-UE location.

C. JCAS Coverage Probability

IV. Numerical Results & Discussions

The results compare JCAS coverage probability (CP) across sensing and communication thresholds, corridor length and radius, and antenna beamwidths. Directional beams improve performance, with gains depending on corridor dimensions.

IV. Numerical Results & Discussions

  • The numerical analysis compares analytical results with Monte Carlo simulations, using L = 100 m, R = 5, and N = 10 as representative defaults.Other defaults include α = 2.2, m = 2, (θc, θs) = (−10, −10) dB, (ϕ3dB, θ3dB) = (45°, 45°), Gmax = 8 dBi, and σ = 0.7 m².Source: §IV · p. 4
  • Sensing CP degrades more smoothly than communication CP, and the 3GPP antenna array gives notable sensing gains even with wide beams.The paper attributes the difference to sensing SIR's round-trip radar propagation, antenna-gain terms, and radar cross section.Source: §IV · pp. 4–5, Figure 2 · p. 5
  • JCAS CP is maximized for large-radius, short corridors; directional-beam gains are consistent for shorter corridors and more pronounced at larger radii for longer corridors.Increasing corridor radius makes interfering UAV-UEs more likely to fall outside the antenna mainlobe, reducing aggregate interference.Source: §IV · p. 5, Figure 3 · p. 5, Figure 4 · p. 5
  • For shorter corridors, increasing radius improves JCAS CP more from small to moderate values than at larger values, where gains weaken and eventually saturate.This radius trend is not uniform across shorter-corridor cases.Source: §IV · p. 5, Figure 4 · p. 5

V. Conclusion

The conclusion highlights how UAV-BS beam directivity and corridor dimensions shape JCAS performance in the proposed aerial-corridor framework. It identifies unknown, noncooperative UAV tracking as future work.

V. Conclusion

  • More directional UAV-BS beams notably improve JCAS performance for shorter corridors; for longer corridors, the gain is more pronounced at larger corridor radii.The paper also notes that directivity trends are weaker for shorter corridors.Source: §V · p. 6
  • The framework's interference-mitigation findings may also be relevant to counter-UAS surveillance, where a UAV of interest must be tracked in dense aerial traffic..Source: §V · p. 6
  • Extending the framework to unknown and noncooperative UAVs is identified as future work.Source: §V · p. 6
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