⚡ electrical engineering

Movable Antenna for Integrated Sensing and Communication in Air Sea Ground Networks

This paper proposes a movable antenna framework for integrated sensing and communication in air-sea-ground networks that optimizes antenna sub-array positions, orientations, and beamforming to maximize both communication and sensing rates, demonstrating superior performance and trade-off capabilities compared to conventional stationary antenna systems.

Original authors: Ahmed A. Al-habob, Octavia A. Dobre, Yindi Jing

Published 2026-07-21

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CC0 1.0

Original authors: Ahmed A. Al-habob, Octavia A. Dobre, Yindi Jing

Original paper dedicated to the public domain under CC0 1.0 (http://creativecommons.org/publicdomain/zero/1.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to talk to a friend across a crowded room while simultaneously trying to listen for a specific sound, like a bird chirping. In the world of wireless technology, this is the daily struggle of "Integrated Sensing and Communication" (ISAC). It's a clever idea where one system tries to do two jobs at once: sending data (like your phone messages) and sensing the environment (like a radar spotting a drone). The problem is, these two jobs often fight each other. The antennas that are perfect for shouting a message to a friend might be terrible for listening to a whisper from a bird. Usually, these antennas are stuck in one spot, like statues, unable to move even when the wind changes or the friend walks away. This paper explores a new way to solve this by giving the antennas the ability to move and turn, turning a static statue into a flexible, dancing performer that can find the perfect spot to do both jobs at once.

The researchers behind this study, Ahmed A. Al-habob, Octavia A. Dobre, and Yindi Jing, are tackling a very specific and complex scenario: connecting the sky, the sea, and the ground. They are designing a system for "Air–Sea–Ground Networks," which means they are trying to talk to Unmanned Aerial Vehicles (UAVs) flying above, ships and buoys floating on the water, and regular people or devices on the land. In this chaotic mix of moving targets and different environments, a fixed antenna is like a lighthouse that can only shine in one direction; it struggles to keep up with everything happening around it.

The paper proposes a solution using "Movable Antennas." Think of these not as a single antenna, but as a team of smaller antenna groups (called sub-arrays) attached to the base station by robotic arms. These arms can physically slide the antennas to new locations and twist them to face new directions. The authors set up a mathematical game where the goal is to get the best possible result for both talking to the devices and sensing the targets at the same time. They didn't just guess how to move them; they created a smart algorithm. First, they used a technique called K-means clustering (which is like sorting a messy pile of toys into neat groups based on where they are) to figure out which way the antennas should point. Then, they used a method called Particle Swarm Optimization (inspired by how a flock of birds searches for food together) to slide the antennas to their best physical spots. Finally, they fine-tuned the signals using advanced math to ensure the messages are clear and the sensing is sharp.

When the researchers ran their simulations, the results were promising. They found that this "dancing antenna" system significantly outperformed the old, stationary systems. In their tests, the movable setup improved the overall performance, offering a much better balance between how fast data could be sent and how accurately targets could be sensed. For instance, when they tested with different numbers of antenna groups (specifically 4, 8, and 16 sub-arrays), the system got better as they added more movable groups, even if the total number of tiny antenna pieces stayed the same. The simulations showed that by giving the antennas the freedom to move and rotate, the system could handle the tricky mix of flying drones, floating ships, and ground users much more effectively than if the antennas were frozen in place. The paper concludes that this flexible approach is a powerful way to make future networks smarter and more efficient, though these results are currently based on computer simulations rather than real-world field tests.

Technical Summary: Movable Antenna for Integrated Sensing and Communication in Air–Sea-Ground Networks

Problem Statement
Integrated Sensing and Communication (ISAC) aims to combine sensing and communication functionalities by sharing hardware and radio resources. However, ISAC systems face inherent challenges, including conflicting beamforming goals and competition for the same resources. Conventional fixed antenna systems, even those utilizing massive MIMO, suffer from static radiation patterns and fixed orientations, which limit their ability to adapt to spatially diverse scenarios involving high-mobility users and targets. In the context of Air–Sea-Ground networks—which integrate Unmanned Aerial Vehicles (UAVs), sea surface stations, and ground users—these limitations are exacerbated by the heterogeneity of devices, diverse locations, and the need for reliable connectivity in remote or maritime environments. Fixed antennas often require a large number of elements to track rapid channel variations and are susceptible to severe path loss and directional misalignment at high frequencies.

Methodology
The paper proposes a movable antenna framework where the base station is equipped with $K$ antenna sub-arrays. Unlike fixed arrays, these sub-arrays can dynamically adjust their 3D positions ( $\psi_k$ ) and 3D orientations ( $\phi_k$ ) via extendable and rotatable rods. The system model accounts for heterogeneous communication channels (LoS for UAVs, specific path loss for sea stations, and Rician fading for ground users) and sensing channels for a set of targets.

The core of the work is a multi-objective optimization problem formulated to maximize a weighted sum of the communication sum data rate and the sensing sum rate. The optimization variables include:

Sub-array placement and orientation: The 3D coordinates and rotation angles of each sub-array.
Beamforming: Transmit beamforming vectors for communication and sensing, and receive beamforming vectors for target sensing.

The problem is subject to practical constraints, including total transmit power budgets, minimum separation distances between sub-arrays to avoid coupling, and geometric constraints ensuring sub-arrays point outward from the base station center without mutual blockage.

To solve this non-convex optimization problem, the authors develop a hybrid solution approach:

Receive Beamforming: Optimized using a Generalized Eigenvector method to maximize the Signal-to-Interference-plus-Noise Ratio (SINR) for sensing.
Transmit Beamforming: Designed using a Successive Convex Approximation (SCA) approach to handle the non-convexity of the rate maximization problem.
Sub-array Orientation: Determined via a K-means clustering algorithm. Devices and targets are clustered, and the sub-arrays are oriented to point toward the cluster centroids.
Sub-array Placement: Optimized using a Particle Swarm Optimization (PSO) algorithm to find the best 3D locations within the feasible space that satisfy geometric constraints and maximize the objective function.

Key Contributions

Framework Development: The paper establishes a movable antenna-based framework specifically tailored for ISAC in Air–Sea-Ground networks, addressing the unique challenges of heterogeneous device types (UAVs, sea stations, ground users) and targets.
Optimization Formulation: A multi-objective optimization problem is formulated to jointly maximize communication and sensing rates under practical constraints on movable antenna placement and orientation.
Algorithmic Solution: A novel solution approach is proposed that integrates K-means clustering for orientation, PSO for placement, SCA for transmit beamforming, and generalized eigenvectors for receive beamforming.

Results
Simulation results, based on parameters such as 6 UAVs, 6 sea stations, 6 ground users, and 3 targets, demonstrate the following:

Convergence: The proposed algorithm (Algorithm 3) exhibits good convergence, stabilizing the objective value within a few iterations.
Performance Gain: The movable antenna framework significantly outperforms conventional stationary antenna arrays in terms of the combined sensing and communication objective.
Scalability: Performance improves as the number of sub-arrays increases (even with a fixed total number of antenna elements), indicating that increased spatial degrees of freedom enhance system capability.
Trade-off Management: The framework provides a flexible trade-off between communication data rates and sensing rates. By adjusting the weighting factor $\omega$ , the system can prioritize either objective while maintaining superior overall performance compared to fixed antenna scenarios.
Robustness: The system performance scales positively with the number of devices and targets, leveraging spatial diversity to improve channel quality.

Significance
The paper claims that the developed movable antenna framework offers a remarkable improvement in ISAC performance for Air–Sea-Ground networks compared to traditional stationary arrays. By dynamically reconfiguring the location and orientation of antenna sub-arrays, the system effectively exploits spatial degrees of freedom to resolve conflicting beamforming goals and mitigate channel impairments. This approach provides a viable solution for enhancing data rates and sensing accuracy in complex, heterogeneous, and high-mobility environments where fixed antenna systems are limited by static radiation patterns.

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