⚡ electrical engineering

Enhancing AAV-Enabled Secure Communications via Synthetic Aperture Beamforming

This paper proposes a synthetic aperture beamforming framework for single-antenna autonomous aerial vehicles (AAVs) that optimizes flight deployment, transmit precoding, and received beamforming to maximize channel capacity and physical layer security in virtual MIMO broadcast channels facing hybrid wiretapping threats.

Original authors: Bin Qiu, Wenchi Cheng, Hongxiang He, Jiangzhou Wang

Published 2026-03-04

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CC0 1.0

Original authors: Bin Qiu, Wenchi Cheng, Hongxiang He, Jiangzhou Wang

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 whisper a secret to a friend across a crowded, noisy room, but there are two problems:

Eavesdroppers: People are trying to listen in on your conversation.
Jamming: Some people are shouting loudly to drown out your voice so your friend can't hear you.

Usually, to solve this, you might need a super-powerful speaker (which drains your battery) or a team of many people standing in a line to create a "wall of sound." But what if you are a single, small drone (an AAV) with a tiny battery and only one tiny microphone? You can't build a big wall, and you can't shout too loud without running out of power.

This paper presents a clever trick called Synthetic Aperture Beamforming. Here is how it works, explained simply:

1. The "Ghost Array" Trick

Normally, to focus a signal like a laser beam, you need a big antenna with many parts (like a large satellite dish). Our drone is too small to carry a big dish.

The Solution: Instead of standing still, the drone flies back and forth along a specific path.

The Analogy: Imagine you are painting a giant mural, but you only have a tiny paintbrush. Instead of trying to paint the whole wall at once, you move your brush to different spots, pause, paint a little, move to the next spot, and paint again.
The Magic: Even though the drone is one single point at any given moment, by moving to specific spots and sending signals at precise times, it tricks the receiver into thinking there is actually a giant team of drones standing there all at once. This creates a "virtual" giant antenna out of a single moving one.

2. The "Secret Dance" (Optimizing the Path)

Just flying back and forth isn't enough. If the drone stops at random spots, the signal gets messy. The researchers figured out the perfect dance steps for the drone.

The Analogy: Think of a choir. If everyone stands in a perfect line, they sound great. If they stand in a messy pile, it sounds terrible. The paper calculates the exact mathematical "sweet spots" (called Fekete points) where the drone should pause.
The Result: When the drone pauses at these specific spots, the signals combine perfectly to create a super-strong beam aimed exactly at the friend (the Base Station) and almost zero energy aimed at the eavesdroppers.

3. The "Whisper vs. Shout" Strategy

The goal is to use as little battery as possible while keeping the message secret.

The Analogy: Instead of shouting to be heard over the noise (which wastes energy and alerts the eavesdroppers), the drone uses the "virtual antenna" to focus its whisper directly into the friend's ear.
The Benefit: Because the signal is so focused, the drone doesn't need to shout. This saves battery life and makes it very hard for the eavesdroppers to hear anything because the signal is like a laser pointer: bright in one spot, invisible everywhere else.

4. Fighting the Noise (Jamming)

The bad guys aren't just listening; they are also shouting (jamming) to confuse the friend.

The Solution: The friend (the Base Station) uses a special filter (called LCMV).
The Analogy: Imagine the friend is wearing noise-canceling headphones. But instead of just canceling all noise, these headphones are smart. They know exactly where the shouting is coming from and create a "silence bubble" in that specific direction, while keeping the whisper from the drone crystal clear.

Why is this a big deal?

Efficiency: It solves the problem of small drones having weak batteries. They don't need to be big or carry heavy equipment.
Security: It makes the communication "invisible" to spies. The signal is so focused that if you aren't standing in the exact right spot, you hear nothing but static.
Flexibility: It turns a single, simple drone into a powerful communication tool that can adapt to different situations, even if the bad guys are moving around.

In a nutshell: This paper teaches a single, small drone how to fly a specific "secret dance" to pretend it is a giant army of antennas. This allows it to whisper secrets clearly to a friend while making the signal disappear for anyone else trying to listen or shout.

1. Problem Statement

The paper addresses the challenge of securing Air-to-Ground (A2G) communications in the presence of hybrid wiretapping environments. In this scenario, a single-antenna Mobile Autonomous Aerial Vehicle (AAV) acts as a transmitter to a multi-antenna Base Station (BS). The system faces threats from:

Passive Eavesdroppers (Eves): Attempting to intercept confidential data.
Active/Intelligent Attackers: Capable of both eavesdropping and jamming legitimate channels simultaneously (full-duplex attacks).

Key Constraints & Challenges:

Hardware Limitations: Single AAVs have strict Size, Weight, and Power (SWaP) constraints, preventing them from carrying large-scale antenna arrays required for traditional spatial degrees of freedom (DoFs).
Multi-AAV Collaboration Issues: While multi-AAV swarms offer solutions, they suffer from synchronization difficulties, high inter-node communication overhead, and individual heterogeneity.
Energy Efficiency: AAVs rely on onboard batteries; traditional Artificial Noise (AN) techniques consume valuable transmit power, reducing efficiency.
Goal: Maximize system channel capacity and ensure Physical Layer (PHY) security by optimizing flight node deployment, transmission strategies, precoding, and beamforming, while minimizing transmit power.

2. Methodology

The authors propose a Synthetic Aperture Beamforming approach where a single-antenna mobile AAV flies along a predefined trajectory to create a Virtual Non-Uniform Linear Array (NULA). This virtual array provides spatial gain toward the BS while suppressing signals toward eavesdroppers.

The solution is decomposed into four interconnected optimization problems:

A. Virtual Array Deployment (AAV Node Positioning)

Objective: Maximize the product of the eigenvalues of the channel gain matrix to maximize channel capacity.
Analysis: The authors derive the asymptotic expression of channel eigenvalues as the rotation offset approaches zero.
Optimal Solution: They prove that the optimal deployment of the $L$ $L$ flight nodes follows the Fekete-point distribution (or Gauss-Lobatto points).
- If the number of data streams $K$ equals the number of nodes $L$ , nodes are distributed as Fekete points.
- If $K < L$ , nodes are grouped into $K$ compact clusters, where the centers of these clusters follow the Fekete distribution. This creates a virtual array that maximizes spatial diversity.

B. Transmission Symbol Strategy

The AAV transmits sub-symbol blocks sequentially as it moves through different flight nodes.
The same data stream is repeated at each node position within a specific time window, allowing the receiver to combine signals and achieve spatial diversity gain, effectively mimicking a multi-antenna system.

C. Transmit Precoding (Power Minimization)

Objective: Minimize transmit power consumption while meeting Quality of Service (QoS) for the BS, leakage tolerance for Eves, and per-node power constraints.
Formulation: A non-convex optimization problem is formulated.
Solution:
- The uncertainty in Eve's channel information is handled using a worst-case scenario model with bounded uncertainty sets.
- The S-Procedure is applied to transform semi-infinite constraints into Linear Matrix Inequalities (LMIs).
- The problem is reformulated as a Semidefinite Program (SDP).
- Since the SDP relaxation may not yield a rank-one solution, randomization techniques are used to generate a feasible precoding vector.
Benefit: This approach achieves Low Probability of Detection (LPD) by minimizing power leakage and avoiding the energy cost of Artificial Noise.

D. Received Beamforming (Jamming Suppression)

Objective: Suppress jamming signals from active attackers while maintaining a distortionless response for the desired signal.
Method: The Linearly Constrained Minimum Variance (LCMV) method is employed.
Robustness: The paper extends this to a Robust Adaptive Beamforming design to handle uncertainties in the BS's channel estimation (e.g., due to wind jitter or positioning errors), formulating a max-min fairness problem.

3. Key Contributions

Novel Synthetic Aperture Framework: Proposes a secure transmission scheme using a single-antenna mobile AAV to form a virtual array, overcoming SWaP limitations and avoiding the complexity of multi-AAV synchronization.
Theoretical Optimization of Deployment: Derives the analytical relationship between channel eigenvalues and node positions, proving that Fekete-point distribution is the asymptotically optimal strategy for maximizing channel capacity in virtual NULA systems.
Energy-Efficient Secure Precoding: Develops a power-minimization algorithm that guarantees QoS and security against hybrid attackers without using energy-intensive Artificial Noise.
Robustness Extensions: Extends the framework to high-dimensional virtual arrays (2D planar) and robust adaptive beamforming to handle real-world uncertainties (wind, positioning errors).

4. Simulation Results

The authors validate their approach through numerical simulations with the following findings:

Channel Capacity: The proposed scheme significantly outperforms traditional Zero-Forcing (ZF) with Uniform Linear Arrays (ULA), especially in high-SNR regimes. Larger aperture sizes ( $D$ ) and optimal node deployment lead to higher capacities.
Spatial Gain & Security: The transmit beam pattern shows a sharp gain peak directed at the BS and deep nulls (low gain) directed at the Eves. This effectively prevents eavesdropping even if attackers know the signal structure.
Jamming Suppression: The received beampattern successfully forms deep nulls at the angles of arrival of jamming signals while preserving the signal path to the BS.
Energy Efficiency: As the number of AAV nodes increases, the required transmit power decreases. The proposed scheme consumes less power than the ZF-ULA baseline to achieve the same SNR.
Robustness: The proposed robust beamforming maintains a higher Signal-to-Jamming-plus-Noise Ratio (SJNR) compared to non-robust methods under channel uncertainty.

5. Significance

This research provides a critical pathway for deploying secure, high-capacity communications in resource-constrained aerial networks. By leveraging synthetic aperture beamforming, the paper demonstrates that a single, low-cost AAV can achieve performance levels comparable to complex multi-antenna systems.

Practical Impact: It offers a solution for emergency response, remote area coverage, and military applications where traditional encryption is insufficient, and hardware limitations prevent the use of large antenna arrays.
Theoretical Advance: The connection between Fekete points and optimal virtual array deployment in MIMO channels provides a new theoretical foundation for future aerial communication system designs.
Security Paradigm: It shifts the focus from high-power jamming/AN to intelligent spatial filtering and power minimization, offering a more sustainable and stealthy approach to PHY security.