RHOSI: Efficient Anti-Jamming Resource Allocation with Holographic Surfaces in UAV-enabled ISAC

This paper proposes RHOSI, an efficient resource allocation framework that leverages a Reconfigurable Holographic Surface (RHS)-aided UAV to jointly optimize beamforming, phase shifts, and spatial deployment, thereby significantly enhancing the anti-jamming resilience and throughput of Integrated Sensing and Communication (ISAC) systems.

The Gist

Imagine you are trying to have an important conversation with a friend in a crowded, noisy room, while also trying to listen for a specific sound (like a fire alarm) that might be hidden by the noise. Now, imagine someone is standing right next to you with a megaphone, screaming loudly to drown out both your conversation and the alarm. This is the challenge faced by modern wireless networks: they need to do two things at once—send data (communication) and detect objects (sensing)—but they are constantly being attacked by "jammers" (the megaphone screamers).

This paper introduces a clever solution called RHOSI to solve this problem. Here is how it works, broken down into simple concepts:

1. The Problem: The "Noisy Room"

In our future 6G networks, we want to use the same radio waves to talk to our phones and to act like radar to see cars or drones. However, bad actors can send "jamming" signals to block these waves. Traditional ground-based systems are like people standing in a room with walls; if the jammer is there, or if a building blocks the view, the system fails.

2. The Hero: The Flying Mirror (RHS-UAV)

The authors propose using a Drone (UAV) equipped with a special technology called a Reconfigurable Holographic Surface (RHS).

• The Analogy: Think of the RHS as a giant, magical, flying mirror made of thousands of tiny, adjustable tiles.
• How it works: Unlike a normal mirror that just reflects light at a fixed angle, this "smart mirror" can instantly change the shape of its surface. It can bend radio waves like a wizard bending light.
• The Mission: The drone flies into the air to get a clear line of sight, bypassing buildings. It then uses its "smart mirror" to:
  1. Boost the good signals: It focuses the radio waves like a laser beam directly at your phone or the target object.
  2. Cancel the bad signals: It creates "noise-canceling" zones to block the jammer's megaphone from reaching your phone.

3. The Strategy: The "Three-Way Dance" (RHOSI)

The paper's main contribution is a smart algorithm called RHOSI. It doesn't just turn the mirror on; it coordinates a complex dance between three partners to save energy and win against the jammer:

1. The Base Station (The Speaker): It decides how to shout (beamforming). Instead of shouting in all directions, it aims its voice precisely.
2. The Drone/Mirror (The Conductor): It decides where to fly and how to tilt its mirror tiles. It moves to the perfect spot to catch the signal and reflects it perfectly to the destination.
3. The Algorithm (The Choreographer): This is the brain. It constantly calculates: "If the drone moves here, and the mirror tilts that way, and the base station shouts this loud, can we hear each other clearly while using the least amount of battery power?"

4. The Result: Smarter, Stronger, and Cheaper

The researchers tested this idea with computer simulations. Here is what they found:

• Beating the Jammer: Even when the "screamer" (jammer) gets very loud, the RHOSI system keeps the conversation going. The flying mirror creates a "safe zone" that the jammer can't penetrate easily.
• Saving Energy: By working together perfectly, the system doesn't need to shout as loudly to be heard. It uses less battery power than older methods.
• The "Magic" of More Antennas: The more "tiles" (antennas) the system has, the better it gets at focusing the signal, much like having more eyes to see around a corner.

The Big Picture

In simple terms, this paper says: "Don't just fight the noise; outsmart it."

Instead of trying to overpower a jammer with brute force (which wastes energy), we use a flying, shape-shifting mirror to bend the radio waves around obstacles and block the enemy. It's like playing a game of "Marco Polo" where you can move your voice to the other side of the wall and whisper directly into your friend's ear, while the person trying to eavesdrop is left shouting into a void.

This technology is a big step toward the 6G networks of the future, where our phones and self-driving cars will stay connected and safe, even in the most chaotic and hostile environments.

Technical Summary

Here is a detailed technical summary of the paper "RHOSI: Efficient Anti-Jamming Resource Allocation with Holographic Surfaces in UAV-enabled ISAC."

1. Problem Statement

The paper addresses the vulnerability of Integrated Sensing and Communication (ISAC) systems to hostile jamming, particularly in terrestrial environments where Line-of-Sight (LoS) blockages and interference degrade performance.

• Context: While ISAC is a key enabler for 6G, it faces challenges from malicious jammers and physical obstacles.
• Specific Challenge: The authors focus on a scenario where a Reconfigurable Holographic Surface (RHS) is mounted on an Unmanned Aerial Vehicle (UAV). The UAV is deployed to create virtual LoS links for sensing and communication while countering a ground-based jammer.
• Objective: The goal is to minimize total network power consumption (including transmit power and UAV aerodynamic power) while satisfying strict constraints:
  • Minimum communication data rates for users ($R_{min}$).
  • Minimum echo Signal-to-Interference-plus-Noise Ratio (SINR) for target detection ($\gamma_T$).
  • UAV flight dynamics (velocity, acceleration, and trajectory constraints).
  • BS transmit power limits.
• Complexity: The problem is formulated as a non-convex Mixed-Integer Non-Linear Program (MINLP) due to the highly coupled variables: transmit beamforming vectors, RHS phase shifts, and UAV 2D positioning/velocity.

2. Methodology: The RHOSI Framework

The authors propose a framework called RHOSI (Resource allocation for anti-jamming HOlographic Surface UAV-enabled ISAC). The core methodology involves an Alternating Optimization (AO) algorithm that iteratively solves three sub-problems to find a high-quality suboptimal solution.

A. System Model

• Architecture: A dual-function Base Station (BS) with $N_t$ antennas serves $K$ users and a specific target. A single-antenna jammer interferes with the system.
• RHS-UAV: The UAV carries an RHS with $M$ reflective elements. It dynamically adjusts its position ($Q[n]$) and phase shifts ($\Theta[n]$) to steer signals.
• Channel Modeling:
  • BS-Jammer-User Links: Modeled with Rician fading (LoS + NLoS components).
  • BS-RHS and Jammer-RHS Links: Modeled as LoS channels with specific array response vectors based on angles of arrival (AoA).
  • Sensing: Performance is evaluated via beampattern gain and echo SINR, accounting for jamming interference on the reflected path.
• Power Model: Includes BS transmit power, circuit power, and a detailed aerodynamic power model for the UAV (profile, parasite, and induced power) based on flight velocity.

B. Optimization Algorithm (RHOSI)

The non-convex problem is decomposed into three blocks, solved iteratively:

1. Active Beamforming (BS): Optimized using Successive Convex Approximation (SCA) to adjust transmit weights ($w_k[n]$) for users, maximizing signal strength while minimizing interference.
2. Passive Beamforming (RHS): Optimized using a penalty-based technique to adjust phase shifts ($\Theta[n]$). This reshapes the wireless environment to constructively combine signals for users/targets and destructively interfere with the jammer's path.
3. UAV Trajectory & Positioning: Optimized using SCA to determine the UAV's horizontal coordinates ($Q[n]$) and velocity ($v[n]$). This ensures the UAV maintains optimal geometry for LoS links while adhering to kinematic constraints.

Key Techniques Used:

• Semi-Definite Relaxation (SDR): To handle rank constraints in beamforming.
• Big-M Method: To handle binary/integer constraints related to phase shifts or specific logical conditions.
• Successive Convex Approximation (SCA): To linearize non-convex constraints and objectives for iterative solving.

3. Key Contributions

1. Novel Integration: First to propose a joint optimization framework combining RHS, UAV mobility, and ISAC specifically for anti-jamming scenarios.
2. Joint Resource Allocation: Simultaneously optimizes three critical dimensions:
   • BS active beamforming.
   • RHS passive phase shifts.
   • UAV spatial deployment (trajectory and position).
3. Algorithmic Innovation: Development of the RHOSI algorithm, which effectively tackles the non-convex MINLP problem by decomposing it into solvable sub-problems using SCA and penalty methods.
4. Comprehensive Modeling: Incorporates realistic UAV aerodynamic power consumption and detailed channel models (including Rician fading and array responses) into the resource allocation problem.

4. Simulation Results

The authors evaluated RHOSI against baselines (Random RHS Deployment and Discrete Phase Shifts) under various conditions:

• Power vs. Antenna Count (Fig. 2):

  • RHOSI demonstrates that increasing the number of BS antennas significantly reduces total power consumption.
  • Reasoning: More antennas provide higher spatial degrees of freedom (DoFs), allowing for more precise beamforming and interference nulling.
  • Trade-off: Stricter QoS requirements (higher $R_{min}$) naturally increase power consumption but RHOSI maintains efficiency.

• Sum Rate vs. Jamming Power (Fig. 3):

  • As jamming power ($g_{Jam}$) increases, all schemes degrade, but RHOSI maintains the highest achievable sum rate.
  • Resilience: The performance curve of RHOSI flattens at high jamming powers, indicating that the RHS passive beamforming gain becomes increasingly effective at suppressing interference and preserving signal integrity.
  • Comparison:
    • Random Deployment: Performs worst, showing that unoptimized placement cannot effectively harness spatial diversity.
    • Discrete Phase Shifts: Performs better than random but worse than RHOSI, highlighting the benefit of continuous phase control in holographic surfaces.

5. Significance and Conclusion

• Robustness: The study proves that integrating mobile RHS with UAVs creates a highly resilient ISAC architecture capable of withstanding severe adversarial jamming.
• Energy Efficiency: By jointly optimizing trajectory and beamforming, the system achieves significant power savings compared to static or non-optimized approaches.
• 6G Implications: The work provides a concrete pathway for 6G networks to utilize intelligent, reconfigurable environments (RHS) and aerial platforms to ensure reliable sensing and communication in contested or obstructed environments.
• Future Work: The authors suggest extending the framework to multi-UAV scenarios and considering imperfect Channel State Information (CSI).

In summary, RHOSI represents a sophisticated approach to 6G resource management, leveraging the synergy between mobility (UAV), reconfigurability (RHS), and joint sensing/communication to solve the critical problem of jamming in wireless networks.