⚡ electrical engineering

RIS-Assisted Radar-Communication Coexistence: Detection Analysis with Channel Uncertainties

This paper proposes an RIS-assisted radar-communication coexistence framework for uncoordinated settings with channel uncertainties, introducing practical non-coherent and mismatched coherent maximum-likelihood detectors that effectively mitigate strong random radar interference without requiring precise phase tracking or instantaneous CSI.

Original authors: Rawan Derbas, Shimaa Naser, Hamad Yahya, Sanjeev Gurugopinath, Paschalis C. Sofotasios, Sami Muhaidat

Published 2026-08-25

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CC BY 4.0

Original authors: Rawan Derbas, Shimaa Naser, Hamad Yahya, Sanjeev Gurugopinath, Paschalis C. Sofotasios, Sami Muhaidat

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ 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

In the crowded sky of modern wireless technology, two very different systems are increasingly forced to share the same space. On one side, radar systems act as the eyes of the world, scanning for aircraft, vehicles, and weather with powerful, high-energy pulses. On the other, communication networks carry our data, voice, and video, relying on delicate, continuous streams of information. When these two systems operate near each other without a shared plan, the radar's powerful bursts can drown out the quiet whispers of the communication network, creating a chaotic environment where data is lost. This is particularly true for users located in "exclusion zones," areas where radar is allowed to operate freely but where communication towers are forbidden to protect the radar's sensitive measurements. For a person in such a zone, the radar interference is so strong and its timing so unpredictable that standard methods of receiving a signal simply fail.

To solve this, engineers have turned to a technology called a reconfigurable intelligent surface. Imagine a large, flat panel made of thousands of tiny, passive mirrors. Unlike a traditional antenna that needs power to transmit a signal, this surface does not generate energy; instead, it can be programmed to gently nudge incoming radio waves, changing their direction and timing to guide them exactly where they are needed. By placing such a surface inside a radar exclusion zone, it is possible to bounce a communication signal around obstacles and deliver it to a user who would otherwise be cut off. However, this solution introduces its own set of challenges. The radar interference is not just loud; it is also random, making it impossible to predict its exact phase, or timing alignment. Furthermore, the intelligent surface itself is not perfect; the tiny mirrors have slight manufacturing errors, and the system does not always know the exact condition of the air between the transmitter and the receiver. These imperfections mean that the sophisticated mathematical tricks usually used to clean up a signal often break down, leaving the user with a garbled message.

A team of researchers set out to design a new way for communication receivers to listen in this difficult environment. They focused on a scenario where a user is trapped inside a radar exclusion zone, relying on a reconfigurable intelligent surface to receive a signal while being bombarded by unpredictable radar noise. The researchers knew that trying to track the exact timing of the radar interference was futile, and that assuming the system knew the channel perfectly was unrealistic. Instead of fighting the uncertainty, they developed two new methods for the receiver to make sense of the noise. The first method is a non-coherent approach, which essentially means the receiver looks at the strength of the signal without trying to match the precise timing of the waves. This method is designed to work even when the intelligent surface has small errors in how it reflects the signal. The second method is a "mismatched" approach, which assumes the receiver has a rough idea of the channel conditions but not a perfect one, allowing it to function even when the data it has about the environment is slightly wrong.

The researchers built a detailed mathematical model of this situation, treating the radar interference as a constant but random force that shifts unpredictably. They then derived the best possible way for a computer to decide which message was sent, given these messy conditions. They found that the most accurate method, known as the maximum-likelihood detector, could be simplified into a practical rule that does not require tracking the radar's random phase. This simplified rule works by calculating the most likely signal based on the average behavior of the system, effectively ignoring the impossible task of predicting the exact moment the radar pulse arrives. They also created a second rule for when the system has imperfect knowledge of the channel, using a statistical approximation to handle the uncertainty.

To see if these ideas worked, the researchers ran extensive computer simulations. They tested how the system performed under different levels of radar interference and with varying numbers of reflecting elements on the intelligent surface. The results showed that their new non-coherent detector performed almost exactly as well as the theoretically perfect detector, proving that it is a viable, practical solution. The simulations revealed a critical finding: when radar interference is very strong, the system hits a "floor" where no amount of extra power can improve the connection; the error rate stops dropping and stays stuck. However, the researchers discovered that increasing the number of tiny reflecting elements on the intelligent surface significantly lowers this error floor. By adding more elements, the system becomes much more robust, delaying the point where interference takes over and allowing the connection to survive in much harsher conditions.

The study also highlighted how different types of errors affect the system. The researchers found that errors in the surface's phase configuration and errors in the channel knowledge degrade performance in distinct ways, but both can be mitigated by scaling up the size of the intelligent surface. In one specific test, increasing the number of reflecting elements from twelve to fifteen allowed the system to tolerate a six-decibel increase in radar interference power while maintaining the same level of reliability. This suggests that the physical size of the intelligent surface is a key factor in overcoming the limitations of uncoordinated radar and communication systems. The work confirms that while the environment is chaotic, a carefully designed receiver can still find the signal, provided the intelligent surface is large enough to smooth out the rough edges of reality.

Technical Summary: RIS-Assisted Radar-Communication Coexistence with Channel Uncertainties

Problem Statement
This paper addresses the challenge of maintaining reliable communication for users located inside radar exclusion zones within a Radar-Communication Coexistence (RCC) framework. In these uncoordinated settings, communication users (CUs) are restricted from operating near radar systems to prevent interference with radar operations. Consequently, CUs rely on Reconfigurable Intelligent Surfaces (RISs) deployed within the exclusion zone to receive signals from a base station (BS) located outside the zone.

The system faces three critical practical impairments that degrade conventional detection methods:

Unknown Radar Interference Phase: CUs experience strong radar interference where the amplitude is relatively constant, but the phase is random, unknown, and difficult to track due to propagation delays.
RIS Phase Errors: Hardware limitations cause the RIS reflecting elements to deviate from their ideal phase shifts, disrupting coherent combining.
Imperfect Channel State Information (CSI): Acquiring accurate CSI in RIS-assisted links is difficult due to the passive nature of the RIS and high channel dimensionality, leading to estimation errors.

These factors render conventional coherent detectors, which rely on precise channel and interference phase knowledge, ineffective or overly complex.

Methodology
The authors develop a signal model for an RIS-assisted RCC system where the direct BS-to-CU path is blocked, and the RIS is positioned to minimize direct radar illumination. The received signal includes the desired RIS-reflected signal, additive white Gaussian noise, and radar interference modeled as an additive term with known power but unknown, uniformly distributed phase.

The paper proposes two Maximum-Likelihood (ML) based detection frameworks tailored to specific impairment scenarios:

Non-Coherent Detector (RIS Phase Uncertainty):
- Designed for a setting where the BS has perfect CSI for RIS configuration, but the CU operates with only statistical CSI and faces RIS phase errors.
- The authors derive an exact likelihood expression by marginalizing over the unknown radar phase and the distribution of the RIS channel gain.
- A closed-form detector is derived for the low-to-moderate Signal-to-Interference-plus-Noise Ratio (SINR) regime. This detector explicitly incorporates an attenuation factor ( $\eta$ ) to quantify the performance loss due to RIS phase errors.
- Performance is analyzed using Pairwise Error Probability (PEP) and Union Bound Bit Error Rate (BER) approximations.
Mismatched Coherent Detector (Imperfect CSI):
- Designed for a setting where the RIS phase shifts are configured based on estimated channels, but the estimates contain errors (modeled via a correlation coefficient $\rho$ ).
- The received signal model includes an effective channel gain and an additional noise term introduced by the channel estimation errors.
- A mismatched ML detector is derived, which treats the radar interference as noise in the low-to-moderate SINR regime.
- The PEP analysis involves complex integrals due to the correlation between the channel gain and the noise variance. To make the analysis tractable, the authors employ Gauss-Chebyshev Quadrature (GCQ) to approximate the PEP and derive a Nearest Neighbor Union Bound (NNUB) for tighter performance bounds at high SINR.

Key Contributions

System Modeling: The paper establishes a comprehensive signal model for uncoordinated RCC that explicitly accounts for unknown radar interference phase, RIS phase errors, and imperfect CSI.
Detector Design: Two practical ML-based detectors are developed: a non-coherent detector that avoids tracking the radar phase and incorporates RIS phase uncertainty, and a mismatched coherent detector that functions with imperfect CSI.
Analytical Framework: The authors derive exact likelihood expressions and closed-form detectors for specific regimes. They provide tractable approximations for the PEP and BER using Union Bounds and GCQ, avoiding the intractability of exact integration for the mismatched case.
Performance Validation: Extensive Monte Carlo simulations are conducted to validate the analytical derivations, showing that the proposed detectors closely match the performance of optimal ML detectors.

Results and Findings
Numerical and analytical results demonstrate the following:

Detector Accuracy: The proposed non-coherent detector closely tracks the optimal ML detector across a broad range of operating conditions, validating its practical utility with lower implementation complexity.
Interference-Limited Regime: Strong radar interference creates an interference-limited regime where the BER saturates to a floor, regardless of increases in transmit power.
Impact of RIS Elements: Increasing the number of RIS reflecting elements ( $N$ ) significantly improves system robustness. A larger $N$ lowers the BER and delays the onset of the error floor by enhancing the coherent combining gain and channel hardening effects.
Impairment Sensitivity: Both RIS phase errors and imperfect CSI degrade performance, but through distinct mechanisms. However, increasing the number of RIS elements substantially alleviates the negative impact of these impairments compared to random-phase RIS configurations.
Phase Compensation: Phase-compensated RIS configurations offer significant performance gains over random-phase configurations, even in the presence of imperfect CSI, though the advantage diminishes at very high interference power levels.

Significance
The paper claims that its primary significance lies in bridging the gap between theoretical RIS-assisted RCC designs and the practical receiver-side challenges of real-world deployments. By explicitly addressing the unknown radar phase and hardware/channel uncertainties, the proposed framework offers a robust solution for communication users in exclusion zones where conventional coherent reception is unfeasible. The work provides design insights indicating that while strong interference imposes fundamental limits, strategic deployment of larger RIS arrays can effectively mitigate these limits and maintain reliable communication links.

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