Optimized Analog Beamforming for Integrated Sensing and Communication with Self-Interference Suppression

المفاهيم الأساسية

An optimization framework is proposed to design optimal analog beamforming vectors for integrated sensing and communication (ISAC) systems, which also addresses the problem of self-interference suppression in the analog domain.

الملخص

The paper presents an optimization framework for analog beamforming in the context of monostatic integrated sensing and communication (ISAC) systems. The key objective is to design the transmit (TX) and receive (RX) beamforming vectors to optimize the detection performance of the sensing functionality, while also satisfying the communication requirements and suppressing self-interference in the analog domain.
The authors first formulate a joint optimization problem that aims to maximize the minimum signal power in the main lobe of the beampattern, subject to constraints on sidelobe levels, self-interference suppression, communication SNR, and antenna power limitations. Due to the non-convex nature of the problem, the authors propose to decouple it into separate TX and RX optimization problems, which can be approximated using a superiorized iterative projection algorithm.
The proposed approach is shown to outperform the popular mean squared error (MSE) minimization technique in terms of detection performance, while also achieving satisfactory self-interference suppression. Numerical results demonstrate the advantages of the proposed framework in terms of receiver operating characteristics (ROC) and angular power spectrum (APS) compared to the MSE-based approach.

الإحصائيات

Sentence

We can now correlate yr(n) with the known signal s(n) and apply the generalized likelihood ratio test [19] (∀n ∈Z \ [N] s(n) = 0):

maxn′∈[N]

where η ∈R+ is a parameter that trades off the probability of false alarm PFA and the probability of detection PD.

Specifically, PFA and PD are given by χ22 and χ22(ρ), the central and non-central chi distributions with two degrees of freedom, respectively, and ρ the non-centrality parameter

ρ = α2q⋆N

اقتباسات

Quote

It is known that, for a fixed PFA, PD =: fD (ρ ; PFA) is monotonically increasing in ρ [19].

الرؤى الأساسية المستخلصة من

Optimized Detection with Analog Beamforming for Monostatic Integrated Sensing and Communication

by Rodr... في arxiv.org 04-15-2024

https://arxiv.org/pdf/2404.08455.pdf

استفسارات أعمق

How can the proposed framework be extended to handle dynamic targets with varying velocities

To extend the proposed framework to handle dynamic targets with varying velocities, we can incorporate the Doppler effect into the system model. By redefining the one-dimensional delta function in the delay domain to a two-dimensional delta in the delay-Doppler plane, we can account for the targets' velocities. This adjustment allows us to track moving targets and adapt the beamforming strategy dynamically. Additionally, the channel model needs to be modified to include the velocity information of the targets, enabling the system to adjust the beamforming vectors in real-time based on the targets' movements.

What are the potential challenges and trade-offs in implementing the analog beamforming solution in a practical ISAC system

Implementing the analog beamforming solution in a practical ISAC system poses several challenges and trade-offs. One challenge is the hardware complexity involved in designing analog beamformers that can effectively suppress self-interference while maintaining communication performance. This complexity can lead to increased power consumption and cost. Another challenge is the limited flexibility of analog beamforming compared to digital beamforming, as analog beamformers lack the ability to adapt to changing channel conditions or optimize performance based on feedback.
In terms of trade-offs, there is a balance between achieving high detection performance for sensing targets and meeting the communication requirements of the system. Optimizing the analog beamforming vectors for target detection may result in suboptimal communication performance, and vice versa. Additionally, there may be trade-offs between SI suppression and beamforming efficiency, as increasing SI suppression capabilities can impact the overall system performance and complexity.

How can the optimization problem be further refined to jointly optimize the sensing and communication performance, rather than treating them as separate objectives

To refine the optimization problem to jointly optimize sensing and communication performance, we can introduce a multi-objective optimization framework. By formulating the problem as a multi-objective optimization task, we can consider both sensing and communication objectives simultaneously. This approach allows us to explore the trade-offs between optimizing beamforming for target detection and meeting communication requirements.
In the refined optimization problem, we can define objective functions that capture the performance metrics for both sensing (e.g., target detection probability) and communication (e.g., signal-to-noise ratio). By incorporating constraints that balance these objectives, such as constraints on SLL, SI suppression, and SNR, we can optimize the analog beamforming vectors to achieve a balance between sensing and communication performance. Additionally, techniques like Pareto optimization can be employed to find the optimal trade-off solutions between the competing objectives.