ISAC with Backscattering RFID Tags: Joint Beamforming Design

TL;DR

The simulation results demonstrate that, under different communication SINR requirements, joint beamforming optimization outperforms the zero-forcing-based method in terms of achievable detection distance, offering a promising approach for the ISAC-backscattering systems.

Abstract

In this paper, we explore an integrated sensing and communication (ISAC) system with backscattering RFID tags. In this setup, an access point employs a communication beam to serve a user while leveraging a sensing beam to detect an RFID tag. Under the total transmit power constraint of the system, our objective is to design sensing and communication beams by considering the tag detection and communication requirements. First, we adopt zero-forcing to design the beamforming vectors, followed by solving a convex optimization problem to determine the power allocation between sensing and communication. Then, we study a joint beamforming design problem with the goal of minimizing the total transmit power while satisfying the tag detection and communication requirements. To resolve this, we re-formulate the non-convex constraints into convex second-order cone constraints. The simulation results demonstrate that, under different communication SINR requirements, joint beamforming optimization outperforms the zero-forcing-based method in terms of achievable detection distance, offering a promising approach for the ISAC-backscattering systems.

Figures (5)

• Figure 1: This figure illustrates the considered MIMO ISAC system, where an access point transmits sensing and communication waveforms to communicate with a user while detecting a passive RFID tag. This system could be applicable, for instance, in the context of RFID-aided inventory management and surveillance systems within a warehouse, as depicted in the figure.
• Figure 2: The achievable detection distance of different tag directions, where the user's position remains fixed at $(5/\sqrt{2}, 5/\sqrt{2})$. (a) and (b) show the performance under low and high user SINR requirements, respectively.
• Figure 3: The CDF of the coverage ratio across different user positions. At each user position, the detection coverage is computed as the average ratio of the achievable detection distance to the upper bound across different angles. In this simulation, the number of antennas is $4$, and $\rm{SINR}_u$ is set to $0$ dB.
• Figure 4: The total allocated power with different tag directions, where the user's position is at $(5/\sqrt{2}, 5/\sqrt{2})$, and the tag's distance is fixed at $6$ meters. In this simulation, the number of antennas is $4$, and $\rm{SINR}_u$ is set to $0$ dB.
• Figure 5: The beamforming patterns of the proposed solutions. The number of antennas is $8$. The directions of the tag and the user remain fixed at $90^{\circ}$ and $135^{\circ}$. (a) shows the beamforming pattern of joint beamforming optimization, and (b) depicts the beamforming pattern of zero-forcing based method.