Digital Twin-Based Beamforming for Interference Mitigation in AF Relay MIMO Systems

TL;DR

A novel prediction-assisted optimization (PAO) framework for beamform design in AF relay-assisted multiuser MIMO systems is introduced and results show the trade-off between localization accuracy and beamforming performance and confirm that PAO maintains robustness even in the presence of localization errors while reducing the need for real-world measurements.

Abstract

Beamforming in multiple-input multiple-output (MIMO) systems should take interference mitigation into account. However, for beamform design, accurate channel state information (CSI) is needed, which is often difficult to obtain due to channel variability, feedback overhead, or hardware constraints. For example, amplify-and-forward (AF) relays passively forward signals without measurement, precluding full CSI acquisition to and from the relay. To address these issues, this paper introduces a novel prediction-assisted optimization (PAO) framework for beamform design in AF relay-assisted multiuser MIMO systems. The proposed solution in the AF relay aims at maximizing the signal-plus-interference-to-noise ratio (SINR). Unlike other methods, PAO relies solely on received power measurements, making it suitable for scenarios where CSI is unreliable or unavailable. PAO consists of two stages: a supervised-learning-based neural network (NN) that predicts the positions of transmitters using signal observations, and an optimization algorithm, guided by a digital twin (DT), that iteratively refines the beam direction of the relay in a simulated radio environment. As a key contribution, we validate the proposed framework using realistic measurements collected on a custom-built experimental millimeter wave (mmWave) platform, which enables training of the NN model under practical wireless conditions. The estimated information is then used to update the digital twin with knowledge of the surrounding environment, enabling online optimization. Numerical results show the trade-off between localization accuracy and beamforming performance and confirm that PAO maintains robustness even in the presence of localization errors while reducing the need for real-world measurements.

Figures (13)

• Figure 1: System model. The direct link between the target sta to the Wifi ap is blocked, so transmission is assisted by an af relay.
• Figure 2: Block diagram of the proposed pao framework for relay beam configuration. The architecture integrates a Measurement Module, Switching Control unit, sl-based localization block, and dt-assisted optimization engine, enabling dynamic switching between measurement and communication modes to achieve efficient beam optimization without full csi acquisition.
• Figure 3: Photograph of the mmWave relay prototype composed of two Sivers EVK06003 beamforming modules, integrating 16-element antennas and RF front-end functionalities for transmission and reception in the 60 GHz band.
• Figure 4: The transmitter array consists of 30 positions, organized into two parallel rows along the longer sides of a $1.5~\mathrm{m} \times 1~\mathrm{m}$ rectangle in the $x$-$y$ plane. Each row contains 15 positions, evenly spaced at $0.1~\mathrm{m}$. Positions A and B denote the first and last transmitter locations along each row.
• Figure 5: Recreated 3D dt of the experimental environment employed for simulations, reproducing all structural and spatial features. The model incorporates walls, doors, windows, furniture, and other relevant elements of the setup. Furthermore, the spatial disposition of the af relay, transmitters, and receivers is modeled according to the experimental configuration.