Abstract
IEEE Internet of Things Journal (2024) This paper introduces an approach to harness digital twin (DT) technology in
the realm of integrated sensing and communications (ISAC) in the
sixth-generation (6G) Internet-of-everything (IoE) applications. We consider
moving targets in a vehicular network and use DT to track and predict the
motion of the vehicles. After predicting the location of the vehicle at the
next time slot, the DT designs the assignment and beamforming for each vehicle.
The real time sensing information is then utilized to update and refine the DT,
enabling further processing and decision-making. This model incorporates a
dynamic Kalman gain, which is updated at each time slot based on the received
echo signals. The state representation encompasses both vehicle motion
information and the error matrix, with the posterior Cram\'er-Rao bound (PCRB)
employed to assess sensing accuracy. We consider a network with two roadside
units (RSUs), and the vehicles need to be allocated to one of them. To optimize
the overall transmission rate while maintaining an acceptable sensing accuracy,
an optimization problem is formulated. Since it is generally hard to solve the
original problem, Lagrange multipliers and fractional programming are employed
to simplify this optimization problem. To solve the simplified problem, this
paper introduces both greedy and heuristic algorithms through optimizing both
vehicle assignments and predictive beamforming. The optimized results are then
transferred back to the real space for ISAC applications. Recognizing the
computational complexity of the greedy and heuristic algorithms, a
bidirectional long short-term memory (LSTM)-based recurrent neural network
(RNN) is proposed for efficient beamforming design within the DT. Simulation
results demonstrate the effectiveness of the DT-based ISAC network.