Toward Real-Time Digital Twin of Physical Reality via Intelligent Wireless Resource Allocation

Enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communication (URLLC) are two important wireless communication traffics to build a digital twin of physical reality. Therein, eMBB and URLLC traffics are to transmit high-quality sensed data and critical commands, respectively. To support these two important traffics, we develop an intelligent resource allocation mechanism. First, we model the time-frequency resource allocation as an optimization problem aiming to maximize the throughput for the eMBB traffics according to their urgency subject to the constraint on the successful transmission for the URLLC traffics. In this way, the amount of resources allocated to each traffic can be appropriately determined without causing waste in resource usage. Secondly, we propose a feasible low-complexity solution for the optimization problem by relaxing it and then applying linear programming. Thirdly, to address the possible failure of the algorithm due to the relaxation, we propose a post-processing by puncturing the resource initially allocated to eMBB traffics and thereafter reallocating this resource to URLLC traffics. By such, the characteristic of the eMBB traffic, i.e., high throughput, and that of the URLLC traffic, i.e., low latency and ultra reliability, can be achieved. We perform system-level simulations on Matlab 5 G simulation platform to evaluate the performance of the proposed mechanism under different scenarios. Simulations show that the proposed mechanism achieves better performance compared to existing schemes regarding the total eMBB throughput and URLLC failure probability on all the scenarios.