Search

Extended reality (XR) is an emerging 6G application with unique traffic characteristics and requirements, calling for innovative Ultra-Reliable and Low-Latency Communication (URLLC) technologies. This paper investigates multi-user scheduling to meet XR services’ hard-latency constraints. Specifically, we focus on a periodical traffic model, where the latency constraint for transmitting each XR frame is less than the inter-arrival time. We describe the system as a periodic Markov Decision Process (MDP) with the performance metric being the probability of successful transmission within the latency constraint. We then obtain the maximum success probability and the optimal scheduling based on the optimal value function. In the case of homogeneous arrivals, we construct a lower bound of the optimal value function. Based on this, we propose an online multi-user scheduling policy that determines scheduling decisions by solving a series of nonlinear Knapsack Problems (KPs) in polynomial time. Our analysis demonstrates that the scheduling scheme is asymptotically optimal with increasing users. Furthermore, we extend the online scheduling scheme to heterogeneous arrivals and present extensions for practical scenarios with multiple resource blocks, quasi-periodical arrivals, random frame sizes, and time-correlated channel fading. Finally, simulation results show that the proposed scheduler achieves near-optimal performance and outperforms other benchmark schedulers.

Millimeter-wave (mmWave) is highly susceptible to obstacles and requires significant directivity of beams. To address these challenges, a promising solution is to deploy antenna arrays at base stations (BSs) and reconfigurable intelligent surfaces (RISs). Antenna arrays enable 3D beamforming and provide highly directional beams, while RISs create favorable transmission environments. In this paper, we propose an analytical framework to quantify the coverage performance of the RIS-assisted mmWave cellular network with 3D beamforming. Modeling obstacles and RISs with a line Boolean model and BSs with a Poisson point process (PPP), we provide the distance distribution between a UE and its associated RIS by evaluating the impact of the RIS orientation. Moreover, we adopt a 3D sectorized flat-top antenna model to characterize the interference introduced by dynamic directional beams (i.e., mainlobe/sidelobe). Furthermore, we derive the signal-to-interference ratio (SIR) coverage probability, where an equivalent PPP is proposed to enhance the computation efficiency. Numerical results validate the accuracy of our analysis and show that the proposed network achieves significant coverage improvement. We also investigate the impacts of the key parameters on the coverage probability, providing useful insights for deploying RISs and antenna arrays in mmWave cellular networks.