Latency Minimization for Hybrid-Frequency UHD Upload in Double-IRS-Aided HSR Networks

Abstract

Real-time mechanical fault diagnosis in high-speed railway (HSR) networks requires ultra-reliable and low-latency upload of ultra-high-definition (UHD) video streams. However, energy constraints of trackside cameras and severe transmission latency pose critical challenges. This paper proposes a novel 6G infrastructure-to-vehicle (I2V) architecture employing double intelligent reflecting surfaces (IRSs) to enhance wireless powered communication network (WPCN) and hybrid-frequency data transmission. Crucially, to guarantee the quality of experience (QoE) for in-cabin passengers using Mobile Multimedia Broadcasting Services (MBMS), a strict zero-forcing spatial interference isolation constraint is imposed via the window-mounted IRS. We formulate a weighted latency minimization problem and develop a block coordinate descent (BCD) algorithm. Downlink energy beamforming and uplink information transmission are alternately optimized utilizing difference of convex (DCA) and semi-definite relaxation (SDR) techniques. Additionally, a low-complexity heuristic algorithm is proposed to mitigate the severe Doppler spread induced by train mobility. Simulation results demonstrate that the proposed scheme significantly reduces upload latency to meet stringent URLLC thresholds while ensuring interference isolation within the carriage.