Spatial-Temporal Resource Optimization for Uneven-Traffic LEO Satellite Systems: Beam Pattern Selection and User Scheduling

With the commercial deployment of low earth orbit (LEO) satellites, the future integrated 6G-satellite system represents an excellent solution for ubiquitous connectivity and high-throughput data service to massive users. Due to the heterogeneity of users’ traffic profiles, uneven traffic distribution among beams or users often occurs in LEO satellite systems. Conventional satellite payloads with fixed beam radiation patterns may result in large gaps between requested and allocated capacity. The advances of flexible satellite payloads with dynamic beamforming capabilities enable spot beams to adjust their coverage and adaptively schedule users, thus offering spatial-temporal domain flexibility. Motivated by this, as an early attempt, we investigate how adaptive beam patterns with flexible user scheduling schemes can help alleviate mismatches of requested-transmitted data in uneven-traffic and full-frequency reuse LEO systems. We formulate an optimization problem to jointly determine beam patterns, power allocation, user-LEO association, and user-slot scheduling. The problem is identified as mixed-integer nonconvex programming. We propose an efficient iterative algorithm to solve the problem by first determining beam patterns and user associations at the frame scale, followed by optimizing power allocation and user scheduling at the timeslot scale. The four-decision components are iteratively updated to improve the overall performance. Numerical results demonstrate the benefits brought by adaptive beam patterns and their effectiveness in reducing the mismatch effect in uneven-traffic LEO systems.