  The regenerative capabilities of next-generation satellite systems offer a
novel approach to design low earth orbit (LEO) satellite communication systems,
enabling full flexibility in bandwidth and spot beam management, power control,
and onboard data processing. These advancements allow the implementation of
intelligent spatial multiplexing techniques, addressing the ever-increasing
demand for future broadband data traffic. Existing satellite resource
management solutions, however, do not fully exploit these capabilities. To
address this issue, a novel framework called flexible resource management
algorithm for LEO satellites (FLARE-LEO) is proposed to jointly design
bandwidth, power, and spot beam coverage optimized for the geographic
distribution of users. It incorporates multi-spot beam multicasting, spatial
multiplexing, caching, and handover (HO). In particular, the spot beam coverage
is optimized by using the unsupervised K-means algorithm applied to the
realistic geographical user demands, followed by a proposed successive convex
approximation (SCA)-based iterative algorithm for optimizing the radio
resources. Furthermore, we propose two joint transmission architectures during
the HO period, which jointly estimate the downlink channel state information
(CSI) using deep learning and optimize the transmit power of the LEOs involved
in the HO process to improve the overall system throughput. Simulations
demonstrate superior performance in terms of delivery time reduction of the
proposed algorithm over the existing solutions.
