Handover-Aware Power Minimization for Networked LEO Satellite Communications: Joint Cooperative Beamforming and Scheduling

TL;DR

An iterative algorithm is developed that combines a reweighted $\ell_2$ surrogate with a penalty-based relaxation and a fractional-programming inner loop, yielding a sequence of convex second-order cone programs to address scheduling-induced combinatorial sparsity and nonconvex fractional rate constraints.

Abstract

Networked low Earth orbit (LEO) satellite constellations enabled by inter-satellite links offer a promising path toward ubiquitous broadband non-terrestrial services. However, fast orbital motion induces frequent scheduling updates and handovers, while stringent on-board constraints (e.g., limited radio-frequency chains) tightly couple user scheduling with cooperative beamforming. This paper investigates handover-aware power-efficient downlink transmission in networked LEO systems under statistical channel state information. We introduce a two-segment frame structure that separates handover-related operations from user-plane transmission, and propose a power consumption model that captures both the switching cost of newly established satellite-user links and the reduced effective transmission window during handover. Using a hardening-bound ergodic-rate metric, we formulate a per-frame network-wide power minimization problem with joint cooperative beamforming and implicit scheduling under segmented quality-of-service constraints, per-satellite power budgets, and serving-cardinality limits. To address scheduling-induced combinatorial sparsity and nonconvex fractional rate constraints, we develop an iterative algorithm that combines a reweighted $\ell_2$ surrogate with a penalty-based relaxation and a fractional-programming inner loop, yielding a sequence of convex second-order cone programs. Simulations based on time-varying orbital dynamics with frame-wise serving-set evolution and maritime user data quantify the power-handover tradeoff and demonstrate consistent power savings and improved feasibility over non-cooperative and pre-scheduled cooperative baselines.

Figures (8)

• Figure 1: Illustration of system model. The cooperating leo satellites are typically separated by hundreds to a few thousand kilometers, depending on the orbital configuration and the selected serving set.
• Figure 2: Illustration of the considered frame structure for networked leo satellite communications. Each frame has duration $T$. At the beginning of every frame, the serving satellite set $\mathcal{L}^{(k)}$ is re-evaluated and the joint cooperative beamforming and scheduling variables are updated. If the user scheduling differs from the previous frame, a handover procedure incurs a delay $\tau_{\mathrm{HO}}T$, after which downlink data transmission proceeds for the remaining $(1-\tau_{\mathrm{HO}})T$.
• Figure 3: Frame-wise variation of the serving satellite set $\mathcal{L}^{(k)}$ due to leo orbital motion. Panels (a)-(d) show the topology snapshots for frames 1-4.
• Figure 4: Time-evolution performance under dynamic topology over $6$ consecutive frames. (a) The serving set $\mathcal{L}^{(k)}$ varies across frames due to orbital motion. (b) Network-wide power consumption versus frame index.
• Figure 5: Average network-wide power consumption and feasibility rate versus the per-ut qos requirement. (a) Average power consumption computed over feasible trials. (b) Feasibility rate.

Theorems & Definitions (2)

• Remark 1
• Remark 2