Toward Communication-Efficient Space Data Centers: Bottlenecks, Architectures, and New Paradigms

TL;DR

This paper explores communication bottlenecks in Space Data Centers and proposes semantic communication as a solution to enable scalable, energy-efficient orbital AI infrastructure.

Contribution

It systematically analyzes communication constraints in SDCs and demonstrates the potential of semantic communication to reduce uplink data transmission.

Findings

Semantic communication can significantly reduce uplink data pressure.

A multi-layer heterogeneous SDC framework is feasible under energy and thermal constraints.

Open challenges for scalable deployment are outlined.

Abstract

The rapid growth of foundation model training and large-scale AI services has driven ground data centers toward unprecedented power densities, intensifying challenges in energy supply, cooling, and spatial scalability. Space Data Centers (SDCs) have emerged as a promising paradigm for hosting energy-intensive computing infrastructures in orbit, leveraging continuous solar energy and radiative cooling advantages. However, unlike ground facilities primarily constrained by power and site availability, SDCs are fundamentally limited by communication capability. The gap between petabit-scale internal data exchange in ground data centers and the gigabit-scale capacity of ground-space links forms a critical bottleneck. This article systematically analyzes communication constraints in SDC architectures and explores semantic communication as a key enabling paradigm. By transmitting compact, task-relevant semantic representations instead of raw data, uplink pressure can be substantially reduced. The feasibility of communication-efficient orbital AI infrastructures is demonstrated through the evaluation of a multi-layer heterogeneous SDC framework consisting of relay satellites and orbital computing nodes operating under coupled energy and thermal constraints. The article further outlines open research challenges toward scalable deployment.