卫星互联网的高效频谱共享研究综述

周述淇; 陈发堂

doi:10.13756/j.gtxyj.2026.250126

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卫星互联网的高效频谱共享研究综述

无线光通信技术 | 更新时间：2026-10-21

- 卫星互联网的高效频谱共享研究综述
- A Survey on Efficient Spectrum Sharing in Satellite Internet
- 光通信研究 2026年第5期页码：262-272
- 作者机构：
  
  1.上海思朗科技股份有限公司，西安　710000
  2.重庆邮电大学　重庆市移动通信技术重点实验室，重庆　400065
- 作者简介：
  
  周述淇，工程师。E-mail：234279535@qq.com
- 基金信息：
  
  重庆市自然科学基金创新发展联合基金资助项目(CSTB2023NSCQ-LZX0114)
- DOI：10.13756/j.gtxyj.2026.250126
  中图分类号： TN929.5
- 收稿：2025-03-30，
  
  修回：2025-04-23，
  
  纸质出版：2026-10-10
- 稿件说明：
移动端阅览
周述淇，陈发堂. 卫星互联网的高效频谱共享研究综述[J]. 光通信研究，2026(5): 262-272.

Zhou S Q, Chen F T. A Survey on Efficient Spectrum Sharing in Satellite Internet[J]. Study on Optical Communications, 2026 (5): 262-272.
周述淇，陈发堂. 卫星互联网的高效频谱共享研究综述[J]. 光通信研究，2026(5): 262-272. DOI： 10.13756/j.gtxyj.2026.250126.

Zhou S Q, Chen F T. A Survey on Efficient Spectrum Sharing in Satellite Internet[J]. Study on Optical Communications, 2026 (5): 262-272. DOI： 10.13756/j.gtxyj.2026.250126.

摘要

卫星互联网的发展对频谱资源的高效利用提出了更高要求，传统固定分配方式难以满足动态环境需求。文章研究了非正交多址接入、认知无线电、区块链和人工智能在频谱共享中的应用，分析其适用性、优势及局限性，阐述了其在频谱共享中的应用原理，并综述当前研究进展，从适用场景、计算复杂度、频谱利用率、抗干扰能力以及误码率等维度进行对比分析。分析结果表面：非正交多址接入适用于高用户密度场景但受连续干扰消除算法限制，认知无线电依赖高精度感知技术，区块链提升频谱交易公平性但实时性较差，人工智能具备动态频谱资源预测但受限于数据质量。最后，结合多种技术的优势，提出若干建议，旨在为工程应用提供参考。

Abstract

Despite the rapid expansion of satellite internet constellations for global coverage

static spectrum allocation cannot cope with the time-varying propagation and traffic dynamics of space-ground integrated networks. Here we systematically survey four dominant technical paradigms for spectrum sharing including Non-Orthogonal Multiple Access (NOMA)

Cognitive Radio (CR)

blockchain and Artificial Intelligence (AI). We elaborate their fundamental operating principles

map the full body of contemporary literature

and benchmark their trade-offs across five core quantitative dimensions including applicable deployment scenarios

computational overhead

spectrum utilization efficiency

interference resilience and bit-error performance. Our cross-technology comparison reveals distinct intrinsic limitations. The NOMA is well-suited for high-user-density scenarios but suffers performance degradation from Successive Interference Cancellation (SIC) residual noise. The CR performance relies on high-precision wideband spectrum sensing. The blockchain enables verifiable

equitable spectrum trading at the cost of substantial transaction latency. The data-driven AI facilitates predictive dynamic resource scheduling yet is constrained by the scarcity and quality of training datasets. By integrating the advantages of multiple technologies

we propose several recommendations aimed at providing a reference for future standardized engineering implementations.

关键词

Keywords

references

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