Spread spectrum scheme design and resource allocation for internet of things over micro-satellites

doi:10.3785/j.issn.1008-973X.2026.12.011

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (12): 2652-2662 DOI: 10.3785/j.issn.1008-973X.2026.12.011

Spread spectrum scheme design and resource allocation for internet of things over micro-satellites

Zhejian ZHENG1,2,3(

),Xiaojun JIN1,2,3,*(

),Zhonghe JIN1,2,3

1. Micro-Satellite Research Center, Zhejiang University, Hangzhou 310027, China
2. Key Laboratory of Micro-Nano Satellite Research of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
3. Huanjiang Laboratory, Zhuji 311899, China

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Abstract

A multi-user spread spectrum communication scheme integrating cyclic extended synchronization code（CESC）with belief propagation（BP）decoding and successive interference cancellation（SIC）was proposed in order to enhance user access performance and interference suppression capability in massive-user access scenarios. The detection probability of weak-signal users was enhanced by compensating their synchronization code length. The access success rate was increased by 3.06% and the single-access cost was reduced by 40.52 bits compared with the power control strategy. A grouping and priority-based packet scheduling （GPPS）strategy was introduced in a hybrid grant-based and grant-free framework in order to improve transmission performance. A portion of spreading codes was allocated exclusively to grant-based users, which led to 13.05% increase in average throughput compared with the grant-free slotted ALOHA scheme. A topology-aware distributed resource allocation（TDRA）strategy was proposed in order to enhance the utilization efficiency of codes, enabling spatial reuse of resources. The average number of available spreading codes was improved by 216.37%, and the average throughput was improved by 226.86% compared with the fixed resource allocation（FRA）strategy. Results indicate that the proposed method can significantly strengthen the interference suppression and resource allocation capabilities of micro-satellite internet of things (IoT) under massive user access.

Key words： satellite internet of things micro-satellite spread-spectrum communication resource allocation packet scheduling topology-aware

Received: 04 December 2025 Published: 21 October 2026

CLC:

TP 393

Fund: 国家自然科学基金资助项目（62073289）.

Corresponding Authors: Xiaojun JIN E-mail: 22324035@zju.edu.cn;axemaster@zju.edu.cn

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Cite this article:

Zhejian ZHENG,Xiaojun JIN,Zhonghe JIN. Spread spectrum scheme design and resource allocation for internet of things over micro-satellites. Journal of ZheJiang University (Engineering Science), 2026, 60(12): 2652-2662.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.12.011 OR https://www.zjujournals.com/eng/Y2026/V60/I12/2652

小卫星物联网扩频体制设计与资源分配策略

为了提升海量用户接入场景下的用户接入性能与干扰抑制能力，提出将循环扩展同步码（CESC）与置信传播（BP）译码及串行干扰消除（SIC）融合的多用户扩频通信方案，通过对弱信号用户的同步码长度进行补偿，提升捕获概率. 与功率控制策略相比，该方案的接入成功率提高了3.06%，单次接入成本降低了40.52 bit. 为了提升传输性能，提出授权与免授权混合体系下基于分组与优先级的数据包调度（GPPS）策略，将部分扩频码作为授权用户专用传输码，使平均吞吐率较免授权时隙ALOHA方案提升了13.05%. 为了进一步提高码利用率，提出基于拓扑感知的分布式资源分配（TDRA）策略以实现资源的空间复用，较固定资源分配（FRA）策略，平均可支配扩频码数量提升了216.37%，平均吞吐率提升了226.86%. 研究结果表明，所提方法可以显著增强微小卫星物联网应对海量用户接入的干扰抑制与资源分配能力.

关键词： 卫星物联网, 微小卫星, 扩频通信, 资源分配, 数据包调度, 拓扑感知

Fig.1 Schematic diagram of urban node and device cluster distribution used in simulation

Tab.1 Downlink channel parameters generated by different Walsh codes in satellite internet of things

Fig.2 Modulation process for satellite IoT downlink traffic channels

Tab.2 Parameters of preferred m-sequence pairs for Gold sequence generation in satellite IoT uplink channels

Fig.3 Design of synchronous frame header

Fig.4 Access and transmission process of user link

Fig.5 Transmission and reception process of satellite IoT multi-user uplink channel

Fig.6 SIC process in coupled system

Fig.7 Flowchart of grouping and priority-based packet scheduling algorithm

Fig.8 Schematic diagram of inter-satellite earth occultation at critical condition

Tab.3 Physical layer simulation parameters for access performance in dynamic scenarios

Fig.9 Average access cost under dynamic scenarios for varying user arrival rates and access strategies

Fig.10 Access success rate under dynamic scenarios for varying user arrival rates and access strategies

Fig.11 Throughput comparison of single-satellite system under different access schemes

Fig.12 Comparison of available spreading codes for SoI under different constellation scales and code resource allocation strategies

Fig.13 Throughput comparison of SoI under different constellation scales and code resource allocation strategies


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