卫星物联网容量增强的波束优化设计技术研究

刘子威; 徐圆圆; 边东明; 张更新

doi:10.11999/JEIT231120

卫星物联网容量增强的波束优化设计技术研究

doi: 10.11999/JEIT231120

1.
南京邮电大学通信与信息工程学院南京 210003
2.
陆军工程大学通信工程学院南京 210007

基金项目: 国家自然科学基金(61971440, 62271266, U21A20450)，江苏省自然科学基金重大项目(BK20192002)

详细信息

作者简介:
刘子威：男，博士，副教授，研究方向为卫星通信、干扰分析、多用户检测、自适应阵列处理、通信信号处理

徐圆圆：女，硕士生，研究方向为卫星通信、随机接入

边东明：男，博士，教授，研究方向为卫星通信、深空通信

张更新：男，博士，教授，研究方向为卫星通信、深空通信、空间信息网络

通讯作者:
刘子威　lzw@njupt.edu.cn

中图分类号: TN927.2
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- 被引次数: 0
出版历程
- 收稿日期: 2023-10-17
- 修回日期: 2024-06-17
- 网络出版日期: 2024-12-24

Research on Beam Optimization Design Technology for Capacity Enhancement of Satellite Internet of Things

1.
School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China

Funds: The National Natural Science Foundation of China (61971440, 62271266, U21A20450), The Natural Science Foundation of Jiangsu Province Major Project (BK20192002).

摘要

摘要: 卫星物联网终端低功耗、轻控制的设计需求导致系统采用常规随机接入协议时易发生大量碰撞，难以满足系统吞吐量要求。现有容碰撞随机接入技术依赖功率控制、波形积累的方式，在实际中难以实现。该文分析了功率域碰撞分离所需条件，提出面向功率域信号分离的辅助波束设计方案，在常规接收波束外增设辅助接收波束，通过优化辅助波束增益构造接收信号信噪比差异，支撑碰撞信号分离。仿真表明，所提方案能够显著提升随机接入的吞吐量。
- 卫星物联网 /
- 随机接入 /
- 信号分离 /
- 波束形成
Abstract: Objective Under the hundreds of kilometers of transmission distance in low-orbit satellite communication, both power consumption and latency are significantly higher than in ground-based networks. Additionally, many data collection services exhibit short burst characteristics. Conventional resource reservation-based access methods have extremely low resource utilization, whereas dynamic application-based access methods incur large signaling overhead and fail to meet the latency and power consumption requirements for satellite Internet of Things (IoT). Random access technology, which involves competition for resources, can better accommodate the short burst data packet services typical of satellite IoT. However, as the load increases, data packet collisions at satellite access points lead to a sharp decline in actual throughput under medium and high loads. In terrestrial wireless networks, technologies such as near-far effect management and power control are commonly employed to create differences in packet reception power. However, due to the large number of terminals covered and the long distance between the satellite and the Earth, these techniques are unsuitable for satellite IoT, preventing the establishment of an adequate carrier-to-noise ratio. Developing separation conditions suitable for satellite IoT access scenarios is a key research focus. Considering the future development of spaceborne digital phased array technology, this paper leverages the data-driven beamforming capability of the on-board phased array and introduces the concept of spatial auxiliary channels. By employing a sum-and-difference beam design method, it expands the dimensions for separating collision signals beyond the time, frequency, and energy domains. This approach imposes no additional processing burdens on the terminal and aligns with the low power consumption and minimal control design principles for satellite IoT. Methods To address packet collision issues in hotspot areas of satellite IoT services, this study extends the conventional time-slot ALOHA access framework by introducing an auxiliary receiving beam alongside the random access of conventional receiving beams. The main and auxiliary beams simultaneously receive signals from the same terminal. By optimizing the main lobe gain of the auxiliary beam, a difference in the Signal-to-Noise Ratio (SNR) between the signals received by the main and auxiliary beams is established. This difference is then separated using Successive Interference Cancellation (SIC) technology, leveraging the correlation between the received signals of the auxiliary and main beams to support the separation of collision signals and ensure reliable reception of satellite IoT signals. Results and Discussions Firstly, the system throughput of the proposed scheme is simulated (Fig. 4). The theoretical throughput derived in the previous section is consistent with the simulation results. When the normalized load reached 1.8392, the maximum system throughput is 0.81085 packets/slot. Compared with existing methods such as SA, CRDSA, and IRSA, the proposed scheme demonstrated improved system throughput and packet loss rate performance in both peak and high-load regions, with a peak throughput increase of approximately 120%. Secondly, the influence of amplitude, phase, and angle measurement errors on system performance is evaluated. The angle measurement error had a greater effect on throughput performance than amplitude and phase errors. Amplitude and phase errors had a smaller effect on the main lobe gain but a larger effect on the sidelobe gain (Tables 3-5). Therefore, angle measurement errors have a considerable effect on throughput improvement. Regarding beamwidth, as beamwidth increased, the roll-off of the corresponding difference beam with 10 array elements is gentler than that with 32 array elements. However, the peak gain of the auxiliary beam decreased, leading to reduced system throughput for configurations with larger main lobe widths. Conclusions This paper presents an auxiliary beam design strategy for power-domain signal separation in satellite IoT scenarios, aiming to improve system throughput and packet loss rate performance. The approach incorporates spatial domain processing and proposes the concept of auxiliary receiving beams. By generating a difference beam derived from the main beam and using it as the auxiliary beam, the scheme constructs the required SNR difference for power-domain signal separation, enhancing the probability of successfully receiving collided signals. Simulation results indicate that, compared with SA, the peak system throughput increased by 120%, with significant improvements observed. Furthermore, the scheme demonstrated robustness by tolerating moderate system and measurement errors, facilitating large-capacity random access for satellite IoT terminals.
- Satellite internet of things /
- Random access /
- Signal separation /
- Beamforming

HTML全文

图 1 卫星物联网数据包上行随机接入场景

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图 2 碰撞数据包分离流程

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图 3 辅助波束设计示意图

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图 4 基于辅助波束接入方案与SA/CRDSA/IRSA吞吐量性能对比

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图 5 32阵元测角误差下基于辅助波束接入方案性能

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表 1 仿真参数

参数	数值
载波频率(GHz)	2
阵元间隔(m)	波长/2
星地距离(km)	1000
终端发送功率(dBW)	–10
终端发送增益(dBi)	0
等效噪声温度(K)	290
带宽(kHz)	20
分离门限(dB)	10

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表 2 32阵元测角误差下碰撞信号分离成功率(%)

碰撞数据包个数	无误差	$\sigma = \dfrac{1}{{10}}\beta $	$\sigma = \dfrac{1}{5}\beta $
2	75	42.33	23.55
3	20.33	5.72	1.75
4	3.75	0.66	0.1
5	0.72	0.07248	0.0052
6	0.0667	0.0026	0.0002
7	0.028571	0.00051429	0

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表 3 测角误差下系统吞吐量提升(%)

波束宽度	无误差	$\sigma = \dfrac{1}{{10}}\beta $	$\sigma = \dfrac{1}{5}\beta $
$3.2^\circ $	120.22	55.77	27.43
$10.2^\circ $	117.10	54.18	26.65

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表 4 幅相误差下系统吞吐量提升(%)

波束宽度(°)	$ \begin{gathered} {\sigma _{\text{a}}} = 6\% \\ {\sigma _{\text{p}}} = 1\% \\ \end{gathered} $	$ \begin{gathered} {\sigma _{\text{a}}} = 10\% \\ {\sigma _{\text{p}}} = 5\% \\ \end{gathered} $
$3.2$	110.27	107.56
$10.2$	84.92	79.00

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表 5 幅相误差和测角误差下系统吞吐量提升(%)

波束宽度	$\sigma = \dfrac{1}{{10}}\beta $	$\sigma = \dfrac{1}{5}\beta $
$3.2^\circ $	56.23	28.76
$10.2^\circ $	48.49	16.75

下载: 导出CSV

参考文献(24)

[1]	CASINI E, DE GAUDENZI R, and DEL RIO HERRERO O. Contention resolution diversity slotted ALOHA (CRDSA): An enhanced random access schemefor satellite access packet networks[J]. IEEE Transactions on Wireless Communications, 2007, 6(4): 1408–1419. doi: 10.1109/TWC.2007.348337.
[2]	FEI Changjiang, JIANG Bin, XU Kun, et al. An intelligent load control-based random access scheme for space-based internet of things[J]. Sensors, 2021, 21(4): 1040. doi: 10.3390/s21041040.
[3]	SRIVATSA C R and MURTHY C R. On the impact of channel estimation on the design and analysis of IRSA based systems[J]. IEEE Transactions on Signal Processing, 2022, 70: 4186–4200. doi: 10.1109/TSP.2022.3186539.
[4]	RAMATRYANA I N A and SHIN S Y. NOMA-based CRDSA with access control for next-generation IoT networks[C]. 2021 International Conference on Information and Communication Technology Convergence, Jeju Island, Republic of Korea, 2021: 997–1001. doi: 10.1109/ICTC52510.2021.9620221.
[5]	ALVI S, DURRANI S, and ZHOU Xiangyun. Enhancing CRDSA with transmit power diversity for machine-type communication[J]. IEEE Transactions on Vehicular Technology, 2018, 67(8): 7790–7794. doi: 10.1109/TVT.2018.2831926.
[6]	CLAZZER F, PAOLINI E, MAMBELLI I, et al. Irregular repetition slotted ALOHA over the Rayleigh block fading channel with capture[C]. 2017 IEEE International Conference on Communications, Paris, France, 2017: 1–6. doi: 10.1109/ICC.2017.7996796.
[7]	WANG Qiwei, REN Guangliang, GAO S, et al. A framework of non-orthogonal slotted aloha (NOSA) protocol for TDMA-based random multiple access in IoT-oriented satellite networks[J]. IEEE Access, 2018, 6: 77542–77553. doi: 10.1109/ACCESS.2018.2883399.
[8]	ZHAO Bo, REN Guangliang, and ZHANG Huining. Random pattern multiplexing for random access in IoT-oriented satellite networks[J]. IEEE Systems Journal, 2020, 14(3): 4089–4100. doi: 10.1109/JSYST.2019.2927319.
[9]	BAI Jialing and REN Guangliang. Polarized MIMO slotted ALOHA random access scheme in satellite network[J]. IEEE Access, 2017, 5: 26354–26363. doi: 10.1109/ACCESS.2017.2774247.
[10]	WIESELTHIER J E, EPHREMIDES A, and MICHAELS L A. An exact analysis and performance evaluation of framed ALOHA with capture[J]. IEEE Transactions on Communications, 1989, 37(2): 125–137. doi: 10.1109/26.20080.
[11]	阚鹏程, 王子宁, 孔槐聪, 等. 基于下行NOMA的多波束卫星通信稳健波束成形算法[J]. 天地一体化信息网络, 2021, 2(4): 53–59. doi: 10.11959/j.issn.2096-8930.2021043. KAN Pengcheng, WANG Zining, KONG Huaicong, et al. Robust beamforming algorithm for multibeam satellite communication based on downlink NOMA[J]. Space-Integrated-Ground Information Networks, 2021, 2(4): 53–59. doi: 10.11959/j.issn.2096-8930.2021043.
[12]	CHU Jianhang, CHEN Xiaoming, ZHONG Caijun, et al. Robust design for NOMA-based multibeam LEO satellite internet of things[J]. IEEE Internet of Things Journal, 2021, 8(3): 1959–1970. doi: 10.1109/JIOT.2020.3015995.
[13]	梁宇宏, 邓宓原, 张云, 等. 一种新型和差旁瓣抑制阵列天线[J]. 电波科学学报, 2022, 37(1): 137–145. doi: 10.12265/j.cjors.2020224. LIANG Yuhong, DENG Miyuan, ZHANG Yun, et al. A novel sum-difference side lobe suppression array antenna[J]. Chinese Journal of Radio Science, 2022, 37(1): 137–145. doi: 10.12265/j.cjors.2020224.
[14]	LING Binjian, DONG Chao, DAI Jincheng, et al. Multiple decision aided successive interference cancellation receiver for NOMA systems[J]. IEEE Wireless Communications Letters, 2017, 6(4): 498–501. doi: 10.1109/LWC.2017.2708117.
[15]	胡亚, 王永庆, 吴嗣亮, 等. 利用频域连续性检测非合作突发通信信号[J]. 北京理工大学学报, 2012, 32(10): 1071–1076. doi: 10.3969/j.issn.1001-0645.2012.10.015. HU Ya, WANG Yongqing, WU Siliang, et al. Detection of non-cooperative burst signals employing continuity in frequency domain[J]. Transactions of Beijing institute of Technology, 2012, 32(10): 1071–1076. doi: 10.3969/j.issn.1001-0645.2012.10.015.
[16]	吴迪, 葛临东, 王彬. 突发信号存在性自适应盲检测算法[J]. 计算机应用, 2010, 30(8): 2221–2223,2234. doi: 10.3724/SP.J.1087.2010.02221. WU Di, GE Lindong, and WANG Bin. Adaptive blind presence detection algorithm for burst signals[J]. Journal of Computer Applications, 2010, 30(8): 2221–2223,2234. doi: 10.3724/SP.J.1087.2010.02221.
[17]	郭书涵, 胡国平, 赵方正, 等. 基于深度卷积神经网络的DOA估计[J]. 空军工程大学学报, 2023, 24(4): 62–68. doi: 10.3969/j.issn.2097-1915.2023.04.0010. GUO Shuhan, HU Guoping, ZHAO Fangzheng, et al. A DOA estimation based on deep convolutional neural network[J]. Journal of Air Force Engineering University, 2023, 24(4): 62–68. doi: 10.3969/j.issn.2097-1915.2023.04.0010.
[18]	张明洋, 查淞元, 刘雨东. 基于特征值聚类的MUSIC算法[J]. 西北工业大学学报, 2023, 41(3): 574–578. doi: 10.1051/jnwpu/20234130574. ZHANG Mingyang, CHA Songyuan, and LIU Yudong. MUSIC algorithm based on eigenvalue clustering[J]. Journal of Northwestern Polytechnical University, 2023, 41(3): 574–578. doi: 10.1051/jnwpu/20234130574.
[19]	员琳红. 和差多波束跟踪算法及工程实现[D]. [硕士论文], 西安电子科技大学, 2012. YUAN Linhong. Sum-difference muti-beam tracking algorithms and hardware implementation[D]. [Master dissertation], Xidian University, 2012.
[20]	武楠, 匡镜明, 王华. 卫星通信接收机同步技术[M]. 北京: 北京理工大学出版社, 2018. WU Nan, KUANG Jingming, and WANG Hua. Synchronization Techniques in Satellite Communications Receivers[M]. Beijing: Beijing Institute of Technology Press, 2018.
[21]	STAHL V, FISCHER A, and BIPPUS R. Quantile based noise estimation for spectral subtraction and Wiener filtering[C]. 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing, Istanbul, Turkey, 2000: 1875–1878. doi: 10.1109/ICASSP.2000.862122.
[22]	李延梅. 一种改进的遗传算法及应用[D]. [硕士论文], 华南理工大学, 2012. LI Yanmei. An improved genetic algorithm and its application[D]. [Master dissertation], South China University of Technology, 2012.
[23]	葛培明. 改进的遗传算法及其在工程优化中的应用[D]. [博士论文], 西南交通大学, 2006. doi: 10.7666/d.y884718. GE Peiming. Improved genetic algorithms and applications in engineering optimization[D]. [Ph. D. dissertation], Southwest Jiaotong University, 2006. doi: 10.7666/d.y884718.
[24]	蔡明頔. 求解优化问题最优解的两种改进的遗传算法[D]. [硕士论文], 哈尔滨师范大学, 2014. CAI Mingdi. Two kinds of improved genetic algorithm to solve the optimal solution of the optimization problem[D]. [Master dissertation], Harbin Normal University, 2014.