在线学习辅助的智能接收机设计与实现

doi:10.11959/j.issn.1000-436x.2024012

通信学报 ›› 2024, Vol. 45 ›› Issue (1): 18-30.doi: 10.11959/j.issn.1000-436x.2024012

• 专题：面向有人无人协同的智能通信与组网技术 • 上一篇

在线学习辅助的智能接收机设计与实现

孔凌劲, 梅锴, 刘潇然, 熊俊, 赵海涛, 魏急波

国防科技大学电子科学学院，湖南长沙 410073

修回日期:2023-11-09 出版日期:2024-01-01 发布日期:2024-01-01
作者简介:孔凌劲（1999- ），男，湖北咸宁人，国防科技大学博士生，主要研究方向为机器学习、物理层传输技术等
梅锴（1993- ），男，四川仁寿人，国防科技大学博士生，主要研究方向为机器学习、物理层传输技术等
刘潇然（1992- ），男，河南洛阳人，博士，国防科技大学讲师，主要研究方向为无线通信信号处理技术、多载波波形设计和智能通信技术
熊俊（1987- ），男，江西丰城人，博士，国防科技大学副研究员，主要研究方向为协同通信、物理层安全和网络资源分配等
赵海涛（1981- ），男，山东昌乐人，博士，国防科技大学教授、博士生导师，主要研究方向为认知无线电网络、自组织网络、协同通信等
魏急波（1967- ），男，湖北汉川人，博士，国防科技大学教授、博士生导师，主要研究方向为无线通信网络协议、通信信号处理、协同通信、认知无线电网络等
基金资助:
国家自然科学基金资助项目(61931020);国家自然科学基金资助项目(62101569)

Design and implementation of online learning assisted intelligent receiver

Lingjin KONG, Kai MEI, Xiaoran LIU, Jun XIONG, Haitao ZHAO, Jibo WEI

College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Revised:2023-11-09 Online:2024-01-01 Published:2024-01-01
Supported by:
The National Natural Science Foundation of China(61931020);The National Natural Science Foundation of China(62101569)

摘要/Abstract

摘要：

为了解决复杂场景下的可靠通信问题，设计了一种在线学习辅助的正交频分复用（OFDM）智能接收机。该接收机能够判断信道环境是否发生改变，并在线收集样本数据进行训练，形成当前环境下最佳的接收参数。在OFDM系统的信道估计模块中，设计了基于样本含噪均方误差（MSE）的性能比较器作为信道环境变化的判断依据，并采用轻量化的神经网络结构以实现快速在线训练。最后，通过通用软件无线电外设（USRP）进行了实现和验证。仿真和空口实验表明，所提接收机能够有效感知并适应新的信道环境，并且在导频数量受限的情况下，接收性能和收敛速度均优于现有的机器学习方法。

关键词: 机器学习, 智能接收机, 在线训练, 正交频分复用

Abstract:

To address the issue of reliable communication under complicated scenarios, an online learning-assisted intelligent OFDM receiver was proposed.The variations of the channel environment could be precepted by the receiver, and the optimal parameters of the receiver under the current scenario were obtained by collecting data and training online.In the channel estimation module of the OFDM system, a performance comparator based on the mean square error of noisy channel samples was designed as the indicator of channel environment variations.To accelerate the online training progress, a lightweight neural network structure was applied.The proposed method was further implemented and verified based on universal software radio peripherals.The numerical simulation and over-the-air experimental results demonstrate that the proposed receiver can perceive and adapt to new environments effectively, and outperforms existing machine learning methods in terms of receiving performance and convergence rate with a limited number of pilots.

Key words: machine learning, intelligent receiver, online training, OFDM

中图分类号:

TN929.5

孔凌劲, 梅锴, 刘潇然, 熊俊, 赵海涛, 魏急波. 在线学习辅助的智能接收机设计与实现[J]. 通信学报, 2024, 45(1): 18-30.

Lingjin KONG, Kai MEI, Xiaoran LIU, Jun XIONG, Haitao ZHAO, Jibo WEI. Design and implementation of online learning assisted intelligent receiver[J]. Journal on Communications, 2024, 45(1): 18-30.

图/表 16

图1

图2

图3

图4

表1

图5

表2

图6

图7

图8

图9

表3

图10

图11

图12

图13

参考文献 32

[1]	赵海涛, 高士顺, 王海军 ,等. 无人机自主通信和组网能力评估方法[J]. 通信学报, 2020,41(8): 87-98.
	ZHAO H T , GAO S S , WANG H J ,et al. Evaluation method for autonomous communication and networking capability of UAV[J]. Journal on Communications, 2020,41(8): 87-98.
[2]	王海军, 赵海涛, 任保全 ,等. 一种面向无人机智能通信的信息物理融合框架[J]. 中国科学:信息科学, 2022,52(11): 2141-2154.
	WANG H J , ZHAO H T , REN B Q ,et al. Cyber-physical framework for UAV intelligent communications[J]. Scientia Sinica (Informationis), 2022,52(11): 2141-2154.
[3]	尹浩, 魏急波, 赵海涛 ,等. 一种面向复杂场景的无线通信节点智能适变架构[J]. 中国科学:信息科学, 2021,51(2): 294-304.
	YIN H , WEI J B , ZHAO H T ,et al. An intelligent adaptative architecture for wireless communication in complex scenarios[J]. Scientia Sinica (Informationis), 2021,51(2): 294-304.
[4]	FELIX A , CAMMERER S , D?RNER S ,et al. OFDM-autoencoder for end-to-end learning of communications systems[C]// Proceedings of the 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). Piscataway:IEEE Press, 2018: 1-5.
[5]	ZHAO Z Y , VURAN M C , GUO F J ,et al. Deep-waveform:a learned OFDM receiver based on deep complex-valued convolutional networks[J]. IEEE Journal on Selected Areas in Communications, 2021,39(8): 2407-2420.
[6]	YE H , LI G Y , JUANG B H . Power of deep learning for channel estimation and signal detection in OFDM systems[J]. IEEE Wireless Communications Letters, 2018,7(1): 114-117.
[7]	HONKALA M , KORPI D , HUTTUNEN J M J . DeepRx:fully convolutional deep learning receiver[J]. IEEE Transactions on Wireless Communications, 2021,20(6): 3925-3940.
[8]	LI A , ME Y , XUE S Y ,et al. A carrier-frequency-offset resilient OFDMA receiver designed through machine deep learning[C]// Proceedings of the 2018 IEEE 29th Annual International Symposium on Personal,Indoor and Mobile Radio Communications (PIMRC). Piscataway:IEEE Press, 2018: 1-6.
[9]	D?RNER S , CAMMERER S , HOYDIS J ,et al. Deep learning based communication over the air[J]. IEEE Journal of Selected Topics in Signal Processing, 2018,12(1): 132-143.
[10]	YANG Y W , GAO F F , MA X L ,et al. Deep learning-based channel estimation for doubly selective fading channels[J]. IEEE Access, 2019,7: 36579-36589.
[11]	ZENG T Y , LI J Y , HU M S ,et al. Toward higher performance for channel estimation with complex DnCNN[J]. IEEE Communications Letters, 2020,24(1): 198-201.
[12]	梅锴, 赵海涛, 刘潇然 ,等. 高效的基于数据与模型的信道估计算法[J]. 通信学报, 2022,43(1): 59-70.
	MEI K , ZHAO H T , LIU X R ,et al. Efficient model-and-data based channel estimation algorithm[J]. Journal on Communications, 2022,43(1): 59-70.
[13]	FARSAD N , GOLDSMITH A . Neural network detection of data sequences in communication systems[J]. IEEE Transactions on Signal Processing, 2018,66(21): 5663-5678.
[14]	CAMMERER S , GRUBER T , HOYDIS J ,et al. Scaling deep learning-based decoding of polar codes via partitioning[C]// Proceedings of 2017 IEEE Global Communications Conference. Piscataway:IEEE Press, 2017: 1-6.
[15]	ZHANG Y , DOSHI A , LISTON R ,et al. DeepWiPHY:eep learning-based receiver design and dataset for IEEE 802.11ax systems[J]. IEEE Transactions on Wireless Communications, 2021,20(3): 1596-1611.
[16]	JIANG P W , WANG T Q , HAN B ,et al. AI-aided online adaptive OFDM receiver:design and experimental results[J]. IEEE Transactions on Wireless Communications, 2021,20(11): 7655-7668.
[17]	XIONG K X , ZHAO Z Y , HONG W ,et al. Few-shot learning in wireless networks:a meta-learning model-enabled scheme[C]// Proceedings of the 2022 IEEE International Conference on Communications Workshops (ICC Workshops). Piscataway:IEEE Press, 2022: 415-420.
[18]	HWANG C H , LAI G L , CHEN S C . Spectrum sensing in wideband OFDM cognitive radios[J]. IEEE Transactions on Signal Processing, 2010,58(2): 709-719.
[19]	AL-HABASHNA A , DOBRE O A , VENKATESAN R ,et al. Second-order cyclostationarity of mobile WiMAX and LTE OFDM signals and application to spectrum awareness in cognitive radio systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2012,6(1): 26-42.
[20]	LIEN S Y , SHIEH S L , HUANG Y ,et al. 5G new radio:waveform,frame structure,multiple access,and initial access[J]. IEEE Communications Magazine, 2017,55(6): 64-71.
[21]	NEUMANN D , WIESE T , UTSCHICK W . Learning the MMSE channel estimator[J]. IEEE Transactions on Signal Processing, 2018,66(11): 2905-2917.
[22]	LI Y , CIMINI L J , SOLLENBERGER N R . Robust channel estimation for OFDM systems with rapid dispersive fading channels[J]. IEEE Transactions on Communications, 1998,46(7): 902-915.
[23]	LIU J , MEI K , ZHANG X C ,et al. Online extreme learning machine-based channel estimation and equalization for OFDM systems[J]. IEEE Communications Letters, 2019,23(7): 1276-1279.
[24]	MEI K , LIU J , ZHANG X Y ,et al. A low complexity learning-based channel estimation for OFDM systems with online training[J]. IEEE Transactions on Communications, 2021,69(10): 6722-6733.
[25]	MEI K , LIU J , LIU X R ,et al. LMMSE channel estimation for OFDM systems with channel correlation function selection[J]. IET Communications, 2021,15(17): 2159-2175.
[26]	MEI K , LIU J , ZHANG X C ,et al. Performance analysis on machine learning-based channel estimation[J]. IEEE Transactions on Communications, 2021,69(8): 5183-5193.
[27]	LUAN D X , THOMPSON J S . Channelformer:attention based neural solution for wireless channel estimation and effective online training[J]. IEEE Transactions on Wireless Communications, 2023,22(10): 6562-6577.
[28]	3GPP. Evolved universal terrestrial radio access (E-UTRA); User equipment (UE) radio transmission and reception:TS 36.101[S]. 2011.
[29]	3GPP. Universal mobile telecommunications system (UMTS); Deployment aspects:TR 25.943 V6.0.0[S]. 2004.
[30]	IEEE. IEEE draft standard for information technology – telecommunications and information exchange between systems local and metropolitan area networks – specific requirements part 11:wireless LAN medium access control (MAC) and physical layer (PHY) specifications amendment enhancements for high efficiency WLAN[S]. 2019.
[31]	JIANHAN L , RON P , NIHAR J ,et al. TGax channel model[S]. 2014.
[32]	ETSI. Broadband radio access networks (BRAN); High performance radio logical area network (HIPERLAN) type 2; Requirements and architectures for wireless broadband access[S]. 1999.

方法	网络层	数据维度	激活函数
Online CE	输入（LS估计）	144	无
	输出	288
SwitchNet	输入（LS估计）	144	无
	FC₁、FC₂、FC₃	288、288、288
	输出α₁FC₁+α₂FC₂+	288
	α₃FC₃
TransferNet	输入（LS估计）	144	无
	FC	288
	输出	288

参数	参数值
SNR/dB	5~25
损失函数	MSE
初始化学习率	1×10⁴
优化器	Adam
训练轮次	100
训练数据量/组	15 000

模型	Model A	Model B	Model C	Model D	Model E	Model F
Model A	0	0	0	0	0	0
Model B	1	0	0	0	0	0
Model C	1	0.994 5	0	0	0	0
Model D	1	1	0.634 2	0	0	0
Model E	1	1	1	1	0	0
Model F	1	1	1	1	0	0

在线学习辅助的智能接收机设计与实现

Design and implementation of online learning assisted intelligent receiver

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 16

参考文献 32

相关文章 15

Metrics

推荐阅读 0

[1]	刘乔寿, 周雄, 刘爽, 邓义锋. 基于深度强化学习的OFDM自适应导频设计[J]. 通信学报, 2023, 44(9): 104-114.
[2]	期治博, 杜磊, 霍如, 杨帆, 黄韬. 基于边缘计算的多摄像头视频协同分析方法[J]. 通信学报, 2023, 44(8): 14-26.
[3]	陈晓霖, 昝道广, 吴炳潮, 关贝, 王永吉. 面向纵向联邦学习的对抗样本生成算法[J]. 通信学报, 2023, 44(8): 1-13.
[4]	巩小雪, 庞嘉豪, 张琦涵, 徐长乐, 秦文帅, 郭磊. 基于机器学习的光网络干扰攻击检测、识别与恢复方法[J]. 通信学报, 2023, 44(7): 159-170.
[5]	杨光, 吴朝阳, 聂敏, 闫晓红, 江帆. 基于CWGAN-SLM的多小波OFDM系统峰均比抑制算法研究[J]. 通信学报, 2023, 44(4): 99-110.
[6]	戴千一, 张斌, 郭松, 徐开勇. 基于多分类器集成的区块链网络层异常流量检测方法[J]. 通信学报, 2023, 44(3): 66-80.
[7]	郭漪, 王翊卿, 樊媛媛, 刘刚. 基于子载波补给索引调制的OFDM传输方案[J]. 通信学报, 2023, 44(2): 104-111.
[8]	于季弘, 林子砚, 叶能, 杨凯, 安建平. 基于三方生成对抗网络的隐蔽通信方法[J]. 通信学报, 2023, 44(11): 225-236.
[9]	何高峰, 魏千峰, 肖咸财, 朱海婷, 徐丙凤. 支持数据隐私保护的恶意加密流量检测确认方法[J]. 通信学报, 2022, 43(2): 156-170.
[10]	邢旺, 唐晓刚, 周一青, 张冲, 潘振岗. 面向OTFS的时延-多普勒域信道估计方法综述[J]. 通信学报, 2022, 43(12): 188-201.
[11]	陆彦辉, 柳寒, 李航, 朱光旭. 基于多鉴别器生成对抗网络的时间序列生成模型[J]. 通信学报, 2022, 43(10): 167-176.
[12]	冯智斌, 徐煜华, 杜智勇, 刘鑫, 李文, 韩昊, 张晓博. 对抗智能干扰的主动防御技术[J]. 通信学报, 2022, 43(10): 42-54.
[13]	许琳森, 杨维, 田洪现. 超声波透金属通信中信道交织器的设计和性能分析[J]. 通信学报, 2022, 43(10): 1-11.
[14]	彭长根, 高婷, 刘惠篮, 丁红发. 面向机器学习模型的基于PCA的成员推理攻击[J]. 通信学报, 2022, 43(1): 149-160.
[15]	梅锴, 赵海涛, 刘潇然, 刘军, 熊俊, 任保全, 魏急波. 高效的基于数据与模型的信道估计算法[J]. 通信学报, 2022, 43(1): 59-70.