基于CWGAN-SLM的多小波OFDM系统峰均比抑制算法研究

doi:10.11959/j.issn.1000-436x.2023069

通信学报 ›› 2023, Vol. 44 ›› Issue (4): 99-110.doi: 10.11959/j.issn.1000-436x.2023069

基于CWGAN-SLM的多小波OFDM系统峰均比抑制算法研究

杨光, 吴朝阳, 聂敏, 闫晓红, 江帆

西安邮电大学通信与信息工程学院（人工智能学院），陕西西安 710121

修回日期:2023-02-21 出版日期:2023-04-25 发布日期:2023-04-01
作者简介:杨光（1977- ），女，陕西西安人，博士，西安邮电大学副教授、硕士生导师，主要研究方向为无线网络传输技术、卫星通信、网络协议设计等
吴朝阳（1998- ），男，陕西扶风人，西安邮电大学硕士生，主要研究方向为OFDM关键技术和生成对抗网络
聂敏（1964- ），男，陕西乾县人，博士，西安邮电大学教授、硕士生导师，主要研究方向为无线通信、量子信息处理等
闫晓红（1974- ），男，陕西乾县人，博士，西安邮电大学副教授，主要研究方向为多小波理论应用、扩频通信等
江帆（1982- ），女，江苏盐城人，博士，西安邮电大学教授、硕士生导师，主要研究方向为无线资源管理、D2D 通信技术边缘计算及缓存技术等
基金资助:
国家自然科学基金资助项目(61971348);国家自然科学基金资助项目(62071377);陕西省自然科学基础研究计划基金资助项目(2021JM-464)

Research on PAPR reduction algorithm based on CWGAN-SLM for multi-wavelet OFDM system

Guang YANG, Zhaoyang WU, Min NIE, Xiaohong YAN, Fan JIANG

School of Communication and Information Engineering ＆School of Artificial Intelligence, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Revised:2023-02-21 Online:2023-04-25 Published:2023-04-01
Supported by:
The National Natural Science Foundation of China(61971348);The National Natural Science Foundation of China(62071377);The Natural Science Basic Research Program of Shaanxi Province(2021JM-464)

摘要/Abstract

摘要：

为满足未来6G星地一体化系统对正交频分复用（OFDM）技术低峰均比（PAPR）的需求，首先提出一种将选择映射（SLM）算法与多小波OFDM技术相结合的算法，但此算法降低PAPR的幅度有限，并且计算复杂度较高。因此，进一步提出基于条件 Wasserstein 生成对抗网络（CWGAN）和 SLM 的峰均比抑制算法，即CWGAN-SLM算法，并将其应用于多小波OFDM系统，该算法通过引入CWGAN，生成多个时域备选信号，以达到降低峰均比的目的。仿真结果表明，CWGAN-SLM算法能够有效降低系统的PAPR和计算复杂度，而且具有较低的误码率，与GAN和WGAN相比，CWGAN具有训练容易、稳定性强且PAPR性能好的特点。

关键词: 星地一体化, 正交频分复用, 峰均比, 多小波OFDM, 计算复杂度

Abstract:

In order to meet the demand for low peak to average ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) technology in the future 6G satellite-ground integrated system, an algorithm combining selective mapping (SLM) algorithm and multi-wavelet OFDM technology was proposed firstly.However, the PAPR reduction was limited and the computational complexity was high.To solve this problem, a multi-wavelet OFDM PAPR reduction algorithm based on conditional Wasserstein generative adversarial network (CWGAN) and SLM was proposed, which was called CWGAN-SLM algorithm.CWGAN was introduced to generate more time-domain alternative signals to reduce the PAPR in the CWGAN-SLM algorithm.Simulation results indicate that the CWGAN-SLM algorithm greatly reduces the PAPR of the system and the computational complexity, and has a lower bit error rate.Compared with the GAN and WGAN, the CWGAN has the advantages of easy training, strong stability and good PAPR performance.

Key words: satellite-ground integration, OFDM, PAPR, multi-wavelet OFDM, computational complexity

中图分类号:

TN919.3

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.

Guang YANG, Zhaoyang WU, Min NIE, Xiaohong YAN, Fan JIANG. Research on PAPR reduction algorithm based on CWGAN-SLM for multi-wavelet OFDM system[J]. Journal on Communications, 2023, 44(4): 99-110.

图/表 17

图1

图2

图3

图4

图5

图6

图7

图8

表1

3种SLM算法的计算复杂度对比"

SLM算法	复数乘法	复数加法
传统SLM	$\frac{N K}{2} lb N$	$N K lb N$
TR-SLM	$\frac{N K}{2} lb N + 2 K (N - 1)$	$N K lb N + 9 N K + K (N - 1)$
CWGAN-SLM	14N	16N

表1

表2

图9

图10

表3

图11

图12

图13

表4

参考文献 21

[1]	赵亚军, 郁光辉, 徐汉青 . 6G移动通信网络:愿景、挑战与关键技术[J]. 中国科学:信息科学, 2019,49(8): 963-987.
	ZHAO Y J , YU G H , XU H Q . 6G mobile communication networks:vision,challenges,and key technologies[J]. Scientia Sinica (Informationis), 2019,49(8): 963-987.
[2]	王悦, 王权, 张德鹏 ,等. 低轨卫星通信系统与5G通信融合的应用设想[J]. 卫星应用, 2019(1): 54-59.
	WANG Y , WANG Q , ZHANG D P ,et al. Application assumption of fusion of LEO satellite communication system and 5G communication[J]. Satellite Application, 2019(1): 54-59.
[3]	CUI X , LIU K , LIU Y . Novel linear companding transform design based on linear curve fitting for PAPR reduction in OFDM systems[J]. IEEE Communications Letters, 2021,25(11): 3604-3608.
[4]	SONG H J , LEE N . Terahertz communications:Challenges in the next decade[J]. IEEE Transactions on Terahertz Science and Technology, 2021,12(2): 105-117.
[5]	ZHANG Y , WANG Z , HUANG Y ,et al. A digital signal recovery technique using DNNs for LEO satellite communication systems[J]. IEEE Transactions on Industrial Electronics, 2020,68(7): 6141-6151.
[6]	XING Z T , LIU K M , TANG B H ,et al. Novel PAPR reduction scheme based on piecewise nonlinear companding transform in OFDM systems[J]. IEEE Communications Letters, 2020,24(8): 1757-1761.
[7]	SGHAIER M , ABDELKEFI F , SIALA M . Efficient embedded signaling through Alamouti STBC precoders in MIMO-OFDM systems[C]// Proceedings of 2013 IEEE Wireless Communications and Networking Conference (WCNC). Piscataway:IEEE Press, 2013: 4053-4058.
[8]	FATHY S A , EL-MAHALLAWY M S , HAGRAS E A A . C8.SLM technique based on particle swarm optimization algorithm for PAPR reduction in wavelet-OFDM systems[C]// Proceedings of 2015 32nd National Radio Science Conference (NRSC). Piscataway:IEEE Press, 2015: 163-170.
[9]	LEE K S , CHO Y J , WOO J Y ,et al. Low-complexity PTS schemes using OFDM signal rotation and pre-exclusion of phase rotating vectors[J]. IET Communications, 2016,10(5): 540-547.
[10]	季策, 祝雯靖, 魏颖 ,等. 降低OFDM系统PAPR的改进SLM算法[J]. 通信学报, 2018,39(4): 152-158.
	JI C , ZHU W J , WEI Y ,et al. Improved SLM algorithm for PAPR reduction in OFDM system[J]. Journal on Communications, 2018,39(4): 152-158.
[11]	WANG X , JIN N D , WEI J D . A model-driven DL algorithm for PAPR reduction in OFDM system[J]. IEEE Communications Letters, 2021,25(7): 2270-2274.
[12]	KIM M , LEE W , CHO D H . A novel PAPR reduction scheme for OFDM system based on deep learning[J]. IEEE Communications Letters, 2018,22(3): 510-513.
[13]	LIU Z J , HU X , HAN K ,et al. Low-complexity PAPR reduction method for OFDM systems based on real-valued neural networks[J]. IEEE Wireless Communications Letters, 2020,9(11): 1840-1844.
[14]	NGUYEN M T , KADDOUM G , SELIM B ,et al. Deep unfolding network for PAPR reduction in multicarrier OFDM systems[J]. IEEE Communications Letters, 2022,26(11): 2616-2620.
[15]	SAROWA S , KUMAR N , AGRAWAL S ,et al. Evolution of PAPR reduction techniques:a wavelet based OFDM approach[J]. Wireless Personal Communications, 2020,115(2): 1565-1588.
[16]	ALMUTAIRI A F , KRISHNA A . Filtered-orthogonal wavelet division multiplexing (F-OWDM) technique for 5G and beyond communication systems[J]. Scientific Reports, 2022,12:4607.
[17]	SUHARTOMO A , VINCENT V . Comparison of BER performance for M-ary QAM and PSK on DWT-based OFDM system with PTS technique through AWGN channel[C]// Proceedings of 2020 IEEE International Conference on Sustainable Engineering and Creative Computing (ICSECC). Piscataway:IEEE Press, 2020: 5-10.
[18]	YAN X H , LI J , WU Z Y . 5G new radio with orthogonal multiwavelet packet division multiplexing[C]// Proceedings of 2022 IEEE 2nd International Conference on Electronic Technology,Communication and Information (ICETCI). Piscataway:IEEE Press, 2022: 996-1002.
[19]	GOODFELLOW I J , POUGET-ABADIE J , MIRZA M ,et al. Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems. New York:ACM Press, 2014: 2672-2680.
[20]	LI Y , CUI W G , GUO Y Z ,et al. Time-varying system identification using an ultra-orthogonal forward regression and multiwavelet basis functions with applications to EEG[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018,29(7): 2960-2972.
[21]	KOLEV V , COOKLEV T , KEINERT F . Design of a simple orthogonal multiwavelet filter by matrix spectral factorization[J]. Circuits,Systems,and Signal Processing, 2020,39(4): 2006-2041.

仿真参数	数值
卫星轨道高度/km	1 500
载波频率/GHz	20
信道类型	CDL-A
系统模型	MWOFDM
多小波基函数	SA4
子载波个数/个	4 096
备选信号个数/个	16
调制/解调方式	QPSK

仿真参数	GAN	WGAN	CWGAN
生成器卷积层个数	3	3	3
判别器卷积层个数	4	4	4
生成器激活函数	ReLU	ReLU	ReLU
判别器激活函数	LeakyReLU	LeakyReLU	LeakyReLU
判别器的最后一层	Sigmoid	—	Sigmoid
epoch	2 000	2 000	2 000
Batch_size	64	64	64
生成器学习率	0.000 1	0.000 1	0.000 1
判别器学习率	0.000 4	0.000 4	0.000 4
条件信息	无	无	有
优化算法	Adam	RMSProp	RMSProp

算法	第1次/s	第2次/s	第3次/s	平均运行时间/s
传统SLM	1.105	0.998	1.058	1.053
TR-SLM	4.194	4.007	4.138	4.113
MDTR	0.421	0.406	0.415	0.414
PRNet	0.298	0.314	0.308	0.306
RV-NN	0.407	0.397	0.385	0.396
PR-DUN	0.357	0.367	0.368	0.364
GAN-SLM	1.525	1.687	1.592	1.601
WGAN-SLM	0.781	0.826	0.759	0.789
CWGAN-SLM	0.476	0.435	0.461	0.457

基于CWGAN-SLM的多小波OFDM系统峰均比抑制算法研究

Research on PAPR reduction algorithm based on CWGAN-SLM for multi-wavelet OFDM system

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 21

相关文章 15

Metrics

推荐阅读 0

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	李果, 文妮, 宫丰奎, 张剑, 张思瀚. 部分频带干扰下的OFDM系统干扰检测与分集抑制算法[J]. 通信学报, 2021, 42(9): 194-204.
[6]	宫丰奎, 文妮, 李果, 高洋. 基于CAZAC序列的低复杂度抗频偏同步算法[J]. 通信学报, 2021, 42(2): 64-71.
[7]	邢智童, 李云, 彭德义, 张本思, 刘凯明, 刘元安. OFDM中一种有效的基于分段非线性压扩的PAPR抑制算法[J]. 通信学报, 2021, 42(12): 44-53.
[8]	贾科军,杨博然,曹明华,黎锁平,郝莉. 可见光通信自适应O-OFDM符号分解串行传输系统设计与研究[J]. 通信学报, 2020, 41(9): 179-189.
[9]	许琳森,杨维,田洪现,吴韬. 超声波透金属通信中子载波快速筛选比特填充算法[J]. 通信学报, 2020, 41(11): 30-39.
[10]	王怡,王亚萍. M分布星地激光通信链路相干正交频分复用系统误码性能研究[J]. 通信学报, 2020, 41(10): 179-187.
[11]	贾敏,敬晓晔,刘晓锋,刘枫,郭庆,顾学迈. 基于业务优先级的认知卫星网络频谱分配方法[J]. 通信学报, 2019, 40(4): 140-148.
[12]	崔苗,喻鑫,李学易,张广驰,刘怡俊,林凡. 多载波无线携能通信的上下行链路联合资源分配[J]. 通信学报, 2019, 40(3): 206-214.
[13]	马英杰,赵耿,魏占祯,李兆斌,鞠磊. 基于量子混沌映射降低OFDM系统PAPR的算法研究[J]. 通信学报, 2019, 40(1): 195-200.
[14]	刘业君,刘玉莹,汉鹏超,王继东,郭磊. OFDM无源光网络中多业务分层带宽分配算法[J]. 通信学报, 2018, 39(9): 84-93.
[15]	季策,祝雯靖,魏颖,贾佃霞. 降低OFDM系统PAPR的改进SLM算法[J]. 通信学报, 2018, 39(4): 152-158.