基于SLM-PTS算法融合的NC-OFDM峰均比优化

doi:10.11959/j.issn.1000-0801.2022155

电信科学 ›› 2022, Vol. 38 ›› Issue (7): 63-74.doi: 10.11959/j.issn.1000-0801.2022155

基于SLM-PTS算法融合的NC-OFDM峰均比优化

周杰¹^,², Esono Mikue Bernardo Esono¹, 王学英¹, 周惠婷¹, 罗宏²

¹ 南京信息工程大学电子与信息工程学院，江苏南京 210044
² 南京信息工程大学图书馆，江苏南京 210044

修回日期:2022-07-06 出版日期:2022-07-20 发布日期:2022-07-01
作者简介:周杰（1964- ），男，博士，南京信息工程大学电子与信息工程学院教授、图书馆馆长，主要研究方向为无线通信、大规模MIMO等
Esono Mikue Bernardo Esono（1993- ），男，南京信息工程大学电子与信息工程学院硕士生，主要研究方向为OFDM通信、OpenWrt路由器、无线 5G 模块/4G 模块/Wi-Fi 模块STM32控制板
王学英（1995- ），女，南京信息工程大学电子与信息工程学院硕士生，主要研究方向为无线通信理论、信道建模
周惠婷（1995- ），女，南京信息工程大学电子与信息工程学院硕士生，主要研究方向为无线通信理论、OFDM通信
罗宏（1968- ），女，南京信息工程大学图书馆副研究馆员，主要研究方向为无线通信理论、信息服务与用户研究
基金资助:
国家自然科学基金资助项目(61971167);国家自然科学基金资助项目(62101275);国家自然科学基金资助项目(62101274);江苏省信息与通信工程优势学科建设项目

PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems

Jie ZHOU¹^,², Bernardo Esono Esono Mikue¹, Xueying WANG¹, Huiting ZHOU¹, Hong LUO²

¹ School of Electronic and Information, Nanjing University of Information Science and Technology, Nanjing 210044, China
² Library, Nanjing University of Information Science and Technology, Nanjing 210044, China

Revised:2022-07-06 Online:2022-07-20 Published:2022-07-01
Supported by:
The National Natural Science Foundation of China(61971167);The National Natural Science Foundation of China(62101275);The National Natural Science Foundation of China(62101274)

摘要/Abstract

摘要：

基于非连续正交频分复用（non-continuous orthogonal frequency division multiplexing，NC-OFDM）模型，提出和研究了选择映射（selected mapping，SLM）算法和部分传输序列（partial transmit sequence，PTS）算法，及其SLM-PTS融合优化技术，设计了融合模型和改进流程。仿真结果与其他文献方法进行了对比，验证了SLM-PTS的融合具有优秀的峰值平均功率比（peak to average power ratio，PAPR）降低能力，但缺点是算法实现复杂度过高。因此，又进一步提出了互补型映射和限幅的联合算法（SLM-Clipping）融合解决方案，并利用深度学习方法建立PAPRnet模型。仿真结果验证了此算法对NC-OFDM系统具有PAPR良好的抑制效果，而且能够提高仿真运算效率。

关键词: NC-OFDM, 选择映射算法, 部分传输序列算法, 融合算法, 深度学习算法

Abstract:

Based on the non-continuous orthogonal frequency division multiplexing (NC-OFDM) model, a fusion optimization technology based on selected mapping (SLM) algorithm and partial transmit sequence (PTS) algorithm was proposed, and a system model of fusion technology was designed.Through simulation comparison with other literature methods, it was verified that the SLM-PTS fusion technology had excellent peak to average power ratio (PAPR) reduction ability, but the algorithm implementation complexity was too high.Therefore, a complementary SLM-Clipping fusion solution was proposed, and the deep learning method PAPRnet model was construted.The simulation results verif that prove the effectiveness of the method, the algorithm has an excellent PAPR suppressed effect on the NC-OFDM system, and greatly improves the computational efficiency.

Key words: NC-OFDM, SLM algorithm, PTS algorithm, fusion algorithm, deep learning algorithm

中图分类号:

TP319

周杰, Esono Mikue Bernardo Esono, 王学英, 周惠婷, 罗宏. 基于SLM-PTS算法融合的NC-OFDM峰均比优化[J]. 电信科学, 2022, 38(7): 63-74.

Jie ZHOU, Bernardo Esono Esono Mikue, Xueying WANG, Huiting ZHOU, Hong LUO. PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems[J]. Telecommunications Science, 2022, 38(7): 63-74.

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参考文献 16

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网络层名称	参数	输出特征大小
输入层	—	(None,2,512)
全连接层1（Dense1）	512×1 024(weight)+1 024(bias)=525 312	(None,2,1 024)
BN1	4 096	(None,2,1 024)
激活函数	ReLU	(None,2,1 024)
全连接层（Dense 2）	1 024×1 024(weight)+1024(bias)=1 049 600	(None,2,1 024)
BN2	4 096	(None,2,1 024)
激活函数	ReLU	(None,2,1 024)
全连接层3（Dense 3）	512×1 024(weight)+512(bias)=524 800	(None,2,512)
BN3	2 048	(None,2,512)
激活函数	ReLU	(None,2,512)
输出层	—	(None,2,512)

参数	值
子载波数量	256
分的块数	2、4、8、16
调制方式	QPSK
性能度量	CCDF
符号数	1 000
采样因子	4

基于SLM-PTS算法融合的NC-OFDM峰均比优化

PAPR optimization based on SLM and PTS algorithms in NC-OFDM systems

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