FRFT-Based Interference Suppression for OFDM Systems in IoT Environment

被引:30
作者
Zhang, Chengwen [1 ,2 ]
Shi, Jun [1 ,2 ]
Zhang, Zheming [1 ,2 ]
Liu, Yutao [2 ]
Hu, Xudong [1 ,2 ]
机构
[1] Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China
[2] Sci & Technol Commun Networks Lab, Shijiazhuang 050081, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
OFDM; Interference suppression; Time-frequency analysis; Chirp; Bandwidth; Long Term Evolution; LoRa; interference suppression; fractional Fourier transform; chirp signal; SAMPLING THEOREM; MODULATION; TRANSFORM;
D O I
10.1109/LCOMM.2019.2939236
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
The OFDM technology has been widely used in 4G, 5G, and other wireless communication systems for its anti-frequency selective fading and high spectral efficiency. However, OFDM systems are very sensitive to narrowband interference (NBI). There are many existing NBI suppression algorithms for OFDM systems. Unfortunately, in these existing algorithms, the NBI is typically modeled as a sinusoidal signal. With the widespread use of LoRa, which is one of the most imposing IoT technologies, the sinusoidal interference model is unreasonable since LoRa adopts non-stationary chirp signals. Toward this end, we study LoRa interference suppression for OFDM Systems (IEEE 802.11ah). First, we derive the analytical expression of LoRa signal in the fractional Fourier transform (FRFT) domain. Then, we present the principle and procedure of the proposed FRFT-domain interference suppression algorithm for OFDM systems interfered by LoRa. Simulation results show that the proposed FRFT-domain algorithm can significantly improve the BER performance of OFDM systems interfered by LoRa at different signal to interference ratios, compared with the frequency-domain algorithm.
引用
收藏
页码:2068 / 2072
页数:5
相关论文
共 13 条
[1]   Internet of Things for Smart Cities: Interoperability and Open Data [J].
Ahlgren, Bengt ;
Hidell, Markus ;
Ngai, Edith C. -H. .
IEEE INTERNET COMPUTING, 2016, 20 (06) :52-56
[2]   Receiver for IEEE 802.11ah in Interference Limited Environments [J].
Bishnu, Abhijeet ;
Bhatia, Vimal .
IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (05) :4109-4118
[3]   Closed-Form Approximation of LoRa Modulation BER Performance [J].
Elshabrawy, Tallal ;
Robert, Joerg .
IEEE COMMUNICATIONS LETTERS, 2018, 22 (09) :1778-1781
[4]   Symbol Error Probability Analysis of DFrFT-Based OFDM Systems With CFO and STO in Frequency Selective Rayleigh Fading Channels [J].
Kumar, Atul ;
Magarini, Maurizio .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (01) :64-81
[5]   Block Sparse Bayesian Learning-Based NB-IoT Interference Elimination in LTE-Advanced Systems [J].
Liu, Sicong ;
Yang, Fang ;
Song, Jian ;
Han, Zhu .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2017, 65 (10) :4559-4571
[6]   RELATIONSHIPS BETWEEN THE RADON-WIGNER AND FRACTIONAL FOURIER-TRANSFORMS [J].
LOHMANN, AW ;
SOFFER, BH .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 1994, 11 (06) :1798-1811
[7]   Radio Resource Management Scheme in NB-IoT Systems [J].
Malik, Hassan ;
Pervaiz, Haris ;
Alam, Muhammad Mahtab ;
Le Moullec, Yannick ;
Kuusik, Alar ;
Imran, Muhammad Ali .
IEEE ACCESS, 2018, 6 :15051-15064
[8]   Detection and parameter estimation of multicomponent LFM signal based on the fractional Fourier transform [J].
Qi, L ;
Tao, R ;
Zhou, SY ;
Wang, Y .
SCIENCE IN CHINA SERIES F-INFORMATION SCIENCES, 2004, 47 (02) :184-198
[9]   Compact Fractional Fourier Domains [J].
Serbes, Ahmet .
IEEE SIGNAL PROCESSING LETTERS, 2017, 24 (04) :427-431
[10]   A Sampling Theorem for Fractional Wavelet Transform With Error Estimates [J].
Shi, Jun ;
Liu, Xiaoping ;
Sha, Xuejun ;
Zhang, Qinyu ;
Zhang, Naitong .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2017, 65 (18) :4797-4811