Feature Detection Based Spectrum Sensing in NOMA System

被引：0

作者：

Wu, Jingyi ^{[1
]}

Xu, Tianheng ^{[1
,2
]}

Zhou, Ting ^{[1
]}

Wang, Kaijie ^{[1
]}

机构：

[1] Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai 201210, Peoples R China

[2] Shanghai Frontier Innovat Res Inst, Shanghai, Peoples R China

来源：

MOBILE NETWORKS AND MANAGEMENT, MONAMI 2021 | 2022年 / 418卷

基金：

中国国家自然科学基金;

关键词：

NOMA; Spectrum sensing; Feature detection; Cyclic delay diversity; NONORTHOGONAL MULTIPLE-ACCESS; CYCLIC DELAY DIVERSITY; COGNITIVE RADIO; UPLINK NOMA; CHALLENGES; MODULATION; VISION;

D O I：

10.1007/978-3-030-94763-7_16

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Non-orthogonal multiple access (NOMA) technology allows multiple users to share the same spectrum resource. Meanwhile, the technology of spectrum sensing enables us to find the free time of the spectrum. Both two technologies can significantly improve spectrum efficiency. In this paper, we attempt to combine the two techniques, for meeting the higher demand of spectrum resources requirements in future communication. We propose a transceiver architecture by the combination of two techniques. And we verify the feasibility of this scheme. The experiments and obtained data reveal that the proposed method is feasible. And it manifests to have a good detection performance.

引用

页码：201 / 217

页数：17

共 50 条

[31] A Cooperative Spectrum Sensing Method Based on a Feature and Clustering Algorithm
Wang, Yonghua
Zhang, Shunchao
Wan, Pin
Zhang, Yongwei
Li, Xingcheng
2018 CHINESE AUTOMATION CONGRESS (CAC), 2018, : 1029 - 1033
[32] Optimization of Sensing Time for Efficient Spectrum Utilization in NOMA Based Cognitive Radio Networks
Rajpoot, Deepika
Verma, Pankaj
OPTICAL AND WIRELESS TECHNOLOGIES, OWT 2020, 2022, 771 : 471 - 481
[33] Feature based person detection beyond the visible spectrum
Jungling, Kai
Arens, Michael
2009 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPR WORKSHOPS 2009), VOLS 1 AND 2, 2009, : 296 - 303
[34] Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA
Wang, Bichai
Dai, Linglong
Mir, Talha
Wang, Zhaocheng
IEEE COMMUNICATIONS LETTERS, 2016, 20 (07) : 1473 - 1476
[35] Spectrum Sensing Using Adaptive Threshold Based Energy Detection in Cognitive Radio System
Murti, Budi Bayu
Hidayat, Risanuri
Wibowo, Sigit Basuki
2021 4TH INTERNATIONAL SEMINAR ON RESEARCH OF INFORMATION TECHNOLOGY AND INTELLIGENT SYSTEMS (ISRITI 2021), 2020,
[36] Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing
Tian, Zhi
Tafesse, Yohannes
Sadler, Brian M.
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, 2012, 6 (01) : 58 - 69
[37] Stealthy-Attacker Detection With a Multidimensional Feature Vector for Collaborative Spectrum Sensing
Wang, Jinlong
Yao, Junnan
Wu, Qihui
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2013, 62 (08) : 3996 - 4009
[38] Spectral Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing
Lagunas, Eva
Najar, Montse
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2015, 14 (07) : 3978 - 3990
[39] An expert algorithm for spectrum sensing and signal detection in NOMA-enabled 5G networks
Khodadad, Farid Samsami
Janalizadeh, Shakiba
EXPERT SYSTEMS WITH APPLICATIONS, 2023, 214
[40] Cyclostationary Feature Detection Based Spectrum Sensing Technique of Cognitive Radio in Nakagami-m Fading Environment
Ghosh, Deborshi
Bagchi, Srijibendu
COMPUTATIONAL INTELLIGENCE IN DATA MINING, VOL 2, 2015, 32 : 209 - 219

← 1 2 3 4 5 →