뉴럴 네트워크를 활용한 재밍 탐지 및 회피 알고리즘
Jamming Detection and Avoidance Algorithm using Neural Networks
- 주제(키워드) Jamming detection and avoidance algorithm , Classify the jamming signal , Neural networks
- 발행기관 아주대학교
- 지도교수 김재현
- 발행년도 2018
- 학위수여년월 2018. 2
- 학위명 석사
- 학과 및 전공 일반대학원 전자공학과
- 실제URI http://www.dcollection.net/handler/ajou/000000027649
- 본문언어 영어
- 저작권 아주대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
Recently, jamming signal causes a serious damage to wireless communication. Jamming signal can be a threat not only to the industries and the commercials, but also to the military. There are two steps to reduce the damage caused by the jamming signal. The first step is detection and identifying the jamming signal. To properly cope with the wireless communication environment, identify the jamming signals such as types, cycle time and frequency are necessary. The second step is effectively avoidance the jamming signal with the identified information. In this thesis, we identify and classify the jamming signal using artificial neural networks (ANN). Then we propose the jamming signal avoiding algorithm by changing the transmission frequency band. We consider jamming signals as Chirp and OFDM signal that changes the radiation time and frequency step size. Using this proposed jamming avoidance algorithm, it can classify the jamming signal according to set of radiation time and frequency step size. Also we change the transmission frequency band according to classification of jamming signal. As a result, BER performance decreased sharply when using proposed jamming avoidance algorithm. In the environment where jamming signals exist, the proposed algorithm can be very effective to detect and avoid jamming signal.
more목차
Chapter 1. Introduction 1
1.1 Background and Motivation 1
1.2 Contribution 2
1.3 Overview 2
Chapter 2. Related Work 3
2.1 Types of Jammers 3
2.2 Related Work for Jamming Detection 9
Chapter 3. Wireless Communication System Model 14
3.1 Communication Channel Model 14
3.2 Jamming Model 16
3.3 Modulation Scheme 21
3.4 Preliminary Simulation Result 23
Chapter 4. Proposed Detection and Avoidance Algorithm 30
4.1 Jamming Detection Classifier 30
4.2 Jamming Avoidance Algorithm 36
Chapter 5. Performance Evaluation 38
5.1 Probability of Detect Jamming Signal 38
5.2 BER using Avoiding Algorithm 42
Chapter 6. Conclusion 46
References 47
Abstract 51
