무선 통신에서 심층 신경망(DNN)과 심층 강화 학습(DRL)의 적용을 탐색하고 무선 통신 개발을 가속화한다. 본 연구는 한 명의 기본 사용자로 구성된 간단한 인지 무선 시나리오를 제안한다. 보조 사용자 한 명과 함께. 보조 사용자는 주파수 자원을 공유하려고 시도합니다. 기본 사용자 DNN 및 DRL을 기반으로 하는 지능형 전력 알고리즘 모델이 구축된다. MATLAB 플랫폼을 사용하여 모델을 시뮬레이션합니다. 서로 다른 전략에 따른 알고리즘 모델의 성능 분석에서 두 번째 전력 제어 전략이 첫 번째 전력 제어 전략보다 더 보수적인 것으로 나타났다. 두 번째 전력 제어 전략은 첫 번째 전력 제어 전략보다 더 많은 반복을 경험했다. 성공률 측면에서 두 번째 전력 제어 전략은 첫 번째 전력 제어 전략보다 반복 횟수가 더 많습니다. 평균 전송 횟수는 같은 변화 추세를 보이지만 성공률은 1에 이를 수 있다. 기존의 분산 클러스터링 및 전력 제어(DCPC) 알고리듬과 비교했을 때, 이 연구에서 알고리듬의 수렴률이 더 높은 것은 분명하다. DRL에 기반한 제안된 DQN 알고리듬은 수렴을 달성하기 위해 몇 가지 단계만 필요로 하며, 이는 그 효과를 검증한다.
제 1편 : MATLAB 기본편
1. MATLAB 기본사용편 ···················· 003
1.1 MATLAB 시작하기 ·························· 003
명령창(command Window)에서의 입력 005
도움말(Help)의 이용 ······························· 007
1.2 입력 오류의 수정 ····························· 008
계산의 중지 ·············································· 009
MATLAB 종료하기 ································· 009
1.3 연산과 변수의 할당 ·························· 009
연산자 우선순위 ······································· 011
내장함수 ···················································· 012
1.4 데이터의 표현 ··································· 013
1.5 변수의 처리 ······································· 015
변수 이름 ·················································· 015
clear 명령어 ············································· 016
특수변수와 정수 ······································· 017
whos 명령어 ············································ 017
1.6 벡터와 행렬 ······································· 018
벡터 ··························································· 018
행렬 ·························································· 023
스크린 출력과 억제 ································· 024
1.7 랜덤(Random)수와 복소수 ·············· 025
랜덤 수 ····················································· 025
복소수 ······················································· 027
1.8 기호를 이용한 연산 ·························· 028
기호식에서의 치환 ··································· 029
1.9 코드 파일 ·········································· 030
스크립트 코드 파일 ································· 030
코멘트의 추가 ·········································· 032
함수 코드 파일 ···································· 033
사용자 정의함수 ······································ 036
1.10 간단한 그래프의 생성 ····················· 037
ezplot을 이용한 그래프 ·························· 037
plot을 이용한 그래프 ·························· 039
3차원 그래프 ··········································· 042
1.11 MATLAB과 엑셀(Excel)의 접속 043
엑셀 데이터 불러오기 ····························· 043
데이터 가져오기 옵션 ························· 046
스크립트 생성 옵션 ································· 049
함수 생성 옵션 ········································ 049
생성된 데이터를 엑셀파일로 저장하기 ·· 050
제 2편 : 연구논문
Application of deep neural network and deep reinforcement learning in
wireless communication
1. Introduction 52
2. Literature review 52
3. Methods 54
4. Results and discussion 60
5. Conclusions 63
6. References 63
