發(fā)布時間：2018-02-09 20:20

  本文關(guān)鍵詞： 認知無線電 資源分配 智能決策引擎 遺傳算法 案例推理　出處：《電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：智能決策引擎是認知無線電(Cognitive Radio,CR)系統(tǒng)的核心模塊,通過自身的優(yōu)化決策和學(xué)習(xí)推理功能,實現(xiàn)系統(tǒng)資源的最佳配置。OFDM(Orthogonal Frequency Division Multiplexing)技術(shù)是一種高效的多載波調(diào)制技術(shù),已廣泛應(yīng)用到當前主流的無線通信技術(shù)中,適合應(yīng)用于認知無線電系統(tǒng)。本文從人工智能技術(shù)在OFDM系統(tǒng)資源分配應(yīng)用的角度出發(fā),研究了認知無線電智能決策引擎系統(tǒng)的設(shè)計。智能決策引擎能夠根據(jù)感知的外界信息,結(jié)合通信業(yè)務(wù)要求、歷史經(jīng)驗以及約束規(guī)則,利用內(nèi)部的優(yōu)化決策和學(xué)習(xí)推理模塊進行智能決策,自適應(yīng)地調(diào)整和配置系統(tǒng)資源。本文的研究主要分為以下四個部分:本文第一部分主要研究OFDM系統(tǒng)資源分配以及人工智能算法。分析了OFDM系統(tǒng)動態(tài)資源分配的優(yōu)化準則:邊界自適應(yīng)(Margin Adaptive,MA)和速率自適應(yīng)(Rate Adaptive,RA)準則;同時研究了四種優(yōu)化決策算法和四種學(xué)習(xí)推理算法的工作原理及應(yīng)用,并對這些優(yōu)化算法和學(xué)習(xí)算法進行對比分析,為后續(xù)的資源分配和智能決策引擎的研究提供理論指導(dǎo)。本文第二部分研究了以最小化發(fā)射功率為目標的OFDM系統(tǒng)資源分配算法。在分析遺傳操作算子的不同取值對遺傳算法搜索性能影響的基礎(chǔ)上,提出了一種基于改進遺傳算法、以最小化系統(tǒng)發(fā)射功率為目標的資源分配算法,然后分別使用TDMA-OFDM、FDMA-OFDM、多用戶貪婪注水算法、基本遺傳算法和改進遺傳算法進行子載波、比特和功率分配,并對這幾種算法的性能和復(fù)雜度進行了對比分析。本文第三部分研究了以最大化容量為目標的OFDM系統(tǒng)資源分配方法。在研究最優(yōu)以及次優(yōu)子信道和功率分配算法原理的基礎(chǔ)上,提出了一種基于遺傳算法、最大化系統(tǒng)容量為目標的子信道和功率聯(lián)合分配算法。重點研究了在滿足系統(tǒng)目標(最大化系統(tǒng)容量和用戶傳輸速率比例公平性要求)和約束條件(發(fā)射功率和誤碼率約束)下,遺傳算法染色體、目標函數(shù)和評價函數(shù)的設(shè)計,并仿真驗證了多目標優(yōu)化問題中不同加權(quán)因子取值對遺傳算法搜索性能的影響;對兩種次優(yōu)算法、遺傳算法和改進遺傳算法的性能進行了綜合對比分析。本文第四部分主要研究了基于遺傳算法和案例推理的OFDM系統(tǒng)智能決策引擎的設(shè)計。給出了智能決策引擎優(yōu)化模塊和學(xué)習(xí)模塊的協(xié)同工作機制,并通過幾個實例場景仿真驗證了系統(tǒng)的運行機理。智能決策引擎在滿足系統(tǒng)通信目標和約束條件的情況下,根據(jù)檢測的信道信息,采用遺傳算法和案例推理進行智能決策,自適應(yīng)地調(diào)整系統(tǒng)的子載波、比特、發(fā)射功率和調(diào)度周期等配置參數(shù),來適應(yīng)外界環(huán)境的變化。
[Abstract]:Intelligent decision engine is the core module of cognitive radio cognitive radio (CR) system. The optimal configuration of system resources. OFDM orthogonal Frequency Division Multiplexing (OFDM) is an efficient multicarrier modulation technology. Has been widely used in the current mainstream wireless communication technology, suitable for the cognitive radio system. This article from the artificial intelligence technology in the OFDM system resource allocation application point of view, In this paper, the design of cognitive radio intelligent decision engine system is studied. The intelligent decision engine can combine the requirements of communication service, historical experience and constraint rules according to the perceived external information. Using the internal optimization decision and learning reasoning module to make intelligent decision, The research of this paper is divided into four parts: the first part of this paper mainly studies OFDM system resource allocation and artificial intelligence algorithm, and analyzes the dynamic resource allocation of OFDM system. The criteria are boundary adaptive margin adaptive MAand rate adaptive rate adaptive rama; At the same time, the working principle and application of four optimization decision algorithms and four learning reasoning algorithms are studied, and these optimization algorithms and learning algorithms are compared and analyzed. In the second part of this paper, we study the resource allocation algorithm of OFDM system with the aim of minimizing the transmit power, and analyze the different values of genetic operators. On the basis of the effect on the search performance of genetic algorithm, This paper presents a resource allocation algorithm based on improved genetic algorithm, which aims at minimizing the transmission power of the system, and then uses TDMA-OFDM FDMA-OFDM, multi-user greedy water flooding algorithm, basic genetic algorithm and improved genetic algorithm to carry out subcarriers, respectively. Bit and power allocation, The performance and complexity of these algorithms are compared and analyzed. In the third part of this paper, the resource allocation method of OFDM system aiming at maximizing capacity is studied. On the basis of studying the principle of optimal and suboptimal subchannel and power allocation algorithm, A genetic algorithm is proposed. A joint subchannel and power allocation algorithm with maximum system capacity as its target. The emphasis is placed on the study of system objectives (maximization of system capacity and user transmission rate proportional fairness requirements) and constraints (transmit power sum). Error rate constraint, The design of chromosome, objective function and evaluation function of genetic algorithm, and the simulation results show the influence of different weighting factors on the search performance of genetic algorithm. The performance of genetic algorithm and improved genetic algorithm are compared and analyzed synthetically. In the 4th part of this paper, the design of intelligent decision engine of OFDM system based on genetic algorithm and case-based reasoning is studied, and the intelligent decision engine is given. The cooperative working mechanism of the module and the learning module, The mechanism of the system is verified by the simulation of several examples. The intelligent decision engine adopts genetic algorithm and case-based reasoning to make intelligent decision according to the detected channel information and meets the system communication objectives and constraints. The configuration parameters of the system such as subcarrier bit transmit power and scheduling period are adaptively adjusted to adapt to the change of the external environment.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN929.53

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