本文選題：認知無線網(wǎng)絡(luò) + 頻譜感知��； 參考：《解放軍信息工程大學》2014年博士論文

【摘要】：認知無線網(wǎng)絡(luò)(Cognitive Radio Networks, CRN)是解決當前“頻譜資源匱乏”問題的有效方法之一,近年來已成為通信領(lǐng)域的研究熱點。該技術(shù)的基本思想是在保證授權(quán)用戶(或主用戶)一定服務(wù)質(zhì)量的前提下,使非授權(quán)用戶(或次用戶)“伺機地”接入臨時空閑的無線頻段,從而使無線頻譜資源得到更為充分、合理的利用。認知無線網(wǎng)絡(luò)中的一項關(guān)鍵技術(shù)是頻譜感知,用于發(fā)現(xiàn)無線網(wǎng)絡(luò)環(huán)境中的空閑頻譜資源。從網(wǎng)絡(luò)分層的角度來看,頻譜感知技術(shù)可以分為物理層頻譜感知和MAC.層及跨層感知兩類。其中,物理層頻譜感知主要關(guān)注如何通過有效的信號檢測算法,對信道的占用狀況做出快速、準確的判斷；而MAC層及跨層感知則是借鑒了跨層優(yōu)化的思想,通過對感知參數(shù)和策略的選取及優(yōu)化,來進一步提高物理層頻譜感知的性能。目前,由于數(shù)字信號處理技術(shù)的先期發(fā)展,使得物理層頻譜感知算法已具有了深厚的理論基礎(chǔ)。相對而言,有關(guān)MAC層及跨層感知的研究還處于起步階段。為此本文依托國家.863探索導向類項目“基于行為預測的認知網(wǎng)絡(luò)資源優(yōu)化分配技術(shù)”,基于現(xiàn)有的物理層頻譜感知算法,對MAC層及跨層感知中多個重要感知參數(shù)和策略的選取方法進行了優(yōu)化研究,其中主要包括：感知周期、感知時間長度、感知信道策略以及協(xié)作頻譜感知中的融合參數(shù)。主要研究成果包括：(1)為提高多信道條件下的頻譜資源檢測率,提出了一種多信道協(xié)作頻譜感知周期優(yōu)化算法。通過分析頻譜感知過程中可能影響次用戶發(fā)現(xiàn)或使用空閑頻譜資源的兩種情況,基于連續(xù)時間馬爾科夫理論推導出衡量多信道頻譜資源檢測率的目標函數(shù),并將各信道感知周期的選取建模為一個帶約束條件的多目標優(yōu)化問題。在求解該多目標優(yōu)化問題的過程中,為避免傳統(tǒng)“權(quán)重法”所固有的主觀因素對解的質(zhì)量影響較大這一問題,文章采用了相對復雜的遺傳算法對其求解。另外,為提高遺傳算法在本文應(yīng)用背景下的算法性能,文章還對其進行了改進,使算法的收斂速度以及最終解的質(zhì)量均得到了較大提高。所提算法與現(xiàn)有主流算法的區(qū)別在于：在多信道感知周期的現(xiàn)有研究中,大多簡單假設(shè)次用戶是以相同的周期對各信道進行感知,而本文算法充分考慮了各授權(quán)信道間的負載差異,分別以不同的周期對其感知,從而使多信道條件下的頻譜資源檢測率得到了較大提高。(2)基于協(xié)作頻譜感知的認知無線網(wǎng)絡(luò)中,已有研究表明增加參與協(xié)作頻譜感知的次用戶數(shù)量能夠提高感知性能以及信道的吞吐量。然而,由于信道容量的限制,次用戶數(shù)量的不斷增加并不會使信道吞吐量無限提高,反而會使次用戶平均可獲得的吞吐量不斷降低。因此，本文認為認知無線網(wǎng)絡(luò)的研究不能僅以提高頻譜利用率為目標,還應(yīng)考慮次用戶平均可獲得的吞吐量大小,即以次用戶平均吞吐量作為認知無線網(wǎng)絡(luò)性能的衡量標準更能直接體現(xiàn)次用戶的利益。為此本文以最大化次用戶平均吞吐量為目標,基于k/N融合準則,對感知時間和融合參數(shù)的選取方法進行了優(yōu)化研究。證明了對于任意給定的融合參數(shù),次用戶的平均吞吐量是感知時間的凸函數(shù),并基于該特性提出了交叉迭代算法進行二維優(yōu)化。仿真結(jié)果表明,當信噪比為-10dB時,相對于僅考慮感知時間或融合參數(shù)的一維優(yōu)化算法,所提算法可使次用戶的平均吞吐量提高20%以上。(3)認知無線網(wǎng)絡(luò)不僅要具有自適應(yīng)性,更應(yīng)具備一定的智能性。在未知無線環(huán)境參數(shù)以及網(wǎng)絡(luò)動態(tài)特性的前提下,為使次用戶能夠從多個授權(quán)信道中選擇吞吐量回報較高的信道優(yōu)先進行感知,文章將強化學習理論引入到認知無線網(wǎng)絡(luò)中,以最大化次用戶吞吐量為目標,提出了一種基于強化學習的智能信道選擇算法。該算法利用了強化學習理論中的在線交互式學習技術(shù),使次用戶僅通過不斷與環(huán)境進行交互學習,便能夠逐步改進其行為策略,使吞吐量回報逐漸得以提高。另外,該算法還借鑒了模擬退火算法的思想對信道的選擇動作進行優(yōu)化,使之能夠從學習階段平滑地過渡到使用階段。仿真結(jié)果表明,相對于現(xiàn)有信道選擇算法,本文算法可有效提高次用戶的吞吐量,并且在主用戶使用規(guī)律發(fā)生變化時,能夠自動實現(xiàn)二次收斂,可作為認知無線電系統(tǒng)邁向智能化的一種嘗試。(4)針對認知無線網(wǎng)絡(luò)中次用戶節(jié)點能量受限問題,文章引入能量傳輸效率作為評價次用戶能量有效性的指標,并根據(jù)主用戶非時隙返回信道可能與次用戶發(fā)生碰撞的特點,基于連續(xù)時間馬爾科夫理論對次用戶的頻譜感知和接入活動進行建模,提出了一種聯(lián)合考慮感知時間和接入概率的能效優(yōu)化算法。文中證明了的確存在最優(yōu)的感知時間,能夠使次用戶的能量有效性達到最優(yōu)。此外,基于最優(yōu)感知時間,本文還提出了一種能量有效的頻譜接入策略。區(qū)別于傳統(tǒng)接入策略,次用戶在發(fā)現(xiàn)空閑信道的情況下并不直接進行接入,而是先根據(jù)信道空閑時間的統(tǒng)計規(guī)律以及次用戶的接入時間,計算接入后與返回主用戶的碰撞概率,再依概率進行接入,從而使次用戶的能量有效性得到了較大提高。
[Abstract]:Cognitive Radio Networks (CRN) is one of the effective methods to solve the current "lack of spectrum resources". In recent years, it has become a research hotspot in the field of communication. The basic idea of this technology is to make unauthorized users (or sub users) "server" on the premise of guaranteeing the quality of service of authorized users (or main users). A key technology in cognitive wireless network is spectrum sensing, which is used to discover free spectrum resources in wireless network environment. From the point of view of network stratification, frequency spectrum sensing technology can be divided into physical layer spectrum sensing and MA. The C. layer and the cross layer perception are two categories. Among them, the physical layer spectrum perception mainly focuses on how to make a quick and accurate judgment on the occupancy of the channel by effective signal detection algorithm, while the MAC layer and cross layer perception refer to the idea of cross layer optimization, and further improve the physical layer through the selection and optimization of the perceptual parameters and strategies. The performance of spectrum sensing. At present, due to the advance of digital signal processing technology, the spectrum sensing algorithm of physical layer has a profound theoretical basis. Relative, the research on MAC layer and cross layer perception is still in the initial stage. This paper relies on the national.863 exploration oriented project "cognitive network based on behavior prediction" Based on the existing physical layer spectrum sensing algorithm, this paper optimizes the selection methods of several important perceptual parameters and strategies in MAC layer and cross layer perception, which mainly include the perception period, the perception time length, the perceptual channel strategy and the fusion parameters in the cooperative spectrum sensing. The results include: (1) a multi channel cooperative spectrum sensing cycle optimization algorithm is proposed to improve the spectrum resource detection rate under multi channel conditions. By analyzing two situations that may affect the secondary user discovery or the use of free spectrum resources in the spectrum sensing process, the multichannel spectrum is derived to measure the multichannel spectrum based on the continuous temporal Markov theory. The objective function of the detection rate of resources is modeled as a multi-objective optimization problem with constraint conditions. In order to avoid the problem that the subjective factors inherent in the traditional "weight method" have great influence on the quality of the solution, the article adopts a relatively complex heredity in solving the multi-objective optimization problem. In addition, in order to improve the performance of the algorithm in the background of the genetic algorithm, the paper also improves the algorithm, which makes the convergence speed of the algorithm and the quality of the final solution greatly improved. The difference between the proposed algorithm and the existing mainstream algorithm is that in the existing research of the multi-channel perception period, the algorithm is mostly simple. It is assumed that the sub user is aware of the channels in the same cycle, and this algorithm fully considers the load differences between the authorized channels and perceiving them in different cycles, so that the detection rate of the spectrum resources under the multi channel condition has been greatly improved. (2) in the cognitive wireless network based on cooperative spectrum sensing, the research has already been studied. The increase in the number of sub users participating in cooperative spectrum sensing can improve the perceptual performance and the throughput of the channel. However, the increase in the number of sub users will not increase the throughput of the channel indefinitely because of the limitation of the capacity of the channel. On the contrary, the throughput of the sub users will be reduced. The research of line network should not only aim at improving the spectrum utilization, but also take into account the average throughput size of the secondary users. That is, the average throughput of the sub user as a measure of the performance of the cognitive wireless network can directly reflect the interests of the secondary users. Therefore, the aim of this paper is to maximize the average throughput of the user and based on the k/N fusion. According to the criterion, the selection method of perceptual time and fusion parameter is optimized. It is proved that the average throughput of the sub user is the convex function of the perceptual time for any given fusion parameter, and the cross iteration algorithm is proposed for two-dimensional optimization based on this characteristic. The proposed algorithm can improve the average throughput of the secondary users by more than 20%. (3) the cognitive wireless network should not only have adaptability, but also have a certain intelligence. Under the premise of unknown wireless environment parameters and network dynamic characteristics, the sub user can get from multiple authorized channels. In this paper, the channel preference for higher throughput is preferred, and the reinforcement learning theory is introduced into the cognitive wireless network. In order to maximize the secondary user throughput, an intelligent channel selection algorithm based on reinforcement learning is proposed. This algorithm makes use of the online interactive learning technology in the reinforcement learning theory to make the sub user. Through interactive learning with the environment, the behavior strategy can be improved gradually and the throughput returns are gradually improved. In addition, the algorithm also uses the thought of simulated annealing algorithm to optimize the channel selection action, making it smooth transition from the learning stage to the use stage. There is a channel selection algorithm. This algorithm can effectively improve the throughput of the secondary users, and can automatically achieve two convergence when the rule of the main user changes, which can be used as an attempt to intelligentize the cognitive radio system. (4) the energy transfer is introduced in this paper for the problem of the energy limitation of the sub user nodes in the cognitive wireless network. Efficiency is an index to evaluate the energy efficiency of secondary users, and based on the characteristics of the collision between the main users and the secondary users in the non time slot. Based on the continuous time Markov theory, the spectrum perception and access activities of the secondary users are modeled, and an energy efficiency optimization algorithm which combines the perception time and the access probability is proposed. It is proved that there is an optimal perception time that can make the energy efficiency of the secondary user optimal. In addition, based on the optimal perception time, this paper also proposes an energy efficient spectrum access strategy, which is different from the traditional access strategy. The statistical law of the idle time of the channel and the access time of the sub user are used to calculate the collision probability with the return of the main user after the access, and then the access is carried out according to the probability, thus the energy efficiency of the secondary users is greatly improved.
【學位授予單位】：解放軍信息工程大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TN925

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