網(wǎng)絡(luò)控制外文文獻(xiàn)翻譯

1、<p><b>　　摘要</b></p><p>　　該文提出了一種在認(rèn)知無線網(wǎng)絡(luò)控制信道帶寬受限條件下基于信任度的雙門限協(xié)同頻譜感知算法。首先每個認(rèn)知用戶基于雙檢測門限獨(dú)立進(jìn)行頻譜感知，但只有部分可靠的認(rèn)知用戶通過控制信道向認(rèn)知無線網(wǎng)絡(luò)基站發(fā)送本地感知結(jié)果。當(dāng)所有的用戶都不可靠時，選取信任度最高的認(rèn)知用戶發(fā)送本地感知結(jié)果進(jìn)行判決。理論分析和仿真表明，同常規(guī)能量檢測算法相比較，該算

2、法能夠在控制信道帶寬受限條件下，以較少的網(wǎng)絡(luò)開銷獲得更好的頻譜感知性能。</p><p>　　關(guān)鍵詞：認(rèn)知無線電；頻譜感知；信任度；雙門限</p><p><b>　　1引言</b></p><p>　　隨著無線通信技術(shù)的飛速發(fā)展，有限的頻譜資源與不斷增長的無線通信需求的矛盾越來越突出。然而根據(jù)現(xiàn)有的固定分配頻譜資源策略，絕大多數(shù)頻譜資源得不到

3、有效利用。據(jù)FCC 的調(diào)查統(tǒng)計，70%的已分配頻譜資源沒有得到有效利用。為了提高頻譜資源的利用率，認(rèn)知無線電技術(shù)由Joseph Mitola Ⅲ 提出并得到了廣泛的關(guān)注。頻譜感知技術(shù)是認(rèn)知無線電網(wǎng)絡(luò)的支撐技術(shù)之一。通常它又可以分為能量檢測法、匹配濾波器法和循環(huán)平穩(wěn)特征法[4]。能量檢測算法因?yàn)閼?yīng)用簡單且無需知道任何授權(quán)用戶信號的先驗(yàn)知識成為研究熱點(diǎn)。認(rèn)知用戶在接入授權(quán)頻帶之前，必須首先感知該頻帶空閑即授權(quán)用戶沒有工作，否則會對授權(quán)用戶造

4、成干擾。一旦授權(quán)用戶重新工作，認(rèn)知用戶必須退避，實(shí)現(xiàn)在不對授權(quán)用戶產(chǎn)生干擾的情況下對頻譜資源的共享。由于實(shí)際信道中的多徑和陰影效應(yīng)，單個認(rèn)知用戶頻譜感知的性能并不樂觀，針對這個問題D. Cabric等人提出了協(xié)同頻譜感知算法[5]-[6]。協(xié)同頻譜感知算法性能較好，但是當(dāng)認(rèn)知用戶數(shù)量很大的時候，控制信道的帶寬將不夠用。文獻(xiàn)[7]中提出了一種在控制信道帶寬受限條件下的基于雙檢測門限的頻譜感知算法，該算法能夠以較小的網(wǎng)</p>

5、<p><b>　　2系統(tǒng)模型</b></p><p>　　假設(shè)一個認(rèn)知無線電網(wǎng)絡(luò)有N個認(rèn)知用戶和一個認(rèn)知無線網(wǎng)絡(luò)基站，如圖1 所示。認(rèn)知無線網(wǎng)絡(luò)基站負(fù)責(zé)管理和聯(lián)系N個認(rèn)知用戶，在收到認(rèn)知用戶的檢測報告后做出最終判決。</p><p>　　圖1. 認(rèn)知無線電網(wǎng)絡(luò)示意圖</p><p>　　頻譜感知的實(shí)質(zhì)是一個二元假設(shè)問題，即<

6、/p><p><b>　　（1）</b></p><p>　　其中x(t)代表認(rèn)知用戶接收到的信號，s(t)表示授權(quán)用戶的發(fā)送信號，h(t)代表授權(quán)用戶與認(rèn)知用戶之間信道的衰落因子。代表授權(quán)用戶沒有工作，代表授權(quán)用戶正在工作。設(shè)是認(rèn)知用戶接收信號的能量，根據(jù)能量檢測理論[8]，服從以下分布：</p><p><b>　　（2）</b

7、></p><p>　　其中表示瞬時信噪比，并且其服從均值為的指數(shù)分布，表自由度為2m的中心卡方分布，代表自由度為非中心參數(shù)為的卡方分布，表示時間帶寬積。</p><p>　　在能量檢測算法本地判決中，每個認(rèn)知用戶把接收到的能量跟預(yù)設(shè)的門限進(jìn)行比較，如圖2（a）所示。當(dāng)時，本地能量檢測器做出本地判決，表示授權(quán)用戶在工作，否則判決 D 為 0。而雙門限能量檢測算法本地判決如圖3(b)所

8、示，本地能量檢測器判決規(guī)則如下：</p><p><b>　?。?）</b></p><p>　　其中ND表示認(rèn)知用戶接受到的能量值不可靠，認(rèn)知用戶不作出任何判決，發(fā)送感知報告給認(rèn)知無線電網(wǎng)絡(luò)基站。如果出現(xiàn)所有認(rèn)知用戶都不作出判決的情況，則選擇信用度最高的認(rèn)知用戶依據(jù)單門限能量檢測算法作出本地判決。并發(fā)送感知報告給認(rèn)知無線電網(wǎng)絡(luò)基站。</p><p

9、>　　圖2.（a）一般能量檢測算法本地判決示意圖</p><p>　　（b）雙門限能量檢測算法本地判決示意圖</p><p>　　信用度獲取方法采取文獻(xiàn)[9]的方法：在最開始階段，認(rèn)知無線電網(wǎng)絡(luò)基站把每個認(rèn)知用戶數(shù)目的可信度設(shè)為0，當(dāng)某認(rèn)知用戶本地判決結(jié)果與認(rèn)知無線電網(wǎng)絡(luò)基站的最終判決結(jié)果一致時，該認(rèn)知用戶可信度加1，否則減1。假設(shè)認(rèn)知用戶i的可信度是，則其更新過程如（4）：<

10、/p><p><b>　　（4）</b></p><p>　　其中是認(rèn)知用戶傳送給認(rèn)知無線電網(wǎng)絡(luò)基站的判決結(jié)果，是認(rèn)知無線電網(wǎng)絡(luò)基站的最終判決結(jié)果。</p><p>　　據(jù)文獻(xiàn)[8]可知，認(rèn)知用戶在高斯信道下的平均檢測概率、平均漏檢概率和平均虛警概率如下所示：</p><p><b>　　(5)</b>

11、</p><p><b>　　(6)</b></p><p><b>　　(7)</b></p><p>　　出于對授權(quán)用戶的保護(hù)，認(rèn)知無線電網(wǎng)絡(luò)基站最終采用OR準(zhǔn)則作出判決。</p><p><b>　　3頻譜感知性能分析</b></p><p>&l

12、t;b>　　3.1網(wǎng)絡(luò)開銷</b></p><p>　　在1bit量化條件下，代表歸一化平均感知位數(shù)，和分別代表K個已向認(rèn)知無線電網(wǎng)絡(luò)基站發(fā)送數(shù)據(jù)和N-K個未向認(rèn)知無線電網(wǎng)絡(luò)基站發(fā)送報告。</p><p>　　則：，。設(shè)和，則劃歸一劃平均感知位數(shù)如式8所示：</p><p><b>　　(8)</b></p>&

13、lt;p><b>　　定義：， 則：</b></p><p><b>　　(9)</b></p><p><b>　　由9式可得：</b></p><p>　　可知：基于雙門限的協(xié)同頻譜檢測算法的網(wǎng)絡(luò)開銷始終小于常規(guī)的能量檢測算法。</p><p><b>　　

14、3.2檢測性能分析</b></p><p>　　設(shè)和別表示?在假設(shè)和下的概率分布，則根據(jù)文獻(xiàn)[10]可知:</p><p><b>　　(10)</b></p><p>　　= (11)</p><p>　　顯然，。假設(shè)，分別代表在授權(quán)用戶在工作和授權(quán)用戶未工作情況下沒有認(rèn)知用戶發(fā)送感知報

15、告，即當(dāng)K=0時，則：</p><p><b>　　(12)</b></p><p><b>　　(13)</b></p><p>　　基于雙門限的頻譜感知算法在瑞利信道下的虛警概率，漏檢概率和檢測概率分別為：</p><p><b>　　(14)</b></p>

16、<p>　　= (15)</p><p><b>　　(16)</b></p><p><b>　　其中：</b></p><p><b>　　=</b></p><p>　　=

17、 (17)</p><p><b>　　(18)</b></p><p><b>　　則：</b></p><p><b>　　(19)</b></p><p><b>　　(20)</b></p><p>　　由上式可知當(dāng)=0時，

18、此算法與常規(guī)算法相同。當(dāng)參與協(xié)同的認(rèn)知用戶數(shù)目N較大時，，則基于雙門限的頻譜檢測算法的檢測性能與常規(guī)能量算法的檢測性能近似，可知在控制信道帶寬受限制的情況下以較小的性能損失大大降低了網(wǎng)絡(luò)開銷。</p><p><b>　　4 仿真及分析</b></p><p>　　本節(jié)通過計算機(jī)仿真來評估所提出的基于信任度的雙門限協(xié)同頻譜感知算法的性能。仿真參數(shù)設(shè)置如表1 所示。&l

19、t;/p><p><b>　　表1 仿真參數(shù)設(shè)置</b></p><p>　　圖3 給出了在的情況下算法的檢測性能?？梢钥闯鐾Ｒ?guī)能量檢測算法相比較，本文所提出算法的檢測性能得到了明顯的改善。例如當(dāng)時，基于信任度的雙門限協(xié)同頻譜感知算法的檢測概率比常規(guī)能量檢測算法高出0.019。</p><p><b>　　圖3檢測性能示意圖</b

20、></p><p>　　圖4 描述了在不同的條件下，基于信任度的雙門限協(xié)同頻譜感知算法對網(wǎng)絡(luò)開銷的影響。同常規(guī)能量檢測算法即=0時相比較，本文所提出算法的歸一化平均感知位數(shù)急劇下降，控制信道帶寬與認(rèn)知用戶數(shù)量之間的矛盾得到了緩解。例如當(dāng)，= 0.01 時，基于信任度的雙門限協(xié)同頻譜感知算法的歸一化平均感知位數(shù)下降了38%。當(dāng)，=0.001時，歸一化平均感知位數(shù)則下降了44%</p><p

21、>　　圖4 不同條件下算法對網(wǎng)絡(luò)開銷的影響</p><p><b>　　5結(jié)束語</b></p><p>　　頻譜感知技術(shù)是認(rèn)知無線電網(wǎng)絡(luò)的支撐技術(shù)之一。當(dāng)認(rèn)知用戶數(shù)量很大的時候，控制信道的帶寬將不夠用。本文提出了認(rèn)知無線電環(huán)境下一種基于信任度的雙門限協(xié)同頻譜感知算法。每個認(rèn)知用戶基于雙檢測門限獨(dú)立進(jìn)行頻譜感知，但只有部分可靠的認(rèn)知用戶通過控制信道向認(rèn)知無線網(wǎng)絡(luò)

22、基站發(fā)射感知報告。當(dāng)所有的用戶都不可靠時，選取信任度最高的認(rèn)知用戶發(fā)射感知報告進(jìn)行判決。本文對該算法進(jìn)行了性能分析并通過仿真表明，本文方法比較常規(guī)能量檢測算法，在減小網(wǎng)絡(luò)開銷的同時提高了檢測性能。</p><p><b>　　參考文獻(xiàn)</b></p><p>　　[1] Federal Communications Commission. Spectrum Polic

23、y Task Force, Rep. ET Docket no. 02-135 [R]. Nov. 2002.</p><p>　　[2] J. Mitola and G. Q. Maguire. Cognitive radio: Making software radios more personal[C],IEEE Personal Communication. vol. 6, pp. 13–18, Aug.

24、 1999.</p><p>　　[3] S. Haykin. Cognitive radio: brain-empowered wireless communications [J]. IEEE J. Sel. Areas Communication. vol. 23, pp. 201–220, Feb. 2005.</p><p>　　[4] AKYLDIZ IF. Next gene

25、ration/dynamic spectrum access/cognitive radio wireless networks: A Survey [J]. ELSEVIER Computer Networks, 2006(50):2127-2159.</p><p>　　[5] D. Cabric, S. M. Mishra, and R. W. Brodersen. Implementation issue

26、s in spectrum sensing for cognitive radios[C]// in Proc. Of A silomar Conf. on Signals, Systems, and Computers, Pacific Grove,CA, USA, Nov. 7-10, 2004, pp. 772 - 776.</p><p>　　[6] A.Ghasemi and E. S. Sousa.

27、 Collaborative spectrum sensing for opportunistic access in fading environments[C]// in Proc. 1st IEEES ymp. New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, Nov. 8–11, 2005, pp. 131–136.</p><

28、;p>　　[7] Chunhua Sun, Wei Zhang, Letaief K.B. Cooperative spectrum sensing for cognitive radios under bandwidth constraints[C]// in Proc. IEEE WCNC, March 11-15, 2007, pp. 1-5.</p><p>　　[8] H. Urkowitz. E

29、nergy detection of unknown deterministic signals [C]. Proceedings of IEEE, vol.55, pp. 523-531, April 1967.</p><p>　　[9] Ruiliang Chen, Jung-Min Park, Kaigui Bian. Robust Distributed Spectrum Sensing in Cogn

30、itive Radio Networks[C]. in Proc. IEEEINFOCOM, April 2008, pp. 1876-1884.</p><p>　　[10] F. F. Digham, M. -S. Alouini, and M. K. Simon. On the energy detection of unknown signals over fading channels[C]. in P

31、roc. IEEE ICC, Anchorage, AK, USA, May 11-15, 2003, pp. 3575–3579.</p><p><b>　　附原文：</b></p><p>　　A New Cooperative Spectrum Sensing Algorithm</p><p>　　for Cognitive Radi

32、o Networks</p><p>　　Abstract—spectrum sensing is a critical phase in building a cognitive radio network. However, the bandwidth for reporting secondary users’ sensing results will be insufficient, when the n

33、umber of secondary user is very large. In this paper, we propose a new cooperative spectrum sensing algorithm to alleviate the bandwidth problem of reporting channel. Compared with conventional method, only the secondary

34、 users with reliable information are allowed to report their sensing results. When no user wi</p><p>　　Keywords—cognitive radio; cooperative spectrum sensing; double threshold; reputation</p><p>

35、;　　Ⅰ. INTRODUCTION</p><p>　　Due to the increasingly development of wireless applications, more and more spectrum resources are needed to support numerous emerging wireless service. However, recent measuremen

36、ts by Federal Communication Commission (FCC) have shown that 70% of the allocated spectrum in US is not utilized [1]. In order to increase the efficiency of spectrum utilization, cognitive radio technology was recently

37、proposed [2], [3]. </p><p>　　A requirement of cognitive radios is that their transmission should not cause harmful interference to primary users. Namely, the secondary users can use the licensed spectrum as

38、 long as the primary user is absent. However, when the primary user comes back into operation, the secondary users should vacate the spectrum instantly to avoid interference with the primary user. Accordingly, spectrum s

39、ensing is a crucial phase in building a cognitive radio system. </p><p>　　One of the great challenges of implementing spectrum sensing is the hidden terminal problem which caused by the fading of the channel

40、s and the shadowing effects. In order to deal with the hidden terminal problem, cooperative spectrum sensing has been studied to improve the spectrum sensing performance [4], [5]. In[6], due to control channel for each c

41、ognitive radio to report its sensing result is usually bandwidth limited, a censoring method which has two thresholds is given to decrease the aver</p><p>　　In this paper, we present a new double threshold c

42、ooperative spectrum sensing method with reputation. In our system, every cognitive user will firstly obtain an observation independently and only the users with reliable information send their local decisions to the com

43、mon receiver based on double thresholds. If no user is reliable, only the cognitive user with the highest reputation is selected to sense the spectrum. Simulation results show that the spectrum sensing performance under

44、AWGN chann</p><p>　　The rest of the paper is organized as follows. In section Ⅱ, system model is briefly introduced. Sensing performance is analyzed in Section Ⅲ. In Section Ⅳ, we present the simulation resu

45、lts of our cooperative spectrum sensing method. Finally, we draw our conclusions in Section Ⅴ.</p><p>　　II. SYSTEM MODEL</p><p>　　In cognitive radio systems, spectrum sensing is a critical eleme

46、nt as it should be firstly performed before allowing secondary users to access a vacant licensed channel. Cooperative spectrum sensing has been widely used to detect the primary user with a high agility and accuracy. The

47、 essence of spectrum sensing is a binary hypothesis-testing problem:</p><p>　　:primary user is absent;</p><p>　　:primary user is present.</p><p>　　For implementation simplicity, we

48、restrict ourselves to energy detection in the spectrum sensing. The local spectrum sensing is to decide between the following two hypotheses:</p><p><b>　　(1)</b></p><p>　　Where is t

49、he signal received by secondary user, is primary user’s transmitted signal,is AWGN, and is the temporary amplitude gain of the channel. </p><p>　　According to energy detection theory [7], we have the follow

50、ing distribution:</p><p><b>　　(2)</b></p><p>　　Where is the energy value collected by secondary user, is instantaneous SNR and follows exponentially distribution with the mean value

51、 , is the time bandwidth product of the energy detector,represents a central chi-square distribution with 2m degrees of freedom and. represents a non-central chi-square distribution with degrees of freedom and a non-cen

52、trality parameter .</p><p>　　In conventional energy detection method, the local decision is made by comparing the observation with a pre-fixed threshold as Fig.1 (a). When the collected energy exceeds the t

53、hreshold , decision will be made. Otherwise decision will be made. In contrast, the system model which has two thresholds of our interest is shown inFig.1 (b). Where “ Decision ” and “Decision ” represent the absence a

54、nd the presence of licensed user, respectively.“No decision” means that the observation is not reliabl</p><p>　　Reputation is obtained based on the accuracy of cognitive user’s sensing results. The reputatio

55、n value is set to zero at the beginning. Whenever its local spectrum sensing report is consistent with the final sensing decision, its reputation is incremented by one; otherwise it is decremented by one. Under this rule

56、, assuming the th cognitive user’s reputation value is 1, the last sensing report of cognitive user send to common receiver is , and the final decision is ,then is updated according t</p><p>　　For the cogn

57、itive radio users with the energy detector, the average probabilities of detection, the average probabilities of missed detection, and the average probabilities of false alarm over AWGN channels are given, respectively,

58、by [7]:</p><p><b>　　(3)</b></p><p><b>　　(4)</b></p><p><b>　　(5)</b></p><p>　　Where , are complete and incomplete gamma function resp

59、ectively, and is the generalized Marcum function. </p><p>　　In this paper, we consider cooperative spectrum sensing with 1bit quantization. Let represent the normalized</p><p>　　Fig1. (a)Conv

60、entional detection method </p><p>　　(b)Double threshold energy detection method</p><p>　　average number of sensing bit. Let and represent he event that the

61、re are K unlicensed users reporting 1-bit decision and N-K users not reporting to the common receiver, respectively. The , .and then the average number of sensing bits for our method can be derived as:</p><p&g

62、t;<b>　?。?）</b></p><p>　　For simplicity, we define:</p><p>　　， （7）</p><p>　　Let denote the normalized average number of sensing bits, then, we obtain as fo

63、llows：</p><p><b>　?。?）</b></p><p>　　From (8), It can be seen that, the normalized average number of sensing bits is always smaller than 1. the communication traffic of our method i

64、s are deduced as opposed to the conventional energy detection method.</p><p>　　III. THE PERFORMANCE ANALYSIS OF SPECTRUM SENSING</p><p>　　In this section, the spectrum sensing performance of the

65、 proposed method will be analyzed. Assume the control channel between the unlicensed users and the common receiver is perfect, the local decisions are reported without any error. Let and denote the cumulative distri

66、bution function (CDF) of the local test statistic under the hypothesis and , respectively. Then, we have [10]:</p><p><b>　　(9)</b></p><p><b>　　(10)</b></p><p&

67、gt;　　Obviously,，.</p><p>　　If no any local decision is reported to the common receiver, i.e., K=0 , we call that fail sensing. For this case, the common receiver will request the user which has the highest r

68、eputation to send its local decision based on conventional energy detection method. Let and denote the probability of fail sensing under hypothesis and , respectively. Here we have:</p><p><b>　　(11)<

69、;/b></p><p><b>　　(12)</b></p><p>　　Apparently, and .In our scheme, the false alarm probability ,the detection probability,and the missing probability :</p><p><b&g

70、t;　　(13)</b></p><p>　　= (14)</p><p><b>　　(15)</b></p><p>　　For simplicity, we assume the channel between the unlicensed users and the base station

71、are ideal, the local decision will be reported without any error. So stand for the probability of the event that under hypothesis , all the K users claim and other N-K users make no local decisions. </p><p&

72、gt;<b>　　=</b></p><p>　　= （16）</p><p><b>　?。?7）</b></p><p><b>　　（18）</b></p><p><b>　?。?9）</b>&l

73、t;/p><p>　　IV. SIMULATION RESULTS</p><p>　　In this section, some simulation results are presented to illustrate the system performance of our cooperative spectrum sensing algorithm based on reputat

74、ion. The results of the conventional one threshold energy detection method are also shown for a comparison. In our simulation, the common simulation parameters are given as follows:</p><p>　　Table 1. Simula

75、tion parameters</p><p>　　Fig.2 depicts the performance of cooperative spectrum sensing and ..It can be observed that, compared it with the conventional method, the detection performance has improved signific

76、antly. For example, while = 0.001, our method achieves extra 0.019 detection probability. Fig.3 shows the decrease of the normalized transmission bits for different values of fail sensing, i.e. = 0, 0.001, 0.01, 0.1. Com

77、pared with conventional method, i.e., when = 0, the normalized average number of sensing bits is </p><p>　　Fig 2.vs., </p><p>　　Fig 3.vs.,=00,0.001,0.01,0.1</p><p>　　V. CONCLUSION&

78、lt;/p><p>　　In this paper, a new scheme in cooperative spectrum sensing for cognitive radio networks under bandwidth constraints was proposed. In our method, only the secondary users with reliable information a

79、re allowed to report their sensing results. When no user has reliable information, only he secondary user with highest reputation will report its sensing result. We analyzed the closed expression for the probability of

80、the detection and the false-alarm. From the preliminary simulation results, we dem</p><p>　　REFERENCES</p><p>　　[1] Federal Communications Commission. Spectrum Policy Task Force, Rep. ET Docket

81、no. 02-135 [R]. Nov. 2002.</p><p>　　[2] J. Mitola and G. Q. Maguire. Cognitive radio: Making software radios more personal[C],IEEE Personal Communication. vol. 6, pp. 13–18, Aug. 1999.</p><p>　　

82、[3] S. Haykin. Cognitive radio: brain-empowered wireless communications [J]. IEEE J. Sel. Areas Communication. vol. 23, pp. 201–220, Feb. 2005.</p><p>　　[4] AKYLDIZ IF. Next generation/dynamic spectrum acces

83、s/cognitive radio wireless networks: A Survey [J]. ELSEVIER Computer Networks, 2006(50):2127-2159.</p><p>　　[5] D. Cabric, S. M. Mishra, and R. W. Brodersen. Implementation issues in spectrum sensing for cog

84、nitive radios[C]// in Proc. Of A silomar Conf. on Signals, Systems, and Computers, Pacific Grove,CA, USA, Nov. 7-10, 2004, pp. 772 - 776.</p><p>　　[6] A.Ghasemi and E. S. Sousa. Collaborative spectrum sensi

85、ng for opportunistic access in fading environments[C]// in Proc. 1st IEEES ymp. New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, Nov. 8–11, 2005, pp. 131–136.</p><p>　　[7] Chunhua Sun, Wei

86、Zhang, Letaief K.B. Cooperative spectrum sensing for cognitive radios under bandwidth constraints[C]// in Proc. IEEE WCNC, March 11-15, 2007, pp. 1-5.</p><p>　　[8] H. Urkowitz. Energy detection of unknown de

87、terministic signals [C]. Proceedings of IEEE, vol.55, pp. 523-531, April 1967.</p><p>　　[9] Ruiliang Chen, Jung-Min Park, Kaigui Bian. Robust Distributed Spectrum Sensing in Cognitive Radio Networks[C]. in P

88、roc. IEEEINFOCOM, April 2008, pp. 1876-1884.</p><p>　　[10] F. F. Digham, M. -S. Alouini, and M. K. Simon. On the energy detection of unknown signals over fading channels[C]. in Proc. IEEE ICC, Anchorage, AK,