• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Joint Optimal Energy-Efficient Cooperative Spectrum Sensing and Transmission in Cognitive Radio

    2017-05-08 11:32:01WeizhiZhongKunqiChenXinLiu
    China Communications 2017年1期

    Weizhi Zhong, Kunqi Chen, Xin Liu ,

    1 College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;

    2 School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China

    I.INTRODUCTION

    CR has been proposed to solve the spectrumscarcity problem, owing to its opportunistic transmission and dynamic spectrum access capabilities [1].In CR, SU can utilize the licensed spectrum dynamically when the spectrum is temporally unused [2-4].To avoid interference to the normal communication of primary user (PU), SU needs to use spectrum sensing to detect whether PUs are present or not before its secondary transmissions [5].If the presence of the PU is detected, SU should postpone its transmissions.However, reliable spectrum sensing cannot always been guaranteed due to multipath fading, shadowing and hidden terminal problem.CSS has thus been proposed to improve the sensing accuracy.In CSS, several SUs sense the spectrum independently and then deliver their sensing data to a fusion center that makes the final judgment of the PU status [6].

    Most of previous works in the literature are focused on optimizing either cooperative sensing or transmission parameters to maximize the throughput of the whole CR system [7,8].The optimal sensing- throughput tradeoff is investigated in [9] for the single-channel CR,and the multi-channel CR is also discussed in[10].Both of [9] and [10] assume each SU to have a fixed transmission power and ignore the considerable potential gain of dynamic resource allocation.Therefore, the joint optimization of spectrum sensing and resource al-location is investigated in [11] and [12] for the cases of single user and multiple users respectively, which considers the dynamic allocation and increases the throughput of the CR system observably.

    In this paper, a joint optimal energy-efficient cooperative spectrum sensing(CSS) and transmission in multi-channel CR is proposed to improve the energy efficiency(EE) in cognitive radio(CR).

    All the researches above pay attention to the throughput of CR.Nevertheless, due to the popularity and significance of green communication, EE has spurred great interest in recent years.Since most of the mobile terminals in wireless networks are battery-powered, EE is critical to the service life of these terminals[8,13].In the literature, energy-efficient cooperative sensing for CR networks is investigated in [14,15].The authors aim at maximizing the energy-efficient capacity via optimizing either the sensing time or detection threshold.The optimal energy-efficient power allocation is studied in [7], which assumes the sensing factors fixed.Furthermore, the joint optimization of energy-efficient sensing and transmission in the single-channel CR is considered in[16,17].However, to reduce the complexity and non-convexity of the initial optimization issues, most of the energy-efficient optimizations ignore the miss detection, where SUs fail to detect the presence of PU and access the spectrum, thus yielding awful energy waste and deviation from the real communication Moreover, the joint optimization of energy-efficient CSS and transmission in multi-channel CR has not been considered in current researches.

    Fig.1 Cooperative spectrum sensing model

    Hence, in this paper, we investigate the joint optimization of cooperative spectrum sensing,including sensing time and the number of cooperative users, and data transmission, including proportion of bandwidth and transmission power, for multi-channel CR.Besides, we also consider miss detection to formulate a holistic and significant energy-efficient optimization scheme.

    The rest of this paper is organized as follows.The system model is introduced and an energy-efficient optimization problem is formulated in Section Ⅱ .Then, in Section Ⅲ , a combined algorithm of bi-level optimization,Polyblock optimization and Dinkelbach’s optimization is proposed to solve the proposed non-convex optimization problem.Simulation results and conclusions are finally presented in Section Ⅳ and V, respectively.

    II.SYSTEM MODEL

    In this paper, the cognitive radio network(CRN) consists ofLlicensed channels andNsecondary users.The bandwidth of each licensed channel isB.

    We assume the system is strictly synchronized and a time-slot scheme is considered which divides the communication time into several slots with lengthT.In order to simplify the problem, each PU is also assumed to keep their status unchanged in each time slot.Moreover, each slot contains three phases[12], including local sensing phase, cooperative phase and transmission phase.The timeslot structure is shown in Fig.1.

    First, some SUs are chosen to perform the cooperative sensing.During the local sensing phase, they sense the PU status independently with a sampling rateThen, in the cooperative phase, these SUs deliver their own sensing statistics to a fusion center that makes the final judgment of the PU status [18].To save the bandwidth of the common channel and prevent the conflict of cooperative information of the SUs, the delivery can be implemented via time division multiple access (TDMA).Finally, in the transmission phase, all SUs access the usable channels to transmit their data according to the judgment of the fusion center.

    2.1 Cooperative spectrum sensing

    During the local spectrum sensing phase, each SU senses the signal by spanning the objective channels.The signal received by their receivers follows a binary hypothesis as follows [9]:

    Energy detection is the most widely used spectrum sensing method because it can be easily implemented without knowing any prior knowledge from the PU.Energy detection is conducted by comparing the metrical energy statistic of the received PU signal with a pre-configured decision threshold.If the energy is higher than the threshold, the PU is assumed to be busy; otherwise, the PU would be judged as idle [19-22].

    Using energy detection, the energy statistic of each SU can be given as

    According to CSS, the fusion center collects energy statistics from all SUs and makes the final judgment of the PU status.IfkSUs are chosen to sense PU cooperatively, the global energy statistics collected by the fusion center can be given as

    Furthermore, if the pre-configured decision threshold isthe false alarm and detection probabilities of CSS can be given, respectively, by following [9,11] as

    2.2 Throughput of SU

    In the transmission phase, each SU is assigned a portion of each usable channel at a certain transmission power [12].In this paper,we assume theith SU is assigned a portionof thejth channel, which can be realized via either the Filter Bank Multicarrier (FBMC) or orthogonal frequency division multiple access (OFDMA), at a transmission power.Moreover, the channel gains from the PU transmitter to the SU receiver and from the SU transmitter to its own receiver are respectively denoted byand.

    a) Channeljis indeed idle and no false alarm appears.The probability of this scenario iswhereis the idle probability of channelj.In this case, the SU’s achievable transmission rate is

    In CR system, SU can access one channel only when the PU status in this channel is judged as idle.Actually, SU may access the channel in the following two scenarios [9,12].

    b) Channel j is actually busy but it is not accurately detected.The probability of this scenario iswhereis the busy probability of channelj.In this case, the PU’s signal serves as interference to SU’s communication; hence the SU’s achievable transmission rate is

    In addition, we suppose that the length of the time slot used by each cooperative SU to deliver local sensing data to the fusion center isξ.When using TDMA, the cooperative overhead is given as

    Then the transmission duration is given as

    Therefore, the throughput of all SUs is given as

    2.3 Traditional optimization model

    For the sake of explanation and contrast, we first propose a joint global optimization of CSS and transmission in multi-channel CR to maximize the throughput.

    To ensure the accuracy of spectrum sensing, a lower limit of cooperative detection probability should be set beforehand and the traditional throughput maximization problem is formulated in [9] as

    Problem P0:

    However, Problem P0 does not consider CSS and the transmission optimization.Generally speaking, in CSS, the false alarm and detection probabilities mainly depend on the local sensing duration and the number of cooperative SUsk.In fact, the increase ofandkleads to a higher detection probability and a lower false alarm probability, which implies a better sensing capability, but also means less time and power for the later data transmission.Thus, there is a tradeoff between CSS and throughput.

    Hence, we first upgrade the tradition optimization model by further considering CSS and transmission optimization as follows

    Problem P1:

    Although Problem P1 is a new optimization model, its objective function is just a part of Problem P2 introduced in detail later.Therefore, Problem P1 can be solved by a combination of bi-level optimization and Polyblock optimization.Moreover, a similar basic model is proposed in [12] and the optimization procedure in [12] can be taken as reference to solve Problem P1.

    Hereinafter, we formulate our EE maximization model and make a comparison between the two models to demonstrate our scheme by some simulations.

    2.4 Energy efficiency maximization model

    When concerning energy efficiency, a good choice for the objective function of the optimization problem is one that measures the number of bits that can be transmitted per joule of energy consumed [13].In a communication system, a high power is often required to reach a high signal-noise-rate (SNR) level,which can lead to a lower bit-error-rate (BER)and thus a higher throughput.However, a high power means low battery life for the terminals and is also not eco-friendly.Thus the objective function of EE can be defined as a tradeoff between the throughput and the entire power consumed in a time slot, including both the sensing and transmission power.Furthermore,this tradeoff can be formulated as follows

    The objective function in (14) was introduced in [23] and has been adopted as the definition of EE in many researches [8, 24-25].

    According to (14), we formulate the objective function of EE in CSS as (15) shown in the bottom at this page.

    Based on the above analysis, the EE maximization problem can be formulated as Problem P2 shown in the bottom at this page.

    II.JOINT EE OPTIMIZATION OF COOPERATIVE SPECTRUM SENSING AND TRANSMISSION

    Problem P2 is actually a mixed-integer non-convex problem, which is usually NP-hard to solve directly.To solve Problem P2,we first transform the problem into a low complexity form.

    Becausekis within a finite integer set, we can optimizekby exhaustive searching.Next,we mainly investigate the optimization of

    Problem P2:with a specifickvalue as Problem P4 (with a specifickvalue) shown in the bottom at this page.

    However, Problem P4 is still not convex.To solve this problem, we adopt the bi-level optimization to divide Problem P4 into two sub-level optimization problems.The lower one is to optimizeandwith specifickandwhile the upper one is to optimize.The lower level problem is given as Problem P5(with specifickandvalue) shown in the bottom at this page.

    which is equivalent to Problem P6 (with specifickandvalue) shown in the bottom at this page.

    Problem P3:

    Problem P4 (with a specifickvalue):

    Problem P5 (with specifickandvalue):

    Problem P6 (with specifickandvalue):

    Problem P7:

    The Problem P6 is a fractional optimization problem.The numerator and denominator of its objective function can be denoted asandrespectively, i.e.(17)shown in the bottom at this page.

    Notice that the feasible region of Problem P6 is a convex, compact and connected set.Bothandare continuous and positive-valued functions in the feasible region of Problem P6.Meanwhile,is a concave function from [26], andis a linear function.Therefore, based on [27] and[28], Problem P6 can be solved by Dinkelbach’s optimization, where the fractional problem is represented by an equivalent parametric program that is convex for a concave-linear fractional program and can be rapidly solved by an iterative procedure.

    The feasible region of Problem P6 is denoted as.Then, the procedure of Dinkelbach’s optimization for Problem P6 is described as Algorithm 1.

    Thus, the optimal solution offor specifickandcan be obtained.For Problem P7, we have the following lemma.

    Lemma 1: Functionsandare monotonically increasing functions with respect towithin the interval

    Proof: SupposewhereWhenandthe optimal solutions ofareandrespectively.Since the functionis a monotonically increasing function with respect towithin the intervalwe have

    According to lemma 1, the objective function of Problem P7 is the difference of two monotonically increasing functions whose intervals are both non-negative.Thus, by introducing a variable, Problem P7 can be transformed into a monotonic optimization problem, which can be solved through Polyblock optimization [29].

    Problem P8:

    Algorithm 1 Dinkelbach’s optimization for Problem P6 (with specific k and τ value)

    Algorithm 2 Polyblock optimization for Problem P9 (with a specific k value)

    Algorithm 3 k -Exhaustion Algorithm for Problem P3

    Table I The SCSNR of each SU on each licensed channel

    Problem P9:

    Problem P9 is a monotonic optimization problem because its objective function and constraints are all monotonically increasing functions.Since bothτandare non-negative, Problem P9 can be solved by Polyblock optimization [30,31] as shown in Algorithm 2.

    Thus, with a specifick,the outputxof Algorithm 2 is the optimal solution of the upper Problem P9 andBased on the above analysis, we can now use the exhaustive algorithm to searchkand formulate a general algorithm to solve Problem P3 as shown in Algorithm 3.

    IV.SIMULATION RESULTS

    In this section, simulation results are presented by comparing the energy efficiency maximization model (EEMM) with the traditional throughput maximization model (TMM) to demonstrate our scheme.

    Consider a CR network consisting ofN=6 SUs andL=8 licensed channels.Each channel spans a bandwidthThe sampling frequency isthe length of time slot isthe time used by each cooperative SU to deliver local sensing data to the fusion center isthe transmission power of PU signal isthe sensing power of each cooperative SU isthe maximal transmission power of each SU isand the lower limit of detection probability isBoth the idle and busy probabilities of these channels are

    In the simulations, with different pairs of specifickandτ, we can obtain the optimal utilities of Problem P2 (i.e.EEMM) and Problem P1 (i.e.TMM) by Algorithm 1 and the interior-point method, respectively.

    First, we demonstrate the effect, caused by the local sensing durationτand the number of chosen cooperative sensing SUsk,on EE and throughput in both the two models (EEMM and TMM).Fig.2 shows the EE of the whole CR systemversusτwith differentkwhenandFig.3 shows the throughput of the whole CR systemversusτwith differentkwhenandIt can be seen that, with the increase ofτ, all the optimal utilities first increase and then decrease.In fact, the increase ofτleads to a lower false alarm probability,which implies a better sensing capability and thus a higher EE and throughput of the CR system.Thus the optimal utilities increase at first.However, with the increase ofτ, the effect is wearing off.Whenτreaches a certain value, the loss of transmission time, caused by the increase ofτ, becomes dominant.Hence,the optimal utilities decrease later.Besides, the increase ofkalso has a similar effect on the optimal utilities.We also indicate the optimal, achieved by Algorithm 2 with differentkindependently, and the corresponding utilities in Fig.2.It can be seen that each of the marked utilities is very close to their correspondingk-fixed global optimal utilities, which perfect-ly verifies the effectiveness of our proposed algorithm.In addition, by comparing the utilities of differentwe find out the global optimal solution through Algorithm 3.Whenk=4 andτ=0.0743s, the CR system based on EEMM can achieve the global optimal utility.Moreover, although the obtainedof EEMM is less than that of TMM, the obtainedof EEMM is much more than that of TMM.This result verifies the principles of both EEMM and TMM perfectly.

    Table II The TCSNR of each SU on each licensed channel

    Fig.2versus with different k when

    Fig.3versus with different k when

    Fig.6 k versuswith different models

    Then, we focus on the effect of the lower limit of cooperative detection probabilityin our scheme.In the following simulations, the optimal solutions of EEMM and TMM are respectively given by Algorithm 3 and a similar Polyblock optimization proposed in [12].For the sake of demonstration, we use power utilization ratiowhich represents the power consumption required to accomplish the desired communication preference when the maximum power limit is given, to describe the usage of power in CR system.Furthermore,can be formulated as the ratio of the total consumed power to the total maximal transmission power of all SUs, i.e.

    Finally, we demonstrate the effect of the maximal transmission power of each SUin our scheme.Fig.7, Fig.8 and Fig.9 showsversuswith different models,respectively.From Fig.7, it can be seen that, asgrows,of TMM first increases and then decreases.The prime increase is due to the increase of the throughput.However, sinceof TMM is always 100%, whenreaches a certain value, the excessive consumption of power becomes dominant, which finally leads to the decrease of EE.In contrast, EEMM can limit the power consumption effectively.In Fig.7,with the increase ofof EEMM also increases at first, which is due to the increase of the throughput.However, whenrises toreaches an upper bound and almost keeps invariable later, which indicates the increase ofgradually becomes a waste of resources and the limit on power consumption in EEMM is effective.Moreover, like Fig.4, the obtainedof EEMM is always higher than that of TMM, even in the common ascent stage.In Fig.8, the initial stage of EEMMshows dramatic fluctuations.According to Fig.9, the fluctuations are mainly caused by the interaction of the increase ofkandEven whenrises to 0.0060W, wherekincreases from 3 to 4, the trend of the curve also experiences a slight change.Moreover, oncekis fixed, the useless increase ofgradually becomes more and more severe and directly leads to the periodic decreases of.

    V.CONCLUSION

    Fig.7versus with different models

    Fig.8versus with different models

    Fig.9 k versus with different models

    In this paper, a joint optimal energy-efficient CSS and transmission in multi-channel CR system is investigated.A joint optimization problem is formulated to maximize EE by jointly optimizing local sensing time, number of cooperative sensing SUs, transmission bandwidth and power.This non-convex problem is effectively solved by a combination of bi-level optimization, Polyblock optimization and Dinkelbach’s optimization.Finally, we demonstrate the effectiveness of EEMM by comparing it with TMM.Simulation results show that EEMM has a significant improvement on the EE performance and performs a valid limit to the power consumption.

    ACKNOWLEDGEMENTS

    This work was supported by the National Natural Science Foundations of China under Grant Nos.61301105, 61401288 and 61601221;the Natural Science Foundations of Jiangsu Province under Grant No.BK20140828; the China Postdoctoral Science Foundations under Grant Nos.2015M581791 and 2015M580425;and the Fundamental Research Funds for the Central Universities under Grant No.DUT16RC(3)045.

    [1] T.Cui, F.Gao, A.Nallanathan.“Optimization of Cooperative Spectrum Sensing in Cognitive Radio”.IEEE Transactions on Vehicular Technology,vol.60, no.4, pp 1578- 1589, 2011.

    [2] W.Krenik, A.Batra.“Cognitive radio techniques for wide area networks”.Design Automation Conference, 2005.Proceedings.vol.30, no.5, pp 409-412, 2005.

    [3] S.Huang, X.Liu, Z.Ding.“Optimal Transmission Strategies for Dynamic Spectrum Access in Cognitive Radio Networks”.IEEE Transactions on Mobile Computing, vol.8, no.12, pp 1636-1648,2009.

    [4] S.Bayhan, F.Alagoz.“Scheduling in Centralized Cognitive Radio Networks for Energy Efficiency”.IEEE Transactions on Vehicular Technology,vol.62, no.2, pp 582-595, 2013.

    [5] H.Jiang, L.Lai, R.Fan, et al.“Optimal selection of channel sensing order in cognitive radio”.IEEE Transactions on Wireless Communications,vol.8, no.1, pp 297-307, 2009.

    [6] J.Marinho, E.Monteiro.“Cooperative Sensing-Before -Transmit in Ad-Hoc Multi-hop Cognitive Radio Scenarios”.Lecture Notes inComputer Science, pp 186-197, 2012.

    [7] Y.Wang, W.Xu, K.Yang, et al.“Optimal Energy-Efficient Power Allocation for OFDM-Based Cognitive Radio Networks”.IEEE Communications Letters, vol.16, no.9, pp 1420-1423, 2012.

    [8] M.Haddad, Y.Hayel, O.Habachi.“Spectrum Coordination in Energy-Efficient Cognitive Radio Networks”.IEEE Transactions on Vehicular Technology, vol.64, no.5, pp 2112-2122, 2015.

    [9] Y.C Liang, Y.Zeng, E.C.Y Peh.“Sensing-throughput tradeoff for cognitive radio networks”.IEEE Transactions on Wireless Communications, vol.7, no.4, pp 1326-1336, 2008.

    [10] R.Fan, H.Jiang.“Optimal multi-channel cooperative sensing in cognitive radio networks”.IEEE Transactions on Wireless Communications,vol.9, no.3, pp 1128-1138, 2010.

    [11] X.Liu, G.Bi, Y.L Guan, et al.“Joint optimization algorithm of cooperative spectrum sensing with cooperative overhead and sub-band transmission power for wideband cognitive radio network”.Transactions on Emerging Telecommunications Technologies, vol.26, no.4, pp 586-597,2015.

    [12] R.Fan, H.Jiang, Q.Guo, et al.“Joint Optimal Cooperative Sensing and Resource Allocation in Multichannel Cognitive Radio Networks”.IEEE Transactions on Vehicular Technology, vol.60,no.2, pp 722 - 729, 2011.

    [13] F.Meshkati, H.V Poor, S.C Schwartz.“Energy-Efficient Resource Allocation in Wireless Networks”.IEEE Signal Processing Magazine, vol.24,no.3, pp 58-68, 2007.

    [14] O.Ergul, O.B Akan.“Energy-efficient Cooperative Spectrum Sensing for Cognitive Radio Sensor Networks”.IEEE Symposium of Computers and Communications (ISCC), pp 465-469, 2013.

    [15] E.C.Y Peh, Y.C Liang, Y.L Guan, et al.“Energy-Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks”.Global Telecommunications Conference (GLOBECOM 2011), IEEE, pp 1-5, 2011.

    [16] Y.Gao, W.Xu, K.Yang, et al.“Energy-efficient transmission with cooperative spectrum sensing in cognitive radio networks”.Wireless Communications and Networking Conference (WCNC),IEEE, pp 7-12, 2013.

    [17] X.Wu, J.L Xu, M.Chen, et al.“Optimal Energy-Efficient Sensing and Power Allocation in Cognitive Radio Networks”.Mathematical Problems in Engineering, no.3, pp 1-7, 2014.

    [18] X.Liu.“A new sensing-throughput tradeoff scheme in cooperative multiband cognitive radio network”.Inter- national Journal of Network Management, vol.24, no.3, pp 200-217, 2014.

    [19] Z.P Liang, R.Z Song, Q.Z Lin, et al.“A double-module immune algorithm for multi-objective optimization problems”,Applied Soft Computing,vol.35, no.C, pp 161-174, 2015.

    [20] Q.Z Lin, J.Y Chen, “A novel micro-population immune multiobjective optimization algorithm”,Computers & Operations Research, vol.40, no.6,pp.1590-1601, 2013.

    [21] J.Y Chen, Q.Z Lin, Q.B Hu, “Application of novel clonal algorithm in multiobjective optimization”,International Journal of Information Technology& Decision Making, vol.9, no.2, pp.239-266,2010.

    [22] J.Y Chen, Q.Z Lin, L.L Shen, “An immune-inspired evolutionary strategy for constrained optimization problems”, International Journal on Artificial Intelligence Tools, vol.20, no.3, pp.549-561,2011.

    [23] V.Shah, N.B Mandayam, D.J Goodman.“Power control for wireless data based on utility and pricing”.IEEE International Symposium on Personal, Indoor and Mobile Radio Communications,vol.3, pp 1427-1432, 1998.

    [24] C.U Saraydar, N.B Mandayam, D.Goodman.“Pricing and Power Control in a Multicell Wireless Data Network”.IEEE Journal on Selected Areas in Communications, vol.19, no.10, pp 1883-1892, 2001.

    [25] F.Meshkati, H.V Poor, S.C Schwartz, et al.“An energy-efficient approach to power control and receiver design in wireless data networks”.IEEE Transactions on Communications, vol.53, no.11, pp 1885-1894, 2005.

    [26] X.Gong, S.A Vorobyov, C.Tellambura.“Joint Bandwidth and Power Allocation With Admission Control in Wireless Multi-User Networks With and Without Relaying”.IEEE Transactions on Signal Processing, vol.59, no.4, pp 1801-1813, 2011.

    [27] W.Dinkelbach.“On Nonlinear Fractional Programming”.Management Science, vol.13, no.7,Series A, Sciences, pp 492-498, 1967.

    [28] S.Schaible.“Fractional Programming.II, On Dinkelbach’s Algorithm”.Management Science,vol.22, no.8, pp 868-873, 1976.

    [29] S.Alizamir.“Monotonic Optimization”.Encyclopedia of Optimization, pp 2316-2323, 2009.

    [30] S.Boyd, L.Vandenberghe.“Convex Optimization”.Cambridge University Press, 2004.

    [31] C.Floudas, P.M.Pardalos.“Encyclopedia of Optimization”.Springer-Verlag GmbH, 2008.

    亚洲国产精品999在线| 欧美最黄视频在线播放免费 | 国产熟女午夜一区二区三区| 天堂中文最新版在线下载| 99久久人妻综合| 国产亚洲精品久久久久久毛片| 精品福利观看| 欧美最黄视频在线播放免费 | 搡老熟女国产l中国老女人| 国产成年人精品一区二区 | а√天堂www在线а√下载| 国产精品一区二区三区四区久久 | 亚洲五月天丁香| 亚洲精品美女久久久久99蜜臀| 精品久久久久久,| 日本黄色视频三级网站网址| 国产精品一区二区精品视频观看| 国产成人精品久久二区二区免费| 国产精品永久免费网站| 亚洲欧美精品综合久久99| 亚洲成国产人片在线观看| 午夜免费激情av| 日韩精品中文字幕看吧| 亚洲精品一二三| 99久久久亚洲精品蜜臀av| 中文亚洲av片在线观看爽| 午夜激情av网站| 水蜜桃什么品种好| 成年版毛片免费区| 国产成+人综合+亚洲专区| 亚洲午夜理论影院| 我的亚洲天堂| 91国产中文字幕| av超薄肉色丝袜交足视频| 日韩欧美免费精品| 久久中文字幕人妻熟女| 久久久精品欧美日韩精品| 久久久久久大精品| 精品国产乱码久久久久久男人| 在线免费观看的www视频| 国产蜜桃级精品一区二区三区| 国产精品香港三级国产av潘金莲| 一区二区日韩欧美中文字幕| 人人妻,人人澡人人爽秒播| 一二三四在线观看免费中文在| 精品卡一卡二卡四卡免费| www国产在线视频色| tocl精华| 丝袜人妻中文字幕| 成年人黄色毛片网站| 真人一进一出gif抽搐免费| 久久亚洲真实| 中文字幕人妻丝袜一区二区| 午夜免费成人在线视频| 757午夜福利合集在线观看| 午夜91福利影院| 91精品国产国语对白视频| 精品卡一卡二卡四卡免费| 久久久久久久久中文| 人人妻人人添人人爽欧美一区卜| 人人澡人人妻人| 久久影院123| 我的亚洲天堂| 搡老乐熟女国产| 精品国产乱子伦一区二区三区| 两个人看的免费小视频| 国产高清videossex| 国产成人欧美在线观看| 国产av又大| tocl精华| 亚洲人成电影观看| 亚洲精品中文字幕一二三四区| 美女高潮到喷水免费观看| 亚洲成人国产一区在线观看| 欧美日韩中文字幕国产精品一区二区三区 | 亚洲av电影在线进入| 国产一区二区三区综合在线观看| 涩涩av久久男人的天堂| 最好的美女福利视频网| 国产伦一二天堂av在线观看| 香蕉久久夜色| 老司机深夜福利视频在线观看| 悠悠久久av| 精品人妻在线不人妻| 免费女性裸体啪啪无遮挡网站| 精品熟女少妇八av免费久了| 伦理电影免费视频| 老司机亚洲免费影院| 欧美日韩黄片免| 国产精品国产av在线观看| 亚洲av美国av| 久久久精品欧美日韩精品| 日韩av在线大香蕉| 最近最新免费中文字幕在线| 麻豆av在线久日| 成年人黄色毛片网站| 国产精品秋霞免费鲁丝片| 成人国语在线视频| 欧美另类亚洲清纯唯美| 伦理电影免费视频| 可以在线观看毛片的网站| 窝窝影院91人妻| 黄色怎么调成土黄色| 在线观看免费高清a一片| 国产精品乱码一区二三区的特点 | 在线国产一区二区在线| 日韩免费高清中文字幕av| 亚洲人成网站在线播放欧美日韩| 日韩免费av在线播放| 久久国产乱子伦精品免费另类| 纯流量卡能插随身wifi吗| 纯流量卡能插随身wifi吗| 亚洲美女黄片视频| av欧美777| 日韩欧美三级三区| 一进一出好大好爽视频| 人人妻人人爽人人添夜夜欢视频| 国产一区二区三区综合在线观看| 久久久久久大精品| 国产精品爽爽va在线观看网站 | av福利片在线| 亚洲av熟女| 久久人妻福利社区极品人妻图片| 日本精品一区二区三区蜜桃| avwww免费| 久久精品国产亚洲av香蕉五月| 国产精品免费一区二区三区在线| 亚洲av成人av| 亚洲av电影在线进入| 满18在线观看网站| 老鸭窝网址在线观看| 国产精品久久久久久人妻精品电影| а√天堂www在线а√下载| 一级片免费观看大全| 国产精品 国内视频| 久久久久久久久久久久大奶| 日本a在线网址| 国产午夜精品久久久久久| 一a级毛片在线观看| 巨乳人妻的诱惑在线观看| 天天躁狠狠躁夜夜躁狠狠躁| 亚洲片人在线观看| 久久精品国产清高在天天线| 亚洲精品美女久久av网站| 国产av精品麻豆| 日韩人妻精品一区2区三区| 久久久国产成人免费| 99在线视频只有这里精品首页| 97超级碰碰碰精品色视频在线观看| 亚洲第一欧美日韩一区二区三区| 国产亚洲欧美精品永久| 亚洲精品国产色婷婷电影| 可以在线观看毛片的网站| 曰老女人黄片| 麻豆久久精品国产亚洲av | 一级片免费观看大全| 国产欧美日韩精品亚洲av| 香蕉丝袜av| 后天国语完整版免费观看| 99久久精品国产亚洲精品| 国产高清videossex| 国产1区2区3区精品| 成人精品一区二区免费| 亚洲欧美精品综合一区二区三区| 国产精品乱码一区二三区的特点 | 老司机福利观看| 在线播放国产精品三级| 精品日产1卡2卡| 亚洲国产中文字幕在线视频| 看免费av毛片| 久久精品亚洲熟妇少妇任你| 久久久久久久久久久久大奶| 日日干狠狠操夜夜爽| 久久人妻熟女aⅴ| 亚洲人成电影观看| 亚洲一卡2卡3卡4卡5卡精品中文| 波多野结衣一区麻豆| 男女高潮啪啪啪动态图| 侵犯人妻中文字幕一二三四区| 婷婷精品国产亚洲av在线| 久久精品影院6| 在线观看日韩欧美| 热re99久久国产66热| 丝袜在线中文字幕| 欧美日韩中文字幕国产精品一区二区三区 | 黑人巨大精品欧美一区二区mp4| 亚洲精品久久成人aⅴ小说| 一级,二级,三级黄色视频| 夫妻午夜视频| 曰老女人黄片| 精品一区二区三卡| 一级,二级,三级黄色视频| 久久午夜综合久久蜜桃| 国产精品98久久久久久宅男小说| 精品国产美女av久久久久小说| 亚洲一码二码三码区别大吗| 午夜久久久在线观看| 亚洲专区国产一区二区| 国产91精品成人一区二区三区| 免费人成视频x8x8入口观看| www.熟女人妻精品国产| 久久香蕉精品热| 岛国视频午夜一区免费看| 69精品国产乱码久久久| 亚洲在线自拍视频| 精品国产超薄肉色丝袜足j| 另类亚洲欧美激情| 男人的好看免费观看在线视频 | 大码成人一级视频| 欧美不卡视频在线免费观看 | 欧美午夜高清在线| 91av网站免费观看| e午夜精品久久久久久久| 免费一级毛片在线播放高清视频 | 国产深夜福利视频在线观看| 午夜免费观看网址| 色精品久久人妻99蜜桃| 人妻久久中文字幕网| 女性被躁到高潮视频| av在线播放免费不卡| 亚洲一码二码三码区别大吗| av网站在线播放免费| 国产精品乱码一区二三区的特点 | 欧美激情 高清一区二区三区| 久久精品成人免费网站| 美女福利国产在线| 99热只有精品国产| 国产成人欧美在线观看| 好男人电影高清在线观看| 夜夜爽天天搞| 亚洲专区字幕在线| 国产欧美日韩精品亚洲av| 少妇的丰满在线观看| 亚洲精品在线美女| 亚洲七黄色美女视频| 精品一区二区三区av网在线观看| 国产又色又爽无遮挡免费看| 久久这里只有精品19| 亚洲一区中文字幕在线| 亚洲第一青青草原| 亚洲成国产人片在线观看| 亚洲熟女毛片儿| 久久久精品欧美日韩精品| 级片在线观看| 久久国产精品男人的天堂亚洲| 两性夫妻黄色片| 日韩免费高清中文字幕av| 亚洲精品一二三| 极品人妻少妇av视频| 午夜激情av网站| 亚洲中文av在线| 精品欧美一区二区三区在线| www.自偷自拍.com| 亚洲一区二区三区欧美精品| 国产伦一二天堂av在线观看| 天堂动漫精品| xxx96com| 男人舔女人下体高潮全视频| 国产麻豆69| 少妇 在线观看| 成人亚洲精品一区在线观看| 黑人欧美特级aaaaaa片| 婷婷六月久久综合丁香| 校园春色视频在线观看| 国产欧美日韩一区二区三区在线| 丝袜人妻中文字幕| 欧美日韩瑟瑟在线播放| 国产一区二区在线av高清观看| 欧美另类亚洲清纯唯美| 亚洲成人免费av在线播放| 中文字幕人妻熟女乱码| 国产亚洲av高清不卡| 大陆偷拍与自拍| 亚洲欧洲精品一区二区精品久久久| 国产精品影院久久| 亚洲色图 男人天堂 中文字幕| 国产精品乱码一区二三区的特点 | bbb黄色大片| 久久香蕉精品热| 久久精品国产亚洲av高清一级| 9色porny在线观看| 国产欧美日韩一区二区三| 亚洲成a人片在线一区二区| 一边摸一边做爽爽视频免费| 大型av网站在线播放| 日日夜夜操网爽| 国产高清激情床上av| e午夜精品久久久久久久| 99久久久亚洲精品蜜臀av| 一级片免费观看大全| 9191精品国产免费久久| 国产黄a三级三级三级人| 日韩欧美在线二视频| 波多野结衣高清无吗| 久久久久国内视频| 欧美 亚洲 国产 日韩一| 亚洲熟妇熟女久久| 欧美最黄视频在线播放免费 | 少妇的丰满在线观看| 麻豆久久精品国产亚洲av | 老熟妇仑乱视频hdxx| 午夜福利在线观看吧| 美国免费a级毛片| 啦啦啦 在线观看视频| 久久久久精品国产欧美久久久| 成年人黄色毛片网站| 日韩精品中文字幕看吧| 天堂中文最新版在线下载| 男男h啪啪无遮挡| 欧美激情 高清一区二区三区| 欧洲精品卡2卡3卡4卡5卡区| 天天躁狠狠躁夜夜躁狠狠躁| 国产区一区二久久| 韩国精品一区二区三区| 后天国语完整版免费观看| 老熟妇乱子伦视频在线观看| 18禁美女被吸乳视频| 最近最新中文字幕大全电影3 | 一区二区三区精品91| 精品国产乱子伦一区二区三区| 最好的美女福利视频网| 免费在线观看黄色视频的| 国产午夜精品久久久久久| 操美女的视频在线观看| 女同久久另类99精品国产91| 18禁美女被吸乳视频| 久久久久久久久中文| 久热这里只有精品99| 热99re8久久精品国产| a级毛片黄视频| 国产精品爽爽va在线观看网站 | 欧美日韩视频精品一区| 欧美精品亚洲一区二区| 国产精品日韩av在线免费观看 | 动漫黄色视频在线观看| 一级,二级,三级黄色视频| 国产伦一二天堂av在线观看| 国产黄a三级三级三级人| 男女高潮啪啪啪动态图| 一级a爱片免费观看的视频| 麻豆国产av国片精品| a级毛片黄视频| 欧美日韩亚洲综合一区二区三区_| 日日爽夜夜爽网站| 宅男免费午夜| 男女高潮啪啪啪动态图| 91麻豆精品激情在线观看国产 | www.999成人在线观看| 精品日产1卡2卡| 亚洲精品在线观看二区| 亚洲人成伊人成综合网2020| 韩国精品一区二区三区| 亚洲人成电影免费在线| 久久婷婷成人综合色麻豆| av网站免费在线观看视频| 国产精品久久久久成人av| 女性生殖器流出的白浆| 国产激情欧美一区二区| www.999成人在线观看| 成人影院久久| 88av欧美| 两个人看的免费小视频| 久久精品国产清高在天天线| 成人影院久久| 宅男免费午夜| 国产aⅴ精品一区二区三区波| 宅男免费午夜| 国产精品一区二区在线不卡| 国产91精品成人一区二区三区| 老司机福利观看| av天堂在线播放| 亚洲人成电影免费在线| 亚洲专区中文字幕在线| 欧美日韩视频精品一区| 亚洲专区国产一区二区| 欧美成人性av电影在线观看| 脱女人内裤的视频| 久久天堂一区二区三区四区| 国内久久婷婷六月综合欲色啪| 日韩 欧美 亚洲 中文字幕| 国产97色在线日韩免费| ponron亚洲| 香蕉国产在线看| 黑人巨大精品欧美一区二区mp4| 久久久久久久精品吃奶| 久久 成人 亚洲| 99精品欧美一区二区三区四区| 精品久久蜜臀av无| 国产精品久久久av美女十八| 99久久精品国产亚洲精品| 亚洲av成人不卡在线观看播放网| 一级作爱视频免费观看| 女同久久另类99精品国产91| 日韩人妻精品一区2区三区| 欧美激情久久久久久爽电影 | 很黄的视频免费| 亚洲专区中文字幕在线| 精品久久久久久电影网| 国产亚洲精品第一综合不卡| 国产97色在线日韩免费| 亚洲美女黄片视频| 欧美一级毛片孕妇| 国产精品久久久人人做人人爽| av网站在线播放免费| 亚洲国产毛片av蜜桃av| 亚洲成国产人片在线观看| 亚洲自拍偷在线| 两人在一起打扑克的视频| 亚洲av成人不卡在线观看播放网| 老司机午夜十八禁免费视频| 久久久国产欧美日韩av| 少妇的丰满在线观看| 国产野战对白在线观看| 夜夜夜夜夜久久久久| 亚洲精品一区av在线观看| 精品无人区乱码1区二区| 老汉色∧v一级毛片| 久久午夜亚洲精品久久| 一进一出抽搐gif免费好疼 | 悠悠久久av| 成年人黄色毛片网站| 亚洲精品中文字幕在线视频| 国产精华一区二区三区| 日本黄色视频三级网站网址| 国产精品美女特级片免费视频播放器 | 最近最新免费中文字幕在线| 无限看片的www在线观看| 极品教师在线免费播放| 欧美不卡视频在线免费观看 | 亚洲一区二区三区色噜噜 | 男男h啪啪无遮挡| 久久久久久久午夜电影 | 久久久久久久久久久久大奶| 欧美日韩精品网址| 99国产精品一区二区蜜桃av| 变态另类成人亚洲欧美熟女 | 国产精品永久免费网站| av视频免费观看在线观看| 国产高清videossex| 亚洲成人国产一区在线观看| 久久热在线av| 国产欧美日韩精品亚洲av| 日日摸夜夜添夜夜添小说| 久久久水蜜桃国产精品网| 免费观看精品视频网站| 琪琪午夜伦伦电影理论片6080| 在线国产一区二区在线| 久久中文字幕一级| 免费日韩欧美在线观看| av网站免费在线观看视频| 日本欧美视频一区| 国产亚洲欧美98| 亚洲成人精品中文字幕电影 | 韩国精品一区二区三区| 成年女人毛片免费观看观看9| 婷婷六月久久综合丁香| 国产成人av激情在线播放| 女生性感内裤真人,穿戴方法视频| 亚洲国产欧美网| 少妇裸体淫交视频免费看高清 | 亚洲国产精品一区二区三区在线| 少妇 在线观看| 欧美精品一区二区免费开放| 亚洲少妇的诱惑av| 国产激情久久老熟女| 久久中文看片网| 亚洲 欧美一区二区三区| 亚洲人成77777在线视频| 免费在线观看视频国产中文字幕亚洲| 日韩人妻精品一区2区三区| 国产一区二区三区视频了| 18禁黄网站禁片午夜丰满| 国产三级在线视频| 国产成人啪精品午夜网站| 国产一卡二卡三卡精品| 免费一级毛片在线播放高清视频 | 少妇的丰满在线观看| 国产精品久久久人人做人人爽| 看免费av毛片| 国产有黄有色有爽视频| 亚洲欧美精品综合一区二区三区| 国产精品偷伦视频观看了| 国产深夜福利视频在线观看| 自拍欧美九色日韩亚洲蝌蚪91| 一进一出好大好爽视频| 亚洲中文日韩欧美视频| 高清黄色对白视频在线免费看| 精品少妇一区二区三区视频日本电影| 99精品久久久久人妻精品| 午夜免费成人在线视频| 午夜久久久在线观看| 免费在线观看完整版高清| 美女福利国产在线| 人成视频在线观看免费观看| 国产无遮挡羞羞视频在线观看| 午夜老司机福利片| 国产真人三级小视频在线观看| 黑人欧美特级aaaaaa片| 午夜福利免费观看在线| 久久久久国产精品人妻aⅴ院| 18禁裸乳无遮挡免费网站照片 | 亚洲av片天天在线观看| 国产成人精品无人区| 黄网站色视频无遮挡免费观看| 久久这里只有精品19| 啦啦啦免费观看视频1| 搡老乐熟女国产| 露出奶头的视频| 久久精品亚洲熟妇少妇任你| 国产成人影院久久av| 精品电影一区二区在线| 国产精品久久久久成人av| 最近最新免费中文字幕在线| 一进一出抽搐gif免费好疼 | 国产一区二区三区综合在线观看| 美国免费a级毛片| 电影成人av| 欧美日韩亚洲高清精品| 久久久久国产一级毛片高清牌| 色老头精品视频在线观看| 亚洲av熟女| 丰满饥渴人妻一区二区三| 热99re8久久精品国产| 日韩精品免费视频一区二区三区| 亚洲欧美日韩高清在线视频| 国产午夜精品久久久久久| 亚洲国产精品合色在线| 久久九九热精品免费| 婷婷精品国产亚洲av在线| 男女高潮啪啪啪动态图| 露出奶头的视频| 亚洲欧美日韩另类电影网站| 18禁黄网站禁片午夜丰满| 午夜a级毛片| 精品国内亚洲2022精品成人| 中文字幕人妻丝袜制服| 久久香蕉精品热| 亚洲人成伊人成综合网2020| 黄色女人牲交| 精品国产国语对白av| 国产精品久久视频播放| 大型黄色视频在线免费观看| 亚洲avbb在线观看| 欧美日本中文国产一区发布| 女性被躁到高潮视频| 久久午夜亚洲精品久久| 国产又爽黄色视频| 精品国产乱子伦一区二区三区| 男女之事视频高清在线观看| 亚洲精品国产区一区二| 欧美成人免费av一区二区三区| 老汉色av国产亚洲站长工具| 老熟妇乱子伦视频在线观看| 国产国语露脸激情在线看| 两个人免费观看高清视频| 丁香六月欧美| 交换朋友夫妻互换小说| 黑人巨大精品欧美一区二区蜜桃| 法律面前人人平等表现在哪些方面| 亚洲中文av在线| 97人妻天天添夜夜摸| 日韩有码中文字幕| 一区福利在线观看| 真人做人爱边吃奶动态| 一级片免费观看大全| 天天躁夜夜躁狠狠躁躁| 亚洲精品国产色婷婷电影| 露出奶头的视频| 日韩中文字幕欧美一区二区| 国产亚洲欧美98| 国产精品二区激情视频| 美女国产高潮福利片在线看| 别揉我奶头~嗯~啊~动态视频| 久久国产精品男人的天堂亚洲| 免费观看精品视频网站| 夜夜爽天天搞| 伊人久久大香线蕉亚洲五| 免费av中文字幕在线| 国产精品自产拍在线观看55亚洲| 中文亚洲av片在线观看爽| 夜夜夜夜夜久久久久| 国产精品一区二区三区四区久久 | 午夜精品久久久久久毛片777| 天天影视国产精品| 国产激情欧美一区二区| 亚洲精华国产精华精| 嫩草影视91久久| 国产高清videossex| 香蕉丝袜av| 亚洲一区二区三区色噜噜 | 村上凉子中文字幕在线| 国产精品一区二区免费欧美| 大型av网站在线播放| 男人舔女人的私密视频| www.熟女人妻精品国产| 俄罗斯特黄特色一大片| 国产伦一二天堂av在线观看| 国产av又大| 亚洲片人在线观看| 99久久99久久久精品蜜桃| tocl精华| 精品一品国产午夜福利视频| 咕卡用的链子| 如日韩欧美国产精品一区二区三区| 天天影视国产精品| 免费一级毛片在线播放高清视频 | 亚洲欧美日韩高清在线视频| 啪啪无遮挡十八禁网站| 乱人伦中国视频| 最近最新中文字幕大全电影3 | 少妇被粗大的猛进出69影院| 91在线观看av| 一区在线观看完整版| 国产高清激情床上av| 国产成人av激情在线播放|