Xây dựng các thuật toán phân bố tài nguyên vô tuyến cho mạng vô tuyến nhận thức

Xây dựng các thuật toán phân bố tài nguyên vô tuyến cho mạng vô tuyến nhận thức Xây dựng các thuật toán phân bố tài nguyên vô tuyến cho mạng vô tuyến nhận thức Xây dựng các thuật toán phân bố tài nguyên vô tuyến cho mạng vô tuyến nhận thức luận văn tốt nghiệp,luận văn thạc sĩ, luận văn cao học, luận văn đại học, luận án tiến sĩ, đồ án tốt nghiệp luận văn tốt nghiệp,luận văn thạc sĩ, luận văn cao học, luận văn đại học, luận án tiến sĩ, đồ án tốt nghiệp

HÉLIO AUGUSTO MUZAMANE MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY - HÉLIO AUGUSTO MUZAMANE TELECOMMUNICATIONS DEVELOPMENT OF RADIO RESOURCE ALLOCATION METHODS FOR COGNITIVE RADIO NETWORKS MASTER OF SCIENCE 2014B HA NOI – 2016 MINISTRY OF EDUCATION AND TRAINING HANOI UNIVERSITY OF SCIENCE AND TECHNOLOGY HÉLIO AUGUSTO MUZAMANE DEVELOPMENT OF RADIO RESOURCE ALLOCATION METHODS FOR COGNITIVE RADIO NETWORKS MAJOR : TELECOMMUNICATIONS MASTER THESIS IN SCIENCE SCIENTIFIC SUPERVISOR: Associate Prof Eng NguyễnVănĐức Hà Nội – 2016 List of Figures Figure 2.1 Schematic block diagram of a digital radio [8] Figure 2.2 Spectrum utilization [9] .8 Figure 2.3 Spectrum holes concepts [9] Figure 2.4 Infrastructure-based CR network architecture [8] 11 Figure 3.1 Structure and spectral characteristic of multicarrier transmission system [12] 14 Figure 3.2 Structure and spectral characteristic of OFDM transmission scheme [7] 15 Figure 3.3 Multiple access techniques used in OFDM systems [12] 16 Figure 3.4 Linear receive-antenna combining [13] 17 Figure 3.5 Linear receive antenna combining [13] 18 Figure 3.6 NR x NT MIMO system [12] 23 Figure 3.7 Modal decomposition when CSI is available at the transmitter side [12] 26 Figure 3.8 The r virtual SISO channels obtained from the modal decomposition of a MIMO channel [12] 27 Figure 3.9 Water-filling power allocation algorithm 29 Figure 3.10 Coexistence of PU and SU scenario 35 Figure 4.1 Co-existance of PU and SU at the same geographical space 42 Figure 4.2 Power allocation by water filling 48 Figure 4.3 Water filling configuration for the centered sub-carriers 52 Figure 4.4 The power profile structure for the adjacent sub-carriers .54 Figure 4.5 Power Allocation for 128 sub-carriers with 10 chosen adjacent subcarriers .55 Figure 4.6 Power Allocation for 128 sub-carriers with nulling the adjacent subcarriers .56 Figure 4.7 Transmission capacity .57 i Figure 4.8 Transmission capacity of the CR user Vs SNR for the 128 sub-carriers 58 Figure 4.9 Power Allocation for 64 sub-carriers with 10 chosen adjacent subcarriers .59 Figure 4.10 Transmission capacity of the CR user Vs SNR for the 64 sub-carriers 60 Figure 4.11 Interference Comparison of G.P Algorithm Vs Bansal's Scheme A .61 ii List of Acronyms 4G Fourth Generation 5G Fifth Generation ALOHA Additive Links On-line Hawaii Area BTS Base Station Transceiver System CR Cognitive Radio CSI Channel State Information DFT Direct Fourier Transform DSP Digital Signal Processors FCC Federal Communications Commission FCC Federal Communications Commission FPGA Field Programmable Gate Arrays GHz Giga-Hertz G-P Geometric Progression GPP General-Purpose Processors ICI Inter-Carrier Interference IF Intermediate Frequency IFT Inverse Fourier Transform ISI Inter-Symbol Interference ISM Industrial, Scientific, and Medical iii Kbps Kilo-bits per second Km Kilo-meters LAN Local Area Network LEO Low-Earth Orbit LTE Long Term Evolution MA Margin Adaptive Mbps Mega-bits per second MC-MR Multi-Channel Multi-Radio MEO Medium-Earth Orbit MIMO Multiple Input Multiple Output MRC Maximum-Ratio Combining MTSO Mobile Telephone Switching Office mW Milli-watts OFDM Orthogonal Frequency for Division Multiplexing OFDMA Orthogonal Frequency Division for Multiple Access PSD Power Spectral Density PSTN Public-Switched Telephone Network PU Primary User QoS Quality Of Service iv RA Rate Adaptive RF Radio Frequency SIMO Single Input Multiple Output SIMO Single-Input And Multi-Output SISO Single Input Single Output SNR Signal to Noise Ratio SU Secondary User TV Television UWB Ultra-Wide Band ZMCSCG Zero-Mean Circular Symmetric Complex Gaussian v Acknowledgements I would like to thank my scientific supervisor, Associate Prof Eng NguyễnVăn Đức, and Phd Nguyễn Tiến Hòa for their kindly support during the course of this thesis I also would like to thank my lovely parents for their unconditional presence and my adorable family, who always support me in my whole life Without their support, I could not have had the opportunity to even start my studies vi Abstract In this thesis the radio resource allocation methods are presented, taking to further analysis the ODFM based Cognitive Radio for wireless communications The classical algorithms for power allocation are deeply studied and a new algorithm applied to the adjacent sub-carriers is proposed in order to develop the CR performance obtaining a good approximation to the expected results The channel capacity is maximized keeping the interference introduced to PU below a certain threshold and furthermore the interference is also taken to be the cost function to minimize keeping the QoS in an acceptable range Cognitive Radio systems are designed to be able to occupy the portion of the unused frequency bands and they also must be aware of the interference caused to or by the possible groups of both adjacent PU’s and SU’s bands The resource allocation is formulated as a pack containing many problems to be modeled for the good or acceptable operating performance Starting from the basic principles, such as power control and multiple access, coverage moving to the optimization techniques for resource allocation, including formulation and analysis [1] Water filling algorithm is proposed to solve the problem of resource allocation as it allocates much amount of power to the sub-channels experiencing relatively high SNR than others Along with the water filling scheme, a different algorithm is proposed to allocate the power for the group of adjacent sub-carriers as they play a significant role in terms of interference to the PU’s bands The performance of all these algorithms is verified using MATLAB simulation making comparison with the other algorithms previously studied by different authors vii Index List of Figures i List of Acronyms iii Acknowledgements vi Abstract vii CHAPTER - Introduction 11 1.1 Thesis outline 12 1.2 Thesis organization 13 1.3 Chapter Conclusion 13 CHAPTER - Cognitive Radio Networks 2.1 Introduction 2.2 Evolution of Wireless Communication Systems 2.3 Software Defined Radio 2.4 Cognitive Radio Networks 2.4.1 Spectrum usage 2.4.2 Cognitive radio concept 2.5 Chapter Conclusion 12 CHAPTER - Resource Allocation Techniques for Wireless Communication Networks 13 3.1 Introduction 13 3.2 Multiple Access Methods 13 3.2.1 OFDM 13 the bandwidth is the main parameter that gives a considerable increase of the capacity values as in our system we use the entire bandwidth Figure 4.7 Transmission capacity For the capacity, here we start by showing the advantage of having the CSI and used for the prediction of the channel We can note form the Figure 4.7 that the better performance is obtained by applying the CSI in our system 57 Figure 4.8 Transmission capacity of the CR user Vs SNR for the 128 sub-carriers The transmission capacity of the CR user versus SNR is plotted in the Figure 4.8for G-P, sub-carrier Nulling Algorithm and the Bansal’s scheme A It can be observed from the simulation results, that after nulling, 12 adjacent sub-carriers will not be used, in order to reduce interference to PU However when the PU can operate with lower QoS level, this leads to waste of frequencies bands for CR transmission So, exploiting the adjacent sub-carriers by using G-P algorithm the considerable increase of channel capacity performance is achieved This result is because of the tradeoff between the interference that is reduced by deactivating the adjacent sub-carriers and the capacity that can be achieved by keeping them active 58 Figure 4.9 Power Allocation for 64 sub-carriers with 10 chosen adjacent subcarriers The Figures 4.9 and Figure 4.10 represent an extension of the analysis in which the new distribution is extended to 64 sub-carriers systems and the power distributions and channel capacity are illustrated respectively 59 Figure 4.10 Transmission capacity of the CR user Vs SNR for the 64 sub-carriers 60 Figure 4.11 Interference Comparison of G.P Algorithm Vs Bansal's Scheme A In Figure 4.11, we present the interference comparing with the system in [23], where the achieved QoS is approximated but the main parameter that differ the two systems is the interference caused by their adjacent sub-carriers to the PU bands To refer that in our system the distribution of the power to the adjacent sub-carriers makes the interference become lower We have compared our scheme also with the power assignment Scheme A of [23] in which it is approximated to a steep ladder configuration Here the power is distributed such that the sub-carriers that are adjacent to the PU bands are given the power which increases in P, what means that if the first sub-carrier is assigned the power P the next should be assigned 2P This increment of power is also done as we move away from the PU band The results in Figure 4.11show that our system has relatively much greater performance if we look at the 61 interference caused by the adjacent sub-carriers which in some points as the highest by an oval, the interference can reach considerable difference as it allocates much more power to the farther sub-carriers relatively to this in Scheme A 4.7 Chapter Conclusion In this section an OFDM based Cognitive Radio system was analyzed as a case study taking into account the conditions of a real wireless system characterized by the real channel bandwidth and channel conditions with the parameters approximated to a real scenario The problem formulation was analyzed in two cases, in the first where the objective was to maximize the total transmission rate of the CR keeping the interference to the PU system below a certain threshold and in the second case where the cost function is changed to the interference caused by the SU system while keeping the performance of the system in a given limit of better QoS We have obtained a solution solved by water filing technique for the centered group of CR user and for the adjacent group of CR sub-carriers the new proposed Geometric Progression Technique was implemented for the power allocation 62 Conclusion and Future work In this thesis the power allocation techniques are studied and a new and efficient algorithm is proposed for the power allocation to the adjacent sub-carriers Many schemes where analyzed The performance of the proposed algorithm was analyzed comparing its total transmission capacity and of the other systems in which the adjacent subcarriers nulling technique is applied and the power distribution follows a different policy In other hand the comparison was also done regarding to the interference caused by the proposed scheme and the scheme A also presented It’s noted that the power allocation seems to be better without any power increase requirement The last analysis is done changing the maximization problem of the classical algorithms, where we have proposed the interference as the cost function The results show that the proposed maximization scenario fits with the expected values In the future works we would like to improve the accuracy of our proposed algorithm 63 Bibliography [1] Zhu Han and K.J Ray Liu, Resource Allocation for Wireless Networks New York: Cambridge University Press, 2008 [2] Simon Haykin, Communication Systems, 4th ed New York: John Wiley & Sons, 2001 [3] Linda K Moonre, Cognitive Radio S Insight United States, 2014 [4] Yan et al Zang, Cognitive Radio New York, 2010 [5] Afif Osseiran, Federico Boccardi, and Volker Braun, "Scenarios for 5G mobile and wireless communications: the vision of the METIS project," vol 52, 2014 [6] graduatedegrees [7] Andrea Goldsmith, Wireless Communications.: Stanford University, 2004 [8] Yang Xiao and Fei Hu, Cognitive Radio Networks.: Taylor & Francis Group, LLC, 2009 [9] FCC, "ET Docket No 03-237 Notice of inquiry and notice of proposed Rulemaking," November 2003 [10] Ian F Akyildiz, Won-Yeon Lee, Mehmet C Vuran, and Shantidev Mohanty, "NeXt generation/dynamic spectrum access/cognitive radio wireless networks: A survey," 2006 [11] Joseph Mitola, COGNITIVE RADIO ARCHITECTURE: The Engineering Foundations of Radio XML New Jersey: John Wiley & Sons, 2006, vol III 64 [12] Soo Yong Cho, Jaekwon Kim, Young Won Yang, and Chung-Gu Kang, MIMOOFDM Wireless Communications with MATLAB.: Wiley, 2010 [13] Erik Dahlman, Stefan Parkval, Johan Skold, and Per Beming, 3G Evolution HSPA and LTE for Mobile Broadband Oxford: Elsevier Linacre House, 2007 [14] gaussianwaves.com [Online] http://www.gaussianwaves.com/2014/08/mimo- diversity-and-spatial-multiplexing/ [15] Mostafa Khoshnevisan and J Nicholas Laneman, "Power Allocation in MultiAntenna Wireless Systems Subject to Simultaneous Power Constraints" [16] Meherali Malik Saleh , "Adaptive Resource Allocation in Multiuser OFDM Systems," 2005 [17] C Y Wong, R S Cheng, and K B Letaief, "Multiuser," vol 17, October 1999 [18] Z Shen, J G Andrew, and B L Evans, "Adaptive Resource Allocation in Multiuser OFDM Systems with proportional fairness" [19] J Jang and K B Lee, "Transmit Power Adaptation for Multiuser OFDM Systems," vol 21 [20] Timo Weiss, Joerg Hillembrand, Albert Krohn, and Friedrich Jondral, "Mutual Interference in OFDM-based Spectrum Pooling Systems" [21] H S Shahraki and K Mohamed-Pour, "Power allocation in multiple-input multipleoutput orthogonal frequency division," 2010 [22] Hamid Shahrokh Shahraki and Kamal Mohamed-Pou, "Efficient Power Loading 65 in MIMO-OFDM Based," 2011 [23] Gaurav Bansal , Md Jahangir Hossain, and K Viray Bhargava, "Optimal and Suboptimal Power Allocation Schemes for OFDM-based Cognitive Radio Systems," IEEE Transaction on Wireless Communications, vol 7, 2008 [24] Hoa Tien Nguyen, Helio Augusto Muzamane, and Van Duc Nguyen, "Geometric Progression Algorithm for Adjacent Band Power Allocation in OFDM based Cognitive Radio," 2016 [25] Alexander M Wyglinski , Mazir Nekovee, and Hou Thomas, Cognitive Radio Communication and Network California: Elsevier, 2010 [26] Na Li, Liwen Zhang, and Bing Li, "A New Energy-Efficient Data Transmission Scheme Based on DSC and Virtual MIMO for Wireless Sensor Network," 2015 66 Appendix A closeall clearall clc bw=1400000; %1.4MHz for LTE n_adj=10; %number of the adjacent subcarried N=128; %Number of sub-carriers p_t= 1e-3; %total power as water level -30 dBm Rayleigh_var = 1e-4; %0.429; %Rayleigh variable in watts Noise=1e-4; L= 1e+2; %channelrealizations CSI=[]; %channel state information CSI bw_a=(bw./N).*n_adj; for k=1:N %Channel Generation Z1 = randn(1,L); Z2 = randn(1,L); CSI(k) = ( Rayleigh_var * ( mean(Z1).^2 + mean(Z2).^2) ).^0.5; end CSI_h=CSI; bw_sub=bw./N; %sub-channel band-width CSI=CSI_h*bw_sub; %channel state information for all subcarriers level=1e-3; %water level N1=[1:N]; % to have the subcarriers one-by-one p=[]; % allocated power initialization %Perfoming water filling strategy for n=1:length(N1) if (CSI_h(n)>0 & CSI_h(n)