Interference Mitigation Using Cyclic Autocorrelation and Multi-Objective Optimization

Radio frequency interference on space-to-ground communications links can degrade performance and disrupt the transfer of critical data. These interference events become increasingly likely as more users enter the spectrum, due in part to shared spectrum allocations and scheduling conflicts. If this interference could be detected and mitigated by an automated system, then link performance and reliability in these scenarios could be improved. This report describes the implementation and evaluation of an automated interference mitigation system that provides this functionality. The system uses Cyclic Autocorrelation (CAC) signal processing techniques to monitor the spectrum and detect interfering signals, and it applies a multi-objective optimization approach to mitigate interference by changing link parameters to continuously optimize the link. The implementation was evaluated to characterize its signal detection capabilities for various link qualities and to compare its link management performance to Adaptive Coding and Modulation (ACM) and Constant Coding and Modulation (CCM) when in the presence of randomized interference. In the latter evaluation, the interference mitigation system achieved the highest average throughput in each tested scenario. With these results, the proposed solution provides the groundwork for further automated link management capabilities and continued investigation into interference mitigation approaches

Algorithms for wireless communication systems using SDR platform

Tezin basılısı İstanbul Şehir Üniversitesi Kütüphanesi'ndedir.This thesis presents a detailed study on software based channel emulators and a set of algorithms pertaining to the soft emulator. With the fact that several wireless communications technologies were released in the last decades, there are a lot of challenging issues emerging due to the need for faster and more reliable technologies. From these challenging issues, we have chosen to focus our research on two outstanding challenges: real-time software channel emulator and automatic modulation classification. Recently, there has been an increase in the demand for a reliable and low-cost channel emulator to study the effects of real wireless channels. Hence, in the first part of the thesis, wediscussanimplementationofareal-timesoftwarechannelemulator. Thereal-time fading channel emulator was implemented by using a software defined radio platform. In order to verify the model, the frequency spectrum specifications of the channel generated was checked with a double tone transmitter. Then as a second step of verification, bit error rate (BER) of a real-time Orthogonal Frequency Division Multiplexing system using the Universal Software Radio Peripheral (USRP) and LABVIEW software was compared with the BER floor calculated from the theoretical equations. It has been shown that the developed channel emulator can indeed emulate a fading wireless channel. In the second part of the thesis we focused on covering an issue related to blind estimation or classification of a parameter in wireless communications at the receiver. This problem appears in cognitive radios and some defense applications where the receivers needs to know the type of the modulation of an incoming signal. The efficient automatic modulation classification scheme proposed in this study can be utilized for a group of digitally modulated signals such as QPSK, 16-PSK, 64-PSK, 4-QAM, 16-QAM, and 64QAM. We performed the classification in two stages: first we classified the modulation between QAM and PSK signaling, and then we determined the M-ary order of the modulation by developing Kernel Density Estimation and analyzing the probability density distribution for the real and imaginary parts of the modulated signals. Simulations were carried out to evaluate the performance of the proposed scheme for flat channels. Thus, in this thesis first of all we were able to develop a software based channel emulator. The developed channel emulator can be a very useful tool for other researchers in testing their real-time systems on a verified Doppler channel. Moreover, the emulator can find other applications from education to wireless device developments due to its flexibility. On the other hand, with the automatic modulation classification, the unknown modulation of an incoming signal can be determined. Hence, the two issues can be combined to find applications in cognitive radio developments.Abstract iii Öz v Acknowledgments viii List of Figures xi Abbreviations xiii 1 Introduction and Literature Review 1 1.1 Channel Emulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Automatic Modulation Classification . . . . . . . . . . . . . . . . . . . . . 4 1.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Real Time Fading Channel Emulator using SDR 8 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Implementation of fading channels . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 Implementation of Multipath Doppler Channel . . . . . . . . . . . 13 2.2.2 Specifications of the OFDM system used in verification . . . . . . 14 2.3 Theoretical BER curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4.1 First verification phase . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.4.2 Second verification phase . . . . . . . . . . . . . . . . . . . . . . . 19 2.4.3 Multipath channel simulation results . . . . . . . . . . . . . . . . . 21 2.4.4 Sources of error and mismatch . . . . . . . . . . . . . . . . . . . . 22 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3 Automatic Modulation Classification based on Kernel Density Estimation 25 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.2 Signal model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.3 KDE for the Modulation estimation . . . . . . . . . . . . . . . . . 28 3.2.4 Filtering to improve modulation estimation . . . . . . . . . . . . . 29 3.2.5 AMC proposed flow diagram . . . . . . . . . . . . . . . . . . . . . 31 3.3 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Choosing parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.2 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.3 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 Conclusion and Future Work 40 4.1 Channel emulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2 Automatic Modulation Classification . . . . . . . . . . . . . . . . . . . . . 41 4.3 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 A Proof for equation 2.4 used to calculate the BER for a given fading channel with certain fD 43 B LABVIEW diagram used to generate the curves in Figure 2.14 46 Bibliography 4