Chaotic Communications in MIMO Systems

Multipath fading is one of the primary factors for degrading the performance in a wireless network. Information theoretic and past research suggest the use various diversity techniques to combat fading in wireless networks. Antenna diversity, a form of diversity technique, when incorporated in a wireless transceiver increases the system capacity and is one of the effective methods to combat fading in wireless systems. Also, recent research by Laneman et.al., Sendonaris et.al. suggests that cooperation among users in a wireless networks is an effective approach for a better signal reception in multipath fading environments. The diversity gains obtained by cooperation among the users of a wireless network is termed as cooperative diversity . Although, prior research in cooperative diversity considers users equipped with single antenna, in practical scenarios users may be able to accommodate multiple antennas due to the recent advanced research in semiconductor industry. Hence, the primary purpose of this thesis is to design, simulate and analyze an end-end performance of multi-antenna wireless systems employing cooperative multi antenna relay nodes so as to exploit the cooperative diversity and antenna diversity simultaneously in a wireless networks. Three main contributions to the area of cooperative multiple-input multiple-output (MIMO) wireless systems is presented in this thesis. First, we perform information theoretic analysis to study the impact of antenna arrays on cooperative wireless networks and propose the best possible distribution of antenna arrays among the three terminals of a simple three terminal cooperative relay network. Second, we design, simulate, and analyze a cooperative multiple-input multiple-output (MIMO) wireless systems employing orthogonal space-time block codes as proposed by Alamouti in 1998 with a decode-and-forward (DF) relay terminal. We implement a maximal ratio combining receiver that provides almost twice the diversity gain with respect to point-point multiple input multiple output link. Finally, we implement a practical receiver for cooperative reception using multiple antennas at all nodes based on Bell-Labs Layered Space Time architecture (BLAST). We incorporate a practical adaptive decode-and-forward (DF) relaying technique for reliable signal retransmission for both Alamouti space-time coding and the BLAST schemes. Results presented in terms of bit error rates and throughput show that remarkable performance gains are achievable by combining the concepts drawn from space-time coding, cooperative relaying and array processing.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

Everyone in the world always wants to use a better wireless network which provide high capacity and high data rates. High data rates and capacity can be produce by Multiple Input Multiple Output(MIMO) systems. This paper mainly focused on MIMO systems because the MIMO systems have multiple number of antennas in transmitting and receiving end which can produce more data rates and capacity compared to SISO, SIMO, MISO systems. The multiple antennas allow MIMO systems to produce multiplexing gain and diversity gain. The capacity of MIMO systems will be increased by increased the number of antennas in transmitter and receiver end.

In recent years, a large amount of work on chaos-based cryptosystems has been published (Kocarev (2001); Millerioux et al. (2008)). A general methodology for designing chaotic and hyperchaotic cryptosystems has been developed using the control systems theory (Grassi et al. (1999); Liao et al. (1999); Yang et al. (1997a;b)). The chaotic communication system is closely related to the concept of chaos synchronization. An overview of chaotic secure communication systems can be found in (Yang (2004)). He classified the continuous-time chaotic secure communication systems into four generations. In the third generation, the combination of the classical cryptographic technique and chaotic synchronization is used to enhance the degree of security. Specifically, Yang et al. proposed a new chaos-based secure communication scheme in an attempt to thwart the attacks (Yang et al. (1997a;b)). They have combined both conventional cryptographic method and synchronization of chaotic systems. Their cryptographic method consists of an encryption function (the multi-shift cipher), a decryption function (the inverse of the encryption function), a chaotic encrypter that generates the key signal for the encryption function, and a decrypter that estimates the key signal. The approach has a limitation since the cryptosystem design may fail if different chaotic circuits are utilized. So far, this generation has the highest security in all the chaotic communication systems had been proposed and has not yet broken. From the control theoretic perspective, the transmitter and the receiver in the chaotic communication system can be considered as the nonlinear plant and its observer, respectively. Grassi et al. proposed a nonlinear-observer-based decrypter to reconstruct the state of the encrypter (Grassi et al. (1999); Liao et al. (1999)). They extended the Chua’s oscillator to the observer-based decrypter. The cryptosystem does not require initial conditions of the encrypter and the decrypter belonging to the same basin of attraction. If we can design a decrypter without the knowledge of the parameters of the encrypter, the chaos-based secure communication systems are not secure, because the parameters of the encrypter is selected as static secret keys in the cryptosystem. Parameter identification and adaptive synchronization methods may be effective for intruders in building reconstruction mechanisms, even when a synchronizing system is not available. Therefore, it is important for secure issues to investigate whether adaptive identifiers without the system information of encrypter can be constructed or not. We have recently designed an observer-based chaotic communication system combining the cryptosystems proposed by Grassi et al. (Grassi et al. (1999)) and by Liao et al. (Liao et al. (1999)) that allows us to assign the relative degree and the zeros of its encrypter system (Matsuo et al. (2004)). Specifically, we constructed three cryptosystems based on a Chua’s circuit by assigning its relative degree and zeros. The cryptosystem consists of 20

Multiple-Input Multiple-Output (MIMO) systems that realize high-speed data transmission with multiple antennas at both transmitter and receiver are drawing much attention. In line-of sight (LOS) environments, the performance of MIMO systems depends largely on the difference of the phase difference of direct paths from transmit antennas to each receive antenna. When the phase difference of direct paths are close to each other, the spatial division multiplexing (SDM) channels are not orthogonal to each other so signal detection becomes difficult. In this paper, we propose a MIMO system with relative phase difference time-shift modulation (RPDTM) in Rician fading environments. The proposed scheme transmits independent signals from each antenna at each time slot where the relative phase difference between signal constellations used by transmit antennas varies in a pre-determined pattern. This transmission virtually changes the phase difference of direct paths from transmit antennas to each receive antenna without lowering data rate and without knowledge of the channels. In addition, forward error correction coding (ECC) is applied to exploit the time slots where the receiver can detect the signals easily to improve the detection performance. If there are time slots where the receiver can separate the received signal, the receiver can decode the data by using the time slots and the correlation between data. From the results of computer simulation, we show that MIMO system with RPDTM can achieve the better bit error rate (BER) than the conventional MIMO system. We also show that the MIMO system with RPDTM is effective by about Rician factor K=10dB.

For higher order modulation, the reliabilities of the bits Gray-mapped onto the modulated symbol vary from the most significant bits (MSBs) to the least significant bits (LSBs). This paper introduces one constellation rearrangement method for SISO (Single-Input Single Output) system. For MIMO (Multiple-Input Multiple Output) system, the constellation rearrangement works also for lower order modulation by the construction of spatial signal. One constellation remapping method for MIMO system is also introduced in the paper. The simulation results show the performance gain for the SISO constellation remapping method.

Capacity bounds are calculated for the differential chaos shift keying (DCSK) system with non-coherent detectors, including differentially coherent detector and energy detector, over additive white Gaussian noise (AWGN) and Rayleigh fading channels. Through theoretical analysis and numerical simulations, it is found that the capacity characteristics of the non-coherent DCSK system are significantly different from those of the coherent case. It is shown that there are different capacity bounds corresponding to different spreading factor values. Optimal code rates are found, which minimise the required bit signal-to-noise ratio for a reliable communication system, and they are increasing with the increase of the spreading factor value over an AWGN channel while decreasing over a Rayleigh fading channel. As compared with the AWGN channel, the performance over a Rayleigh fading channel is much more sensitive to the code rate. The new results are useful as benchmark for designing capacity-approaching codes for the non-coherent DCSK system.

In recent times, Chaos communication plays a significant role in addressing the security challenges that are more common in the regular communication systems. By exploiting the chaotic waveform properties, variety of chaos-based communication systems are proposed. Considering the aforementioned facets of communication, this research objective at merging the space-time block code (STBC) and orthogonal differential chaos shift keying (ODCSK) methods to improve the security aspects of multiple-input multiple-output (MIMO) based wireless system. Chaos signal is created by using the method called logistic mapping to achieve better secrecy. STBC method is used during the modulation stage for arranging the chaos signal. This system with different types of STBCs is aimed at analysing the differential chaos shift keying (DCSK) and orthogonal DCSK (ODCSK) modulation in AWGN channel. MATLAB software is used to simulate the Bit Error Rate [BER]. Initial simulation proves that the combination of orthogonal DCSK and Alamouti STBC with MIMO system gives better results for AWGN channel when compared to other combinations.KeywordsDifferential Chaos shift keyingSpace-time block codeMultiple-input multiple-output

Chaotic optical communication at high bit rates has attracted great attention because of its potential application in secure communications. In a chaotic optical communication system, a message can participate in the dynamics of the nonlinear system and further affect the characteristics of the chaotic carrier waveform. Message encoding can also change the synchronization quality if an encoding scheme breaks the symmetry between a transmitter and a receiver. In this presentation, we experimentally investigate the influence of message encoding on the characteristics of chaotic dynamics, chaos synchronization, and chaotic communication, using three encoding schemes, namely chaos shift keying, chaos masking, and chaos modulation, with the setup of a chaotic optical communication system with optoelectronic feedback. The chaos shift keying and chaos modulation schemes are found to be able to increase the complexity of the chaotic carrier waveform due to the random nature of the encoded message. Meanwhile, the chaos shift keying and chaos masking schemes are found to deteriorate the synchronization quality when the amplitude of the message is increased. Consequently, only the chaos modulation scheme has the desired feature in increasing the complexity of the chaotic carrier waveform while maintaining the quality of chaos synchronization, which is an important feature in secure communication.

Design of a mixed fuzzy controller for multiple-input multiple-output systems

In this paper, a new differential coherent chaos-based communication system is proposed and named Reference Flipped-DCSK (RF-DCSK). By utilizing the low correlation value between a chaotic segment and its flipped version, each transmitted signal will be the sum of the information bearing signal and the mirrored version of the reference signal. This enhances the bandwidth efficiency of DCSK by doubling bits rate. Additionally, noise performance is improved at typical values of the spreading factor. According to Gaussian approximation method, theoretical expression for Bit-Error-Rate (BER) is derived. The proposed scheme is simulated and compared with Differential Chaos Shift Keying (DCSK), Correlation Delay Shift Key (CDSK) and High Efficiency -Differential Chaos Shift Keying (HE-DCSK) schemes in Additive White Gaussian Noise (AWGN) environment. Effect of spreading factor is studied. Results show that the proposed scheme clearly outperforms other systems.

In this paper, we propose a multi-user orthogonal frequency-division multiplexing (OFDM) transmission scheme with partial channel reusing between users, suitable for multiple-input multiple-output (MIMO) systems with not-so-large antenna arrays. The spectrum efficiency is improved at the price of adding multiuser interference (MUI) which can be overcome by the multiplexing gain of MIMO. In addition, we propose to employ the spread spectrum OFDM (SS-OFDM) modulation with the Walsh-Hadamard (WH) codebook for such a multi-user system to further mitigate the MUI. The bit error rate (BER) performance of the proposed scheme in the additive white Gaussian noise (AWGN) channels is derived analytically and then verified through Monte-Carlo simulations. Moreover, the proposed SS-OFDM system with partial bandwidth reuse is compared with the OFDM-based solution in a moderate-scale MIMO system with equal gain combining (EGC) precoding. It is shown that the proposed SS-OFDM MIMO system outperforms the classical OFDM approach in frequency-selective fading channels when the number of transmit antennas remains moderate.

This correspondence analyzes the performance of concatenated coding systems and modulation schemes operating over the additive white Gaussian noise (AWGN) channel by examining the loss of capacity resulting from each of the processing steps. The techniques described in this correspondence allow the separate evaluation of codes and decoders and thus the identification of where loss of capacity occurs. Knowledge of this capacity loss is very useful for the overall design of a communications system, e.g., for evaluating the benefits of inner decoders that produce information beyond the maximum-likelihood (ML) estimate. The first two sections of this correspondence provide a general technique for calculating the composite capacity of an orthogonal or a bi-orthogonal code and the AWGN channel in isolation. The later sections examine the composite capacities of an orthogonal or a bi-orthogonal code, the AWGN channel, and various inner decoders including the decoder estimating the bit-by-bit probability of a one, as is used in turbo codes. The calculations in these examples show that the ML decoder introduces a large loss in capacity. Much of this capacity loss can be regained by using only slightly more complex inner decoders, e.g., a detector for M-ary frequency-shift keying (MFSK) that puts out the two most likely frequencies and the probability the ML estimate is correct produces significantly less degradation than one that puts out only the most likely frequency.

In recent years, multiple-input multiple-output (MIMO) systems, which use several transmit and receive antennas, have attracted much attention for high performance radio systems. In MIMO systems, the channel estimation is important to distinguish transmit signals from multiple transmit antennas. While the space-alternating generalized expectation-maximization (SAGE) algorithm is known to offer good channel estimation and data detection. We proposed earlier the minimum mean square error (MMSE)-based SAGE algorithm for MIMO systems where the MMSE estimation is used for channel estimation. We showed that the proposed MMSE-based SAGE algorithm can achieve the better bit error rate (BER) than the maximum likelihood (ML) detection with training symbols. The MMSE channel estimation needs the knowledge of the maximum Doppler frequency F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</sub> for deriving the covariance matrix of the channel and the variance sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of additive white Gaussian noise (AWGN). Additionally, the computation of the MMSE channel estimation requires O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where L is the transmitted frame length. Thus, its computational complexity is high. In this paper, we propose a zero-forcing (ZF)-based SAGE algorithm for channel estimation and data detection in MIMO systems that does not need the knowledge of Fd and sigma <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Since the computation of the proposed ZF-based SAGE algorithm requires O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) operations where N is the number of transmit antennas, its computational complexity is low. We show that the proposed ZF-based tracking SAGE algorithm with less computational complexity can achieve almost the same BER as that of the MMSE-based tracking SAGE algorithm

We propose and experimentally demonstrate a turbulence-resilient free space chaotic optical communication system based on vector optical field manipulation. In this scheme, the utilization of vector beam multiplexing technology not only enhances transmission fidelity in the turbulence link but also increases the transmission capacity of the free-space chaotic communication system. A 22 Gbps non-return-zero on-off keying spatial multiplexed free-space chaotic optical communication that is resistant to turbulence is successfully demonstrated. We investigate and compare the effect of using different optical field modes on the chaotic synchronization quality and communication performance under different turbulence strengths, which includes the scalar Gaussian modes with X and Y polarizations, and V-point 1st-order cylindrical vector modes with azimuthal and radial types. The experimental results reveal that employing vector mode leads to a 10% ∼ 13% improvement in the probability of high-quality synchronization and an 8% ∼ 12% reduction in the communication interruption probability compared to the Gaussian mode. Passive turbulence-resilient technique based on vector optical field manipulation is expected to provide new solutions for developing future FSO chaotic communication systems with large capacity and high transmission reliability.