Efficient blind symbol rate estimation and data symbol detection algorithms for linearly modulated signals

Blind estimation of unknown channel parameters and data symbol detection represent major open problems in non-cooperative communication systems such as automatic modulation classification (AMC). This thesis focuses on estimating the symbol rate and detecting the data symbols. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. The thesis consists of two parts: a symbol rate estimator and a symbol detector. First, the symbol rate is estimated using the EM algorithm. In the EM algorithm, it is difficult to obtain the closed form of the log-likelihood function and the density function. Therefore, both functions are approximated by using the Particle Filter (PF) technique. In addition, the symbol rate estimator based on cyclic correlation is proposed as an initialization estimator since the EM algorithm requires initial estimates. To take advantage of the cyclostationary property of the received signal, there is a requirement that the sampling period should be at least four times less than the symbol period on the receiver side. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage inter-symbol interference, which is caused by over- sampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coe±cients, and the Mixture Kalman Filter (MKF) algorithm is used to marginalize out the fading channel coe±cients. At the end, two resampling schemes are adopted

Joint Blind Symbol Rate Estimation and Data Symbol Detection for Linearly Modulated Signals

This paper focuses on non-data aided estimation of the symbol rate and detecting the data symbols in linearlymodulated signals. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. First, the symbol rate is estimated using the Expectation Maximization (EM) algorithm. However, within the framework of EM algorithm, it is difficult to obtain a closed form for the loglikelihood function and the density function. Therefore, these two functions are approximated in this paper by using the Particle Filter (PF) technique. In addition, a symbol rate estimator that exploits the cyclic correlation information is proposed as an initialization estimator for the EM algorithm. Second, the blind data symbol detector based on the PF algorithm is designed.Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage the intersymbol interference caused by oversampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coefficients, and the Mixture Kalman Filter (MKF) algorithm is used to marginalize out the fading channel coefficients

MIMO-aided near-capacity turbo transceivers: taxonomy and performance versus complexity

In this treatise, we firstly review the associated Multiple-Input Multiple-Output (MIMO) system theory and review the family of hard-decision and soft-decision based detection algorithms in the context of Spatial Division Multiplexing (SDM) systems. Our discussions culminate in the introduction of a range of powerful novel MIMO detectors, such as for example Markov Chain assisted Minimum Bit-Error Rate (MC-MBER) detectors, which are capable of reliably operating in the challenging high-importance rank-deficient scenarios, where there are more transmitters than receivers and hence the resultant channel-matrix becomes non-invertible. As a result, conventional detectors would exhibit a high residual error floor. We then invoke the Soft-Input Soft-Output (SISO) MIMO detectors for creating turbo-detected two- or three-stage concatenated SDM schemes and investigate their attainable performance in the light of their computational complexity. Finally, we introduce the powerful design tools of EXtrinsic Information Transfer (EXIT)-charts and characterize the achievable performance of the diverse near- capacity SISO detectors with the aid of EXIT charts

Efficient blind symbol rate estimation and data symbol detection algorithms for linearly modulated signals

Blind estimation of unknown channel parameters and data symbol detection represent major open problems in non-cooperative communication systems such as automatic modulation classification (AMC). This thesis focuses on estimating the symbol rate and detecting the data symbols. A blind oversampling-based signal detector under the circumstance of unknown symbol period is proposed. The thesis consists of two parts: a symbol rate estimator and a symbol detector. First, the symbol rate is estimated using the EM algorithm. In the EM algorithm, it is difficult to obtain the closed form of the log-likelihood function and the density function. Therefore, both functions are approximated by using the Particle Filter (PF) technique. In addition, the symbol rate estimator based on cyclic correlation is proposed as an initialization estimator since the EM algorithm requires initial estimates. To take advantage of the cyclostationary property of the received signal, there is a requirement that the sampling period should be at least four times less than the symbol period on the receiver side. Second, the blind data symbol detector based on the PF algorithm is designed. Since the signal is oversampled at the receiver side, a delayed multi-sampling PF detector is proposed to manage inter-symbol interference, which is caused by over- sampling, and to improve the demodulation performance of the data symbols. In the PF algorithm, the hybrid importance function is used to generate both data samples and channel model coe±cients, and the Mixture Kalman Filter (MKF) algorithm is used to marginalize out the fading channel coe±cients. At the end, two resampling schemes are adopted

A Linear Multi-User Detector for STBC MC-CDMA Systems based on the Adaptive Implementation of the Minimum-Conditional Bit-Error-Rate Criterion and on Genetic Algorithm-assisted MMSE Channel Estimation

The implementation of efficient baseband receivers characterized by affordable computational load is a crucial point in the development of transmission systems exploiting diversity in different domains. In this paper, we are proposing a linear multi-user detector for MIMO MC-CDMA systems with Alamouti’s Space-Time Block Coding, inspired by the concept of Minimum Conditional Bit-Error-Rate (MCBER) and relying on Genetic-Algorithm (GA)-assisted MMSE channel estimation. The MCBER combiner has been implemented in adaptive way by using Least-Mean-Square (LMS) optimization. Firstly, we shall analyze the proposed adaptive MCBER MUD receiver with ideal knowledge of Channel Status Information (CSI). Afterwards, we shall consider the complete receiver structure, encompassing also the non-ideal GA-assisted channel estimation. Simulation results evidenced that the proposed MCBER receiver always outperforms state-of-the-art receiver schemes based on EGC and MMSE criterion exploiting the same degree of channel knowledge (i.e. ideal or estimated CSI)

Iterative receiver design for MIMO-OFDM systems via Sequential Monte Carlo (SMC) techniques

Master'sMASTER OF ENGINEERIN

Reduced Complexity Sequential Monte Carlo Algorithms for Blind Receivers

Monte Carlo algorithms can be used to estimate the state of a system given relative observations. In this dissertation, these algorithms are applied to physical layer communications system models to estimate channel state information, to obtain soft information about transmitted symbols or multiple access interference, or to obtain estimates of all of these by joint estimation. Initially, we develop and analyze a multiple access technique utilizing mutually orthogonal complementary sets (MOCS) of sequences. These codes deliberately introduce inter-chip interference, which is naturally eliminated during processing at the receiver. However, channel impairments can destroy their orthogonality properties and additional processing becomes necessary. We utilize Monte Carlo algorithms to perform joint channel and symbol estimation for systems utilizing MOCS sequences as spreading codes. We apply Rao-Blackwellization to reduce the required number of particles. However, dense signaling constellations, multiuser environments, and the interchannel interference introduced by the spreading codes all increase the dimensionality of the symbol state space significantly. A full maximum likelihood solution is computationally expensive and generally not practical. However, obtaining the optimum solution is critical, and looking at only a part of the symbol space is generally not a good solution. We have sought algorithms that would guarantee that the correct transmitted symbol is considered, while only sampling a portion of the full symbol space. The performance of the proposed method is comparable to the Maximum Likelihood (ML) algorithm. While the computational complexity of ML increases exponentially with the dimensionality of the problem, the complexity of our approach increases only quadratically. Markovian structures such as the one imposed by MOCS spreading sequences can be seen in other physical layer structures as well. We have applied this partitioning approach with some modification to blind equalization of frequency selective fading channel and to multiple-input multiple output receivers that track channel changes. Additionally, we develop a method that obtains a metric for quantifying the convergence rate of Monte Carlo algorithms. Our approach yields an eigenvalue based method that is useful in identifying sources of slow convergence and estimation inaccuracy.Ph.D.Committee Chair: Douglas B. Williams; Committee Member: Brani Vidakovic; Committee Member: G. Tong zhou; Committee Member: Gordon Stuber; Committee Member: James H. McClella