Low Complexity Blind Equalization for OFDM Systems with General Constellations

This paper proposes a low-complexity algorithm for blind equalization of data in OFDM-based wireless systems with general constellations. The proposed algorithm is able to recover data even when the channel changes on a symbol-by-symbol basis, making it suitable for fast fading channels. The proposed algorithm does not require any statistical information of the channel and thus does not suffer from latency normally associated with blind methods. We also demonstrate how to reduce the complexity of the algorithm, which becomes especially low at high SNR. Specifically, we show that in the high SNR regime, the number of operations is of the order O(LN), where L is the cyclic prefix length and N is the total number of subcarriers. Simulation results confirm the favorable performance of our algorithm

Space-time processing for wireless mobile communications

Intersymbol interference (ISI) and co-channel interference (CCI) are two major obstacles to high speed data transmission in wireless cellular communications systems. Unlike thermal noise, their effects cannot be removed by increasing the signal power and are time-varying due to the relative motion between the transmitters and receivers. Space-time processing offers a signal processing framework to optimally integrate the spatial and temporal properties of the signal for maximal signal reception and at the same time, mitigate the ISI and CCI impairments. In this thesis, we focus on the development of this emerging technology to combat the undesirable effects of ISI and CCL We first develop a convenient mathematical model to parameterize the space-time multipath channel based on signal path power, directions and times of arrival. Starting from the continuous time-domain, we derive compact expressions of the vector space-time channel model that lead to the notion of block space-time manifold, Under certain identifiability conditions, the noiseless vector-channel outputs will lie on a subspace constructed from a set. of basis belonging to the block space-time manifold. This is an important observation as many high resolution array processing algorithms Can be applied directly to estimate the multi path channel parameters. Next we focus on the development of semi-blind channel identification and equalization algorithms for fast time-varying multi path channels. Specifically. we develop space-time processing algorithms for wireless TDMA networks that use short burst data formats with extremely short training data. sequences. Due to the latter, the estimated channel parameters are extremely unreliable for equalization with conventional adaptive methods. We approach the channel acquisition, tracking and equalization problems jointly, and exploit the richness of the inherent structural relationship between the channel parameters and the data sequence by repeated use of available data through a forward- backward optimization procedure. This enables the fuller exploitation of the available data. Our simulation studies show that significant performance gains are achieved over conventional methods. In the final part of this thesis, we address the problem identifying and equalizing multi path communication channels in the presence of strong CCl. By considering CCI as stochasic processes, we find that temporal diversity can be gained by observing the channel outputs from a tapped delay line. Together with the assertion that the finite alphabet property of the information sequences can offer additional information about the channel parameters and the noise-plus-covariance matrix, we develop a spatial temporal algorithm, iterative reweighting alternating minimization, to estimate the channel parameters and information sequence in a weighted least squares framework. The proposed algorithm is robust as it does not require knowledge of the number of CCI nor their structural information. Simulation studies demonstrate its efficacy over many reported methods

A Summative Comparison of Blind Channel Estimation Techniques for Orthogonal Frequency Division Multiplexing Systems

The OFDM techniquei.e. Orthogonal frequency division multiplexing has become prominent in wireless communication since its instruction in 1950’s due to its feature of combating the multipath fading and other losses. In an OFDM system, a large number of orthogonal, overlapping, narrow band subchannels or subcarriers, transmitted in parallel, divide the available transmission bandwidth. The separation of the subcarriers is theoretically optimal such that there is a very compact spectral utilization. This paper reviewed the possible approaches for blind channel estimation in the light of the improved performance in terms of speed of convergence and complexity. There were various researches which adopted the ways for channel estimation for Blind, Semi Blind and trained channel estimators and detectors. Various ways of channel estimation such as Subspace, iteration based, LMSE or MSE based (using statistical methods), SDR, Maximum likelihood approach, cyclostationarity, Redundancy and Cyclic prefix based. The paper reviewed all the above approaches in order to summarize the outcomes of approaches aimed at optimum performance for channel estimation in OFDM system

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE Transactions on Information Forensics and Securit

A genetic algorithm-assisted semi-adaptive MMSE multi-user detection for MC-CDMA mobile communication systems

In this work, a novel Minimum-Mean Squared-Error (MMSE) multi-user detector is proposed for MC-CDMA transmission systems working over mobile radio channels characterized by time-varying multipath fading. The proposed MUD algorithm is based on a Genetic Algorithm (GA)-assisted per-carrier MMSE criterion. The GA block works in two successive steps: a training-aided step aimed at computing the optimal receiver weights using a very short training sequence, and a decision-directed step aimed at dynamically updating the weights vector during a channel coherence period. Numerical results evidenced BER performances almost coincident with ones yielded by ideal MMSE-MUD based on the perfect knowledge of channel impulse response. The proposed GA-assisted MMSE-MUD clearly outperforms state-of-the-art adaptive MMSE receivers based on deterministic gradient algorithms, especially for high number of transmitting users