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Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multi- carrier modulation scheme, which has been adopted for several wireless stan- dards. Systems employing this scheme at the physical layer are sensitive to frequency offsets and that causes Inter Carrier Interference (ICI) and degra- dation in overall system performance of OFDM systems. In this thesis work, an investigation on impairments of OFDM systems will be carried out. Anal- ysis of previous schemes for cancellation of the ICI will be done and a scheme for estimating and compensating the frequency offset based on Unscented Ka- man Filter (UKF) and Extended Kaman Filter (EKF) will be implemented. Analysis on how the UKF improves the Signal to Noise Ratio (SNR); and how well it tracks the frequency offset estimation under Additive White Gaussian Noise (AWGN) channel and flat fading Rayleigh channel will be carried on.

The cyclic prefix (CP) is usually adopted in single carrier frequency domain equalization (SC-FDE) system to avoid inter-block interference (IBI) and inter-symbol interference (ISI) in multipath fading channels. In addition, the use of CP also converts the linear convolution between the transmitted signal and the channel into a circular convolution, leading to significant decrease in receiver equalization. However, the use of CP reduces the bandwidth efficiency. Therefore the SC-FDE system without CP is investigated in this thesis. A number of schemes have been proposed to improve the performance of systems without CP, where both IBI and ICI are dramatically increased. Unfortunately, most of the existing schemes have extremely high computational complexity and are difficult to realize. In this thesis, a novel low-complexity CP reconstruction (CPR) scheme is proposed for interference cancellation, where the successive interference cancellation (SIC) and QR decomposition (QRD) are adopted. In addition, the system performance is further improved by using the fact that the interferences of different symbols are not the same. Simulation experiments are conducted to verify the system performance of the proposed scheme. It is shown that the proposed scheme can effectively reduce the interference, while maintain a low computational complexity.

This dissertation considers several coding techniques based on Reed-Solomon (RS) and low-density parity-check (LDPC) codes. These two prominent families of error-correcting codes have attracted a great amount of interest from both theorists and practitioners and have been applied in many communication scenarios. In particular, data storage systems have greatly benefited from these codes in improving the reliability of the storage media. The first part of this dissertation presents a unified framework based on rate-distortion (RD) theory to analyze and optimize multiple decoding trials of RS codes. Finding the best set of candidate decoding patterns is shown to be equivalent to a covering problem which can be solved asymptotically by RD theory. The proposed approach helps understand the asymptotic performance-versus-complexity trade-off of these multiple-attempt decoding algorithms and can be applied to a wide range of decoders and error models. In the second part, we consider spatially-coupled (SC) codes, or terminated LDPC convolutional codes, over intersymbol-interference (ISI) channels under joint iterative decoding. We empirically observe the phenomenon of threshold saturation whereby the belief-propagation (BP) threshold of the SC ensemble is improved to the maximum a posteriori (MAP) threshold of the underlying ensemble. More specifically, we derive a generalized extrinsic information transfer (GEXIT) curve for the joint decoder that naturally obeys the area theorem and estimate the MAP and BP thresholds. We also conjecture that SC codes due to threshold saturation can universally approach the symmetric information rate of ISI channels. In the third part, a similar analysis is used to analyze the MAP thresholds of LDPC codes for several multiuser systems, namely a noisy Slepian-Wolf problem and a multiple access channel with erasures. We provide rigorous analysis and derive upper bounds on the MAP thresholds which are shown to be tight in some cases. This analysis is a first step towards proving threshold saturation for these systems which would imply SC codes with joint BP decoding can universally approach the entire capacity region of the corresponding systems.

A non-iterative receiver is proposed to achieve near capacity performance on intersymbol interference (ISI) channels. There are two main ingredients in the proposed design. i) The use of a novel BCJR-DFE equalizer which produces optimal soft estimates of the inputs to the ISI channel given all the observations from the channel and L past symbols exactly, where L is the memory of the ISI channel. ii) The use of an encoder structure that ensures that L past symbols can be used in the DFE in an error free manner through the use of a capacity achieving code for a memoryless channel. Computational complexity of the proposed receiver structure is less than that of one iteration of the turbo receiver. We also provide the proof showing that the proposed receiver achieves the i.i.d. capacity of any constrained input ISI channel. This DFE-based receiver has several advantages over an iterative (turbo) receiver, such as low complexity, the fact that codes that are optimized for memoryless channels can be used with channels with memory, and finally that the channel does not need to be known at the transmitter. The proposed coding scheme is universal in the sense that a single code of rate r; optimized for a memoryless channel, provides small error probability uniformly across all AWGN-ISI channels of i.i.d. capacity less than r: This general principle of a proposed non-iterative receiver also applies to other signal processing functions, such as timing recovery, pattern-dependent noise whiten ing, joint demodulation and decoding etc. This makes the proposed encoder and receiver structure a viable alternative to iterative signal processing. The results show significant complexity reduction and performance gain for the case of timing recovery and patter-dependent noise whitening for magnetic recording channels.

Abstract This research attempted to understand the experiences of a cohort of informal and formal science educators and informal science institution (ISI) community representatives during and after completion of a pilot graduate certificate program. Informal science educators (ISEs) find limited opportunities for professional development and support which influence their contributions to America's science literacy and school science education. This emergent design nested case study described how an innovative program provided professional development and enabled growth in participants' abilities to contribute to science literacy. Data were collected through interviews, participant observations, and class artifacts. The program by design and constituency was the overarching entity that accounted for members' experiences. Three principal aspects of the ISI certificate program and cohort which influenced perceptions and reported positive outcomes were (1) the cohort's composition and their collaborative activities which established a vigorous community of practice and fostered community building, mentoring, and networking, (2) long term program design and implementation which promoted experiential learning in a generative classroom, and (3) ability of some members who were able to be independent or autonomous learners to embrace science education reform strategies for greater self-efficacy and career advancement. This research extends the limited literature base for professional development of informal science educators and may benefit informal science institutions, informal and formal science educators, science education reform efforts, and public education and science-technology-society understanding. The study may raise awareness of the need to establish more professional development opportunities for ISEs and to fund professional development. Further, recognizing and appreciating informal science educators as a diverse committed community of professionals who positively influence science education for everyone is essential.

Magnetic recording systems have reached a point where the grain size can no longer be reduced due to energy stability constraints. As a new magnetic recording paradigm, two-dimensional magnetic recording (TDMR) relies on sophisticated signal processing and coding algorithms, a much less expensive alternative to radically altering the media or the read/write head as required for the other technologies. Due to 1) the significant reduction of grains per bit, and 2) the aggressive shingled writing, TDMR faces several formidable challenges. Firstly, severe interference is introduced in both down-track and cross-track directions due to the read/write head dimensions. Secondly, reduction in the number of grains per bit results in variations of bit boundaries which consequently lead to data-dependent jitter noise. Moreover, the bit to grain ratio reduction will cause some bits not to be properly magnetized or to be overwritten which introduces write errors to the system. The nature of write and read processes in TDMR necessitates that the information storage be viewed as a two-dimensional (2D) system. The challenges in TDMR signal processing are 1) an accurate read channel model, 2) mitigating the effect of inter-track interference (ITI) and inter-symbol interference (ISI) by using an equalizer, 3) developing 2D modulation/error correcting codes matching the TDMR channel model, 4) design of truly 2D detectors, and 5) computing the lower bounds on capacity of TDMR channel. The work is concerned with several objectives in regard to the challenges in TDMR systems. 1. TDMR Channel Modeling: As one of the challenges of the project, the 2D Microcell model is introduced as a read channel model for TDMR. This model captures the data-dependent properties of the media noise and it is well suited in regard to detector design. In line with what has been already done in TDMR channel models, improvements can be made to tune the 2D Microcell model for different bit to grain densities. Furthermore, the 2D Microcell model can be modified to take into account dependency between adjacent microtrack borders positions. This assumption will lead to more accurate model in term of closeness to the Voronoi model. 2. Detector Design: The need for 2D detection is not unique to TDMR systems. However, it is still largely an open problem to develop detectors that are close to optimal maximum likelihood (ML) detection for the 2D case. As one of the important blocks of the TDMR system, the generalized belief propagation (GBP) detector is developed and introduced as a near ML detector. Furthermore, this detector is tuned to improve the performance for the TDMR channel model. 3. Channel Capacity Estimation: Two dimensional magnetic recording (TDMR) is a new paradigm in data storage which envisions densities up to 10 Tb/in² as a result of drastically reducing bit to grain ratio. In order to reach this goal aggressive write (shingled writing) and read process are used in TDMR. Kavcic et al. proposed a simple magnetic grain model called the granular tiling model which captures the essence of read/write process in TDMR. Capacity bounds for this model indicate that 0.6 user bit per grain densities are possible, however, previous attempt to reach capacities are not close to the channel capacity. We provide a truly two-dimensional detection scheme for the granular tiling model based on generalized belief propagation (GBP). Factor graph interpretation of the detection problem is provided and formulated in this section. Then, GBP is employed to compute marginal a posteriori probabilities for the constructed factor graph. Simulation results show huge improvements in detection. A lower bound on the mutual information rate (MIR) is also derived for this model based on GBP detector. Moreover, for the Voronoi channel model, the MIR is estimated for the case of constrained and unconstrained input. 4. Modulation Codes: Constrained codes also known as modulation codes are a key component in the digital magnetic recording systems. The constrained code forbids particular input data patterns which lead to some of the dominant error events or higher media noise. The goal of the dissertation in regard to modulation codes is to construct a 2D modulation code for the TDMR channel which improves the overall performance of the TDMR system. Furthermore, we implement an algorithm to estimate the capacity of the 2D modulation codes based on generalized belief propagation (GBP) algorithm. The capacity is also calculated in presence of white and colored noise which is the case for TDMR channel. 5. Joint Detection and Decoding Schemes: In data recording systems, a concatenated approach toward the constrained code and error-correcting code (ECC) is typically used and the decoding is done independently. We show the improvement in combining the decoding of the constrained code and the ECC using GBP algorithm. We consider the performance of a combined modulation constraints and the ECC on a binary-input additive white Gaussian noise (AWGN) channel (BIAWGNC) and also over one-dimensional (1D) and 2D ISI channels. We will show that combining the detection, demodulation and decoding results in a superior performance compared to concatenated schemes.

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This thesis describing basic concepts and parts of OFDM modulator, showing their implementation in Matlab. It’s focused on how to use USRP with Matlab to create fully functional OFDM modulator.

This thesis describing basic concepts and parts of OFDM modulator, showing their implementation in Matlaba. It's also focusing on how to use USRP with Matlab and creating functional OFDM modulator. Main efford is forwarded to practical impelementation of OFDM using available hardware.