The utilization of millimeter wave frequency bands is expected to become prevalent in the following communications systems. However, generating and transmitting communication signals over these frequencies are not as straightforward as in sub-6 GHz frequencies due to complex transceiver structures. As an alternative to conventional transmitter architectures, this paper investigates the implementation of time modulated arrays to effectively modulate and transmit high-quality communication signals at millimeter wave frequencies. By exploiting the array structures and analog beamformers, which are the fundamental components of millimeter wave transmitters, secure and low-cost transmission can be achieved. Though, harmonics of theoretically infinite bandwidth arise as fundamental problem in this approach. Thus, this paper presents a frequency analysis tool for the time-modulated arrays with hardware impairments and shows how controlling the sampling period can reduce the harmonics. Furthermore, the derived results are experimentally verified at 25 GHz with two important remarks. First, the phase error of received signals can be reduced by 32% using the proposed architecture. Second, the harmonics can be significantly suppressed by the correct choice of sampling period for the given hardware.

Reconfigurable holographic surfaces (RHSs) for multi-beam steering applications have been receiving significant attention due to their low fabrication cost and power consumption. RHSs have the advantage of exploiting more antenna elements by configuring only their radiation levels, as opposed to the typical phased antenna arrays which enable the control of each element's phase and amplitude. RHSs can be utilized to realize holographic beamforming with single-feed or multi-feeds, where digital beamforming can also be implemented using multi-feeds. In this paper, first, the holographic beamforming problem is formulated by means of the conventional beamforming expressions using the array factor and the holographic beamforming weights. Then, a zero-forcing beamforming method is developed for a single-feed RHS to generate multi-beam and multi-null patterns. Additionally, a bisectional null scanning approach for beam sweeping is presented to exploit the advantages of RHS for forming wide nulls in the beamspace. Simulation results imply a substantial advantage of such single-feed RHS for high-accuracy beam sweeping.

Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with 40×40 elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.

There have been recently many studies demonstrating that the performance of wireless communication systems can be significantly improved by a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low power requirement and low complexity. This paper presents a measurement-based characterization of RISs for providing physical layer security, where the transmitter (Alice), the intended user (Bob), and the eavesdropper (Eve) are deployed in an indoor environment. Each user is equipped with a software-defined radio connected to a horn antenna. The phase shifts of reflecting elements are software controlled to collaboratively determine the amount of received signal power at the locations of Bob and Eve in such a way that the secrecy capacity is aimed to be maximized. An iterative method is utilized to configure a Greenerwave RIS prototype consisting of 76 passive reflecting elements. Computer simulation and measurement results demonstrate that an RIS can be an effective tool to significantly increase the secrecy capacity between Bob and Eve.

Molecular Communication via Diffusion (MCvD) is a prominent small-scale technology, which roots from the nature. With solid analytical foundations on channel response and advanced modulation techniques, molecular single-input-single-output (SISO) systems are one of the most studied molecular networks in the literature. However, the literature is yet to provide sufficient analytical channel modeling on molecular multiple-output systems with fully absorbing receivers, one of the common applications in the area. In this paper, a channel model for molecular single-input-multiple-output (SIMO) systems is proposed for estimating the channel response of such systems. With the model’s recursive nature, the closed-form solution of the channel response of molecular 2-Rx SIMO systems is analytically derived. A simplified model with lower complexity is also presented at a cost of slightly less accurate channel estimation. The models are extended to the molecular SIMO systems with more than two receivers. The performance of the methods are evaluated for several topologies with different parameters, and the accuracy of the model is verified by comparing to computer-simulated channel estimations in terms of quantitative error metrics such as root-mean-squared error. The performance of the simplified model is verified by the amount of deviation, indicating sufficient channel modeling performance with reduced computational power.

Abstract We study a bipartite graph (BG) construction problem that arises in digital communication systems. In a digital communication system, information is sent from one place to another over a noisy communication channel using binary symbols (bits). The original information is encoded by adding redundant bits, which are then used to detect and correct errors that may have been introduced during transmission. Harmful structures, such as small cycles, severely deteriorate the error correction capability of a BG. We introduce an integer programming formulation to generate a BG for a given smallest cycle length. We propose a branch‐and‐cut algorithm for its solution and investigate the structural properties of the problem to derive valid inequalities and variable fixing rules. We also introduce heuristics to obtain feasible solutions for the problem. The computational experiments show that our algorithm can generate BGs without small cycles in an acceptable amount of time for practically relevant dimensions.

Multiple-input multiple-output (MIMO) transmission approaches have been recently considered in the context of molecular communications due to desirable improvements they provide in terms of communication efficiency. Among these methods, molecular index modulation (molecular-IM) schemes yield a significant improvement in throughput and show promising results for future molecular MIMO research. However, existing molecular-IM methods rely on perfect spatial alignment between corresponding antennas, which may not be the case in a possible practical scenario. Motivated by this practical constraint, this study proposes a novel receiver design for molecular-IM. The proposed decoder is an augmented version of the maximum count decoder (MCD) and operates by merging MCD with a simple linear combining technique. Our numerical results show that the proposed approach yields a desirable robustness against antenna misalignments while still maintaining a simplistic receiver structure.

Cyber-physical systems (CPS) are state-of-the-art communication environments that offer various applications with distinct requirements. However, security in CPS is a nonnegotiable concept, since without a proper security mechanism the applications of CPS may risk human lives, the privacy of individuals, and system operations. In this paper, we focus on PHY-layer security approaches in CPS to prevent passive eavesdropping attacks, and we propose an integration of physical layer operations to enhance security. Thanks to the McEliece cryptosystem, error injection is firstly applied to information bits, which are encoded with the forward error correction (FEC) schemes. Golay and Hamming codes are selected as FEC schemes to satisfy power and computational efficiency. Then obtained codewords are transmitted across reliable intermediate relays to the legitimate receiver. As a performance metric, the decoding frame error rate of the eavesdropper is analytically obtained for the fragmentary existence of significant noise between relays and Eve. The simulation results validate the analytical calculations, and the obtained results show that the number of low-quality channels and the selected FEC scheme affects the performance of the proposed model.

Molecular communication via diffusion (MCvD) is a molecular communication method that utilizes the free diffusion of carrier molecules to transfer information at the nanoscale. Due to the random propagation of carrier molecules, intersymbol interference (ISI) is a major issue in an MCvD system. Alongside ISI, interlink interference (ILI) is also an issue that increases the total interference for the MCvD-based multiple-input-multiple-output (MIMO) approaches. Inspired by the antenna index modulation (IM) concept in traditional communication systems, this paper introduces novel IM-based transmission schemes for MCvD systems. In this paper, molecular space shift keying (MSSK) is proposed as a novel modulation for molecular MIMO systems, and it is found that this method combats ISI and ILI considerably better than the existing MIMO approaches. For nanomachines that have access to two different molecules, the direct extension of MSSK, quadrature MSSK (QMSSK) is also proposed. QMSSK is found to combat ISI considerably well while not performing well against ILI-caused errors. In order to combat ILI more effectively, another dual-molecule-based novel modulation scheme called the molecular spatial modulation (MSM) is proposed. Combined with the Gray mapping imposed on the antenna indices, MSM is observed to yield reliable error rates for molecular MIMO systems.

Performance of analog circuits degrades over time due to several time-dependent degradation mechanisms. Due to the increased aging problems in ever-shrinking dimensions, reliability of complementary metal-oxide-semiconductor analog circuits has become a major concern. Overdesign is a popular aging-aware circuit design approach, where circuit operation is guardbanded by choosing the design point beyond the optimal region. For the sake of reliability, power consumption and chip area are sacrificed in this approach, which is undesirable considering strict energy limitations in modern applications. Conversely, Sense and React (S8R) approach serves the same purpose without any additional power consumption, in which degradation of circuit features is detected by online monitoring and recovered immediately. Furthermore, such systems enable remote control and healing of circuits. However, design of an S8R system is quite complicated. In particular, determination of efficient aging signatures and design of recovery strategy are highly challenging problems. This study thoroughly discusses the design process of S8R systems and proposes computer-aided-design-based design strategies that reduce the designer effort considerably. A novel design automation tool for S8R systems was developed, in which signature selection and recovery determination were integrated. To demonstrate proposed design strategies, two different S8R systems are implemented, simulated, and discussed in detail.

With the growing use of analog circuits in sensor systems for internet of things applications, estimation of their yield has become critical in order to increase the efficiency of large volume manufacturing. In this paper, a methodology to estimate the yield of analog circuits beyond 95% is proposed. The methodology is based on an algorithm that uses adaptive sampling to approach the “tail” region of the initial distribution which contains the dysfunctional units. These units do not satisfy the initial design targets thereby lowering the yield. An inverter and a two-stage operational amplifier have been used to verify the methodology where the reference distribution is based on 10^6 samples for both circuits. Simulation results reveal that the accuracy for 95%, 98%, and 99% yield has been compromised by less than 2.1%, 5.7%, and 7.4%, respectively, whereas the computation cost is reduced by 20x.

NAND flash memories, due to their several advantageous characteristics, have recently dominated the data storage industry and its global market. Currently, multi‐level cell memories, in which each cell can store more than one bit of data resulting in higher data storage capacities, have gained a considerable amount of research interest. However, this comes at the cost of several limitations and increased performance degradation. Various studies have shown that among several error sources in multi‐level cell memories, inter‐cell interference is the most significant one. Therefore, to mitigate the devastating effect of the interference, simple, feasible, and yet efficient equalisation techniques become essential for achieving desired data reliability. In this study, first, a thorough analysis on deriving the distribution of the interference‐free and interference‐affected data is carried out. Then, novel low‐complexity equalisation methods are proposed, and their beneficial complexity‐performance trade‐offs compared with the existing techniques are illustrated. Finally, simulation results are presented to show that the proposed algorithms considerably improve the error performance, while maintaining the low‐complexity constraints.

In this paper, a transfer function that models Molecular Communication via Diffusion (MCvD) between a point transmitter and a fully absorbing circular receiver in an unbounded 2-D environment is analytically derived. The analytical derivation of the transfer function of the 2-D channel in MCvD is proposed for the first time in the literature. The transfer function is obtained by solving the molecule concentration equations around the receiver considering diffusion dynamics, using some initial and boundary conditions, and some reasonable assumptions. Analytical results are compared with the results of Monte Carlo simulations.

Spatially coupled low-density parity-check codes can be decoded using a graph-based message passing algorithm applied across the total length of the coupled graph. However, considering practical constraints on decoding latency and complexity, a sliding window decoding approach is normally preferred. In order to reduce decoding complexity compared with standard parallel decoding schedules, serial schedules can be applied within a decoding window. However, uniform serial schedules within a window do not provide the expected reduction in complexity. Hence, we propose non-uniform schedules (parallel and serial) based on measured improvements in the estimated bit error rate (BER). We show that these non-uniform schedules result in a significant reduction in complexity without any loss in performance. Furthermore, based on observations made using density evolution, we propose a non-uniform pragmatic decoding schedule (parallel and serial) that does not require any additional calculations (e.g., BER estimates) within the decoding process.

In this paper, we study the block error rate (BLER) performance of spatially coupled low-density parity-check (SC-LDPC) codes using a sliding window decoder suited for streaming applications. Previous studies of SC-LDPC have focused on the bit error rate (BER) performance or the frame error rate (FER) performance over the entire length of the code. Here, we consider protograph-based constructions of SC-LDPC codes in which a window decoder continuously outputs blocks in a streaming fashion, and we examine the BLER associated with these blocks. We begin by examining the effect of protograph design on the streaming BLER by varying the block size and the coupling width in such a way that the overall constraint length of the SC-LDPC code remains constant. Next, we investigate the BLER scaling behavior with block size and coupling width. Lastly, we consider the effect of employing an outer code to protect blocks, so that small numbers of residual errors can be corrected by the outer code. Simulation results for the additive white Gaussian noise channel (AWGNC) are included and comparisons are made to LDPC block codes (LDPC-BCs).

In this paper, we present a random puncturing analysis of low-density parity-check (LDPC) code ensembles. We derive a simple analytic expression for the iterative belief propagation (BP) decoding threshold of a randomly punctured LDPC code ensemble on the binary erasure channel (BEC) and show that, with respect to the BP threshold, the strength and suitability of an LDPC code ensemble for random puncturing is completely determined by a single constant that depends only on the rate and the BP threshold of the mother code ensemble. We then provide an efficient way to accurately predict BP thresholds of randomly punctured LDPC code ensembles on the binary-input additive white Gaussian noise channel (BI-AWGNC), given only the BP threshold of the mother code ensemble on the BEC and the design rate, and we show how the prediction can be improved with knowledge of the BI-AWGNC threshold. We also perform an asymptotic minimum distance analysis of randomly punctured code ensembles and present simulation results that confirm the robust decoding performance promised by the asymptotic results. Protograph-based LDPC block code and spatially coupled LDPC code ensembles are used throughout as examples to demonstrate the results.

This paper proposes a novel multi-objective yield aware analog sizing tool that utilizes scrambled Quasi Monte Carlo (QMC) approach for efficient yield estimation and Strength Pareto Evolutionary Algorithm-2 (SPEA2) as a search engine. Analog circuit sizing tools have been utilized for the last two decades to overcome challenging trade-offs in analog circuit design. However, due to the variation phenomenon, some solutions at the Pareto front (PF) move towards the suboptimal region. To overcome this issue, yield aware optimization tools, where yield is given as a new design objective, have been proposed in the last decade. Conventionally, Monte Carlo (MC) approach has been used for the yield estimation. However, large sized MC analysis is a highly inefficient and time consuming process because of the numerous simulations performed during the optimization process. Rather than conventional MC, using QMC, which utilizes Low Discrepancy Sequences (LDS), enhances the synthesis time since, it promises low estimation errors with fewer number of simulations. Thanks to the QMC based variability analysis and multi-objective search engine, a yield aware PF that allows the designer to access all robust solutions can be obtained within an acceptable synthesis time.

In this paper, we provide an efficient way to predict iterative belief propagation (BP) decoding thresholds of randomly punctured low-density parity-check (LDPC) code ensembles on the binary-input additive white Gaussian noise channel (AWGNC), given only the BP threshold of the mother code ensemble on the binary erasure channel (BEC) and the code design rate. We show that the predictions are accurate by comparing them with values calculated by discretized density evolution for a variety of puncturing fractions. We find that the strength and suitability of an LDPC code ensemble for random puncturing over the AWGNC with respect to iterative decoding threshold is completely determined by a single constant θ, and this behavior is demonstrated using both LDPC block code and spatially coupled LDPC code ensembles. Finally, we present simulation results that confirm the excellent decoding performance promised by the asymptotic results.

Polar coding is a recently proposed coding technique that can provably achieve the channel capacity. The polar code structure, which is based on the original 2 × 2 generator matrix, polarises the channels, that is, a portion of the channel capacities approach 1, whereas the remaining channel capacities approach 0. Owing to the specific size of this original generator matrix, polar codes can only have code lengths equal to the powers of 2, resulting in inefficiency for codes of practical lengths. In this study, the performance of finite‐length polar codes over the binary erasure channel is analysed. A normalised polarisation distance measure is defined and polar codes from different generator matrices showing different amount of polarisation are compared using this measure. Encoding structures for these generalised polar codes are proposed and polarisation performances in both asymptotical and finite‐length cases are investigated for generator matrices of size 3 × 3 and 4 × 4. A generalised decoder is also proposed for this generator matrix and its erasure rate is compared with that of the original generator matrix. It is shown that polar codes that have performance similar to the original construction can be constructed and used for a variety of code lengths, not necessarily equal to powers of 2, using generalised generator matrices.

Low-density parity-check convolutional (LDPCC) codes, also known as spatially coupled LDPC codes, can be decoded using a message passing algorithm. In order to limit decoding latency and complexity, windowed decoding can be applied. Updates within the window can be performed either in parallel or serially. However, simulation results show that uniform updating schedules do not provide the expected reduction in complexity when applied within the window. Hence we propose non-uniform schedules for updating the nodes based on measured improvements in the bit error rate. Nodes within the window that stop showing any improvement are excluded from the update list for the next iteration. This results in a reduction of up to 50% in complexity compared to uniform window schedules.

Ring Oscillators (RO) are the main primitives of Physical Unclonable Functions (PUFs) that generate chip specific signatures depending on the uncontrollable components present in the manufacturing process. RO-PUFs are one of the popular PUF types among various structures presented in the literature. However, due to the noisy nature of RO circuits, robust output generation is problematic in RO-PUFs. Maximizing the robustness of a PUF is the main design objective, and analytical solutions have not been developed yet to overcome this problem. In this work, RO structures are analyzed and the effects of RO inverter count and measurement time are examined theoretically and practically in terms of jitter and spatial variation. Next, a design methodology is presented to easily determine the measurement time and RO inverter count for best performing RO-PUFs. In addition to this, the design methodology is practically verified by comparing the jitter and spatial variation to the robustness measurements of previously built RO-PUF circuits.

Low implementation complexity, low delay and close‐to‐optimal performance over a wide variety of channels are some of the advantages of spatially‐coupled low‐density parity‐check (LDPC) codes. However, the error performance of the sliding window decoding scheme that is used to decode these codes is considerably degraded over channels with memory, such as the correlated erasure channel. Employing a block interleaver to encounter this situation is not always a viable option, since it introduces a large amount of delay and cancels out the low‐delay property of the sliding window decoder. Another way to reduce the effects of erasure bursts is to construct a more robust code ensemble by presenting additional code design rules. However, this approach results in additional constraints on the already complicated code construction process. The authors propose a novel communication system that combats the effects of the erasure bursts through the use of a convolutional interleaver. The proposed system combines the inherent convolutional nature of the spatially‐coupled LDPC codes with that of a convolutional interleaver to achieve very low overall delay. The performance of the proposed approach is analysed using the density evolution technique and the performance improvement is demonstrated as a function of the interleaving delay via computer simulations.

Low-density parity-check (LDPC) convolutional codes have been shown to be capable of achieving capacity-approaching performance with iterative message-passing decoding. In the first part of this paper, using asymptotic methods to obtain lower bounds on the free distance to constraint length ratio, we show that several ensembles of regular and irregular LDPC convolutional codes derived from protograph-based LDPC block codes have the property that the free distance grows linearly with respect to the constraint length, i.e., the ensembles are asymptotically good. In particular, we show that the free distance to constraint length ratio of the LDPC convolutional code ensembles exceeds the minimum distance to block length ratio of the corresponding LDPC block code ensembles. A large free distance growth rate indicates that codes drawn from the ensemble should perform well at high signal-to-noise ratios under maximum-likelihood decoding. When suboptimal decoding methods are employed, there are many factors that affect the performance of a code. Recently, it has been shown that so-called trapping sets are a significant factor affecting decoding failures of LDPC codes over the additive white Gaussian noise channel with iterative message-passing decoding. In the second part of this paper, we study the trapping sets of the asymptotically good protograph-based LDPC convolutional codes considered earlier. By extending the theory presented in part one and using similar bounding techniques, we show that the size of the smallest non-empty trapping set grows linearly with the constraint length for these ensembles.

Window decoding schedules are very attractive for message passing decoding of spatially coupled LDPC codes. They take advantage of the inherent convolutional code structure and allow continuous transmission with low decoding latency and complexity. In this paper we show that the decoding complexity can be further reduced if suitable message passing schedules are applied within the decoding window. An improvement based schedule is presented that easily adapts to different ensemble structures, window sizes, and channel parameters. Its combination with a serial (on-demand) schedule is also considered. Results from a computer search based schedule are shown for comparison.

Ensembles of (J,K)-regular low-density parity-check convolutional (LDPCC) codes are known to be asymptotically good, in the sense that the minimum free distance grows linearly with the constraint length. In this paper, we use a protograph-based analysis of terminated LDPCC codes to obtain an upper bound on the free distance growth rate of ensembles of periodically time-varying LDPCC codes. This bound is compared to a lower bound and evaluated numerically. It is found that, for a sufficiently large period, the bounds coincide. This approach is then extended to obtain bounds on the trapping set numbers, which define the size of the smallest, non-empty trapping sets, for these asymptotically good, periodically time-varying LDPCC code ensembles.

Low-density parity-check (LDPC) convolutional codes are capable of achieving excellent performance with low encoding and decoding complexity. In this paper, we discuss several graph-cover-based methods for deriving families of time-invariant and time-varying LDPC convolutional codes from LDPC block codes and show how earlier proposed LDPC convolutional code constructions can be presented within this framework. Some of the constructed convolutional codes significantly outperform the underlying LDPC block codes. We investigate some possible reasons for this “convolutional gain,” and we also discuss the-mostly moderate-decoder cost increase that is incurred by going from LDPC block to LDPC convolutional codes.

It has been suggested that ¿near-codewords¿ may be a significant factor affecting decoding failures of LDPC codes over the AWGN channel. A near-codeword is a sequence that satisfies almost all of the check equations. These near-codewords can be associated with so-called `trapping sets' that exist in the Tanner graph of a code. In this paper, we analyse the trapping sets of protograph-based LDPC convolutional codes. LDPC convolutional codes have been shown to be capable of achieving the same capacity-approaching performance as LDPC block codes with iterative message-passing decoding. Further, it has been shown that some ensembles of LDPC convolutional codes are asymptotically good, in the sense that the average free distance grows linearly with constraint length. Here, asymptotic methods are used to calculate a lower bound on the trapping set growth rates for two ensembles of asymptotically good protograph-based LDPC convolutional codes. This can be used to predict where the error floor will occur for these codes under iterative message-passing decoding.

Message-passing iterative decoders for low-density parity-check (LDPC) block codes are known to be subject to decoding failures due to so-called pseudocodewords. These failures can cause the large signal-to-noise ratio (SNR) performance of message-passing iterative decoding to be worse than that predicted by the maximum-likelihood (ML) decoding union bound.

LDPC convolutional codes have been shown to be capable of achieving the same capacity-approaching performance as LDPC block codes with iterative message-passing decoding. In this paper, asymptotic methods are used to calculate a lower bound on the free distance for several ensembles of asymptotically good protograph-based LDPC convolutional codes. Further, we show that the free distance to constraint length ratio of the LDPC convolutional codes exceeds the minimum distance to block length ratio of corresponding LDPC block codes.

Potentially large storage requirements and long initial decoding delays are two practical issues related to the decoding of low-density parity-check (LDPC) convolutional codes using a continuous pipeline decoder architecture. In this paper, we propose several reduced complexity decoding strategies to lessen the storage requirements and the initial decoding delay without significant loss in performance. We also provide bit error rate comparisons of LDPC block and LDPC convolutional codes under equal processor (hardware) complexity and equal decoding delay assumptions. A partial syndrome encoder realization for LDPC convolutional codes is also proposed and analyzed. We construct terminated LDPC convolutional codes that are suitable for block transmission over a wide range of frame lengths. Simulation results show that, for terminated LDPC convolutional codes of sufficiently large memory, performance can be improved by increasing the density of the syndrome former matrix.

In this paper we study the iterative decoding behavior of time-invariant and time-varying LDPC convolutional codes derived by unwrapping QC LDPC block codes. In particular, for a time-varying LDPC convolutional code, we show that the minimum pseudo-weight of the convolutional code is at least as large as the minimum pseudo-weight of the underlying QC code. We also prove that the unwrapped convolutional codes have fewer short cycles than the QC codes. These results taken together lead to improved BER performance in the low-to-moderate SNR region, where the decoding behavior is influenced by the complete pseudo-codeword spectra and by the Tanner graph cycle histogram, with the time-varying convolutional codes outperforming both the underlying QC block codes and their time-invariant convolutional counterparts.

We propose a novel code design technique for irregular LDPC convolutional codes. The constructed codes can be encoded continuously in real time with the help of a shift-register based encoder. For moderate values of the syndrome former memory, simulation results show that the constructed codes outperform LDPC block codes with comparable hardware (processor) complexity.

Low-density parity-check convolutional codes offer the same good error-correcting performance as low-density parity-check block codes while having the ability to encode and decode arbitrary lengths of data. This makes these codes well suited to certain applications, such as forward error control on packet switching networks. In this paper we propose a decoder architecture for low-density parity-check convolutional codes with very large memories. These codes have very good error correcting properties and as such may be applicable in wireless sensor networks and space communication systems. We discuss a realization of this architecture for a (2048,3,6) code implemented on a field-programmable gate-array.

Iterative message-passing decoders for low-density parity-check (LDPC) block codes are known to be subject to decoding failures due to so-called pseudo-codewords. These failures can cause the large signal-to-noise ratio performance of message-passing decoding to be worse than that predicted by the maximum-likelihood decoding union bound. In this paper we study the pseudo-codeword problem for the class of LDPC convolutional codes decoded continuously using an iterative, sliding window, message-passing decoder. In particular, for an LDPC convolutional code derived by unwrapping a quasi-cyclic LDPC block code, we show that the free pseudo-weight of the convolutional code is at least as large as the minimum pseudo-weight of the underlying quasi-cyclic code. This result parallels the well-known relationship between the free Hamming distance of convolutional codes and the minimum Hamming distance of their quasi-cyclic counterparts. Finally, simulation results are included that show improved performance for unwrapped LDPC convolutional codes compared to their underlying quasi-cyclic codes

While low-density parity-check (LDPC) convolutional codes tend to significantly outperform LDPC block codes with the same processor complexity, large storage requirements and a long initial decoding delay are two issues related to their continuous pipeline decoding architecture [A. Jimenez Feltstrom et al., (1999)]. In this paper, we propose reduced complexity decoding strategies to lessen the storage requirements and the initial decoding delay without significant loss in performance.

In this paper, an optimum sequence estimation algorithm for wireless systems with Alamouti's two transmitter diversity in the presence of multipath fading is proposed. The algorithm is based on a jointly iterative channel and sequence estimation according to the maximum likelihood (ML) criterion, using the expectation-maximization (EM) algorithm employing M-PSK modulation scheme with additive Gaussian noise. The discrete multipath channel is represented in terms of the channel gains from each transmit antenna to the receive antenna. The derived EM algorithm jointly estimates the complex channel parameters of each channel and the data sequence transmitted, iteratively, which converges to the true ML solution. The channel estimation is achieved in a simple way through the iterative equations by decoupling of the signals transmitted from different antennas. The algorithm is applied to the trellis coded modulation systems and efficiency of the algorithm proposed has been shown by the computer simulations. Simulation results show that the EM algorithm converges quickly for fast fading channels. The performance of the EM-based decoder approaches that of the ML receiver which has perfect knowledge of the channel.

The performance of data communication systems over wireless channels is severely degraded by the multipath fading effects. Space-time codes exploit the channel capacity increase by using multiple transmit and/or receive antennas. This ensures space diversity without expanding the required transmission bandwidth. A type of space-time codes, namely orthogonal transmit diversity (OTD), has attracted much attention. This scheme has low complexity and can achieve maximum diversity. However, error performance of OTD systems decreases in the absence of perfect channel state information at the receiver. We present an enhanced power control scheme for OTD systems with two transmit and multiple receive antennas. Simulation results have been presented to show the performance improvement.