This paper investigates the advantages of using communication supported by information from vehicle sensors to improve the performance of the High-Density Platooning (HDPL) application. HDPL is one of the most promising applications in the field of cooperative driving. The goal of this application is to improve fuel and road efficiency without impairing safety. The level of efficiency improvement depends on the instantaneous channel quality. Strong multipath components, which arise from other vehicles, traffic signs, and surrounding buildings in such a highly dynamic vehicular environment, significantly influence the instantaneous link quality between communicating vehicles in a platoon. An emerging concept of vehicle sensor-aided predictive communications has shown its potential to improve direct-link communication. In this paper, the applicability of this approach is evaluated in terms of inter-vehicle distance error, which reflects the difficulties that the control algorithm has to cope with in the communications conditions. The simulation results show that the proposed sensor-based approach significantly improves the platoon performance even if affected by multiple dynamic scattering reflections at the same time.

The U.S. Department of Transportation (USDOT) issued a proposed rule on January 12, 2017 to mandate vehicle-to-vehicle safety communications in light vehicles in the U.S. Cybersecurity and privacy are major challenges for such a deployment. We present a Security Credential Management System (SCMS) for vehicle-to-everything (V2X) communications in this paper, which has been developed by the Crash Avoidance Metrics Partners LLC under a cooperative agreement with the USDOT. This system design is currently transitioning from research to proof-of-concept and is a leading candidate to support the establishment of a nationwide Public Key Infrastructure for V2X security. It issues digital certificates to participating vehicles and infrastructure devices for trustworthy communications among them, which is necessary for safety and mobility applications that are based on V2X communications. The SCMS supports four main use cases, namely, bootstrapping, certificate provisioning, misbehavior reporting, and revocation. The main design goal is to provide both security and privacy to the largest extent reasonable and possible. To achieve a reasonable level of privacy in this context, vehicles are issued pseudonym certificates, and the generation and provisioning of those certificates are divided among multiple organizations. Given the large number of pseudonym certificates per vehicle, one of the main challenges is to facilitate efficient revocation of misbehaving or malfunctioning vehicles, while preserving privacy against attacks from insiders. The proposed SCMS supports all identified V2X use-cases and certificate types necessary for V2X communication security. This paper is based upon work supported by the USDOT. Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors (“we”) and do not necessarily reflect the view of the USDOT.

Achieving high quality measurements with continuous-time Delta-Sigma modulator based micromachined gyroscope systems requires matching the resonant frequencies of the drive and of the sense loops. This work presents a system that synchronizes and calibrates the frequency of the electrical filter to the resonance frequency of the sensor. It is employed in a fourth order continuous-time bandpass Delta-Sigma based gyroscope system. The synchronization process is done in the background without interrupting the normal operation of the gyroscope's closed-loop interface. Simulations show that the proposed synchronization/calibration technique reduces the error in the electrical resonant frequency to less than 0.4% relative to the mechanical resonant frequency.

We introduce a new method for decoding short and moderate-length linear block codes with dense parity check matrix representations of cyclic form. This approach is termed multiple-bases belief-propagation. The proposed iterative scheme makes use of the fact that a code has many structurally diverse parity-check matrices, capable of detecting different error patterns. We show that this inherent code property leads to decoding algorithms with significantly better performance when compared to standard belief-propagation decoding. Furthermore, we describe how to choose sets of parity-check matrices of cyclic form amenable for multiple-bases decoding, based on analytical studies performed for the binary erasure channel. For several cyclic and extended cyclic codes, the multiple-bases belief propagation decoding performance can be shown to closely follow that of the maximum-likelihood decoder.

We compare convolutional codes and LDPC codes with respect to their decoding performance and their structural delay, which is the inevitable delay solely depending on the structural properties of the coding scheme. Besides the decoding performance, the data delay caused by the channel code is of great importance as this is a crucial factor for many applications. Convolutional codes are known to show a good performance while imposing only a very low latency on the data. LDPC codes yield superior decoding performance but impose a larger delay due to the block structure. The results obtained by comparison will also be related to theoretical limits obtained from random coding and the sphere packing bound. It will be shown that convolutional codes are still the first choice for applications for which a very low data delay is required and the bit error rate is the considered performance criterion. However, if one focuses on a low signal-to-noise ratio or if the obtained frame error rate is the base for comparison, LDPC codes compare favorably.

We investigate properties of a channel coding scheme leading to the minimum-possible frame error ratio when transmitting over a memoryless channel with rate R > C. The results are compared to the well-known properties of a channel coding scheme leading to minimum bit error ratio. It is concluded that these two optimization requests are contradicting. A valuable application of the derived results is presented.

We generalize the notion of the stopping redundancy in order to study the smallest size of a trapping set in Tanner graphs of linear block codes. In this context, we introduce the notion of the trapping redundancy of a code, which quantifies the relationship between the number of redundant rows in any parity-check matrix of a given code and the size of its smallest trapping set. Trapping sets with certain parameter sizes are known to cause error-floors in the performance curves of iterative belief propagation (BP) decoders, and it is therefore important to identify decoding matrices that avoid such sets. Bounds on the trapping redundancy are obtained using probabilistic and constructive methods, and the analysis covers both general and elementary trapping sets. Numerical values for these bounds are computed for the [2640, 1320] Margulis code and the class of projective geometry codes, and compared with some new code-specific trapping set size estimates.

We introduce the notion of the stopping redundancy hierarchy of a linear block code as a measure of the tradeoff between performance and complexity of iterative decoding for the binary erasure channel. We derive lower and upper bounds for the stopping redundancy hierarchy via Lovasz's local lemma (LLL) and Bonferroni-type inequalities, and specialize them for codes with cyclic parity-check matrices. Based on the observed properties of parity-check matrices with good stopping redundancy characteristics, we develop a novel decoding technique, termed automorphism group decoding, that combines iterative message passing and permutation decoding. We also present bounds on the smallest number of permutations of an automorphism group decoder needed to correct any set of erasures up to a prescribed size. Simulation results demonstrate that for a large number of algebraic codes, the performance of the new decoding method is close to that of maximum-likelihood (ML) decoding.

A novel soft-decoding method for algebraic block codes is presented. The algorithm is designed for soft-decision decoding and is based on belief-propagation (BP) decoding using multiple bases of the dual code. Compared to other approaches for high-performance BP decoding, this method is conceptually simple and does not change at each stage of the decoding process. With its multiple BP decoders the proposed scheme achieves the performance of a standard BP algorithm with a significantly lower number of iterations per decoder realization. By this means the data delay introduced by decoding is reduced. Moreover, a significant improvement in decoding performance is achieved while keeping the data delay small. It is shown that for selected codes the proposed scheme approaches near maximum likelihood (ML) performance for very small data processing delays.

We investigate the stopping redundancy hierarchy of linear block codes and its connection to permutation decoding techniques. An element in the ordered list of stopping redundancy values represents the smallest number of possibly linearly dependent rows in any parity-check matrix of a code that avoids stopping sets of up to a given size. Redundant parity-check equations can be shown to have a similar effect on decoding performance as permuting the coordinates of the received codeword according to a selected set of automorphisms of the code. Based on this finding we develop new decoding strategies for data transmission over the binary erasure channel that combine iterative message passing and permutation decoding in order to avoid errors confined to stopping sets. We also introduce the notion of s-SAD sets, containing the smallest number of automorphisms of a code with the property that they move any set of not more than s erasures into positions that do not correspond to stopping sets within a judiciously chosen parity-check matrix.

We analyze the effect of redundant parity-check equations on the error-floor performance of low-density parity- check (LDPC) codes used over the additive white Gaussian noise (AWGN) channel. Our findings show that a large number of iterative decoding errors in the [2640,1320] Margulis code, confined to point trapping sets in the standard Tanner graph, can be corrected if only one redundant parity-check equation is added to the decoder's matrix. We also derive an analytic expression relating the number of rows in the parity-check matrix of a code and the parameters of trapping sets in the code's graph.

Several approaches have recently been proposed for the efficient optimum or approximate solution of the detection problem in multiple-input multiple-output transmission systems. These are, however, difficult to compare. In the present work we briefly summarize the most popular and promising of these approaches and offer a way to visualize the tradeoff between complexity of the detection and the achievable power efficiency using "complexity-power diagrams". We conclude that the so-called sphere decoder algorithm is very attractive in terms of average complexity, while for low and constant processing delay lattice reduction with subsequent simple linear or nonlinear detection is more favorable

This paper proposes an optimized delay diversity (ODD) scheme for frequency-selective fading channels. The novel scheme requires knowledge of the channel impulse response (CIR) autocorrelation matrix at the transmitter, but the CIRs themselves have to be available only at the receiver side. A cost function for optimization of the ODD transmit filters is derived and a steepest descent algorithm for iterative calculation of the filter coefficients is provided. In addition, an upper bound on the cost function is derived and employed to prove the asymptotic optimality of the generalized DD (GDD) scheme in [Proc. IEEE Int. Conf. Communications (ICC), (2002) p. 1949] for very high signal-to-noise ratios (SNRs) and transmit filters of maximum length. However, for SNRs of practical interest and reasonable filter lengths, the novel ODD scheme significantly outperforms GDD for both optimum and suboptimum equalizations. It is also shown that, in contrast to the frequency-nonselective case, for frequency-selective channels, transmit diversity schemes designed under the high SNR assumption may perform poorly for practically relevant SNRs.

In this paper, we propose an optimized delay diversity (ODD) scheme for fading intersymbol interference (ISI) channels. The novel scheme requires knowledge of the channel impulse response (CIR) autocorrelation matrix at the transmitter, but the CIRs themselves have to be available only at the receiver side. We derive a cost function for optimization of the ODD transmit filters and we provide a steepest descent algorithm for iterative calculation of the filter coefficients. In addition, an upper bound on the cost function is derived and employed to prove the asymptotic optimality of the generalized DD (GDD) scheme in [1] for very high signal-to-noise (SNR) ratios and transmit filters of maximum length. However, for SNRs of practical interest and reasonable filter lengths the novel ODD scheme significantly outperforms GDD for both optimum and suboptimum equalization.

Algorithms for the detection problem in multiple-input multiple-output (MIMO) transmission systems are compared, and the tradeoff between detection complexity and the achieved power-efficiency is visualized using "power-complexity" diagrams.