Sidelink positioning research predominantly focuses on the snapshot positioning problem, often within the mmWave band. Only a limited number of studies have delved into vehicle-to-anything (V2X) tracking within sub-6 GHz bands. In this paper, we investigate the V2X sidelink tracking challenges over sub-6 GHz frequencies. We propose a Kalman-filter-based tracking approach that leverages the estimated error covariance lower bounds (EECLBs) as measurement covariance, alongside a gating method to augment tracking performance. Through simulations employing ray-tracing data and super-resolution channel parameter estimation, we validate the feasibility of sidelink tracking using our proposed tracking filter with two novel EECLBs. Additionally, we demonstrate the efficacy of the gating method in identifying line-of-sight paths and enhancing tracking performance.

In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first derive a novel, approximate Cramér-Rao bound (CRB) on the connected road user (CRU) position, explicitly taking into account multipath interference, path merging, and the RTT protocol. Capitalizing on tensor decomposition and ESPRIT methods, we propose high-resolution channel parameter estimation algorithms specifically tailored to dense multipath V2X sidelink environments, designed to detect multipath components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using realistic ray-tracing data and antenna patterns, comprehensive simulations are conducted to evaluate channel estimation and positioning performance, indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the proposed algorithms.

Radio positioning is an important part of joint communication and sensing in beyond 5G communication systems. Existing works mainly focus on the mmWave bands and under-utilize the sub-6 GHz bands, even though it is promising for accurate positioning, especially when the multipath is uncomplicated, and meaningful in several important use cases. In this paper, we analyze V2X sidelink positioning and propose a new approximate performance bound that can predict the positioning performance in the presence of severe multipath. Simulation results using ray-tracing data demonstrate the possibility of sidelink positioning, and the efficacy of the new performance bound and its relation with the complexity of the multipath.

This paper describes our work towards preliminary evaluation of LTE/5G cellular communication for robotics motion planning and control. The work comprises the development of communication architecture, motion planning and a demonstrator that validates the feasibility of the proposed approach. In the demonstrated use case, a stationary robot arm picks and places workpieces from/onto a moving mobile platform while relying only on the mobile platform's localization measurements communicated over LTE connection and not using any external sensors. We describe the approach, obtained results, shortcomings, and promising future directions.

The upcoming fifth-generation wireless technology application areas bring new communication performance requirements, mainly in terms of reliability and latency, but also in terms of radio planning, where the further detailed characterization of the wireless channel is needed. To address these demands, we developed an agile multi-node multi-antenna wireless channel sounding system, using multiple software-defined radio devices. The system consists of 12 testbed nodes which are controlled from a centralized testbed server. Each node features a control host computer and two multi-antenna universal software radio peripheral boards. By managing the transmission and reception of reference signals among all the distributed testbed nodes, the system can measure the channel conditions of all multiple independent radio links. At the same time, the distributed architecture of the testbed allows a large number of spatially distributed locations to be covered with only a few redeployments of the testbed nodes. As a consequence of this, the system favors the collection of a large number of distributed channel samples with limited effort within a short dedicated measurement time. In this paper, we detail the general testbed design considerations, along with the specific sounding signal processing implementations. As further support to the system design, we also include the results from different verification and calibration tests, as well as a real measurement application example.

The factories of the future will be highly digitalized in order to enable flexible and interconnected manufacturing processes. Especially wireless technologies will be beneficial for industrial automation. However, the high density of metallic objects is challenging for wireless systems due to multipath fading. In order to understand the signal propagation in industrial environments, this paper provides results from a number of channel measurement campaigns funded by the German research initiative “Reliable wireless communication in the industry”. We give an overview of different measurement scenarios covering visible light communication and radio communication below 6 GHz. We analyze large and small scale parameters as well as delay statistics of the wireless channels. Finally, we discuss the importance of the results for the definition of industrial channel models.

The atomic norm provides a generalization of the $\ell_1$-norm to continuous parameter spaces. When applied as a sparse regularizer for line spectral estimation the solution can be obtained by solving a convex optimization problem. This problem is known as atomic norm soft thresholding (AST). It can be cast as a semidefinite program and solved by standard methods. In the semidefinite formulation there are $O(N^2)$ dual variables which complicates the implementation of a standard primal-dual interior-point method based on symmetric cones. That has lead researcher to consider alternating direction method of multipliers (ADMM) for the solution of AST, but this method is still somewhat slow for large problem sizes. To obtain a faster algorithm we reformulate AST as a non-symmetric conic program. That has two properties of key importance to its numerical solution: the conic formulation has only $O(N)$ dual variables and the Toeplitz structure inherent to AST is preserved. Based on it we derive FastAST which is a primal-dual interior point method for solving AST. Two variants are considered with the fastest one requiring only $O(N^2)$ flops per iteration. Extensive numerical experiments demonstrate that FastAST solves AST significantly faster than a state-of-the-art solver based on ADMM.

In this paper, we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multipath delays to a grid. Our receiver does not impose such a restriction. As a result, it does not suffer from the leakage effect, which destroys sparsity. Communication at near capacity rates in high SNR requires a large modulation order. Due to the close proximity of modulation symbols in such systems, the grid-based approximation is of insufficient accuracy. We show numerically that a state-of-the-art iterative receiver with grid-based sparse channel estimation exhibits a bit-error-rate floor in the high SNR regime. On the contrary, our receiver performs very close to the perfect channel state information bound for all SNR values. We also demonstrate both theoretically and numerically that parametric channel estimation works well in dense channels, i.e., when the number of multipath components is large and each individual component cannot be resolved.

A number of recent works have proposed to solve the line spectral estimation problem by applying off-the-grid extensions of sparse estimation techniques. These methods are preferable over classical line spectral estimation algorithms because they inherently estimate the model order. However, they all have computation times that grow at least cubically in the problem size, thus limiting their practical applicability in cases with large dimensions. To alleviate this issue, we propose a low-complexity method for line spectral estimation, which also draws on ideas from sparse estimation. Our method is based on a Bayesian view of the problem. The signal covariance matrix is shown to have Toeplitz structure, allowing superfast Toeplitz inversion to be used. We demonstrate that our method achieves estimation accuracy at least as good as current methods and that it does so while being orders of magnitudes faster.

We address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coefficients are governed by a Bernoulli-Gaussian prior model turning model order selection into binary sequence detection. Unlike earlier works which retain only point estimates of the frequencies, we undertake a complete Bayesian treatment by estimating the posterior probability density functions (pdfs) of the frequencies and computing expectations over them. Thus, we additionally capture and operate with the uncertainty of the frequency estimates. Aiming to maximize the model evidence, variational optimization provides analytic approximations of the posterior pdfs and also gives estimates of the additional parameters. We propose an accurate representation of the frequency pdfs by mixtures of von Mises pdfs, which yields closed-form expectations. We define the algorithm VALSE in which the estimates of the pdfs and parameters are iteratively updated. VALSE is a gridless, convergent method, does not require parameter tuning, can easily include prior knowledge about the frequencies and provides approximate posterior pdfs based on which the uncertainty in line spectral estimation can be quantified. Simulation results show that accounting for the uncertainty of frequency estimates, rather than computing just point estimates, significantly improves the performance. The performance of VALSE is superior to that of state-of-the-art methods and closely approaches the Cramér-Rao bound computed for the true model order.

Recent progress in wireless receiver design has been towards iterative processing, where channel estimation and decoding is considered a joint optimization problem. Sparse channel estimation is another recent advancement, which exploits the inherent structure of those wireless channels that are composed of a small number of multipath components. In this work we design iterative receivers which incorporate sparse channel estimation. State-of-the-art sparse channel estimators simplify the estimation problem to be a finite basis selection problem by restricting the multipath delays to the discrete domain (i.e. to a grid). Our main contribution is a receiver without such a restriction; the delays are estimated directly as continuous values. As a result, our receiver does not suffer from the leakage effect which destroys sparsity when the delays are restricted to the discrete domain. We discuss certain connections between continuous and discrete domain sparse estimation methods. Our receivers outperform state-of-the-art sparse channel estimation iterative receivers in terms of bit error rate.

This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous parameter space to a fixed grid of points, thus restricting the solution space. In this work, we avoid discretization by working directly with the signal model containing parameterized atoms. Inspired by the “fast inference scheme” by Tipping and Faul we develop a novel sparse Bayesian learning (SBL) algorithm, which estimates the atom parameters along with the model order and weighting coefficients. Numerical experiments for spectral estimation with closely-spaced frequency components, show that the proposed SBL algorithm outperforms state-of-the-art subspace and compressed sensing methods.

In sparse Bayesian learning (SBL) approximate Bayesian inference is applied to find sparse estimates from observations corrupted by additive noise. Current literature only vaguely considers the case where the noise level is unknown a priori. We show that for most state-of-the-art reconstruction algorithms based on the fast inference scheme noise precision estimation results in increased computational complexity and reconstruction error. We propose a three-layer hierarchical prior model which allows for the derivation of a fast inference algorithm that estimates the noise precision with no complexity increase. Numerical results show that it matches or surpasses other algorithms in terms of reconstruction error.

In this paper we discuss the implementation of a Single Carrier Frequency Division Multiple Access (SC-FDMA) transceiver running over the Universal Software Radio Peripheral 2 (USRP2). SC-FDMA is the air interface which has been selected for the uplink in the latest Long Term Evolution (LTE) standard. In this paper we derive an AWGN channel model for SC-FDMA transmission, which is useful for benchmarking experimental results. In our implementation, we deal with signal scaling, equalization and partial synchronization to realize SC-FDMA transmission over a noisy channel at rates up to 5.184 Mbit/s. Experimental results on the Bit Error Rate (BER) versus Signal-to-Noise Ratio (SNR) are presented and compared to theoretical and simulated performance.

Two different approaches for an automotive multi-and broadband monopole antenna for GSM, WLAN and UWB applications will be presented. Both allow covering the complete relevant frequency range worldwide in one antenna structure. The two antennas consist of one broadband structure for frequencies above 1.7 GHz and an additional folded arm for the lower frequencies. The first approach is based on a disc-monopole. In addition it has a folded arm on its top to cover the mobile phone services GSM 900 and AMPS. It has a height of 60 mm. The second approach has a 5 mm reduced height. So it achieves a height of 55 mm, which is the typical goal of many car manufacturers. The antenna is based on a folded metal strip construction. The amplitude and phase behaviour of the two antennas were measured. The derived impulse responses will be discussed with focus on the UWB frequencies.