Standardized (link-level) channel models such as the 3GPP TDL and CDL models are frequently used to evaluate machine learning (ML)-based physical-layer methods. However, in this work, we argue that a link-level perspective incorporates limiting assumptions, causing unwanted distributional shifts or necessitating impractical online training. An additional drawback is that this perspective leads to (near-)Gaussian channel characteristics. Thus, ML-based models, trained on link-level channel data, do not outperform classical approaches for a variety of physical-layer applications. Particularly, we demonstrate the optimality of simple linear methods for channel compression, estimation, and modeling, revealing the unsuitability of link-level channel models for evaluating ML models. On the upside, adopting a scenario-level perspective offers a solution to this problem and unlocks the relative gains enabled by ML.

The mean square error (MSE)-optimal estimator is known to be the conditional mean estimator (CME). This paper introduces a parametric channel estimation technique based on Bayesian estimation. This technique uses the estimated channel parameters to parameterize the well-known LMMSE channel estimator. We first derive an asymptotic CME formulation that holds for a wide range of priors on the channel parameters. Based on this, we show that parametric Bayesian channel estimation is MSE-optimal for high signal-to-noise ratio (SNR) and/or long coherence intervals, i.e., many noisy observations provided within one coherence interval. Numerical simulations validate the derived formulations.

This paper investigates access point (AP) cooperation cluster formation in user-centric cell-free massive MIMO (CF-mMIMO) communication systems characterized by fronthaul links with capacity restrictions. Specifically, we consider an open radio access network (O-RAN) architecture that, although it favors the deployment of ultra-dense networks, is constrained in the number of APs that can be active simultaneously. In this context, we propose an innovative framework termed pixel-based CF-mMIMO, which enables efficient control of both the AP activation and cooperation cluster formation. Recognizing the parallels with pixel-based reconfigurable antennas, the proposed framework allows dynamic reconfiguration of the network coverage map with reasonably low computational cost. The high scalability and performance of the framework are mainly supported by a learning model based on graph neural networks (GNNs) that effectively exploits the existing graph-like structures in CF-mMIMO systems. Extensive simulation experiments demonstrate that the proposed approach achieves competitive spectral efficiency (SE) in challenging scenarios with dense AP deployments and numerous user equipments (UEs).

We compare dirty paper coding (DPC) and linear precoding methods in a reconfigurable intelligent surface (RIS)- aided high-signal-to-noise ratio (SNR) scenario, where the channel between the base station (BS) and the RIS is dominated by a line-of-sight (LOS) component. Furthermore, we consider two groups of users where one group can be efficiently served by the BS, whereas the other one has a negligible direct channel and has to be served via the RIS. Within this scenario, we analytically show fundamental differences between DPC and linear methods. In particular, our analysis addresses two essential aspects, i.e., the orthogonality of the BS-RIS channel with the direct channel and a pseudo-noise term, depending on the number of RIS elements, that is present only for linear precoding techniques. The pseudonoise generally leads to strong limitations for the linear method, especially for random or statistical phase shifts. Moreover, we discuss under which circumstances this pseudo-noise is negligible and in which scenarios DPC and linear precoding lead to the same performance.

We propose decoupling networks for the reconfigurable intelligent surface (RIS) array as a solution to benefit from the mutual coupling between the reflecting elements. In particular, we show that when incorporating these networks, the system model reduces to the same structure as if no mutual coupling is present. Hence, all algorithms and theoretical discussions neglecting mutual coupling can be directly applied when mutual coupling is present by utilizing our proposed decoupling networks. For example, by including decoupling networks, the channel gain maximization in RIS-aided single-input single-output (SISO) systems does not require an iterative algorithm but is given in closed form as opposed to using no decoupling network. In addition, this closed-form solution allows to analytically analyze scenarios under mutual coupling resulting in novel connections to the conventional transmit array gain. In particular, we show that super-quadratic (up to quartic) channel gains w.r.t. the number of RIS elements are possible and, therefore, the system with mutual coupling performs significantly better than the conventional uncoupled system in which only squared gains are possible. We consider diagonal as well as beyond diagonal (BD)-RISs and give various analytical and numerical results, including the inevitable losses at the RIS array. In addition, simulation results validate the superior performance of decoupling networks w.r.t. the channel gain compared to other state-of-the-art methods.

This paper investigates semi-blind channel estimation for massive multiple-input multiple-output (MIMO) systems. To this end, we first estimate a subspace based on all received symbols (pilot and payload) to provide additional information for subsequent channel estimation. This additional information enhances minimum mean square error (MMSE) channel estimation. Two variants of the linear MMSE (LMMSE) estimator are formulated, where the first one solves the estimation within the subspace, and the second one uses a subspace projection as a preprocessing step. Theoretical derivations show that the latter method achieves superior mean square error performance for uncorrelated Rayleigh fading. Further, we provide asymptotical insights on how the proposed MMSE-based channel estimation strategy outperforms the unbiased Cramer-Rao bound. Subsequently, we introduce parameterizations of these semi-blind LMMSE estimators based on two different conditional Gaussian latent models, i.e., the Gaussian mixture model and the variational autoencoder. Both models learn the propagation environment’s underlying channel distribution based on training data and serve as generative priors for our semi-blind channel estimation. Extensive simulations on real-world measurement data and spatial channel models show that the proposed methods achieve superior performance compared to state-of-the-art semi-blind channel estimators in terms of MSE.

Diffusion models (DMs) as generative priors have recently shown great potential for denoising tasks but lack theoretical understanding with respect to their mean square error (MSE) optimality. This paper proposes a novel denoising strategy inspired by the structure of the MSE-optimal conditional mean estimator (CME). The resulting DM-based denoiser can be conveniently employed using a pre-trained DM, being particularly fast by truncating reverse diffusion steps and not requiring stochastic re-sampling. We present a comprehensive (non-)asymptotic optimality analysis of the proposed diffusion-based denoiser, demonstrating polynomial-time convergence to the CME under mild conditions. Our analysis also derives a novel Lipschitz constant that depends solely on the DM’s hyperparameters. Further, we offer a new perspective on DMs, showing that they inherently combine an asymptotically optimal denoiser with a powerful generator, modifiable by switching re-sampling in the reverse process on or off. The theoretical findings are thoroughly validated with experiments based on various benchmark datasets.

Pilot contamination (PC) is a well-known problem that affects massive multiple-input multiple-output (MIMO) systems. When frequency and pilots are reused between different cells, PC constitutes one of the main bottlenecks of the system's performance. In this paper, we propose a method based on the variational autoencoder (VAE), capable of reducing the impact of PC-related interference during channel estimation (CE). We obtain the first and second-order statistics of the conditionally Gaussian (CG) channels for both the user equipments (UEs) in a cell of interest and those in interfering cells, and we then use these moments to compute conditional linear minimum mean square error estimates. We show that the proposed estimator is capable of exploiting the interferers’ additional statistical knowledge, outperforming other classical approaches. Moreover, we highlight how the achievable performance is tied to the chosen setup, making the setup selection crucial in the study of multi-cell CE.

We propose to use Tomlinson-Harashima Precoding (THP) for the reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) broadcast channel where we assume a line of sight (LOS) connection between the base station (BS) and the RIS. In this scenario, nonlinear precoding, like THP or dirty paper coding (DPC), has certain advantages compared to linear precoding as it is more robust in case the BS-RIS channel is not orthogonal to the direct channel. Additionally, THP and DPC allow a simple phase shift optimization which is in strong contrast to linear precoding for which the solution is quite intricate. Besides being difficult to optimize, linear precoding has fundamental limitations when the phases are chosen randomly or based on statistical channel state information (CSI). These limitations do not hold for nonlinear precoding. Moreover, we show that the advantages of THP/DPC are especially pronounced for discrete phase shifts.

Rate splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) are two prospective technologies for improving the spectral and energy efficiency in future wireless communication systems. In this work, we investigate a rate splitting (RS) technique for an RIS-aided system in the presence of only statistical channel knowledge. We propose an algorithm with a quasi closed-form solution based only on the second-order channel statistics, which reduces the design complexity of the system as it does not require estimation of the channel state information (CSI) and optimisation of the precoding filters and phase shifts of the RIS in every channel coherence interval.

This paper investigates using side information in minimum mean square error (MMSE) estimation. We propose a direction-of-arrival (DoA)-aided two-stage channel estimation technique that utilizes information about the dominant direction of the channel. To this end, the decomposition of the MMSE channel estimation into two orthogonal subspaces is formulated. After estimating the channel along the dominant direction, we utilize a Gaussian mixture model to estimate the conditionally Gaussian distributed random vector, which represents the multipath propagation. The proposed two-stage estimator allows pre-computing the respective estimation filters, tremendously reducing the computational complexity. Numerical simulations depict the superior performance of our proposed two-stage estimation approach compared to state-of-the-art methods.

Learning the site-specific distribution of the wireless channel within a particular environment of interest is essential to exploit the full potential of machine learning (ML) for wireless communications and radar applications. Generative modeling offers a promising framework to address this problem. However, existing approaches pose unresolved challenges, including the need for high-quality training data, limited generalizability, and a lack of physical interpretability. To address these issues, we combine the physics-related compressibility of wireless channels with generative modeling, in particular, sparse Bayesian generative modeling (SBGM), to learn the distribution of the underlying physical channel parameters. By leveraging the sparsity-inducing characteristics of SBGM, our methods can learn from compressed observations received by an access point (AP) during default online operation. Moreover, they are physically interpretable and generalize over system configurations without requiring retraining.

Predicting the motion of traffic participants accurately remains a challenging task in the field of automated driving. Especially interactions between traffic participants introduce high complexity and interdependencies into the environment prediction. This work presents the remarkable performance of a Partially Observable Markov Decision Process (POMDP) framework to stochastically predict and safely respond to an interacting environment. The framework is validated for its ability to increase the overall Ego-Vehicle safety by preemptively triggering a de-escalation maneuver. The performance of the framework is analyzed on a publicly available dataset with real-world traffic (Argoverse) and on highly critical simulation scenarios specified by Euro-NCap for emergency braking functions. The results show quantitatively that the proposed framework significantly contributes to an early de-escalation of critical scenarios. Such an early de-escalation increases the safety and comfort of automated vehicles.

Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.

Recent cooperative perception datasets have played a crucial role in advancing smart mobility applications by enabling information exchange between intelligent agents, helping to overcome challenges such as occlusions and improving overall scene understanding. While some existing real-world datasets incorporate both vehicle-to-vehicle and vehicle-to-infrastructure interactions, they are typically limited to a single intersection or a single vehicle. A comprehensive perception dataset featuring multiple connected vehicles and infrastructure sensors across several intersections remains unavailable, limiting the benchmarking of algorithms in diverse traffic environments. Consequently, overfitting can occur, and models may demonstrate misleadingly high performance due to similar intersection layouts and traffic participant behavior. To address this gap, we introduce UrbanIng-V2X, the first large-scale, multi-modal dataset supporting cooperative perception involving vehicles and infrastructure sensors deployed across three urban intersections in Ingolstadt, Germany. UrbanIng-V2X consists of 34 temporally aligned and spatially calibrated sensor sequences, each lasting 20 seconds. All sequences contain recordings from one of three intersections, involving two vehicles and up to three infrastructure-mounted sensor poles operating in coordinated scenarios. In total, UrbanIng-V2X provides data from 12 vehicle-mounted RGB cameras, 2 vehicle LiDARs, 17 infrastructure thermal cameras, and 12 infrastructure LiDARs. All sequences are annotated at a frequency of 10 Hz with 3D bounding boxes spanning 13 object classes, resulting in approximately 712k annotated instances across the dataset. We provide comprehensive evaluations using state-of-the-art cooperative perception methods and publicly release the codebase, dataset, HD map, and a digital twin of the complete data collection environment via https://github.com/thi-ad/UrbanIng-V2X.

Forming effective access point (AP) cooperation clusters is a key challenge in user-centric cell-free massive MIMO (CF-mMIMO). Existing approaches to this task are either computationally prohibitive or overlook the complex interrelationships within communication networks. In this context, we introduce an innovative approach based on graph neural networks (GNNs). By leveraging the inherent graph structure of CF-mMIMO networks, we transform the rate maximization problem into a node classification task, enabling a competitive and robust solution. Simulation results show that the proposed method significantly outperforms conventional baselines in terms of spectral efficiency, computational complexity, and scalability.

We present new fundamental results for the mean square error (MSE)-optimal conditional mean estimator (CME) in one-bit quantized systems for a Gaussian mixture model (GMM) distributed signal of interest, possibly corrupted by additive white Gaussian noise (AWGN). We first derive novel closed-form analytic expressions for the Bussgang estimator, the well-known linear minimum mean square error (MMSE) estimator in quantized systems. Afterward, closed-form analytic expressions for the CME in special cases are presented, revealing that the optimal estimator is linear in the one-bit quantized observation, opposite to higher resolution cases. Through a comparison to the recently studied Gaussian case, we establish a novel MSE inequality and show that that the signal of interest is correlated with the auxiliary quantization noise. We extend our analysis to multiple observation scenarios, examining the MSE-optimal transmit sequence and conducting an asymptotic analysis, yielding analytic expressions for the MSE and its limit. These contributions have broad impact for the analysis and design of various signal processing applications.

In this work, we propose a Gaussian mixture model (GMM)-based pilot design scheme for downlink (DL) channel estimation in single- and multi-user multiple-input multiple-output (MIMO) frequency division duplex (FDD) systems. In an initial offline phase, the GMM captures prior information on the channel statistics through training, which is then utilized for pilot design. In the single-user case, the GMM is utilized to construct a codebook of pilot matrices and, once shared with the mobile terminal (MT), can be employed to determine a feedback index at the MT. This index selects a pilot matrix from the constructed codebook, eliminating the need for online pilot optimization. We further establish a sum conditional mutual information (CMI)-based pilot optimization framework for multi-user MIMO (MU-MIMO) systems. Based on the established framework, we utilize the GMM for pilot matrix design in MU-MIMO systems. The analytic representation of the GMM enables the adaptation to any signal-to-noise ratio (SNR) level and pilot configuration without re-training. Additionally, an adaption to any number of MTs is facilitated. Extensive simulations demonstrate the superior performance of the proposed pilot design scheme compared to state-of-the-art approaches. The performance gains can be exploited, e.g., to deploy systems with fewer pilots.

In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed concept in which a VAE is trained on channel state information (CSI) data and used to parameterize an approximation to the mean squared error (MSE)-optimal estimator. The contributions in this work extend the existing framework from fully-determined (FD) to UD systems, which are of high practical relevance. Particularly noteworthy is the extension of the estimator variant, which does not require perfect CSI during its offline training phase. This is a significant advantage compared to most other deep learning (DL)-based CE methods, where perfect CSI during the training phase is a crucial prerequisite. Numerical simulations for hybrid and wideband systems demonstrate the excellent performance of the proposed methods compared to related estimators.

In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind channel estimation. To this end, a subspace is estimated based on all received symbols and utilized to improve the estimation quality of a Gaussian mixture model-based channel estimator, which solely uses pilot symbols for channel estimation. Both of the proposed approaches allow for parallelization. Even the precomputation of estimation filters, which is beneficial in terms of computational complexity, is enabled by one of the proposed methods. Numerical simulations for real channel measurement data available to us show that the proposed methods outperform the studied state-of-the-art channel estimators.

We consider the problem of sum rate maximization in frequency division duplex (FDD) systems when only imperfect channel state information (CSI) is available. Inspired by the low complexity and generalization ability offered by graph neural networks (GNNs), we propose an end-to-end (E2E) framework for both, precoding and downlink (DL) pilot sequences optimization based on the novel Edge-graph attention network (GAT). The simulation results confirm the potential of the proposed E2E approach to optimize sum rates and its scalability in scenarios with varying numbers of users. Additionally, the superiority of the learned pilot matrix compared to the conventionally employed sub-discrete Fourier transform (DFT) matrix is highlighted.

We present efficient algorithms for the sum-spectral efficiency (SE) maximization of the multi-user reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) broadcast channel based on a zero-forcing approach. These methods conduct a user allocation for which the computation is independent of the number of elements at the RIS, that is usually large. Specifically, two algorithms are given that exploit the line-of-sight (LOS) structure between the base station (BS) and the RIS. Simulations show superior SE performance compared to other linear precoding algorithms but with lower complexity.

This work extends two previously proposed semi-blind channel estimators to a more general multi-user multiple-input-multiple-output (MU-MIMO) system. These estimators utilize data symbols in addition to pilot symbols to enhance the channel estimation quality. Based on all received signals, a subspace is calculated, which enhances the Gaussian mixture model based channel estimator. To estimate this subspace, we consider the inherent additional degrees of freedom in terms of precoding in MU-MIMO systems. Numerical simulations for different scenarios show that the extended methods outperform the studied state-of-the-art channel estimators.

This research focuses on solving challenges related to identifying unfamiliar object categories in the realm of Open-Set Object Detection (OSOD) using infrastructure sensors. Traditional camera-based OSOD systems struggle to generate proposals for dissimilar novel classes due to a lack of feature similarity. This research introduces a novel approach named Fusion Object Detector (FOD), which emphasizes the localization and identification of semantically dissimilar unknown objects through a multimodal fusion architecture involving infrastructure-mounted cameras and LiDARs. FOD leverages a camera-based closed-set object detector for the identification of known class objects, while simultaneously utilizing clusters derived from fused LiDAR point clouds for the detection of unknown class objects. This research work also presents a novel dataset named Thermal camera and LiDAR in Infrastructure Dataset (TLID). TLID comprises fused sensor measurements from multiple thermal cameras and LiDARs mounted in three urban crossings of Ingolstadt city and at CARISSMA outdoor test track. The proposed methodology is evaluated using both an in-house dataset and a publicly available infrastructure dataset for the task of OSOD. The results quantify the importance of multimodal sensor information for the task of identifying dissimilar unknown objects.

This work addresses the fundamental linear inverse problem in compressive sensing (CS) by introducing a new type of regularizing generative prior. Our proposed method utilizes ideas from classical dictionary-based CS and, in particular, sparse Bayesian learning (SBL), to integrate a strong regularization towards sparse solutions. At the same time, by leveraging the notion of conditional Gaussianity, it also incorporates the adaptability from generative models to training data. However, unlike most state-of-the-art generative models, it is able to learn from a few compressed and noisy data samples and requires no optimization algorithm for solving the inverse problem. Additionally, similar to Dirichlet prior networks, our model parameterizes a conjugate prior enabling its application for uncertainty quantification. We support our approach theoretically through the concept of variational inference and validate it empirically using different types of compressible signals.

In this work, we derive a lower bound on the training-based achievable downlink (DL) sum rate (SR) of a multi-user multiple-input-single-output (MISO) system operating in frequency-division-duplex (FDD) mode. Assuming linear minimum mean square error (LMMSE) channel estimation is used, we establish a connection of the derived lower bound on the signal-to-interference-noise-ratio (SINR) to an average MSE that allows to reformulate the SR maximization problem as the minimization of the augmented weighted average MSE (AWAMSE). We propose an iterative precoder design with three alternating steps, all given in closed form, drastically reducing the computation time. We show numerically the effectiveness of the proposed approach in challenging scenarios with limited channel knowledge, i.e., we consider scenarios with a very limited number of pilots. We additionally propose a more efficient version of the well-known stochastic iterative WMMSE (SIWMMSE) approach, where the precoder update is given in closed form.

In this work, we propose an approach to robust precoder design based on a minorization maximization technique that optimizes a surrogate function of the achievable spectral efficiency. The presented method accounts for channel estimation errors during the optimization process and is, hence, robust in the case of imperfect channel state information (CSI). Additionally, the design method is adapted such that the need for a line search to satisfy the power constraint is eliminated, that significantly accelerates the precoder computation. Simulation results demonstrate that the proposed robust precoding method is competitive with weighted minimum mean square error (WMMSE) precoding, in particular, under imperfect CSI scenarios.

Discrete Fourier transform (DFT) codebook-based solutions are well-established for limited feedback schemes in frequency division duplex (FDD) systems. In recent years, data-aided solutions have been shown to achieve higher performance, enabled by the adaptivity of the feedback scheme to the propagation environment of the base station (BS) cell. In particular, a versatile limited feedback scheme utilizing Gaussian mixture models (GMMs) was recently introduced. The scheme supports multi-user communications, exhibits low complexity, supports parallelization, and offers significant flexibility concerning various system parameters. Conceptually, a GMM captures environment knowledge and is subsequently transferred to the mobile terminals (MTs) for online inference of feedback information. Afterward, the BS designs precoders using either directional information or a generative modeling-based approach. A major shortcoming of recent works is that the assessed system performance is only evaluated through synthetic simulation data that is generally unable to fully characterize the features of real-world environments. It raises the question of how the GMM-based feedback scheme performs on real-world measurement data, especially compared to the well-established DFT-based solution. Our experiments reveal that the GMM-based feedback scheme tremendously improves the system performance measured in terms of sum-rate, allowing to deploy systems with fewer pilots or feedback bits.

We propose a versatile feedback scheme for both single- and multi-user multiple-input multiple-output (MIMO) frequency division duplex (FDD) systems. Particularly, we propose utilizing a Gaussian mixture model (GMM) with a reduced number of parameters for codebook construction, feedback encoding, and precoder design. The GMM is fitted offline at the base station (BS) to uplink training samples to approximate the channel distribution of all possible mobile terminals (MTs) within the BS cell. Subsequently, a codebook is constructed, with each element based on one GMM component. Extracting directional information from the codebook or exploiting the GMM’s sample generation ability facilitates joint precoder design for a multi-user MIMO system using state-of-the-art precoding algorithms. After offloading the GMM to the MTs, they can easily determine their feedback by selecting the index of the GMM component with the highest responsibility for their received pilot signal. This strategy exhibits low complexity and supports parallelization. Simulations demonstrate that the proposed approach outperforms conventional methods, which either estimate the channel and utilize a Lloyd codebook or use a deep neural network to determine the feedback in terms of spectral efficiency or sum-rate. The performance gains can be exploited to deploy systems with fewer pilots or feedback bits.

Cardiovascular diseases are a significant cause of mortality worldwide, and the accurate diagnosis of these conditions is essential for effective treatment and management. Electrocardiograms (ECGs) are a common diagnostic tool used by cardiologists, but the manual interpretation of ECGs can be relatively time-consuming and challenging, particularly in cases of atrial fibrillation (AF), which is associated with an increased risk of stroke, heart failure, and other complications. To address the need for reliable and automatic ECG classifiers, we propose a new method using self-supervised learning with a CNNTransformer architecture to improve the ECG classification performance. The proposed model is pre-trained on the China Physiological Signal Challenge 2018 dataset and part of the Physikalisch-Technische Bundesanstalt (PTB) XL dataset using a novel ’nextclip’ prediction task, which asks the model to predict the next small segment of ECG, followed by finetuning on the ECG classification task. Our experimental results demonstrate that our proposed method achieves state-of-the-art results for ECG classification, with an average F1-score of 0.84 and 0.96 for AF detection on the CPSC2018 dataset. The proposed CNNTransformer architecture has shown to be an effective and efficient solution for ECG classification, especially on AF.

One way to improve the estimation of time varying channels is to incorporate knowledge of previous observations. In this context, Dynamical VAEs (DVAEs) build a promising deep learning (DL) framework which is well suited to learn the distribution of time series data. We introduce a new DVAE architecture, called k-MemoryMarkovVAE (k-MMVAE), whose sparsity can be controlled by an additional memory parameter. Following the approach in [1] we derive a k-MMVAE aided channel estimator which takes temporal correlations of successive observations into account. The results are evaluated on simulated channels by QuaDRiGa and show that the k-MMVAE aided channel estimator clearly outperforms other machine learning (ML) aided estimators which are either memoryless or naively extended to time varying channels without major adaptions.

In this work, we study the asymptotic behavior of the zero-forcing precoder based on the least squares (LS) and the linear minimum mean-square error (LMMSE) channel estimates for the downlink (DL) of a frequency-division-duplex (FDD) massive multiple-input-single-output (MISO) system. We show analytically the rather surprising result that zero-forcing precoding based on the LS estimate leads asymptotically to an interference-free transmission, even if the number of pilots used for DL channel training is less than the number of antennas available at the base station (BS). Although the LMMSE channel estimate exhibits a better quality in terms of the MSE due to the exploitation of the channel statistics, we show that in the case of contaminated channel observations, zero-forcing based on the LMMSE is unable to eliminate the inter-user interference in the asymptotic limit of high DL transmit powers. In order for the results to hold, mild conditions on the channel probing phase are assumed. The validity of our analytical results is demonstrated through numerical simulations for different scenarios.

For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parameter learning. Especially for small and sparse graph structures a proper optimization is problematic. To surpass limitations, this work proposes architectural modifications of GATv2. In controlled experiments, it is shown that the proposed model adaptions improve prediction performance in a node-level regression task and make it more robust to parameter initialization. This work aims for a better understanding of the attention mechanism and analyzes its interpretability of identifying causal importance.

Sensors installed in the infrastructure can make a significant contribution to the advancement of Advanced Driver Assistance Systems (ADAS) and connected mobility. Thermal cameras provide protection against the abuse of personalised data and perform robustly in challenging environmental conditions, making them an excellent choice for infrastructural perception. The goal of this work is to solve the crucial problem of Open-Set Recognition (OSR) for thermal camera-based perception systems installed in the infrastructure. In this paper, a novel modular architecture for OSR called Class Specific Experts (CSE) is proposed, in which, class specialization is achieved using individual feature spaces. The proposed methodology can be easily embedded in an object detection setting and provides as a main advantage, the possibility of online incremental learning without catastrophic forgetting. This work also introduces a open-source classification dataset called Infrastructure Thermal Dataset (ITD) containing image snippets captured by a thermal camera mounted in the infrastructure. The proposed approach outperforms the compared baselines for the task of OSR on many publicly available thermal and non-thermal datasets, as well as the new ITD dataset.

In this work, we present new results for the application of rate splitting multiple access (RSMA) to the downlink (DL) of a massive multiple-input-multiple-output system (MIMO) operating in frequency-division-duplex (FDD) mode. We propose a statistical precoding design relying on the channels’ second-order information when one-layer RS is implemented. The advantage of the statistical precoding lies in simplifying the precoder design by basing the optimization objective on the slowly-varying channel covariance matrices instead of the fast-changing instantaneous channel estimates. To this end, we use the so- called bilinear precoder, which was shown in Neumann et al. (2018) and Ben Amor et al. (2020) to have limited performance in the high SNR regime due to the imperfect channel state information (CSI) available at the base station (BS). We formulate the DL throughput maximization problem based on a widely used lower bound on the achievable sum rate and propose an iterative approach to solve the underlying optimization problem. Numerical results demonstrate the benefit of implementing one-layer RS. Furthermore, the proposed iterative approach achieves excellent results in terms of spectral efficiency compared to the state-of-the-art techniques.

To fully unlock the benefits of multiple-input multiple-output (MIMO) networks, downlink channel state information (CSI) is required at the base station (BS). In frequency division duplex (FDD) systems, the CSI is acquired through a feedback signal from the user equipment (UE). However, this may lead to an important overhead in FDD massive MIMO systems. Focusing on these systems, in this study, we propose a novel strategy to design the CSI feedback. Our strategy allows to optimally design variable length feedback, that is promising compared to fixed feedback since users experience channel matrices differently sparse. Specifically, principal component analysis (PCA) is used to compress the channel into a latent space with adaptive dimensionality. To quantize this compressed channel, the feedback bits are smartly allocated to the latent space dimensions by minimizing the normalized mean squared error (NMSE) distortion. Finally, the quantization codebook is determined with$k$-means clustering. Numerical simulations show that our strategy improves the zero-forcing beamforming sum rate by 17%, compared to CsiNetPro. The number of model parameters is reduced by 23.4 times, thus causing a significantly smaller offloading overhead. At the same time, PCA is characterized by a lightweight unsupervised training, requiring eight times fewer training samples than CsiNetPro.

The model order of a wireless channel plays an important role for a variety of applications in communications engineering, e.g., it represents the number of resolvable incident wave-fronts with non-negligible power incident from a transmitter to a receiver. Areas such as direction of arrival estimation leverage the model order to analyze the multipath components of channel state information. In this work, we propose to use a variational autoencoder to group unlabeled channel state information with respect to the model order in the variational autoencoder latent space in an unsupervised manner. We validate our approach with simulated 3GPP channel data. Our results suggest that, in order to learn an appropriate clustering, it is crucial to use a more flexible likelihood model for the variational autoencoder decoder than it is usually the case in standard applications.

We consider channel estimation for systems equipped with an intelligent reflecting surface (IRS). We develop least squares (LS) and minimum mean square error (MMSE) estimation for such systems. The appropriate system models are developed and we also discuss the parameters which can be estimated in such a setup because there exists a difficulty due to the ambiguity for the two channels connecting with the IRS. The MMSE estimator is based on a Kronecker product approximation of the channel covariance matrix. The simulations results illustrate the advantage of the optimized pilots and the optimized phase allocations for the channel estimation.

This paper investigates a channel estimator based on Gaussian mixture models (GMMs). We fit a GMM to given channel samples to obtain an analytic probability density function (PDF) which approximates the true channel PDF. Then, a conditional mean estimator (CME) corresponding to this approximating PDF is computed in closed form and used as an approximation of the optimal CME based on the true channel PDF. This optimal estimator cannot be calculated analytically because the true channel PDF is generally not available. To motivate the GMM-based estimator, we show that it converges to the optimal CME as the number of GMM components is increased. In numerical experiments, a reasonable number of GMM components already shows promising estimation results.

We present distributed methods for jointly optimizing Intelligent Reflecting Surface (IRS) phase-shifts and beamformers in a cellular network. The proposed schemes require knowledge of only the intra-cell training sequences and corresponding received signals without explicit channel estimation. Instead, an achievable sum-rate objective is estimated via sample means and maximized directly. This automatically includes and mitigates both intra- and inter-cell interference provided that the uplink training is synchronized across cells. Different schemes are considered that limit the set of known training sequences from interferers. With MIMO links an iterative synchronous bi-directional training scheme jointly optimizes the IRS parameters with the beamformers and combiners. Simulation results show that the proposed distributed methods show a modest performance degradation compared to centralized channel estimation schemes, which estimate all channels including all cross-channels, and perform significantly better than decentralized channel estimation schemes which ignore the inter-cell interference.

Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity measures or good representations for the traffic scenarios. In this work, an expert-knowledge aided representation learning for traffic scenarios is presented. The latent space so formed is used for successful clustering and novel scenario type detection. Expert-knowledge is used to define objectives that the latent representations of traffic scenarios shall fulfill. It is presented, how the network architecture and loss is designed from these objectives, thereby incorporating expert-knowledge. An automatic mining strategy for traffic scenarios is presented, such that no manual labeling is required. Results show the performance advantage compared to baseline methods. Additionally, extensive analysis of the latent space is performed.

We investigate the distributional shifts between datasets which pose a challenge to validate safety critical driving functions which incorporate Machine Learning (ML)-based algorithms. First, we describe the possible distributional shifts which can occur in highway driving datasets. Following this, we analyze–both qualitatively and quantitatively–-the distributional shifts between two publicly available, and widely used, highway driving datasets. We demonstrate that a safety critical driving function, e.g., a lane change maneuver prediction, trained on one dataset will not generalize as expected to the other dataset in the presence of these distributional shifts. This highlights the impact which distributional shifts can have on safety critical driving functions. We suggest that an analysis of the datasets used to train ML-based algorithms incorporated in safety critical driving functions plays an important role in building a safety-argument for validation.

We propose an innovative machine learning-based technique to address the problem of channel acquisition at the base station in frequency division duplex systems. In this context, the base station reconstructs the full channel state information in the downlink frequency range based on limited downlink channel state information feedback from the mobile terminal. The channel state information recovery is based on a convolutional neural network which is trained exclusively on collected channel state samples acquired in the uplink frequency domain. No acquisition of training samples in the downlink frequency range is required at all. Finally, after a detailed presentation and analysis of the proposed technique and its performance, the “reciprocity of distribution” assumption of the convolutional neural network that is central to the proposed approach is validated with an analysis based on the maximum mean discrepancy metric.

In this paper, we address the challenge of employing Machine Learning (ML) algorithms in safety critical driving functions. Despite ML algorithms demonstrating good performance in various driving tasks, e.g., detecting when other vehicles are going to change lanes, the challenge of validating these methods has been neglected. To this end, we introduce an interpretable Lane Change Detector (LCD) algorithm which takes advantage of the performance of modern ML-based anomaly detection methods. We independently train three Deep Autoencoders (DAEs) on different driving maneuvers: lane keeping, right lane changes, and left lane changes. The lane changes are subsequently detected by observing the reconstruction errors at the output of each DAE. Since the detection is purely based on the reconstruction errors of independently trained DAEs, we show that the classification outputs are completely interpretable. We compare the introduced algorithm with black-box Recurrent Neural Network (RNN)-based classifiers, and train all methods on realistic highway driving data. We discuss both the costs and the benefits of an interpretable classification, and demonstrate the inherent interpretability of the algorithm.

This work gives a summary of our implemented algorithm for dynamic grid map with integrated cell-based velocity estimation. We propose a novel approach to generate prior information for the orientation of newly initialized particles. Our proposal does not rely on any additional input information as maps with the road topology, but does only rely on the vehicle motions and sensor data. Furthermore we show the improvement resulting from this prior information under certain conditions such as turning scenarios and that there is no significant degradation in other conditions.

Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A traffic scenario consists of multiple components, with infrastructure being one of it. In this work, a method to detect novel traffic scenarios based on their infrastructure images is presented. An autoencoder triplet network provides latent representations for infrastructure images which are used for outlier detection. The triplet training of the network is based on the connectivity graphs of the infrastructure. By using the proposed architecture, expert-knowledge is used to shape the latent space such that it incorporates a pre-defined similarity in the neighborhood relationships of an autoencoder. An ablation study on the architecture is highlighting the importance of the triplet autoencoder combination. The best performing architecture is based on vision transformers, a convolution-free attention-based network. The presented method outperforms other state-of-the-art outlier detection approaches.

We study the problem of predicting channel coefficients from one-bit quantized observations in an environment of a moving user who sends pilots to a base station. To start with, we propose a prediction algorithm which consists of two stages. The first stage aims at reconstructing the high-resolution (pre-quantization) receive signal. The second stage then predicts channel coefficients from this reconstructed signal. A drawback of this algorithm is that certain second moments of the channel statistics are required. In case of high-resolution (no quantization) observations, a recently introduced neural network based approach was able to predict channels even without the use of second order statistics. A low-SNR formulation of the proposed two stage algorithm motivates us to employ the neural network based method also in the case of one-bit quantization. Numerical simulations demonstrate the validity of this approach. We observe that the obtained channel predictor can compete with the algorithm that makes use of the second order statistics.

In this paper, we discuss a new approach to direction of arrival estimation for systems with subarray sampling. We propose to estimate the covariance matrix of the full array from the sample covariance matrices of the subarrays using a neural network. This technique enables the estimation of more sources than radio frequency chains by applying a MUSIC estimator to the reconstructed full covariance matrix. The proposed method is able to outperform classical estimators and has some benefits compared to a recently proposed machine learning-based technique for these systems, which models the direction of arrival estimation problem as a end-to-end regression task.

In this paper, we present a machine learning approach for estimating the number of incident wavefronts in a direction of arrival scenario. In contrast to previous works, a multilayer neural network with a cross-entropy objective is trained. Furthermore, we investigate an online training procedure that allows an adaption of the neural network to imperfections of an antenna array without explicitly calibrating the array manifold. We show via simulations that the proposed method outperforms classical model order selection schemes based on information criteria in terms of accuracy, especially for a small number of snapshots and at low signal-to-noise-ratios. Also, the online training procedure enables the neural network to adapt with only a few online training samples, if initialized by offline training on artificial data.

Vehicular safety functions can increase automotive safety by intervening in dangerous situations. However, as such functions rely on sensor measurements to decide actions, they are subject to sensor measurement errors which influence the performance. Therefore, a manufacturer has to design both the sensors and functions in a robust manner considering these errors. A methodology for such a robust design has already been proposed for an automatic emergency braking (AEB) system and is based on a probabilistic quality measure. It is often only possible to evaluate such a probabilistic quality measure through simulations of the system under design. Therefore, a novel approach for efficiently evaluating the probabilistic quality measure through simulations of the AEB system is proposed. The structure of the stochastic problem is analyzed and the new approach derived accordingly. Numerical examples illustrate the savings in computational effort as compared to a Monte Carlo simulation and the accuracy limits. Moreover, the proposed approach generalizes to other vehicular safety systems as well.

Vehicular safety functions that take over the control during dangerous driving situations increase automotive safety. As such functions use the measurements of sensors in order to determine the driving situation, unavoidable sensor measurement errors can have a negative impact on both the safety and the satisfaction of the customer. In this paper, it is shown how a new methodology for the robust design of sensors and functions in vehicular safety considering sensor measurement errors that has already been applied to design an automatic emergency braking (AEB) system can also be applied to design an automatic emergency steering (AES) system. Based on a stochastic model, we formulate the robust design as optimization problems, whose solution yields the optimal parameters for the AES system with respect to a probabilistic quality measure. The probabilistic quality measure is defined similarly to that for the robust design of the AEB system and a closed-form expression is derived for it in case of circular vehicle shapes and an emergency steer intervention with constant lateral acceleration. For more complex vehicle shapes and emergency steer interventions, an approximation of the probabilistic quality measure by a Monte Carlo simulation is proposed in this paper, which leads to a robust design which is based on simulations of the vehicular safety system under design and applicable to other vehicular safety systems as well.

A novel unsupervised outlier score, which can be embedded into graph based dimensionality reduction techniques, is presented in this work. The score uses the directed nearest neighbor graphs of those techniques. Hence, the same measure of similarity that is used to project the data into lower dimensions, is also utilized to determine the outlier score. The outlier score is realized through a weighted normalized entropy of the similarities. This score is applied to road infrastructure images. The aim is to identify newly observed infrastructures given a pre-collected base dataset. Detecting unknown scenarios is a key for accelerated validation of autonomous vehicles. The results show the high potential of the proposed technique. To validate the generalization capabilities of the outlier score, it is additionally applied to various real world datasets. The overall average performance in identifying outliers using the proposed methods is higher compared to state-of-the-art methods. In order to generate the infrastructure images, an openDRIVE parsing and plotting tool for Matlab is developed as part of this work. This tool and the implementation of the entropy based outlier score in combination with Uniform Manifold Approximation and Projection are made publicly available.

It has been shown that rate splitting multiple access (RSMA) promises high data rates in the multi-user multiple-input single-output broadcast channel (MISO BC) with superior performance under imperfect channel state information (CSI) and the possibility to fulfill various quality of service (QoS) constraints. In this work, we use the 1-layer RS approach for the sum rate maximization under a total power constraint in the multi-user multiple-input multiple-output broadcast channel (MIMO BC). We adapt the iterative weighted minimum mean squared error (WMMSE) approach to the MIMO BC. However, we propose an efficient approach that splits the optimization problem into a "private" part based on the previously proposed linear successive allocation (LISA) method, a "common" part carried out by an iteratively SINR maximizing method, and a power allocation part using the waterfilling algorithm. Numerical simulations show performance gains in the medium and high SNR region when applying the proposed method.

So-called improper complex signals have been shown to be beneficial in the single-antenna two-user Gaussian interference channel under the assumptions that all input signals are Gaussian and that we treat interference as noise (TIN). This result has been obtained under a restriction to pure strategies without time-sharing, and it was extended to the case where the rates, but not the transmit powers, may be averaged over several transmit strategies. In this paper, we drop such restrictions and discuss the most general case of coded time-sharing, where both the rates and the powers may be averaged. Since coded time-sharing can in general not be expressed by means of a convex hull of the rate region, we have to account for the possibility of time-sharing already during the optimization of the transmit strategy. By means of a novel channel enhancement argument, we prove a surprising result: proper signals are optimal if coded time-sharing is allowed. In addition to establishing this result, we present an algorithm to compute the corresponding achievable rate region.

We study the capabilities of a filter configuration where a linear filter is applied to an improper vector, and the output is processed by scalar widely linear filters afterwards. Assuming that this filter configuration is used to estimate a vector of interest from a noisy observation, we aim at finding the optimal filter coefficients in the sense of minimizing the mean square error. To this end, we propose a filter design algorithm based on alternating optimization. The resulting filter turns out to achieve an intermediate performance between the optimal widely linear filter and the optimal linear filter. As an application example, we discuss how the considered filter structure fits to the concept of linear transceivers in communication systems.

MIMO Radio-over-Copper (MIMO-RoC) is a transport system for indoor coverage that leverages the pre-existing building's copper cabling infrastructure. In MIMO-RoC, the overall channel from the Base Band Units (BBU) to the end-user is the cascade of a MIMO-radio over a MIMO-cable channel and the analog fronthaul together with a wireless channel enables the transport to the mobile users. The entire system poses two main challenges: i) design low-complexity and flexible allocation strategies between wired and wireless resources; ii) design interference cancellation techniques tailored to the mutually coupled wired-wireless interference. While the former has been extensively investigated, the optimal design of precoding algorithms for the MIMO-RoC downlink is still an open issue and is covered here. In particular, in this paper, the Linear Successive Allocation (LISA) algorithm has been revised and adapted to the MIMO amplify-and-forward (AF) structure of MIMO-RoC. Numerical results validate the proposed method considering a realistic radio environment with 100m copper cable remotization.

As vehicular safety functions that intervene in dangerous driving situations use sensor measurements for interpreting the driving situation, they are typically very vulnerable to sensor imperfections and measurement errors have a negative impact on both the safety and the satisfaction of the customer. Therefore, a new methodology for the robust design of an automatic emergency braking (AEB) system is proposed, which considers sensor measurement errors, selects the best decision rule used by the function of the AEB system for triggering an emergency brake intervention and covers several scenarios in which the designed AEB system is supposed to work. The robust function and sensor design for the AEB system is formulated as optimization problems based on a stochastic model. Numerical examples illustrating the elaborated theoretical results show how the new design methodology provides the designer with design spaces from which the optimal parameter values are chosen, with a ranking of the decision rules based on which the best decision rule is selected and with the worst cases from the set of considered scenarios. Moreover, the proposed design methodology generalizes and can be applied to design functions and sensors of other vehicular safety systems as well.

This paper jointly addresses user scheduling and precoder/combiner design in the downlink of a wideband millimeter-wave communications system. We consider the orthogonal frequency-division multiplexing modulation to overcome the channel frequency selectivity and obtain a number of equivalent narrowband channels. Hence, the main challenge is that the analog preprocessing network is frequency flat and common to all the users at the transmitter side. Moreover, the effect of the signal bandwidth over the uniform linear array steering vectors has to be taken into account to design the hybrid precoders and combiners. The proposed algorithmic solution is based on the linear successive allocation, which greedily allocates streams to different users and computes the corresponding precoders and combiners. By taking into account the rank limitations imposed by the hardware at transmission and reception, the performance loss in terms of achievable sum rate for the hybrid approach is negligible. The numerical experiments show that the proposed method exhibits excellent performance with reasonable computational complexity.

In this paper, the commonly used filtering technique occupancy grid mapping for static environments is extended for dynamic environments. The proposed method is able to estimate velocities indirectly. We apply a distribution model of the respective state variable to estimate the cell dynamics by means of prediction and update cycle, as known by standard tracking filters. Therefore, we present a straight forward derivation of the prediction and update rule. Furthermore, we validate our approach by simple one dimensional simulations, and show how it can be extended into a two dimensional world, including the resulting consequences, e.g. in terms of memory requirements.

Interference during the uplink training phase significantly deteriorates the performance of a massive MIMO system. The impact of the interference can be reduced by exploiting the second-order statistics of the channel vectors, e.g., to obtain the minimum mean squared error estimates of the channel. In practice, the channel covariance matrices have to be estimated. The estimation of the covariance matrices is also impeded by the interference during the training phase. However, the coherence interval of the covariance matrices is larger than that of the channel vectors. This allows us to derive methods for accurate covariance matrix estimation by the appropriate assignment of pilot sequences to the users in consecutive channel coherence intervals. To keep the computational complexity in check, we exploit common structure of the covariance matrices.

Millimeter-wave (mm-wave) communications plays an important role in future cellular networks because of the vast amount of spectrum available in the underutilized mm-wave frequency bands. To overcome the huge free space omnidirectional path loss in those frequency bands, the deployment of a very large number of antenna elements at the base station is crucial. The complexity, power consumption, and costs resulting from the large number of antenna elements can be reduced by limiting the number of RF chains. This leads to hybrid precoding and combining, which, in contrast to the traditional fully digital precoding and combining, moves a part of the signal processing from the digital to the analog domain. This paper proposes new algorithms for the design of hybrid precoders and combiners in a multiuser scenario. The algorithms are based on the previously proposed linear successive allocation method developed for the traditional fully digital version. It successively allocates data streams to users and suppresses the respective interstream interference in two stages, which perfectly matches the hybrid architecture. Furthermore, a low-complexity version is developed by exploiting the typical structure of mm-wave channels. The good performance of the proposed method and its low-complexity version is demonstrated by simulation results.

Digital subscriber line technologies such as G.fast face strong crosstalk between different twisted pair copper wires on higher frequencies. With crosstalk cancellation and interference management techniques, the performance is optimized with respect to high crosstalk. Maximum sum-rate is a widely used objective for groups of multiple subscribers, but does not reflect the subscriber demands and may lead to a high spread of data rates. This paper presents an approach to utilize weighted sumrate optimization to improve data rates for a subset of subscribers with higher demand, resulting in a longer reach for a certain data rate while using free resources from other subscribers. Results for linear and non-linear zero-forcing precoding as well as a gradient approach for linear MMSE precoding are investigated.

Pilot contamination is a throughput limiting factor in cellular massive MIMO systems. Previous work has shown that the impact of pilot-contamination can be reduced by exploiting structural information in form of channel covariance matrices. Additionally, significant gains can be obtained through coordinated user assignment. In this paper, we extend these approaches to a realistic scenario with imperfect knowledge of the channel and its distribution at the base station. We formulate an optimization problem for assigning users to the available pilot sequences, which at the same time takes the estimation of the covariance matrices into account and propose a suboptimal greedy algorithm for efficient implementation. Simulation results with established physical channel models demonstrate the significant performance gains of the proposed method in the case of imperfect knowledge of the covariance matrices at the base station.

We prove convergence of the projected gradient algorithm with inexact projections when applied to linear inverse problems with constraint sets that are unions of subspaces. Such an algorithm is useful for joint angle and delay estimation in MIMO radar, where classical estimators for angle estimation can be integrated into compressive sensing methods for range estimation.

Many variants of the Rapidly-exploring Random Tree (RRT) algorithm use biased-sampling strategies for solving computationally intensive tasks. One of such tasks is the planning of safe trajectories with the simultaneous intervention in both the longitudinal and the lateral dynamics of the vehicle in complex traffic-scenarios with multiple static and dynamic objects. A recently proposed hybrid statistical learning approach uses a 3D convolutional neural network (3D-ConvNet) to predict suitable longitudinal acceleration profiles in combination with an RRT variant called the Augmented CL-RRT algorithm. This algorithm is not effective in complex traffic-scenarios, i.e., traffic scenarios with more than 4 dynamic objects, because of the lack of flexibility and biasing in the longitudinal and the lateral dynamics intervention, respectively. Therefore, an extension to the Augmented CL-RRT algorithm is introduced to improve the longitudinal dynamics intervention with actuator and stable profile constraints and named as the Augmented CL-RRT+ algorithm. A biased-sampling strategy is also proposed based on the predicted longitudinal acceleration and steering wheel angle profiles provided by a trained 3D-ConvNet. Simulations are performed to compare different trajectory planning algorithms based on efficiency and safety. The results show vast improvements in terms of the efficiency without harming the safety.

Recently, ITU finished its standard for G.fast, utilized in so called fiber-to-the-distribution-point (FTTdp) networks, where only the last meters from the fiber link to the customer are bridged by existing copper wires. The target is to increase the data rate over the short copper link up to 1 Gbit/s, which is an increase by the factor of ten compared to VDSL2, by extending the frequency band up to 212 MHz. At higher frequencies, the crosstalk between the lines is amplified, which makes crosstalk management essential to obtain the targeted data rates. Tomlinson-Harashima Precoding (THP) is a non-linear precoding technique, which is considered for the 212 MHz G.fast systems. Up to now, linear zero-forcing (ZF) and weighted minimum mean-square-error (WMMSE) precoding, as well as ZF THP, have been investigated for G.fast. In this paper, we show how WMMSE THP can be utilized in this scenario. We show that WMMSE THP outperforms ZF THP in terms of achievable rate in large systems. However, we see that the rate gain is not significant, although the WMMSE THP solution almost achieves the channel capacity.

Interference coupling in heterogeneous networks introduces the inherent non-convexity to the network resource optimization problem, hindering the development of effective solutions. A new framework based on multi-pattern formulation has been proposed in this paper to study the energy efficient strategy for joint cell activation, user association and multicell multiuser channel allocation. One key feature of this interference pattern formulation is that the patterns remain fixed and independent of the optimization process. This creates a favorable opportunity for a linear programming formulation while still taking interference coupling into account. A tailored algorithm is developed to solve the formulated network energy saving problem in the dual domain by exploiting the problem structure, which gives a significant complexity saving compared to using standard solvers. Numerical results show a huge improvement in energy saving achieved by the proposed scheme.

It is well-known that the use of improper signaling schemes can be beneficial in interference-limited networks. Here we consider an underlay cognitive radio scenario, where a multi-antenna primary user is protected by an interference temperature constraint that ensures a prescribed rate requirement. We study how the interference temperature threshold changes when the interference is constrained to be maximally improper. Since the spatial structure of the impropriety is an additional degree of freedom, we provide the maximum value of the interference threshold that ensures the rate requirement. We illustrate the potential payoffs of improper signaling with some numerical examples, which show that a secondary user can significantly improve its achievable rate with respect to the proper signaling case.

Energy efficiency is besides higher data rates a key to success of the next generation copper access technology, G.fast [1]. Power consumption targets are not only driven by government requirements, but also by the need for access nodes without local power supply, which are fed from the subscriber via reverse power feeding (RPF). G.fast introduces discontinuous operation (DO) to reduce power consumption by switching lines on and off on a short time scale. Implementing DO in combination with precoding requires to maintain precoding performance on active lines while other lines are discontinued. This paper investigates G.fast DO in combination with linear and nonlinear precoding. Spectrum and framing optimization for DO is discussed. Implementation approaches are compared in terms of complexity and power saving capabilities, considering realization aspects as well as standard-related limitations.

Planning of safe trajectories with interventions in both lateral and longitudinal dynamics of vehicles has huge potential for increasing the road traffic safety. Main challenges for the development of such algorithms are the consideration of vehicle nonholonomic constraints and the efficiency in terms of implementation, so that algorithms run in real time in a vehicle. The recently introduced Augmented CL-RRT algorithm is an approach that uses analytical models for trajectory planning based on the brute force evaluation of many longitudinal acceleration profiles to find collision-free trajectories. The algorithm considers nonholonomic constraints of the vehicle in complex road traffic scenarios with multiple static and dynamic objects, but it requires a lot of computation time. This work proposes a hybrid machine learning approach for predicting suitable acceleration profiles in critical traffic scenarios, so that only few acceleration profiles are used with the Augmented CL-RRT to find a safe trajectory while reducing the computation time. This is realized using a convolutional neural network variant, introduced as 3D-ConvNet, which learns spatiotemporal features from a sequence of predicted occupancy grids generated from predictions of other road traffic participants. These learned features together with hand-designed features of the EGO vehicle are used to predict acceleration profiles. Simulations are performed to compare the brute force approach with the proposed approach in terms of efficiency and safety. The results show vast improvement in terms of efficiency without harming safety. Additionally, an extension to the Augmented CL-RRT algorithm is introduced for finding a trajectory with low severity of injury, if a collision is already unavoidable.

The densification and expansion of wireless network pose new challenges on interference management and reducing energy consumption. This paper studies energy-efficient resource management in heterogeneous networks by jointly optimizing cell activation, user association and multicell multiuser channel assignment, according to the long-term average traffic and channel conditions. The proposed framework is built on characterizing the interference coupling by predefined interference patterns, and performing resource allocation among these patterns. In this way, the interference fluctuation caused by (de)activating cells is explicitly taken into account when calculating the user achievable rates. A tailored algorithm is developed to solve the formulated problem in the dual domain by exploiting the problem structure, which gives a significant complexity saving. Numerical results show a huge improvement in energy saving achieved by the proposed scheme. The user association derived from the proposed joint resource optimization is mapped to standard-compliant cell selection biasing. This mapping reveals that the cell-specific biasing for energy saving is quite different from that for load balancing investigated in the literature.

Hybrid copper/fiber networks bridge the gap between the fiber link at the distribution point and the customer by using copper wires over the last meters. The G.fast transmission technology has been designed to be used in such a fiber to the distribution point (FTTdp) network. Crosstalk management using MIMO precoding in downlink and MIMO equalization in uplink is a key to the required performance of FTTdp. Currently, there are mostly linear and nonlinear zero-forcing (ZF) methods discussed for precoding in G.fast. This paper presents a spectrum optimization algorithm for both zero-forcing precoding methods. Minimum mean squared error (MMSE) precoding shows significant advantages under certain channel conditions. A performance comparison of MMSE precoding for G.fast with linear and nonlinear zero-forcing methods indicates that MMSE precoding outperforms standard linear and nonlinear ZF precoding. Applying the proposed spectrum optimization brings all three methods to a similar performance.

We evaluate the performance of a set of low complexity successive interference cancellation (SIC) detection algorithms in comparison to optimal maximum a-posteriori probability (MAP) detection and low complexity linear filter detection in a Turbo multiple-input multiple-output (Turbo-MIMO) system. We show that both linear and SIC soft detection algorithms perform similarly poorly for iterative receivers, even if the channel decoder output is available at the detector. We propose a low complexity combined a-priori/a-posteriori information-based error regularization technique, that improves the performance of the Turbo-MIMO design considerably. With this regularization technique, we show that a decoding gain of 2.2 dB can be achieved in an LTE compliant Turbo-MIMO receiver.

In the downlink (DL) of a multi-user multiple-input and multiple-output (MU-MIMO) system, the maximization of the weighted sum rate with dirty paper precoding (DPC) is treated under multiple linear and linear conic constraints. By network duality, the problem is transformed to a minimax uplink (UL) problem. In the UL, the minimization of the utility with respect to the noise covariance and the maximization of the utility with respect to the transmit covariances is solved either jointly or alternately with the gradient-projection algorithm. The proposed algorithms do not only allow to find the maximum weighted sum rate with respect to conic constraints, they are also efficient implementations with respect to multiple linear constraints.

We evaluate the capacity bounds and the system performance of a set of detection algorithms for multiple-input multiple-output (MIMO) systems focusing on low complexity interference cancellation methods. Since detection and decoding in a bit-interleaved coded modulation system (BICM) is performed separately, the performance in terms of bit error rate depends on both, the signal constellation and the channel encoder design. In order to find a universal measure for the performance of the detection stage, we evaluate the mutual information of the transmit signal and the a-posteriori bit hypotheses probabilities of the respective decoder. The outcome of our analysis provides bounds for the achievable rates using optimal and suboptimal detection algorithms.

Device-to-Device communication as underlay to cellular networks is likely to be incorporated into future communication systems to improve resource efficiency and meet the growing demand for high data rates. In this contribution we consider the uplink of a cellular network that is interfered by a Device-to-Device communication link operating on the same wireless resource. To coordinate interference we devise a new approach for the design of interference covariance shapes for the Device-to-Device link that are superior to state-of-the-art interference temperature constraints in terms of achievable rate regions. The transmit strategies of Device-to-Device and cellular uplink can be determined by solving convex optimization problems.

Hybrid copper/fiber networks bridge the gap between the fiber link and the customer by using copper wires over the last meters. This solution combines energy efficiency and low cost of the copper access network with high data rates of a fiber connection. However, the fiber to the distribution point (FTTdp) network must prove its ability to convey data at fiber speed over copper wire bundles under the spectral constraints of the copper access network. This work investigates achievable data rates of the FTTdp network. It provides an analysis of the sources of performance loss in a system implementation due to complexity limitations. Methods to improve achievable rates are shown, that are based on incorporating the limitations in the optimization process. Achievable data rates are analyzed in terms of rate vs. reach curves, based on a statistical channel model and the ITU standard G.fast. The results indicate that optimized linear methods perform well on shorter lines, while nonlinear methods have advantages for long lines.

This paper considers the so-called partial decode-and-forward (DF) strategy for the Gaussian multiple-input multiple-output (MIMO) relay channel. Unlike for the DF strategy or point-to-point (P2P) transmission from source to destination, for which Gaussian channel inputs are known to maximize the achievable rates, the input distribution that attains the maximum achievable partial DF rate for the Gaussian MIMO relay channel has remained unknown so far. For some special cases, e.g., for relay channels where the partial DF strategy reduces to the DF or P2P transmission, it could be deduced that Gaussian inputs maximize the rate that can be achieved with the partial DF strategy. For the general case, however, the problem has remained open until now. In this paper, we solve this problem by proving that the maximum achievable partial DF rate for the Gaussian MIMO relay channel is always attained by Gaussian channel inputs. Our proof relies on the channel enhancement technique, which was originally introduced by Weingarten et al. to derive the (private message) capacity region of the Gaussian MIMO broadcast channel. By combining this technique with a primal decomposition approach, we first establish that jointly Gaussian source and relay inputs maximize the achievable partial DF rate for the aligned Gaussian MIMO relay channel. Subsequently, we use a limiting argument to extend this result from the aligned to the general Gaussian MIMO relay channel.

Proper (i.e., circularly symmetric) Gaussian signals are known to be capacity-achieving in Gaussian multiple-input multiple-output (MIMO) broadcast channels with proper noise in the sense that the sum rate capacity under a sum power constraint is achievable with proper Gaussian signaling. In this paper, we generalize this statement by proving that the optimality of proper Gaussian signals also holds under a shaping constraint, i.e., a sum covariance constraint instead of a power constraint. Moreover, we show that not only the sum rate optimal point, but the whole capacity region can be achieved with proper Gaussian signals. Finally, we prove that the worst-case noise in a MIMO broadcast channel with shaping constraints is proper.

We present a low complexity soft detector for multiple-input multiple-output (MIMO) channels. Our proposed minimum mean square error successive interference cancellation (MMSE-SIC) detector is based on a regularization mechanism which reduces error propagation in the channel iterative decoder. Although our proposed detector is easy to implement and has a complexity order that is cubic in the number of transmit antennas, it can reach the performance of the soft max-log maximum-likelihood detector (MLD) under realistic system assumptions, as demonstrated in our simulations.

In this paper, a cognitive radio (CR) scenario comprised of a secondary interference channel (IC) and a primary point-to-point link (PPL) is studied, when the former interferes the latter. In order to satisfy a given rate requirement at the PPL, typical approaches impose an interference temperature constraint (IT). When the PPL transmits multiple streams, however, the spatial structure of the interference comes into play. In such cases, we show that spatial interference shaping constraints can provide higher sum-rate performance to the IC while ensuring the required rate at the PPL. Then, we extend the interference leakage minimization algorithm (MinIL) to incorporate such constraints. An additional power control step is included in the optimization procedure to improve the sum-rate when the interference alignment (IA) problem becomes infeasible due to the additional constraint. Numerical examples are provided to illustrate the effectiveness of the spatial shaping constraint in comparison to IT when the PPL transmits multiple data streams.

We propose a new model for the wireless broadcast advantage based on a polymatroid structure. This model is a generalization of the predominating hypergraph model. The polymatroid structure yields a general max-flow min-cut characterization of multicast rate regions, which applies to a large variety of channel, physical layer, and medium access models. It includes the state-of-the-art hypergraph flow regions with lossless and lossy hyperarcs, i.e., Shannon rate models and packet erasure networks. Additionally, it generalizes to various other rate regions, e.g., the cut-set outer bounds for networks of a large variety of independent broadcast channels, including networks of independent Gaussian multiple-input multiple-output channels, and the capacity regions for networks of independent deterministic broadcast channels, which can in general not be modeled by the hypergraph flow model. We propose a dual decomposition approach for network utility optimization problems on the polymatroid broadcast flow region, which subsumes existing dual decomposition approaches based on lossless and lossy hypergraph flow regions. Our approach significantly simplifies the decomposition, especially for lossy hypergraph models in packet erasure networks, by fully exploiting the inherent polymatroid structure of the wireless broadcast. Additionally, it can be directly used to fully characterize and evaluate the cut-set bounds for networks of independent broadcast channels with polymatroid structure without previous knowledge about the relevant cuts.

Recent trends in broadband access technology show the demand to extend the used frequency bands up to hundreds of MHz. Access cables are not built for such high frequencies, and measurements of access cables in this frequency range show a significant change of the cable characteristics compared to low frequencies. The novel modeling approach presented here is designed to be used for evaluation of transmission technologies for fiber-copper hybrid networks, so called FTTdp (Fiber To The distribution point), which enables service providers to serve customers with data rates in the GBit/s range without the requirement to install fiber to the home.

We focus on a linear beamformer design in the downlink with statistical channel state information (CSI) at the transmitter, where the users' ergodic rates are balanced. Simplifying the fading channels to given vectors with random scalar factors, which is a good approximation for rural mobile or satellite communications (SatCom), the stochastic model mismatch is kept small albeit the ergodic rate structure now allows for adapting the perfect CSI balancing algorithms. Although there is no equivalent signal-to-interference-and-noise-ratio (SINR) reformulation for the ergodic constraints, tight inner approximations with SINR structure are found. Based on this observation, a locally optimal sequential approximation strategy is proposed and a fixed point based implementation is provided that requires only few iterations.

Proper Gaussian signals have been shown to be optimal in multiple-input multiple-output (MIMO) broadcast channels from an information theoretic point of view, i.e., capacity can be achieved with a strategy that transmits circularly symmetric complex Gaussian signals. In this work, we show that optimality of proper Gaussian signals does not necessarily hold if the transmit strategy is restricted to widely linear transceivers. The proof is performed by identifying a rate tuple that is achievable in a certain set of channels with widely linear transceivers and improper Gaussian signals, but lies outside the achievable rate region for widely linear transceivers and proper Gaussian signals.

In this paper, we consider achievable rates for the Gaussian multiple-input multiple-output (MIMO) relay channel that can be obtained with the relay using the partial decode-and-forward scheme. The partial decode-and-forward strategy allows to optimize the amount of information the relay has to decode and can hence be seen as a generalization of the decode-and-forward strategy, where the relay must decode the entire source message. Since we cannot determine the maximal achievable partial decode-and-forward rate, we propose a suboptimal approach that is based on zero-forcing the interference the relay would suffer from the part of the source signal that it is not required to decode. For this purpose, a zero-forcing receive filter is introduced at the relay. We then show that, if the receive filter is fixed, standard convex optimization techniques can be used to evaluate the best rate our suboptimal partial decode-and-forward scheme can achieve. Simulation results demonstrate that the coding scheme we propose significantly outperforms the decode-and-forward scheme and/or approximates the cut-set bound for different network scenarios.

In this paper we propose an end-to-end feedback mechanism for intra-session random linear network coding with opportunistic routing in wireless packet networks with lossy links. We focus on bidirectional network coding, i.e., forward and reverse flows between two nodes are coded together, which is key for efficient utilization of the wireless medium as it allows intermediate nodes to relay traffic in both directions with a single transmission. We analyze the performance in terms of decoding and acknowledgement times in a three-node network when nodes are fully backlogged. The results are compared to the theoretic lower bound obtained by solving the network's flow formulation. In addition, we derive symmetric injection rates from the these results which the network should be able to sustain. The evolution of source backlogs and decoding/acknowledgement over time are simulated, demonstrating that backlogs remain bounded. The insight gained will help in developing a generalized feedback model for coded wireless mesh networks, which is to the best of our knowledge an open problem.

Optimization of transmit strategies with linear transceivers in multiple-input multiple-output (MIMO) broadcast channels generally leads to nonconvex problems, which cannot be solved efficiently in a globally optimal manner. Instead, it is necessary to resort to suboptimal algorithms. In this paper, we evaluate the application of a gradient descent algorithm for the optimization of the energy efficiency in such a system. Since the quality of the obtained locally optimal solutions depends on the initialization, a successive stream allocation is introduced and combined with the gradient algorithm. Comparison with a globally optimal reference algorithm for the special case of single receive antennas shows that the obtained solutions are close to the global optimum. For the MIMO case, the energy per bit achievable with dirty paper coding, which is a lower bound for the case of linear transceivers, is used as benchmark, and good performance of the gradient-based methods is shown for MIMO systems as well.

We analyze the achievable sum rate of the Gaussian MIMO broadcast channel. We first consider Multiple-Input Single-Output (MISO) channels and derive the large system limit of the sum capacity as the number of users and transmit antennas go to infinity with a fixed ratio. We then consider Multiple-Input Multiple-Output (MIMO) broadcast channels and fix the number of users and let the number of transmit and receive antennas tend to infinity with fixed ratio. As in this case an asymptotic expression for sum capacity is hard to obtain, we evaluate the large system sum rate corresponding to successive zero-forcing beamforming with Dirty-Paper Coding. The analysis gives a lower bound on the large system sum capacity, which is numerically observed to be quite close. In addition, large system analysis is applied to estimate the relatively small performance losses with respect to sum capacity of successive zero-forcing beamforming with and without Dirty-Paper Coding in finite MISO systems.

The use of proper, i.e., circularly symmetric, complex Gaussian signals for all users is known to be optimal in broadcast channels with proper complex Gaussian noise from an information theoretic point of view, i.e., they are employed in the capacity-achieving strategy. However, such proper per-user transmit signals are not necessarily optimal for problems with quality-of-service (QoS) constraints if the transmit strategy is restricted to widely linear transceivers without time-sharing. This is shown by deriving the QoS feasibility region of the multiple-input multiple-output broadcast channel with improper Gaussian per-user transmit signals and widely linear transceivers.

For MIMO interference networks, uncertainty in the spatial structure of interfering signals is a major source of performance degradation. In this work, we promote the use of linear unitary precoding, as it can be designed such that downlink transmission becomes more robust. Optimizing linear unitary precoding is a combinatorial and nonconvex problem, thus no efficient methods to compute global optimal solutions are available. Therefore, methods with reasonable complexity and acceptable performance are desired and have been investigated in research literature as well as for practical implementation. Contrary to existing work on unitary precoding, which considers receivers with a single antenna, we target scenarios with multiple receive antennas. We introduce and discuss a low-complexity method for successive user and precoder selection to enable unitary precoding for multi-antenna receivers. In addition to interference robustness, our novel method has several advantages for potential implementations, concerning the channel feedback and computation of optimal receive filters. Initial results by numerical simulations indicate that our approach has the potential to outperform existing methods.

This paper addresses utility maximization problems in the half-duplex two-way multiple-input multiple-output (MIMO) relay channel, where the relay uses the decode-and-forward strategy. Perfect channel information at all nodes and a time division duplex communication protocol with per node peak power constraints for every protocol phase are assumed. For this scenario, we show how solutions to the considered class of problems can efficiently be determined by means of a dual decomposition approach.

We focus on the design of linear beamforming based on quality-of-service (QoS) power minimization and rate balancing in the downlink (DL) of a satellite communication system with mobile and static users. Since only the rank-one covariance matrices of the channels to mobile users are known, we introduce average rate requirements for these users contrary to the perfect channel state information (CSI) rate requirements for the static users. Due to the structure of the ergodic rates, we cannot directly resort to the optimization techniques for the purely complete CSI vector broadcast channel (BC). Therefore, we propose two suitable lower and upper bounds on the ergodic rates. Incorporating these bounds in the optimization problems, instead of the ergodic expressions, the usual power minimization algorithms can be applied. This allows the computation of bounds on the optima of the considered problems. Furthermore, employing an additional power allocation on the obtained solutions, we achieve outcomes that reside close to the expected optima.

We consider uni- and bidirectional communication in the half-duplex multiple-input multiple-output (MIMO) relay channel. Assuming perfect channel state information at all nodes and the use of time division duplex communication protocols with a peak power constraint for every protocol phase, we propose a dual decomposition approach to efficiently determine the cut-set bound and the maximum achievable decode-and-forward rate for the Gaussian MIMO relay channel. A general outer bound on the rate regions that can be achieved in the restricted two-way MIMO relay channel is established, and we present an achievable rate region based on the decode-and-forward scheme which is a superset of several previously derived achievable rate regions. Finally, it is shown how the stated outer bound and achievable rate region can also be evaluated by means of the proposed dual decomposition approach, and we discuss how our work may be used for designing optimal protocols.

This article presents comprehensive comparisons of interleave division multiple access (IDMA) and direct sequence code division multiple access (DS-CDMA) in terms of performance and complexity assuming iterative multiuser detection. IDMA can be seen as a special case of DS-CDMA with spreading gain of one using very low rate code and user-specific interleavers for user separation. We focus on three suboptimum linear detectors: minimum mean square error (MMSE), rake (or matched filter), and soft-rake detectors from practical concerns. We analytically prove that the three detectors are equivalent for asynchronous users of IDMA on frequency flat channels for complex modulation alphabets. Such equivalence has been shown only for binary phase shift keying (BPSK) in the literature. The equivalence guarantees the MMSE solution for IDMA without computationally expensive matrix inversions or matrix-vector multiplications. This is generally not the case for DS-CDMA since DS-CDMA is sensitive to user asynchronism. We also discuss complexity aspects when the MMSE detector is used where we focus on essential differences in complexity between IDMA and DS-CDMA, instead of discussing particular complexity reduction techniques. Computer simulations are performed in various scenarios and the performance is analyzed by bit error rate simulations as well as by extrinsic information transfer (EXIT) charts. The analysis reveals the advantages of IDMA over DS-CDMA in terms of performance and complexity under practical considerations, particularly in highly user loaded scenarios.

This paper addresses the optimization of upper and lower bounds on the capacity of the multiple-input multiple-output (MIMO) relay channel. In particular, we show that evaluating the cut-set bound and the maximal achievable decode-and-forward rate is equivalent to solving convex optimization problems, where we assume that perfect channel state information is available at all nodes. Our optimized bounds thus improve on previously published results while they can be efficiently determined using convex programming techniques at the same time.

The problem of stochastic robust sum mean square error (MSE) minimization transceiver design is addressed for multiple-input multiple-output (MIMO) broadcast channels (BCs). The transmitter has only distribution knowledge of the doubly correlated Gaussian channels and the receivers have complete channel state information (CSI). The design is based on an alternating optimization (AO) of the transmit and receive filters. For the users' minimum MSEs (MMSEs), i.e., the achieved MSEs using MMSE equalizers, and the expectations in the AO procedure, novel closed form expressions are calculated via first and second order derivatives of suitable ergodic mutual information expressions.

In this paper, the rate region of the two user MIMO broadcast channel (BC) under linear filtering at high signal-to-noise ratio (SNR) is investigated when time sharing is not available and the transmitter has more antennas than the sum of the receiving antennas. To reach the rate region's boundary, the sum rate is maximized subject to a given ratio between the users rates. The sum rate is first considered asymptotically when the SNR tends to infinity and taken as an affine function of the logarithm of the SNR, with the multiplicative and the additive parameters called the multiplexing gain (MG) and the rate offset (RO), respectively. The maximal MG and the maximal RO are obtained for every rate ratio constraint. Additionally, the asymptotic optimal stream allocations that achieve those values are also derived. Analytical inner and outer bounds, which offer a rough approximation of the boundary but are extremely easy to evaluate even in fading channels, are then developed. The maximization of the rate subject to a rate ratio constraint is then studied at finite SNR. Algorithmic inner and outer bounds for the rate region boundary are derived and shown to be very close to each other and accurate even at intermediate SNR.

This paper investigates multiple input multiple output (MIMO) transmission techniques based on realistic assumptions on feedback of channel state information. We consider three conventional techniques as the baseline: 3GPP long-term evolution (LTE) single user MIMO (SU-MIMO) based on implicit channel feedback, zero-forcing multiuser MIMO (ZF MU-MIMO) based on explicit channel feedback, and ZF MU-MIMO based on implicit channel feedback. SU-MIMO may not be able to exploit the full spatial dimension of the downlink MIMO channel. ZF MU-MIMO has the potential to improve the spectral efficiency, but the explicit channel feedback is not compatible with implicit feedback whereas implicit based ZF MU-MIMO is limited by performance and also the commonly assumed rank restriction makes it impossible to perform dynamic switching of SU/MU MIMO transmission. We propose a new hybrid scheme which enables such dynamic switching of SU/MU MIMO transmission by allowing UE to feed back the implicit channel information without any rank restriction. Computer simulation results show the benefits of the new hybrid scheme, which can properly switch to the better transmission mode in various correlation scenarios.

We consider coordination of transmission strategies in interference networks, where multiple antennas at the transmitters can be used to adjust the spatial signature of the transmitted signal. For single antenna receivers the interference power received can be constraint by so-called interference temperatures, which can be used to coordinate the amount of interference in the network. We recapitulate recent research results on this topic and discuss methods to select interference temperatures that lead to performance gains compared to uncoordinated transmission. A special configuration is to demand interference to be completely eliminated, so-called zero-forcing. Methods based on zero-forcing allow for simple computation of the transmit strategies, while for general temperatures iterative algorithms are required. Strictly enforcing completely orthogonalized transmission drastically reduces the number of active users in the network and is therefore too restrictive for larger networks. We suggest an efficient algorithm that enforces orthogonal transmission only in part, which leads to an increased number of users and significant performance gains, while maintaining the low complex computation of the transmit strategies. The method is based on successive user allocation, that avoids an exhaustive search for the active user set and the user transmitter pairs for which interference should be eliminated.

In this paper we propose a new efficient algorithm for finding good codebook targeting multiple input multiple output (MIMO) precoding transmission. The proposed algorithm is constrained optimization that satisfies a set of design constraints such as unitary, constant modulus, constrained alphabet, and nested. These constraints aim at practically important properties such as low complexity and low peak-to-average-power-ratio and have been carefully taken into account in the codebook design at 3GPP. Our algorithm is based on rotations of the discrete Fourier transform (DFT) matrix and attempts to maximize the minimum chordal distance between any two precoders in the codebook in each rank. The algorithm works iteratively: at each step it identifies a bottleneck precoder in terms of minimum chordal distance and then finds a better precoder replacing the bottleneck precoder. Simulation results illustrate that the codebook found by the proposed algorithm performs better than conventional codebooks in both single user MIMO as well as in dynamic switching of single user and multi user MIMO transmission.

An interference management method for coordinating downlink transmission in a Multiple-Input Multiple-Output (MIMO) cellular network is proposed. The problem is to efficiently manage inter-cell interference in a multi-cell environment, by so-called transmitter cooperation, in order to reduce the diminishing effects of interference on the networks performance. To allow for application in deployable networks, an utmost concern of the presented algorithm is to provide a low-complexity solution avoiding costly combinatorial or non-convex optimization problems. The problem is solved by a network wide successive allocation of data streams and choosing linear transmit and receive filters for each data stream, such that interference is completely avoided. By embedding our new algorithm in a more general framework for interference coordination, we can show relevant gains for network performance where especially the cell-edge users profit.

We study the throughput region of random access in coded wireless packet networks. We propose a distributed and robust fixed point algorithm that determines whether given coded information flow requirements are feasible with random access and computes the minimal required attempt probabilities supporting these requirements. Combined with linear coding subgraph and flow optimization, this yields a distributed algorithm for joint network coding and random access optimization for multicast networks. Numerical examples show that the proposed method reduces the gap of existing approaches to the optimal solution significantly.

Besides the optimal but impractically complex dirty paper coding (DPC), linear precoding and vector precoding (VP) have been proposed for the vector broadcast channel (BC). Linear precoding is simple, as only a linear transform is applied to the data signals, whereas VP necessitates a closest-point search in a lattice to find the perturbation signal which reduces the necessary transmit power. In this paper, we analyze the high signal-to-noise ratio (SNR) performance of VP assuming optimal decoding. As the perturbation process hinders the analytical assessment of the VP performance, lower and upper bounds on the expected data rate of VP systems are reviewed and proposed. Based on these bounds, VP is compared to linear precoding w.r.t. the weighted sum rate, the power resulting from a quality of service (QoS) formulation, and the performance when balancing the rates.

The major problem of multiuser MIMO precoding and scheduling is the required Channel State Information (CSI) at the base station which needs to be provided to the base station via a feedback channel in case of frequency division duplex. Each user quantizes the channel direction information and computes a Channel Quality Indicator (CQI), e.g., based on the Signal-to-Interference-and-Noise Ratio (SINR), both fed back as CSI to the base station. Since the precoder is not known when the feedback information is computed, the SINR needs to be approximated where the quality of the approximation depends strongly on the number of finally scheduled data streams. In this paper, we propose limited feedback schemes which improve the performance of the scheduler by exploiting this dependency and adapting the feedback information accordingly. Three types of feedback schemes are presented: first, feeding back more than one approximation such that the base station can choose the more accurate one depending on the number of finally scheduled data streams, second, a time multiplexing of these approximations in order to decrease the feedback overhead, and third, feeding back the channel magnitude and estimating the approximations via additional statistical measures. Simulation results show that compared to state-of-the-art techniques, the proposed feedback schemes perform well for a wide range of correlation scenarios.

Inter-cell interference diminishes the performance of wireless cellular networks, hence interference management by cooperation of basestations should be employed to combat interference and increase spectral efficiency. Contrary to full cooperation, which renders the network into a super-cell with distributed antennas, we investigate a form of weak cooperation: transmission strategies among the basestations are coordinated, which requires a minor overhead, while the users treat interference of other cells as noise. Although our results are not restricted to reuse partitioning, we assume a set of strategies, each corresponding to a different reuse factor, and assign orthogonal resources to each strategy. Basestation cooperation is realized by dynamically adjusting the resource allocation, so-called fractional reuse partitioning, while the capacity achieving single-cell strategies are employed in each cell in order to optimally manage intra-cell interference exploiting all degrees of freedom offered by multiple antennas at the transmitter and receiver. Efficient operation of a cellular communications network requires interference management in order to achieve high data rates including rate assignment matched to the user demands, which we formulate as network utility maximization problem. We put special emphasis on the popular utilities sum-rate and proportional fairness, either with or without additional quality of service constraints. Finally, we illustrate the performance gain of our method by providing system level simulation results for a three sectorized cellular network with nineteen sites.

We consider the problem of power minimization under per-user quality of service (QoS) constraints (expressed in terms of rates) in parallel multiple-input single-output (MISO) broadcast channels employing linear zero-forcing precoding. Solving the arising scheduling problem by an exhaustive search is prohibitively complex due to its combinatorial nature so that the use of successive user allocation schemes has been proposed. We show that existing schemes lead to strongly suboptimal solutions in systems with a low number of degrees of freedom and that better performance can be achieved with a new scheduling criterion. By introducing additional correction steps, we end up with an efficient close-to-optimum algorithm.

We consider a network of K interfering transmitter-receiver pairs, where each node has N antennas and at most one beam is transmitted per user. We investigate the asymptotic performance of different beamforming strategies, as characterized by the slope and y-axis intercept (or offset) of the high signal-to-noise-ratio (SNR) sum rate asymptote. It is known that a slope (or multiplexing gain) of 2N-1 is achievable with interference alignment. On the other hand, a strategy achieving a slope of only N might allow for a significantly higher offset. Assuming that the number of fully aligned beamformer sets that achieve a slope of 2N-1 is finite for a given channel realization, we approximate the average offset when the best out of a large number L of these sets is selected. We also derive a simple large system approximation for the sum rate of a successive beam allocation scheme when K=N. We show that both approximations accurately predict simulated results for moderate system dimensions and characterize the large-system asymptotes for different relationships between L and N.

Solving Quality of Service constrained optimization problems in the Multiple-Input Multiple-Output (MIMO) broadcast channel usually requires the iterative solution of a weighted sum rate maximization, which exhibits a considerable amount of numerical complexity itself, especially in Orthogonal Frequency Multiplexing (OFDM) systems. In this paper a low complexity framework for these problems is presented, where the allocation of data streams to users and carriers is done successively. An efficient rule for choosing the user and carrier to be allocated in each step is presented for both linear and non-linear precoding leading to small performance losses compared to the optimum.

Kernel regression is introduced as a method for solving ill-posed localization problems. To obtain a unique solution the missing data is augmented by the use of a kernel function that comprises the dynamic behavior of the studied system. The proposed approach is based on the minimization of a cost term which combines a least squares estimator and a regularizer in a reproducing kernel Hilbert space. The solution is represented by a finite number of parameters.While the method works for a large class of positive definite kernels we further point out the impact of the kernel design on the quality of the solution. The design of the preferred kernel function is physically motivated. The validity of the method is demonstrated by a real world problem where the available data origins from unsynchronized and singular range measurements to nodes of unknown position.

Analytical results for the average sum rate achievable in the Multiple-Input Multiple-Output (MIMO) broadcast channel with algorithms relying on full channel state information at the transmitter are hard to obtain in practice. In the large system limit, when the number of transmit and receive antennas goes to infinity at a finite fixed ratio, however, the eigenvalues of many random matrices become deterministic and analytical expressions for the sum rate can be derived in some cases. In this paper we will present large system expressions for the sum rate for three sub-optimum algorithms, namely the Successive Encoding Successive Allocation Method (SESAM), Block Diagonalization and Block Diagonalization with Dirty Paper Coding. In case the large system limit of the sum rate does not exist, we derive lower bounds. By simulation results it is shown that the asymptotic results serve as a good approximation of the system performance with finite system parameters of reasonable size.

In this paper, the rate region of the two user MIMO broadcast channel (BC) with linear filtering at high signal-to-noise ratio (SNR) is studied when time sharing is not available and the transmitter has fewer antennas than the sum of the receiving antennas. To reach the boundary of the rate region, the sum rate is maximized subject to a rate ratio constraint. Furthermore, the sum rate is approximated as an affine function of the logarithm of the SNR and the two parameters of this approximation, which are the multiplexing gain (MG) and the rate offset (RO), are derived. This leads directly to the asymptotic rate region, particularly interesting because it is obtained in simple analytical form and offers a good approximation at high but finite SNR. We then consider the rate region boundary at finite SNR and derive algorithmic bounds for it, which are accurate even at intermediate SNR.

For downlink transmission in a multiuser multiple-input multiple-output (MIMO) communication system, quantized Channel State Information (CSI) is fed back to the base station in an uplink channel of finite rate. The quantized CSI is obtained via Channel Vector Quantization (CVQ) of the so-called composite channel vector, i.e., the product of the channel matrix and an estimation of the receive filter, which cannot be computed exactly at the stage of quantization because of its dependency on the finally chosen precoder. Here, the state-of-the-art approach estimates the receive filter and quantize the composite channel vector such that its Euclidean distance to the estimated composite channel vector is minimized. In this paper, we propose an alternative CVQ method which determines the estimated receive filter vector and the quantized composite channel vector such that the resulting Signal-to-Interference-and-Noise Ratio (SINR), or an approximation thereof, is maximized. Since the SINR is related to the individual user rates, and therefore related to the sum rate of the system, the presented solution aims at maximizing the system sum rate. Simulation results of a multiuser MIMO system with linear zero-forcing preceding show that the proposed schemes achieve significant performance improvements compared to the state-of-the-art method, especially in the low signal-to-noise ratio region.

This paper presents comprehensive comparisons of interleave division multiple access (IDMA) and direct sequence code division multiple access (DS-CDMA) in terms of performance and complexity using iterative multiuser detection technique, where we restrict ourself to three suboptimum linear detectors: minimum mean square error (MMSE), rake (or matched filter), and soft-rake detectors from practical concerns. We first analytically compare these detectors, which are found to be equivalent for IDMA with asynchronous users on flat channels, whereas this does not hold for DS-CDMA, which is sensitive to user asynchronism. This implies that, on flat channels, simple detector suffices to get the MMSE output for IDMA while DS-CDMA requires more complexity such as computing matrix inversions. We also discuss several aspects of complexities for IDMA and DSCDMA when the MMSE detector is used. Computer simulations are performed in various scenarios and the performance is analyzed by bit error rate as well as by extrinsic information transfer chart. The analysis reveals some advantages of IDMA over DS-CDMA, particularly in highly user loaded scenarios.

In this paper, location estimation algorithms for cellular communications systems employing orthogonal frequency division multiplexing (OFDM) are investigated. To determine the location of a mobile station, usually time difference of arrival (TDOA) measurements are performed with at least three base stations using pilot symbols included in the signal streams. With these TDOAs then the location is estimated. However, the timing estimation process is faced by different effects that restrict the performance. For instance, limited number of pilot symbols, inter-cell interference, and multipath propagation decrease the accuracy of the timing estimates, and hence, position estimates. Therefore, we propose to feed back decided data symbols where particularly data-aided timing estimation and interference cancellation are considered in the location estimation context. Simulation results for a 3GPP-LTE system show the ability of these algorithms to increase the overall accuracy and reliability of location estimates.

Pilot-aided channel estimation for multi-carrier systems can be significantly improved by exploiting time and frequency correlations between the channel frequency response coefficients. But in practice, the knowledge of channel correlation function is not accurately available, thereby necessitating the need of an estimator that employs a fixed correlation function and is robust to mismatches with the actual one. While the maximally robust channel estimator for multi-carrier systems for the case of an infinite number of observations is well known for almost a decade, the one for the case of a finite number of observations has been only recently proposed. We extend the proposed maximally robust estimator to the practical case of small finite dimensional pilot grids by taking into account the grid edge effects. This paves the way for application of the estimator to practical systems such as 3G LTE. Simulation results for an LTE uplink system under different transmission scenarios demonstrate the superiority of the proposed maximally robust estimator over the heuristic one by as much as 1.35 dB in terms of the coded BER.

In this paper, we propose an efficient design of proportionality factors in the recently established algorithm named Krylov-proportionate normalized least mean-square (KPNLMS), which is an extention of the PNLMS algorithm to nonsparse (or dispersive) unknown systems by means of a Krylov subspace. The designing task takes a form of minimizing the number of iterations that is needed for an upper bound of the system mismatch to reach a specified target value. The minimization is performed under several constraints related to numerical stability, computational requirements, and nonnegativity, and its closed-form solution is derived. Numerical examples demonstrate that the proposed design significantly reduces the number of iterations needed to achieve target values of system mismatch especially when a low level of system mismatch is required.

The maximization of an increasing function over the set of achievable rates in a multi-user, multi-antenna downlink is addressed. In general, the set of rates achievable by linear precoding and treating interference as noise is nonconvex. As a result, the corresponding utility maximization problem is nonconvex. The rate region can be convexifled by time sharing, and the utility maximization over the convexifled region can be solved via Lagrange duality. Still, subproblems in the dual problem remain nonconvex. It is shown how all the aforementioned nonconvex problems can be solved to global optimality in the framework of monotonic optimization. Moreover, it is investigated to what extent utility is increased by time sharing. While all problems can be solved to global optimality, the resulting computational complexity is rather high, thus the proposed solution strategies mainly provide a benchmark for locally optimum, less complex methods. Numerical results demonstrate that a method which finds stationary points on the boundary of the rate region can provide close-to-optimum performance.

Achieving the capacity region in the MIMO broadcast channel requires the use of dirty paper coding (DPC). When it cannot be afforded to satisfy the requirements of all users by DPC based approaches, it is necessary to identify the users which exhibit the highest performance gain compared to simpler approaches. In this paper we present a user grouping method that aims to identify those users with highest weighted sum rate gain. First some users are excluded by a simple criterion leading to a reduced user set, from which the final user group is selected by a more sophisticated criterion.

In this paper, we consider location estimation algorithms using cellular orthogonal frequency division multiplexing (OFDM) communications systems. In the classical approach the mobile station (MS) determines time difference of arrival (TDOA) information from the received signals of at least three base stations (BSs). With these TDOAs the location of the MS is estimated. However, only at the cell edge a good reception of several BSs is ensured. Especially for future systems targeting a frequency re-use of one, interference is a limiting factor. Hence, in the inner cell it is difficult to detect out-of-cell BSs with sufficient quality. Therefore, we propose an interference cancellation scheme to improve the performance in these critical situations for TDOA positioning. Simulation results for a 3GPP- LTE system show the ability of this approach to increase the accuracy and to extend the coverage of the overall location estimation.

We study wireless multicasting from two sources to two destinations with the assistance of a single half-duplex relay. The objective is to evaluate the throughput and error performance of different analog and digital relay schemes with linear network coding at the relay. The analog relay node forwards either a scaled version of the received signal to the destinations, or alternatively, first filters the received signals to generate a linear Minimum Mean Squared Error (MMSE) estimate, which is subsequently forwarded. The digital relay scheme first detects the source transmissions, combines the packets with a network code, and forwards the resulting symbols to the destinations. For all schemes the destinations recover the source and relay signals by first applying linear MMSE filters, followed by decoding of the source bits. The performance of the schemes are compared in terms of normalized throughput (bits per channel use accounting for the delay due to the relay) and uncoded error probability, given a normalized power constraint. Both narrowband and wideband transmission schemes are considered. Our results show that the analog relay schemes outperform the digital network coding scheme with respect to both throughput and error probability because of error propagation through the relay. Numerical results are presented, which illustrate throughput-reliability trade-offs for all schemes considered.

We study distributed algorithms for updating transmit preceding matrices for a two-user Multi-Input/Multi-Output (MIMO) interference channel. Our objective is to maximize the sum rate with linear Minimum Mean Squared Error (MMSE) receivers, treating the interference as additive Gaussian noise. An iterative approach is considered in which given a set of preceding matrices and powers, each receiver announces an interference price (marginal decrease in rate due to an increase in interference) for each received beam, corresponding to a column of the precoding matrix. Given the interference prices from the neighboring receiver, and also knowledge of the appropriate cross-channel matrices, the transmitter can then update the beams and powers to maximize the rate minus the interference cost. Variations on this approach are presented in which beams are added sequentially (and then fixed), and in which all beams and associated powers are adjusted at each iteration. Numerical results are presented, which compare these algorithms with iterative water-filling (which requires no information exchange), and a centralized optimization algorithm, which finds locally optimal solutions. Our results show that the distributed algorithms perform close to the centralized algorithm, and by adapting the rank of the precoder matrices, achieve the optimal high-SNR slope.

We consider the problem of bargaining over block fading interference channels, where interaction between players takes place over multiple channel realizations. Based on the assumption that the transmitters have conflicting objectives, we use axiomatic bargaining theory to derive optimal rate allocations in each block. In the setup under consideration, the Nash bargaining solution (NBS) is non-causal, i.e., cannot be implemented in a real-world system. We argue that the invariance axiom is superfluous when bargaining over a rate region. Without the invariance axiom, an equivalent solution follows from the maximization of a sum of utilities under minimum utility constraints. This alternative solution is also non-causal. We propose causal approximations to the optimal solutions. The sum utility solution allows for a more systematic approximation than the NBS. Thus, dropping the invariance axiom makes it possible to choose a solution which can be better approximated. We provide numerical results to illustrate the performance of the proposed solutions.

We consider multicast communications from a single source to multiple destinations through a wireless network with unreliable links. Random linear network coding achieves the min-cut flow capacity; however, additional overhead is needed for end-to-end error protection and to communicate the network coding matrix to each destination. We present a joint coding and training scheme in which training bits are appended to each source packet, and the channel code is applied across both the training and data. This scheme allows each destination to decode jointly the network coding matrix along with the data without knowledge of the network topology. It also balances the reliability of communicating the network coding matrices with the reliability of data detection. The throughput for this scheme, accounting for overhead, is characterized as a function of the packet size, channel properties (error and erasure statistics), number of independent messages, and field size. We also compare the performance with that obtained by individual channel coding of training and data. Numerical results are presented for a grid network that illustrate the reduction in throughput due to overhead.

Recently, the capacity region of the Gaussian broadcast channel has been characterized. For a given transmit power constraint, those points on the boundary of the capacity region can be regarded as the set of optimal operational points. The present work addresses the problem of selecting the point within this set that satisfies given constraints on the ratios between rates achieved by the different users in the network. This problem is usually known as rate balancing. To this end, the optimum iterative approach for general MIMO channels is revisited and adapted to an OFDM transmission scheme. Specifically, an algorithm is proposed that exploits the structure of the OFDM channel and whose convergence speed is essentially insensitive to the number of subcarriers. This is in contrast to a straightforward extension of the general MIMO algorithm to an OFDM scheme. Still, relatively high complexity and the need of a time-sharing policy to reach certain rates are at least two obstacles for a practical implementation of the optimum solution. Based on a novel decomposition technique for broadcast channels a suboptimum non-iterative algorithm is introduced that does not require time-sharing and very closely approaches the optimum solution.

Non-linear precoding for point-to-point (P2P) multiple-input multiple-output (MIMO) systems is considered. First, the minimum mean square error (MMSE) optimal vector precoding (VP) is presented for different receiver structures, viz., weighted identity matrix, diagonal matrix, weighted unitary matrix, and matrix without particular structure. Whereas the former two structures can also be applied to the vector broadcast channel, the latter two are only realizable for cooperative receivers. Second, VP is derived that minimizes the MSE but is restricted to maximize the mutual information of the MIMO channel. Third, the corresponding Tomlinson-Harashima precoding (THP) is found by applying the nearest-plane approximation to the computation of the perturbation signal. The resulting maximum mutual information THP clearly outperforms the state-of-the-art P2P-MIMO THP based on the generalized triangular decomposition (GTD) with respect to MSE and BER.

For the separation of the signals for multiple users in the vector broadcast channel (BC), channel state information (CSI) is necessary at the transmitter. Since the transmitter has no access to this information in many cases, the CSI must be fed back from the receivers to the transmitter. Before the feedback, the receivers estimate the CSI and apply a rank reduction possible due to the channel correlations. We propose a joint optimization of the estimation, the rank reduction, and the codebook used for the feedback. Interestingly, the estimator and the rank reduction resulting from this monolithic formulation are independent of the used codebook which can be computed with the generalized Lloyd algorithm. Applying the proposed feedback design to a system with multi-user precoding based on CSI feedback shows the clear superiority of the optimized codebook compared to previous designs.

In this paper we investigate the design of a robust MMSE linear precoding Multiuser Multiple Input Single Output (MU MISO) system with limited feedback that exploits multiple feedback vectors to improve the quality of the available CSI at transmission. We explain how to appropriately exploit this additional past channel information to design the channel estimator, rank basis reduction and the quantizer parameters.

Finding the matrix with the maximum singular value amongst a set of matrices is a common problem occurring in transmit signal processing algorithms for multiuser multiple-input multiple-output (MIMO) systems. However, computing the principal singular value of a matrix is a rather numerically complex task. Furthermore, in many practical scenarios, the number of users is large and for each user this task has to be conducted. In this paper we therefore propose a novel user preselection method which reduces the computational complexity at no performance loss. This is achieved by deselecting some users based on a simple criterion and thus avoiding explicit computations of the singular values of those users. This criterion is based on easily computable bounds for the principal singular values. Finally, a statistical analysis is provided and the application to the Successive Encoding Successive Allocation Method (SESAM) is shown.

For the single-group multicast scenario, where K users are served with the same data by a base station equipped with N antennas, we present two beamforming algorithms which outperform state-of-the-art multicast filters and feature a drastically reduced complexity at the same time. For the power minimization problem, where QoS constraints need to be satisfied, we introduce a successive beamforming filter computation approach aiming at satisfying at least one additional SNR constraint per orthogonal filter update. As long as the number of users K is smaller than the number N of transmit antennas, this procedure delivers excellent results. Our second approach is an iterative update algorithm for the max-min problem subject to a limitation of the transmit power. Given a low-complexity initialization beamformer, we search within the local vicinity of this filter vector for a filter-update preserving the transmit power and achieving a larger minimum SNR. To this end, we improve the weakest user's SNR during each iteration and keep on applying this procedure as long as the updates increase the smallest SNR. Otherwise, we adapt the step-size and continue investigating the local vicinity. It turns out that this novel approach is superior to existing state-of-the-art multicast beamformers for an arbitrary number of users.

Interleave division multiple access (IDMA) recently attracted many research activities because of its excellent performance despite its reasonable low complexity. The low complexity is usually realized by the multiuser detector that applies an approximation similar to the rake receiver for CDMA systems. So far, this type of detector has been most frequently considered in IDMA literature. In this paper we investigate the performance of IDMA based on linearminimummeansquareerror(MMSE) detection. The MMSE detector is more complex than the rake-like approximation. At the price of the complexity, however, it is shown that the MMSE detector brings several advantages over the rake-like approach such as the superior performance on channels with spectrally poor characteristics, effective iterative processing for lower SNR values, faster convergence and therefore shorter decoding delays, and better performance for short block length. We also confirm that the complexity can be drastically reduced by the low rank approximation of the MMSE filter by its multistage representation without compromising on the performance.

The problem of sending channel state information from the receiver to the transmitter of a wireless link is investigated in this paper. If the channel state is Gaussian distributed, this problem is equivalent to that of transmission of a Gaussian source over a noisy channel. We focus on a model in which the source outputs are statistically independent and the feedback channel is either AWGN or Rayleigh fading. Due to the strict delay constraints, information theoretic results are hardly applicable to the analysis of such a setting. As a consequence, despite its simplicity, little is known about its fundamental performance limits. Here, two different delay limited digital transmission approaches and a linear analog transmission approach are discussed and compared. If D channel uses per source output are allowed, it is shown that for the AWGN feedback channel delay limited digital approaches can achieve a distortion decay of at least D/2 dB per dB of SNR. This decay rate is 1 for the linear analog approach regardless D. For Rayleigh feedback channels the distortion decay rate is shown to be upper bounded by 1 for digital approaches and is asymptotically 1 for the analog approach. This fact and simplicity are good reasons for the use of analog transmission for feedback purposes over fading channels.

In this paper, we present a novel algorithm for determining the sum-rate optimal transmit covariance matrices for the MIMO broadcast channel. Instead of optimizing the covariances directly, our algorithm operates on the preceding matrices, i.e., the square roots of the covariances. As a result, no eigenvalue decompositions are required in the iterations, and the complexity per iteration is significantly lower. A look at the convergence over the required number of computations shows a visible advantage over the state-of-the-art sum power iterative waterfilling algorithm. Also, our algorithm allows us to find the optimal sum-rate for an arbitrarily limited number of data streams per user. Finally, with a simple modification, our algorithm can also be used for sum-MSE minimization.

We consider an innovative downlink multiuser MIMO scheme. Several users compete for the available resources in time, frequency and space. The proposed scheme exploits multiuser diversity and uses interference cancellation at the transmitter. We evaluate it for indoor, hot spot and multihop scenarios. Significant gains in terms of sum capacity can be achieved even under line of sight conditions. The theoretical limit can be approximately achieved in most scenarios. A nice feature of the proposed scheme is that it inherently provides some fairness regarding rate distribution among users even though fairness is not explicitly taken into account by the scheduler.

The exploitation of multiuser diversity and the application of multiple antennas at transmitter and receiver are considered to be key technologies for future highly bandwidth-efficient wireless systems. We combine both ideas in a downlink multicarrier transmission scheme where multiple users compete for the available resources in time, frequency and space. The instantaneous channel impulse responses for all users are assumed to be perfectly known at the transmitter. Our proposed algorithm allocates each spatial dimension on a subcarrier to the user which has the highest channel tap gain on the respective spatial dimension. The scheduling strategy is optimized for sum capacity maximization. In this paper, we restrict ourselves to a more illustrative description of the idea rather then providing mathematical details. We demonstrate the potential of the proposed scheme by capacity results for measured real world channels in a large office environment. Finally, video streaming is used as a potential application with high data rate and low latency demands. It is shown that the proposed method has the potential to exploit multiuser diversity while still providing stable video streams even though QoS constraints are not explicitly taken into account by the scheduler.

Inherent inter-symbol and inter-carrier interference elimination ability of cyclic prefixed OFDM transmission fails for the case of multipath fading channels when the channel impulse response (CIR) length exceeds the duration of cyclic prefix (CP). Conventional channel estimation and equalization schemes, if applied to this case of insufficient CP, suffer significant performance degradation. We propose, in this paper, a channel estimation scheme that enables estimation of the complete CIR even beyond the CP length. We then design an optimal MMSE based equalizer for the suppression of insufficient CP generated interference. A robust and low complexity version of this equalizer is also derived. Simulation results for the proposed schemes show significant performance gain at low SNRs and drastic reduction of the error floors at high SNRs and more importantly, as opposed to earlier schemes, without any loss in transmission efficiency.

We consider a multiuser MIMO-OFDMA scheme which exploits multiuser diversity in all dimensions: time, frequency and space. The main contribution of this paper is the evaluation and explanation of multiuser MIMO in a real world scenario, i.e. a large office room, based on measured channels. We report interesting results of a measurement campaign which suggest that significant MIMO gains are possible in an indoor environment even under strong line-of-sight condition as long as the distance of the users from the base station is larger than a reverberation distance which only depends on room surface and material. We show results on the achievable multiuser MIMO data rates for the given scenario compare to theoretical limits and discuss the results in the light of the insights gained from the measurement campaign. We also introduce restrictions on the rate distribution between users, i.e. QoS constraints. It is shown that the theoretical limits can be approximately achieved provided that the users which compete for the spatial resources are carefully chosen.

We propose a receiver for multipoint-to-point systems based on theminimummeansquareerror(MMSE) criterion, where the symbols aredetectedingroupsand already detected symbols are fed back for interference subtraction, as known for decision feedback equalization (DFE). The proposedscaledDFE(SDFE) has two special cases: 1) DFE for a group size of one, i.e., for symbol-by-symbol detection. 2)Maximumlikelihooddetection(MLD), if the group comprises all transmitted symbols. The diversity order of SDFE lies between the poor diversity order of DFE and the full diversity order of MLD. Therefore, SDFE offers a trade-off between complexity due to the group-wise symbol detection and the increased diversity order compared to DFE. We also present an algorithm to compute the SDFE filters with an order of complexity which is the same as that to compute linear equalization filters. Motivated by the promising results of detectors based onlatticereduction(LR), we combine SDFE with LR. The resulting detector exhibits full diversity order and improved performance compared to LR-DFE. The simulations show that SDFE is an interesting generalization of DFE for detectors with zero-forcing constraint, since SDFE even outperforms LR-DFE for realisticsignal-to-noise-ratio(SNR). However, LR-DFE exhibits the best results for an affordable complexity, when dropping the zero-forcing constraint.

In this paper, we present two extensions of the block conjugate gradient (BCG) algorithm, a method which exploits the concept of block Krylov subspaces. First, we extend the BCG algorithm such that it is more flexible concerning the dimension of the block Krylov subspace. Second, a computationally efficient preconditioned BCG (PBCG) algorithm is introduced which turns out to outperform the standard BCG algorithm concerning the complexity-performance ratio. Hence, we provide a powerful implementation for reduced-rank signal processing in the minimum mean square error (MMSE) sense. Simulation results show the gain in rank-flexibility and convergence speed

We address the problem of jointly optimizing the pre coder and the receivers in a multi-user broadcast system for a linear and nonlinear transmitter under sum mean square error (MSE) minimization. By means of alternating optimization (AO), we find an iterative algorithm which always converges to the global optimum. In addition to an elegant initialization of the receivers' weights, we come up with expressions for the speed of convergence. Our algorithm is applicable to both single-antenna and multi-antenna receivers and can be combined with nonlinear Tomlinson-Harashima precoding (THP). For THP, the precoding order can easily be included in the AO. Moreover, we show that for sum MSE minimization the user-wise channels are not necessarily diagonalized and give some ideas how this fact can be exploited

In the work at hand a general procedure to analyze the impact of channel estimation errors on the performance of decomposition techniques for multiuser MIMO channels is presented. In particular, this procedure is applied to a decomposition technique called cooperative zero-forcing with successive encoding and successive allocation method (CZF-SESAM). Based on the resulting analytical expressions the transmitter is able to adjust bit and power loading so that in spite of estimation errors transmission quality requirements can still be met

Points on the boundary of the MIMO broadcast channel (BC) capacity region are achieved by a combination of dirty paper coding (DPC) and linear precoding. The linear precoding determines the covariance matrices of the transmitted signals. Determining the optimum covariance matrices may lead to an undesirably high computational complexity for systems of high dimension. In this paper, an approach to approximate the MIMO BC capacity region is proposed. The proposed method combines DPC with sub-optimum linear precoding matrices that can be computed with low complexity but provide close to optimum performance. Motivated by multiobjective optimization, an efficient algorithm developed for sum-rate maximization is generalized to computing an achievable rate region. Simulation results show that this rate region, which has low complexity in terms of computing the precoding matrices, well approximates the capacity region of the MIMO BC.

In the work at hand relevant issues concerning implementation of optimal and nearly optimal transmission approaches for the MIMO OFDM broadcast channel are discussed. In particular, algorithms proposed to compute optimum covariance matrices are efficiently extended to the multicarrier setting. Furthermore, a method is proposed to transform the resulting vector channels into a set of scalar subchannels over which information can be independently transmitted without incurring any capacity loss. This effective diagonalization of the broadcast channel is most convenient for practical purposes as, so far, existing techniques for coding with side information have exclusively been conceived for scalar subchannels. Finally, we discuss the practical advantages of a suboptimum technique such as the cooperative zero-forcing with successive encoding and successive allocation method (CZF-SESAM). This technique exhibits a nearly optimum performance and significantly simplifies both computation of transmit covariance matrices and downlink signaling.

This paper extends the self-contained theory of linear precoding to the field of covariance based spatio-temporal downlink processing for direct-sequence code-division multiple-access (CDMA) systems and shows the applicability to the release 6 of high-speed downlink packet access (HSDPA). To this end, a unifying theory is developed to formulate the three known linear filters, namely, the transmit matched filter, the transmit zero-forcing filter, and the transmit Wiener filter, as optimization problems even in systems, where only covariance knowledge is available at the transmitter. Second, the solutions of these transmit filters are given for such systems with partial channel state information (CSI). Finally, it is shown how covariance-based linear precoding can be employed in the new generation CDMA system HSDPA, i.e., how channel estimation on the secondary common pilot channel allows for optimum full rank linear precoding employing only partial CSI.

Assuming perfect channel state information at the transmitter of a Gaussian broadcast channel, strategies are investigated on how to assign subchannels in frequency and space domain to each receiver aiming at a maximization of the sum rate transmitted over the channel. For the general sum capacity maximizing solution, which has recently been found, a method is proposed that transforms each of the resulting vector channels into a set of scalar channels. This makes possible to achieve capacity by simply using scalar coding and detection techniques. The high complexity involved in the computation of this optimum solution motivates the introduction of a novel suboptimum zero-forcing allocation strategy that directly results in a set of virtually decoupled scalar channels. Simulation results show that this technique tightly approaches the performance of the optimum solution, i.e., complexity reduction comes at almost no cost in terms of sum capacity. As the optimum solution, the zero-forcing allocation strategy applies to any number of transmit antennas, receive antennas and users

Tomlinson-Harashima precoding (THP) for a system with multiple transmit antennas and non-cooperative receivers is considered (broadcast channel). Design of THP for this channel is typically based on complete CSI at the transmitter, which is not available in mobile wireless systems. For larger Doppler frequencies it even performs significantly worse than linear precoding or simple beamforming due to its high sensitivity to parameter errors. We apply a novel optimization criterion based on partial CSI to THP. For this robust design it is shown that a continuous transition from complete to statistical CSI is achieved and that THP is now guaranteed to perform always better than or equal to linear precoding

A method is presented for efficiently describing the capabilities of the physical layer in a MIMO communication system. Such a description is required in systems that determine the optimum operating point based on information exchange between layers. We characterize the physical layer by means of efficient MSE tuples. Such efficient tuples are found by sampling the boundary of the MSE region. A particularly simple sampling algorithm is derived. We show how the distance between subsequent samples can be directly controlled, allowing us to provide a characterization of the physical layer that is both compact and representative.

We derive a hybrid of beam-forming (BF) and space-time block coding (STBC), where the space-time code is transmitted over the beams generated by the steering vectors corresponding to the channel path directions. This is for the practical case where the transmit array may have adequate information on the departure angles of the dominant paths between transmitter and receiver, but unreliable information on the associated complex path gains. We compute analytically the signal-to-noise ratio (SNR) of the proposed hybrid for the specific case of a two-path channel model and using the orthogonal Alamouti code, and compare the result to the SNR of optimal linear precoding (LP) and the theoretically possible SNR of orthogonal STBC (OSTBC). Simulation results show that the performance of the BF/STBC hybrid can be very close to LP /sup n/der certain conditions - or even better in the practical case where there are phase estimation errors in the path gain estimates employed at the transmitter.

The problem of estimating and predicting the Gramian of a matrix (with rather general structure) of correlated complex Gaussian random variables is addressed. We propose its conditional mean estimator as the optimum Bayesian estimator for a quadratic risk function and present its mean square error (MSE) performance analysis. Numerical results for the example of linear pre-equalization in a wireless communications application show a significantly improved performance of the novel estimator compared to known approaches.

Traditionally, channel estimation and equalization are optimized separately and independently. The channel estimate is simply plugged into the equalizer as if it had no errors. We propose a new method for joint pilot symbol assisted channel estimation and equalization and apply it to the design of the space-time decision feedback equalizer. The explicit solution of this joint approach is obtained with the same order of complexity as a separate design and results in a significant performance improvement. Furthermore, we discuss its relation to robust optimization and regularization techniques for equalizer design.

Common transmit processing concepts are either based on complete, partial, or no channel state information (CSI). But the quality of CSI is crucial for a fair comparison. Therefore, we derive an explicit expression of the bit error probability (binary modulation) for the transmit matched filter, beamforming, and Alamouti space-time block code in case of linear estimation and prediction of the channel coefficients or a delay of the channel estimates. Uncorrelated as well as correlated frequency flat channels are considered. Based on the analytical expressions a comparison for these three representatives for all three types of CSI is made. The break even point between the three schemes is computed numerically, which can serve as a switching point between the concepts.

Nonlinear minimum mean square error Tomlinson-Harashima precoding considered in this paper is an attractive solution for a scenario where a transmitter serves spatially separated receivers and no cooperation among them is possible. Unfortunately, the large performance gain against linear precoding comes along with significantly higher complexity than linear filters in the case of a large number of receivers. We show that superior performance of the nonlinear minimum mean square error Tomlinson-Harashima precoding can be obtained with complexity equivalent to linear precoding. Our proposed algorithm reduces the complexity by a factor of N/sub R/ which is the number of receivers.

Tomlinson-Harashima Precoding (THP) for a wireless system with multiple cooperative transmitters and non-cooperative receivers is considered (downlink channel). In the literature THP has been optimized for this channel assuming complete channel state informatioin (CSI). But quality of CSI is generally poor at the transmitter due to the timevarving channel. We present a robust optimization of THP and a combined optimizlation of THP and channel estimation, which take into account the quality of CSI. They yield a significantly improved robustness towards errors in CSI.

In bottom-up cross-layer optimization, each layer offers a set of feasible operation points to the layer above, enabling the application to decide which parameter setup is optimum. In this paper, the capabilities of the physical layer in a multi-user MISO system are described in terms of the users' outage probabilities. Optimum descriptions are given by sets of efficient outage probability tuples. We first derive a closed-form expression for a user's outage probability in the presence of multi-user interference. Next, the set of efficient outage probability tuples under a simplified SDMA transmit strategy is derived. We show how a good approximate description of the efficient set is found by equidistant sampling. Finally, the performance of the proposed transmit strategy is compared with an orthogonal multiple access scheme, demonstrating that optimum mode selection at the physical layer can lead to significant performance gains at the application layer

We derive the minimum mean square error (MMSE) solution to vector precoding for frequency flat multiuser scenarios with a centralized multi-antenna transmitter. The receivers employ a modulo operation, giving the transmitter the additional degree of freedom to choose a perturbation vector. Similar to existing vector precoding techniques, the optimum perturbation vector is found with a closest point search in a lattice. The proposed MMSE vector precoder does not, however, search for the perturbation vector resulting in the lowest transmit energy, as proposed in all previous contributions on vector precoding, but finds an optimum compromise between noise enhancement and residual interference. We present simulation results showing that the proposed technique outperforms existing vector precoders, as well as the MMSE Tomlinson-Harashima precoder.

In the work at hand, decomposition approaches are investigated for the downlink of a multiuser MIMO setting. The focus is on approaches that use successive encoding to eliminate part of the interference between users or information streams. We start reviewing the well known zero-forcing with successive encoding (ZF-SE) approach and generalize the basic idea behind this technique to arrive at a block ZF-SE that can be applied to the case of users with multiple antennas exploiting their cooperation capability. Aiming at a maximization of the achievable sum-rate we elaborate on the ZF-SE and block ZF-SE approaches to come up with the ZF-SE with successive allocation method (ZF-SESAM) and the cooperative ZF-SE with successive allocation method (CZF-SESAM), respectively. This two approaches proceed successively selecting at each step a user to which the next spatial dimension is assigned. Specifically, we propose a largest gain criterion for this selection and provide some rationale for that. Using an OFDM transmission scheme, Tomlinson-Harashima precoding and a common bit loading algorithm we compare the sum-rate achieved by the different approaches. Finally, some simulation results show that in a large MIMO-OFDM system with a moderate number of users even the weakest user can profit from the increase in sum-rate if compared to a maximally fair system, where the same number of dimensions is assigned to every user.

Enormous capacity advantage can be achieved on a flat MIMO channel compared to single antenna systems. V-BLAST was proposed to obtain such capacity advantage with low complexity. V-BLAST, however, requires multiple matrix (pseudo) inversions that are still computationally intensive for a large number of antennas. Much research has been attracted to reducing the complexity. Our contribution is to show that the MMSE block decision-feedback equalizer equivalent to the MMSE V-BLAST can be calculated via Cholesky factorization of the error covariance matrix with symmetric permutation. Forward and backward filters as well as detection order are jointly optimized with significantly reduced complexity. Simulation results show that MMSE V-BLAST performance can be achieved by the proposed scheme.

The multi-stage transmit Wiener filter (MSTxWF) is presented, an approach to reducing the complexity of the transmit Wiener filter (TxWF). The MSTxWF is found by applying the multi-stage decomposition known from the receive multi-stage Wiener filter (MSWF) to the TxWF. Complexity reduction is achieved by truncating the decomposition. We show that the resulting reduced rank MSTxWF can be interpreted as an approximation of the TxWF in a Krylov subspace, allowing for an efficient computation of the MSTxWF with the Lanczos algorithm. The reduced rank MSTxWF shows near-optimum performance for relatively low rank, making it an interesting alternative to eigenspace-based methods for complexity reduction.

Compared to the conventional full-rank Wiener filter (WF), reduced-rank processing in the minimum mean square error sense is a well-known strategy in order to reduce computational complexity and enhance performance in case of low sample support. In this paper, we reveal the relationship between block Krylov methods and the multi-stage matrix WF (MSMWF) as a reduced-rank matrix WF which estimates a signal vector instead of a scalar. The new insights lead to an implementation of the MSMWF based on Rune's variant of the block Lanczos algorithm which is more flexible with respect to rank selection compared to existing algorithms. Finally, the application to a time-dispersive multiple-input multiple-output (MIMO) system demonstrates the ability of the new algorithm to lessen receiver complexity while maintaining the same level of system performance or even improve it if second order statistics are not perfectly known. Moreover, the MSMWF outperforms the parallel implementation of multi-stage vector WFs with a comparable computational complexity.

Orthogonal space-time block codes (STBC) constitute a simple way of exploiting transmit diversity. If no channel knowledge is available at the transmitter the use of diversity can increase performance significantly. However, if some partial channel state information (CSI) is available, such as knowledge of the transmit correlation matrix, adapting transmission to this knowledge provides additional performance gains. In such case, adaptivity can be introduced by using a unitary eigenbeamformer with beams pointing along the directions of the eigenvectors of the transmit correlation matrix and applying a convenient power loading along the resulting beams. While for Rayleigh-fading channel eigenbeamforming has been shown to be optimum in terms of ergodic capacity, so far, no closed solution for the optimum power loading has been found. In the work at hand, orthogonal STBC are combined with eigenbeamforming in an orthogonal frequency-division multiplexing (OFDM) context and an optimum power loading is found, where optimality refers to an upperbound of pairwise error probability (PEP). The resulting signaling scheme can be viewed as an extension of STBC to OFDM with partial channel knowledge. The solution represents a very interesting trade-off between transmit diversity and antenna gain.

In this letter, we introduce five different strategies of linear transmit signal processing for multiuser multiple-input multiple-output (MIMO) systems and provide performance comparisons in terms of maximum throughput in both uncorrelated and correlated channels when the number of transmit antennas is much larger than the number of receive antennas. It is shown that the multiuser MIMO schemes are preferable to time-division multiple-access (TDMA)-based MIMO schemes, hence demonstrating the power of multiuser MIMO signal processing. Our work also indicates possibilities for future research in finding efficient suboptimal algorithms. As an example, we show that our multiuser MIMO decomposition scheme can improve the maximum throughput compared to TDMA-based MIMO schemes for large number of transmit antennas or high transmit power.

The joint operation of high speed downlink packet access (HSDPA) and adaptive antennas in a WCDMA cellular network is considered. The total throughput per cell of HSDPA depends heavily on the strategy employed by the scheduler. It is argued that the maximum SIR scheduler, which maximizes throughput in the single antenna system by serving only the user with the best momentary channel quality, will not substantially benefit from adaptive antennas due to excess self interference at the receiver. As an alternative a set of scheduling strategies is proposed, which rely on serving simultaneously multiple spatially separated users. System level simulations show that with the spatial division multiple access scheduler it is possible to achieve almost twofold throughput improvement over maximum SIR when using four antennas per sector.

Compared to wired channels, time dispersive radio channels possess more frequent nulls in their spectral characteristics. Thus, the performance of linear filters to compensate intersymbol interference degrades dramatically. The well-known nonlinear decision feedback equalizer (DFE) is one approach to improve this behavior. However, in systems with observations of high dimensionality, the optimum DFE structure is computational intensive. In this paper, we apply the method of the multi-stage Wiener filter (MSWF) to a conventional minimum mean square error (MMSE) DFE in order to reduce computational complexity. The application of the new algorithm to an Enhanced Data rates for GSM Evolution (EDGE) system demonstrates the ability to outperform the even more computational intensive linear Wiener filter (WF).

Linear transmit filters depend on current channel state information, which is available from the uplink channel estimation in time division duplex systems. For multiple antenna elements at the transmitter, we illustrate the influence of out-dated channel estimates on link level performance for symmetric and asymmetric traffic, comparing the transmit matched, zero-forcing, and Wiener filters. A Wiener predictor is proposed to improve channel knowledge of the transmitter. We observe an inherent robustness of the transmit matched filter and explain the occurrence of a minimum in the bit error ratio at a finite SNR for the transmit Wiener filter.

In this article, we present different space-time receive processing techniques for frequency selective multiple input multiple output (MIMO) channels and evaluate their performance. We present the solutions for linear zero-forcing (ZF) and Wiener filter (WF) equalization with latency time optimization and incorporate the Bell Laboratories Layered Space Time (BLAST) architecture to gain diversity. Furthermore, we present systems based on decision feedback equalization (DFE). We also combine this equalization method with the BLAST principle.

We investigate the effects of fading correlations on wireless communication systems employing multiple antennas at both the receiver and the transmitter side of the link, so called multiple-input multiple-output (MIMO) systems. It turns out that the amount of transmitter sided channel knowledge plays an important part when dealing with fading correlations. Furthermore, the possible availability of time diversity in a time-selective channel can have essential influence on performance. To study the influence of time-selectivity, the concept of sample-mean outage is introduced and applied to information theoretic measures, like capacity or cutoff rate. It will be shown, that in some cases correlated fading may offer better performance than uncorrelated fading permits, which is due to exploitable antenna gain, that will also be defined in a general form for MIMO systems.

Adaptive antennas at the base stations have a big potential to increase downlink capacity and coverage in WCDMA systems. One of the most promising techniques of adaptive antenna control is closed-loop transmit diversity. This method achieves beamforming gain as well as diversity gain by feedback of downlink fast fading characteristics from mobile station to base station. Due to the limited feedback rate, typically the best antenna weight vector chosen from a restricted set is reported. If this feedback is subject to transmission errors, two performance degrading effects occur: a suboptimal weight vector is used and channel estimation errors take place at the mobile station. The latter effect arises because the mobile station derives the channel estimate for the dedicated channel from the reported weight vector and the channel estimate per antenna based on the common pilot channel. In effect, feedback errors severely distort this estimate and lead to an error floor effect. However, channel estimation can also be based on the pilot symbols in the dedicated channel itself. This estimate has high variance, but is not distorted by feedback errors. If both estimates are combined in a process called antenna weight verification, performance can be dramatically increased. Within this paper an antenna weight verification scheme for eigenbeamformer is presented, which maximizes the estimation accuracy and eliminates the error floor. This is demonstrated by theoretical analysis and link-level simulations. It is shown that this antenna weight verification reduces the performance degradation to 0.2 dB at 1% frame error rate.

A MIMO channel measurement campaign with a moving mobile has been conducted in Vienna. The measured data will be used to investigate covariance matrices with respect to their dependence on time and frequency. This document focuses on the description of the evaluation techniques which will be applied to the measurement data in the future. The F-eigen-ratio is defined expressing the degradation due to out-dated covariance matrices. Illustrating the derived methods, first results based on the measured data are shown for a simple line-of-sight scenario.

We consider the following receiver architecture for a wireless communication link with one transmit and multiple receive antennas: spatial and temporal rank reduction based on long-term (average) spatio-temporal channel properties followed by space-time processing using maximum-likelihood estimates of the channel coefficients. Receivers based on this architecture use e.g. a temporal or spatio-temporal Rake, beamspace processing, or eigenvalue decomposition. Their performance critically depends on a good choice of the spatio-temporal rank. We define the effective spatio-temporal rank based on the mean square error (MSE) of the receiver's reduced rank channel estimate. Thus, we can determine the optimum rank and get an analytical insight in the fundamental trade-offs involved, when designing such systems. Using this criterion we discuss under which conditions beamforming is optimal compared to diversity combining.

We present space-time transmit filters for FDD DS-CDMA systems based on partial channel state information, i.e. we do not take into account the channel coefficients. Although the FDD transmit zero-forcing filter seems to be the most intuitive approach, we focus on the FDD transmit matched filter and the FDD transmit Wiener filter. Similarly to the respective receive filters and TDD transmit filters, the FDD matched filter maximizes the desired signal portion at the receiver and is optimum for low signal-to-noise-ratio scenarios, whereas the FDD transmit Wiener filter takes into account the noise power at the receiver and is therefore able to find an optimum trade-off between signal maximization and interference suppression. Additionally, we show that the FDD transmit matched filter is a type of eigenbeamforming. The simulation results reveal the excellent performance of the two FDD transmit filters. The FDD transmit Wiener filter even outperforms the TDD transmit matched filter for high signal-to-noise-ratio which is based on the instantaneous channel properties.

The use of multiple receive antennas as well as multiple transmit antennas has attracted worldwide attention to extend the performance of existing mobile communication systems. Therefore, it is of great importance to know the features of the real propagation channel. The real propagation channel is investigated by MIMO channel sounding, where high-resolution parameter estimation schemes are used to resolve the physical channel with respect to its spatial (and temporal) structure. However, due to narrow diffuse scatterers, the commonly used model of discrete wavefronts is no longer valid. We introduce a simple approach to model wavefronts with small angular spread. Additionally, we show that the ESPRIT (estimation of signal parameters by rotational invariance techniques) algorithm with a slight modification is applicable to estimate the direction of arrival and the angular spread jointly. The approach is based on the assumption of Gaussian distributed wavefronts.

We investigate the effects of both fading correlations and transmitter channel knowledge in multiple element antenna (MEA) communication systems. While, for independent and identically distributed fades between receive and transmit antennas, pioneering work showed that a huge increase in capacity is possible for MEA compared to a single antenna system, recent contributions warn that fading correlations destroy most of this advantage. While this is true for zero transmitter channel knowledge, we show however that long-term average channel state information enables the transmitter to efficiently use the fading correlations to its advantage and offers the potential to even increase capacity beyond the one possible for independent fading. A conceived transmit technique is presented that efficiently makes use of fading correlations, and also provides optimum choice of digital modulation schemes that carry the information.

Many services of third-generation (3G) mobile radio systems will have higher data rates in the downlink than in the uplink. We propose to utilize adaptive antennas at the base stations because spatial interference suppression is able to reduce the near-far effect in the downlink of single-user detection direct-sequence (DS) code division multiple access (CDMA) systems. Besides the channel parameters in terms of directions of arrival, delays, and medium-term average path attenuations which are estimated in the uplink, we also take into account the correlation properties of the spreading and scrambling codes. In DS-CDMA the users are distinguished by different spreading codes, which change over time due to scrambling. In Brunner et al. (see Proc. EPMCC, p.375-80, 1999), the beamforming vectors at the base station were computed slotwise, whereas in this paper, we favor a symbol-rate beamforming at the base station. The new approach optimizes the actual values of the decision variables of the RAKE demodulators at the receivers. The superiority of the symbol rate beamforming algorithm compared to slotwise beamforming is shown by bit error rate (BER) simulations.

The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.

Utilizing adaptive antenna arrays at the base stations of next generation mobile communication systems has been proposed as a promising approach to meet future requirements, e.g. spatio-temporal filtering techniques benefit from the detailed knowledge of the directional channel parameters. Unfortunately, in parameter estimation the computation of the signal subspace often turns out to be the most time-consuming part. However, the computational complexity of the subspace estimation can be significantly reduced by means of tracking. To this end, we propose a new technique which is merely based on the projector representation of the subspace. The presented results are based on the DSP implementation of two algorithms for tracking the azimuthal and elevation angles of impinging wavefronts in an alternating mobile communications system.

Polychotomous classification is a widespread task in pattern recognition. A classifier relates an input pattern to a class Ck element of a fixed number K > 2 of classes C1, C2, …, CK. Neural networks for classification are generally based on the trivial 1-out-of-K coding. The advantage of non-trivial codes for discrimination of multiple classes lies in the increased Hamming distance between reference vectors, which makes error detection and even error correction feasible. Dichotomies from non-trivial codes are not as well arranged as from 1-out-of-K coding and may even have worse classification results after training. The code becomes superior to the trivial alternative only if its growing Hamming distance compensates for the increased tendency of single output errors. In this paper, the design of non-trivial error-correcting codes is based on the maximization of a cost function Φ, where the cost function is given by a trade-off between the empirical risk on training samples and a regularization term in the decision space. The introduced algorithm is based on a semi-implicit optimization of the given cost function. The results are related to the coding of multiple classes from a real classification task in handwritten character recognition. The complete system consists of parallel neural networks, each consisting of hidden neurons in the first layer and a Boolean function in the second layer. A comparison between optimized non-trivial codes and the trivial 1-out-of-K code is presented. It is shown that the generally applied 1-out-of-K code is not optimal. The optimum is reached by non-trivial coding without increasing the size of the classification system.

An extension of a feedforward neural network is presented. Although utilizing linear threshold functions and a Boolean function in the second layer, signal processing within the neural network is real. After mapping input vectors onto a discretization of the input space, real valued features of the internal representation of the pattern are extracted. A vectorquantizer assigns a class hypothesis to a pattern based on its extracted features and adequate reference vectors of all classes in the decision space of the output layer. Training consists of a combination of combinatorial and convex optimization. This work has been applied to a standard optical character recognition task. Results and comparison to alternative approaches are presented.

In this paper a statistical point of view of feedforward neural networks is presented. The hidden layer of a multilayer perceptron neural network is identified of representing the mapping of random vectors. Utilizing hard limiter activation functions, the second and all further layers of the multilayer perceptron, including the output layer represent the mapping of a Boolean function. Boolean type of neural networks are naturally appropriate for categorization of input data. Training is exclusively carried out on the first layer of the neural network, whereas the definition of the Boolean function generally remains a matter of experience or due to considerations of symmetry. In this work a method is introduced, how to adapt the Boolean function of the network, utilizing statistical knowledge of the internal representation of input data. Applied to the classification problem of greylevel bitmaps of handwritten characters the misclassification rate of the neural network is approximately reduced by 20%.

Feature extraction is a crucial part of classification procedures. In this paper we present an approach to utilize feature extraction criteria to predict the potential efficiency of a neural network classifier. Statistical and geometrical criteria are introduced for analysis. The complete system of our research consists of a class of generalized Hough-transformations for feature extraction and a subsequent neural network. The neural network performs the classification based on respective features. For an example we concentrated on a pattern recognition problem-the classification of handwritten numerals. As a result of our work we assign two feature extraction criteria to the employed network for a significant estimation of its efficiency.