In-X subnetworks (SNs) encounter significant challenges in achieving hyper-reliable low-latency communication (HRLLC), particularly in hyper-dense deployment scenarios. These challenges stem from rapid and dynamic variations in interference caused by mobility, dynamic channel statistics, and varying traffic patterns. Predictive interference management plays a crucial role in meeting these extreme requirements, by enabling proactive and reliable resource allocation to prevent performance degradation. To address these challenges, we propose a probabilistic interference prediction technique using an inverted quantile transformer (iQTransformer) to learn interference dynamics effectively and capture tail statistics. It predicts interference for multiple sensor-actuator (SA) pairs in the SN with minimal disparity by leveraging their own interference dynamics alongside those of other active SA pairs. We integrate extreme value theory (EVT) with the iQTransformer to handle rare and extreme interference events. Results demonstrate that our proposed method outperforms baseline approaches in terms of prediction accuracy, achieving near-optimal target reliability.

In this paper, we explore a multi-task semantic communication (SemCom) system for distributed sources, extending the existing focus on collaborative single-task execution. We build on the cooperative multi-task processing introduced in [1], which divides the encoder into a common unit (CU) and multiple specific units (SUs). While earlier studies in multi-task SemCom focused on full observation settings, our research explores a more realistic case where only distributed partial observations are available, such as in a production line monitored by multiple sensing nodes. To address this, we propose an SemCom system that supports multi-task processing through cooperation on the transmitter side via split structure and collaboration on the receiver side. We have used an information-theoretic perspective with variational approximations for our end-to-end data-driven approach. Simulation results demonstrate that the proposed co-operative and collaborative multi-task (CCMT) SemCom system significantly improves task execution accuracy, particularly in complex datasets, if the noise introduced from the communication channel is not limiting the task performance too much. Our findings contribute to a SemCom framework capable of handling distributed sources and multiple tasks simultaneously, advancing the applicability of SemCom systems in real-world scenarios.

In wireless communication systems, accurate Channel State Information (CSI) is essential for base stations to perform downlink precoding. While much of the existing research primarily focus on compressing the CSI matrix, they often neglect the impact of subsequent pre-transmission processes such as quantization, channel coding, and modulation. This paper investigates two distinct approaches for CSI dimensionality reduction: TransNet, a transformer-based neural network, and Dynamic Mode Decomposition (DMD), a mathematical decomposition technique for dynamical systems. We analyze how quantization, channel coding, and modulation affect CSI feedback for both methods. Unlike TransNet, DMD can decompose the channel matrix into components (called modes) with varying significance. This decomposition allows for an effective application of Unequal Error Protection (UEP) techniques to DMD modes, which is not feasible with TransNet-based CSI. Simulation results reveal that while the compression performance of TransNet and DMD varies based on factors like target CSI size and channel estimation error, integrating UEP techniques for DMD-based CSI yields superior CSI transmission performance compared to TransNet-based CSI.

In this study, we introduce an innovative signal to interference and noise ratio (SINR) time sequence feedback scheme based on vector quantization variational autoencoder (VQ-VAE). We compress the SINR sequence at the user equipment (UE) side and reconstruct it at the base station (BS) side. The reconstructed sequence is then utilized for SINR prediction at the BS. The VQ-VAE framework compresses SINR sequences into a compact embedding space involving several embedding vectors. Instead of transmitting the entire compressed SINR sequence back, we only need to transmit the index of the corresponding embedded vector. Based on the index, the sequence will be reconstructed. Moreover, a principal component analysis (PCA) based method is employed to reshape the distribution of the embedding space and compression performance is improved consequently. Our numerical simulations demonstrate that VQ-VAE combined with PCA achieves superior reconstruction and prediction accuracy while requiring fewer quantization bits compared to 3GPP commonly used method, differential quantization. Therefore, the proposed scheme is a promising solution for enhancing SINR sequence compression and prediction in wireless communication systems.

We present a 3D seismic exploration and imaging survey conducted by robotic platforms in a hardware-in-the-loop system. To this end, we integrate the adapt-then-combine full waveform inversion (ATC-FWI) over a network of mobile rovers in the ROS2 framework. The ATC-FWI allows for distributed subsurface imaging in a multi-agent network, i.e., each rover obtains a 3D subsurface image via data exchange with other rovers in the network. We demonstrate the capability of our system by performing multiple surveys using a synthetic subsurface model with an anomaly. The rovers acquire seismic data over different measurement areas and perform distributed imaging to reconstruct the subsurface. We show that the rovers are able to image the anomaly and to enhance their subsurface image over multiple measurement stages in different areas.

We focus on a (generic) joint source-channel coding problem, appearing in a broad variety of real-world application. Explicitly, a noisy observation from a user/source signal should be compressed, ahead of getting forwarded over an error-prone and rate-limited channel to a remote processing unit. The design problem shall be formulated in a fashion that the impacts of the forward link are taken into account. Aligned with the Information Bottleneck (IB) method, we consider the Mutual Information (MI) as the fidelity criterion, and work out a data-driven approach to tackle the underlying design problem based upon a finite sample set. For that, we derive a tractable variational lower-bound of the objective functional, and present a general learning architecture which can be used to optimize the given lower-bound by standard training of the encoder and decoder Deep Neural Networks. This approach that is, principally, based upon the (generative) latent variable models, extends the concepts of Variational AutoEncoder (VAE) and Deep Variational Information Bottleneck (Deep VIB) for (remote) source coding to the context of joint source-channel coding. We validate the effectiveness of our approach by several numerical simulations over typical transmission scenarios.

Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in satellite communications. Space-Division Multiple Access (SDMA) precoding is a method that mitigates interference among satellite beams, boosting spectral efficiency. While optimal SDMA precoding solutions have been proposed for ideal channel knowledge in various scenarios, addressing robust precoding with imperfect channel information has primarily been limited to simplified models. However, these models might not capture the complexity of LEO satellite applications. We use the Soft Actor-Critic (SAC) deep Reinforcement Learning (RL) method to learn robust precoding strategies without the need for explicit insights into the system conditions and imperfections. Our results show flexibility to adapt to arbitrary system configurations while performing strongly in terms of achievable rate and robustness to disruptive influences compared to analytical benchmark precoders.

Industrial Ethernet networks are well-established communication systems in industrial production facilities. They are used in particular in applications with high demands on real-time capability and transmission reliability. In the context of applications with mobility requirements, such as mobile robots or rotating machine parts, however, they reach their practicable limits. In these cases, wireless communication systems are necessary. In addition to enabling the aforementioned applications, they promise further advantages, such as cost savings through simplified installation. However, the same requirements are placed on wireless systems as on their wired counterparts. This paper structures these requirements’ implications on industrial communication systems by deriving four mandatory properties that need to be fulfilled by any communication system for industrial applications. Current commercially available technologies are reviewed with respect to the mandatory properties. Addressing their shortcomings, an overview of current research approaches aiming to improve industrial wireless systems in the automation applications is given.

This paper introduces an adaptive pilot design for wireless communication systems, dynamically adjusting the number of pilot symbols based on historical channel information. Focusing on Orthogonal Frequency Division Multiplexing (OFDM) systems formally adopted in 5G networks and is a potential candidate for future 6G systems, we employ Generative Adversarial Networks (GAN) to model and predict channel states by leveraging spectral and temporal correlations within the OFDM resource grid. The predicted channel state is then used alongside the available pilots to perform channel estimation. As the accuracy of channel predictions improves, fewer pilots are needed to achieve the desired channel estimation accuracy. Thus, we propose a scheme to estimate the GAN prediction accuracy, which is essential for determining the required number of pilots. Additionally, the proposed method supports ultra-low latency communication by performing channel estimation within the duration of a single OFDM symbol, making it highly suitable for beyond 5G and 6G networks. Simulation results demonstrate significant improvements in channel estimation accuracy compared to traditional fixed-pilot schemes, thereby reducing the number of required pilots to achieve a target channel accuracy.

The Nyquist-Shannon sampling theorem states that bandlimited signals can be perfectly reconstructed from samples taken at a fixed rate. Signals with varying spectral content are not considered, which leads to an unnecessarily high number of samples in signal intervals with narrowband content. An extension of the Nyquist-Shannon theorem enables the definition of variable bandwidth signals through nonlinear time axis distortion. This technique, known as time warping, enables variable-rate sampling based on instantaneous bandwidth, resulting in sample numbers proportional to the average bandwidth rather than the maximum bandwidth as in classical sampling. In practice, however, the instantaneous bandwidth of a signal is unknown, except for a few analytically determinable exceptions. In this paper, we introduce a novel spectrogram-based algorithm for estimating the instantaneous bandwidth of classically sampled signals, allowing to project them to variable bandwidth signals. We examine the tradeoff between sample reduction and reconstruction accuracy of electrocardiograms and compare the results to classical downsampling.

In multiple-antenna communication systems, it is crucial for the base station to acquire accurate downlink Channel State Information (CSI) to optimize signal transmission through beamforming. However, with the absence of the channel reciprocity, the mobile station must follow the process of channel estimation with feeding the CSI back to the base station. This can introduce a substantial overhead that increases with the number of antennas and the bandwidth. Therefore the CSI must be first compressed and quantized before reporting. In this paper we introduce a novel approach that based on combining Dynamic Mode Decomposition (DMD) with Residual Vector Quantization (RVQ). RVQ adapts the quantization accuracy based on the DMD output, namely the modes. This strategy allows the system to prioritize important feedback data and reduce the overhead bits needed for less critical data. Simulation results show that our approach can reduce the CSI feedback overhead while maintaining the target channel reconstruction accuracy.

The emergence of mega-constellations of interconnected satellites has a major impact on the integration of cellular wireless and non-terrestrial networks, while simultaneously offering previously inconceivable data gathering capabilities. This paper studies the problem of running a federated learning (FL) algorithm within low Earth orbit satellite constellations connected with intra-orbit inter-satellite links (ISL), aiming to efficiently process collected data in situ. Satellites apply on-board machine learning and transmit local parameters to the parameter server (PS). The main contribution is a novel approach to enhance FL in satellite constellations using intra-orbit ISLs. The key idea is to rely on predictability of satellite visits to create a system design in which ISLs mitigate the impact of intermittent connectivity and transmit aggregated parameters to the PS. We first devise a synchronous FL, which is extended towards an asynchronous FL for the case of sparse satellite visits to the PS. An efficient use of the satellite resources is attained by sparsification-based compression the aggregated parameters within each orbit. Performance is evaluated in terms of accuracy and required data transmission size. We observe a sevenfold increase in convergence speed over the state-of-the-art using ISLs, and 10× reduction in communication load through the proposed in-network aggregation strategy.

“Network of subnetworks” is envisioned to be a key enabler in a 6G network with extremely low (100 μs) latency and high-reliability (99.9999%-99.99999%) in demanding applications. However, to achieve this level of performance, it is necessary to introduce a proactive and robust interference estimation considering the random mobility of subnetworks in an ultra-dense environment. We propose long-short term memory (LSTM) to learn the non-linear behavior of interference power time series for robust estimation and prediction in in-X subnetworks. This proposed method empowers to prediction/estimation of interference on the subnetwork itself. The achieved estimation result is compared with the moving average-based and expectation based estimators. Furthermore, we introduce federated learning (FL) in-X subnetworks' interference estimation, which learns cooperatively from the interference power vector of subnetworks participating in training. The results indicate that the proposed FL-based estimator achieves a higher convergence speed and lower estimation error.

In wireless communication, it is essential for the base station (BS) to obtain the downlink channel state information (CSI). In case of the absence of channel reciprocity, the mobile station (MS) needs to report the CSI back to the BS. In mobile multiple input multiple output (MIMO) systems, the CSI feedback overhead grows proportionally with the number of antennas and with the employed bandwidth. Moreover, the channel characteristics change constantly, so the feedback must be reported repeatedly with cautiously designed update intervals depending on how rapidly the channel changes. The increasing CSI overhead becomes a performance bottleneck, therefore it is vital to reduce it while keeping the system performance as good as required. In this paper, we propose a novel method based on designing a dynamical model of a time-varying channel with help of a framework called dynamic mode decomposition (DMD). Reporting the model to the BS gives it the ability to predict the channel state and track its changes over time. Simulation results show that the proposed method can increase the interval duration between the successive feedback updates and thus reduce the average overhead.

The uplink from a very small aperture terminal (VSAT) towards multiple satellites is considered, in this paper. VSATs can be equipped with multiple antennas, allowing parallel transmission to multiple satellites. A low-complexity precoder based on imperfect positional information of the satellites is presented. The probability distribution of the position uncertainty and the statistics of the channel elements are related by the characteristic function of the position uncertainty. This knowledge is included in the precoder design to maximize the mean signal-to-leakage-and-noise ratio (SLNR) at the satellites. Furthermore, the performance w.r.t. the inter-satellite distance is numerically evaluated. It is shown that the proposed approach achieves the capacity for perfect position knowledge and sufficiently large inter-satellite distances. In case of imperfect position knowledge, the performance degradation of the robust precoder is relatively small.

Modern wireless systems require strict key performance indicators (KPIs), such as very high reliability and throughput. Link adaptation (LA) is a core technology used to achieve these targets, with outer loop link adaptation (OLLA) being the most commonly used algorithm due to its feasibility and simplicity. OLLA uses a term called backoff factor to correct the signal to interference and noise ratio (SINR) estimate mapped from channel state information (CSI) to obtain an effective SINR. Based on the effective SINR, modulation and coding scheme (MCS) will be selected. OLLA adjusts the backoff factor with respect to hybrid automatic repeat request (HARQ) in a linear manner. However, this leads to effective SINR fluctuation and quite often results in overestimation. Hence, the performance of the system will be degraded. In this work, we propose a novel algorithm which introduces an adaptive adjustment step size for the backoff factor using an exponentially decaying factor to alleviate this issue. Since the backoff factor is not adjusted in a linear manner like OLLA, we call the proposed novel algorithm non-linear outer loop link adaptation (NOLLA). NOLLA can be regarded as an extension of OLLA that retains low complexity and high feasibility, providing the possibility to improve the link transmission in an uncomplicated way. Numerical evaluations demonstrate that NOLLA achieves higher reliability and throughput in scenarios with and without interference.

Wireless sensor nodes need a drastically reduced technical complexity to fit constraints of future applications. Reducing complexity often results in a degradation of energy and bandwidth efficiency. An interesting new approach that promises to reduce both technical complexity and energy consumption is event-based communication (EBC). While practical low-complexity implementations of such systems have already been proposed, the general question of energy and bandwidth efficiency remains open. In this paper, we compare these between EBC and a system relying on classical uniform sampling. We show that EBC is indeed much more energy efficient, and this comes at the cost of bandwidth efficiency. Therefore EBC is particularly suitable in combination with ultra-wideband communication.

This work analyzes the use of reconfigurable meta-surfaces as a more energy-efficient transceiver technology than traditional active antenna arrays. A wireless link is considered, in which both the transmitter and receiver are equipped with a single antenna that illuminates a passive meta-surface placed in the vicinity of the transmit/receive antenna. The rate and energy efficiency of this system are optimized with respect to the phase shifts applied by the transmit and receive meta-surfaces. Numerical results show that the use of passive meta-surfaces can significantly improve the system energy efficiency without reducing the system rate when compared to a similar multiple-input multiple-output (MIMO) system without meta-surface.

Advances in mobile communication capabilities open the door for closer integration of pre-hospital and in-hospital care processes. For example, medical specialists can be enabled to guide on-site paramedics and can, in turn, be supplied with live vitals or visuals. Consolidating such performance-critical applications with the highly complex workings of mobile communications requires solutions both reliable and efficient, yet easy to integrate with existing systems. This paper explores the application of Deep Deterministic Policy Gradient (DDPG) methods for learning a communications resource scheduling algorithm with special regards to priority users. Unlike the popular Deep-Q-Network methods, the DDPG is able to produce continuous-valued output. With light post-processing, the resulting scheduler is able to achieve high performance on a flexible sum-utility goal.

Mega-constellations of small-size Low Earth Orbit (LEO) satellites are currently planned and deployed by various private and public entities. While global connectivity is the main rationale, these constellations also offer the potential to gather immense amount of data, e.g., for Earth observation. Power and bandwidth constraints together with motives like privacy, limiting delay, or resiliency make it desirable to process this data directly within the constellation. We consider the implementation of on-board federated learning (FL) orchestrated by an out-of-constellation parameter server (PS) and propose a novel communication scheme tailored to support FL. It leverages intraorbit inter-satellite links, the predictability of satellite movements and partial aggregating to massively reduce the training time and communication costs. In particular, for a constellation with 40 satellites equally distributed among five low Earth orbits and the PS in medium Earth orbit, we observe a 29× speed-up in the training process time and a 8× traffic reduction at the PS over the baseline.

Greater capabilities of mobile communications technology enable the interconnection of on-site medical care at a scale previously unavailable. However, embedding such critical, demanding tasks into the already complex infrastructure of mobile communications has proven challenging. This paper explores a resource allocation scenario where a scheduler must balance mixed performance metrics among connected users. To fulfill this resource allocation task, we present a scheduler that adaptively switches between different model-based scheduling algorithms. We make use of a deep Q-Network (DQN) to learn the benefit of selecting a scheduling paradigm for a given situation, combining advantages from model-driven and data-driven approaches. The resulting ensemble scheduler is able to combine its constituent algorithms to maximize a sum-utility cost function while ensuring performance on designated high-priority users.

Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a massive increase in spectral efficiency. However, this would require instantaneous perfect channel state information (CSI) and strong cooperation between satellites. In practice, orbital velocities around 7.5 km/s lead to very short channel coherence times on the order of fractions of the inter-satellite propagation delay, invalidating these assumptions. In this paper, we propose a distributed linear precoding scheme and a GS equalizer relying on local position information. In particular, each satellite only requires information about its own position and that of the GS, while the GS has complete positional information. Due to the deterministic nature of satellite movement this information is easily obtained and no inter-satellite information exchange is required during transmission. Based on the underlying geometrical channel approximation, the optimal inter-satellite distance is obtained analytically. Numerical evaluations show that the proposed scheme is, on average, within 99.8 % of the maximum achievable rate for instantaneous CSI and perfect cooperation.

Low Earth orbit (LEO) satellite constellations rely on inter-satellite links (ISLs) to provide global connectivity. However, one significant challenge is to establish and maintain inter-plane ISLs, which support communication between different orbital planes. This is due to the fast movement of the infrastructure and to the limited computation and communication capabilities on the satellites. In this paper, we make use of antenna arrays with either Butler matrix beam switching networks or digital beam steering to establish the inter-plane ISLs in a LEO satellite constellation. Furthermore, we present a greedy matching algorithm to establish inter-plane ISLs with the objective of maximizing the sum of rates. This is achieved by sequentially selecting the pairs, switching or pointing the beams and, finally, setting the data rates. Our results show that, by selecting an update period of 30 seconds for the matching, reliable communication can be achieved throughout the constellation, where the impact of interference in the rates is less than 0.7% when compared to orthogonal links, even for relatively small antenna arrays. Furthermore, doubling the number of antenna elements increases the rates by around one order of magnitude.

The problem of finding good error correcting codes for short block lenghts and its corresponding decoders is an open research topic. A frequently applied soft decoder is the Belief Propagation (BP) decoder, however with degraded performance in case of short loops in the Tanner graph. This is especially problematic for short length codes as loops of small length are more likely to occur. In this paper, we propose the Machine Learning Scaled Belief Propagation (MLS-BP) to mitigate the performance loss of BP decoding for short length codes by introducing a learned scaling factor for the receive signals. The key point of this approach is the fact that the implementation of the BP decoder is not changed and the simple scaling leads to performance results comparable to other proposed BP improvements.

Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially with system dimensions. Instead of using DNNs, essentially being a black-box, we take a slightly different approach and introduce a probabilistic Continuous relaxation of disCrete variables to MAP detection. Enabling close approximation and continuous optimization, we derive an iterative detection algorithm: Concrete MAP Detection (CMD). Furthermore, extending CMD by the idea of deep unfolding into CMDNet, we allow for (online) optimization of a small number of parameters to different working points while limiting complexity. In contrast to recent DNN-based approaches, we select the optimization criterion and output of CMDNet based on information theory and are thus able to learn approximate probabilities of the individual optimal detector. This is crucial for soft decoding in today’s communication systems. Numerical simulation results in MIMO systems reveal CMDNet to feature a promising accuracy complexity trade-off compared to State of the Art. Notably, we demonstrate CMDNet’s soft outputs to be reliable for decoders.

Consider the following setup: Through ajointdesign, multiple observations of a remote data source shall belocallycompressed before getting transmitted via severalerror-prone, rate-limited forward links to a (distant) processing unit. For addressing this specific instance of multiterminalJoint Source-Channel Codingproblem, in this article, the foundational principle of theInformation Bottleneckmethod is fully extended to obtain purely statistical design approaches, enjoying theMutual Informationas their fidelity criterion. Specifically, the forms of stationary points for two types of distributed compression schemes are characterized here. Subsequently, those acquired solutions are utilized as the centerpiece of the proposed generic, iterative algorithm, termed theMultiterminal Forward-Aware Vector Information Bottleneck (M-FAVIB), for addressing the design optimizations. Leveraging an unfolding trick, it will be proven that both distributed compression schemes fall into the category ofSuccessive Upper-Bound Minimization, ensuring their convergence to a stationary point. Eventually, the effectiveness of the proposed compression schemes will be substantiated as well by means of numerical investigations over some typical transmission scenarios.

The focus is on Wyner-Ziv type distributed fronthaul compression for the uplink of Cloud Radio Access Networks with single-hop topology to leverage the correlation among the received signals of neighboring Radio Access Points. For this, we highlight the relation between the problem at hand and the Chief Executive Officer source coding under logarithmic-loss distortion and depict that the achievability arguments from the latter verify addressing the postulated optimization. Subsequently, we derive the pertinent optimal solution and utilize that as the backbone of the Generalized Distributed Information Bottleneck (G-DIB) routine proposed here to tackle the considered remote source coding problem. As its name suggests, this novel approach in its very core spirit extends the State-of-the-Art Distributed Information Bottleneck (DIB) method by enabling individual rate constraints for various fronthaul links.

Wireless industrial radio communication systems spawn a new set of requirements with focus on high reliability and low latency. These requirements were identified in the Industry 4.0 (I4.0) initiative as well as in 5th Generation (5G) mobile communication standardization in the form of Ultra Reliable Low Latency Communication (URLLC).Specifically Closed-Loop-Control (CLC) applications exhibit periodic deterministic communication with short packets. These applications require ultra low latency which bars the application of retransmissions to improve reliability. Also, many CLC applications are very sensitive to burst errors but can tolerate single packet loss. Therefore, we propose to shift the focus from sum-rate maximization to burst error minimization. As a first step, we perform an extensive burst error analysis of state of the art scheduling and Resource Allocation (RA) strategies. We show that any dynamic RA outperforms a static RA by a large margin.

The Kalman filter as an effective tool to solve the state estimation problem for linear dynamic systems can be derived from a generalized perspective by applying the sum-product message passing over a factor graph. This viewpoint encourages us to visualize the state estimation problem over a network where all the nodes aspire to obtain consensus-based state estimates of a dynamic system by collecting sequential measurements over time. In this work, nodes process in a distributed and cooperative fashion and exchange Gaussian messages among neighbors resulting in a Gaussian belief propagation algorithm. We discuss and illustrate the performance of our proposed method under acyclic and cyclic network typologies.

The main focus will be on the indirect Joint Source-Channel Coding problem in which a noisy observation of the source has to be quantized ahead of transmission over an error-prone forward link to a remote processing unit. To that end, we present here a complete extension to the preliminary Information Bottleneck method by providing the formal optimal solution to this newly established Variational Principle, together with an algorithm, the Forward-Aware Vector Information Bottleneck (FAVIB), to pragmatically tackle its underlying non-convex design optimization. FAVIB extends the current state-of-the-art approaches via capacitating a full sweep over the entire gamut of the trade-off parameter. Consequently, the trajectory of all achievable points in the Information-Compression plane becomes traversable via soft mappings. It will be shown that, by enjoying an inherent error protection, this novel compression scheme can obviate the call for separate channel coding on the forward path.

Consider following setup: A number of observations from a data source shall be compressed jointly prior to a forward transmission via several rate- limited links to a central processing unit. To design the respective quantizers, here, Mutual Information is chosen as the fidelity criterion and the broad-ranging structure of Multivariate Information Bottleneck is then aptly tailored to that purpose. This, indeed, not only yields a novel design approach for the considered distributed scenario but also paves the way towards perceiving the chance of leveraging this flexible conceptual frame in a vast variety of applications regarding digital data transmission. Explicitly, it immediately enables addressing various extensions of the presumed arrangement, incorporating the parallel construction of intertwined compression systems for several correlated sources.

The main focus of this paper is on the problem of noisy source coding wherein observed signals from an inaccessible source shall be compressed. To that end, rather than resorting to the conventional methods from Rate-Distortion theory, the so-called Information Bottleneck paradigm is deployed in order to obtain a highly informative representing signal w.r.t. the given source. An efficient, generic and highly flexible graph-based message passing routine for clustering, known as the Affinity Propagation is successfully applied here as a novel treatment for that purpose. The fundamental differences and the performance-wise comparison w.r.t. the state-of-the-art KL-Means-IB algorithm is provided as well.

We propose a hybrid dictionary approach for distributed kernel-based adaptive learning of a nonlinear function by a network of nodes. The hybrid dictionary incorporates a local part to improve learning of high frequency components in the function within the local domain of each node and a global part to provide a consensus estimate of the function over the whole region of interest. We apply our scheme to the reconstruction of a spatial distribution by a network of mobile nodes. Performance evaluations show that high frequency components are reconstructed accurately by our hybrid dictionary approach while common schemes are not able to recover them completely.

In this paper, we address the problem of distributed state estimation, where a set of nodes are required to jointly estimate the state of a linear dynamic system based on sequential measurements. In our distributed scenario, all the nodes 1) are interested in the full state of the observed system and 2) pursue a consensus-based state estimate with high accuracy. We exploit the equivalent relation between the maximum-a-posteriori (MAP) estimation and the Kalman filter (KF) in the minimum mean square error (MMSE) sense under the Gaussian assumption. Utilizing this relation, a distributed Kalman filtering algorithm is derived, which ensures consensus-based state estimates among nodes and converges to the optimal central KF solution.

In this paper, a novel distributed precoding (DiP) algorithm for ultra-dense small cell (SC) networks is developed, where the SCs cooperate to perform a joint transmission to users (UEs) with limited and individual transmit powers. Different to most state of the art (SotA) DiP algorithms, the proposed precoder design is based on the assumption that each SC has only local channel state information (CSI) available. Additionally, there are no constraints on the number of antennas for each SC, but only one constraint on the sum of all transmit antennas. A solution for the considered problem based on the Lagrangian method of multipliers (MoM) is presented, by formulating the precoder design as a constrained convex optimization problem. The obtained solution can be implemented in a fully distributed way among the SCs by using the preconditioned Richardson (PR) iteration. In numerical simulations, the convergence of the proposed DiP algorithm is verified and it is shown that the sum rate significantly increases, if the SCs cooperate with each other.

In this paper, we introduce a millimeter wave (mmWave) channel sounder design to enable dynamic vehicle-to-vehicle propagation measurements such as short range train-to-train and different car platooning scenarios. The RUSK-DLR channel sounder has been extended with mmWave frontends to up- and down-convert intermediate signals. This combination is enabling us to perform accurate mmWave propagation measurements in a dynamic manner. The design of the frontends and the evaluation through lab tests and outdoor measurements are described. Outdoor measurements covered static and dynamic scenarios. Measurements at ranges up to 110 m were performed. We could accurately record channel impulse responses, phase measurements and delay information in the different scenarios. Furthermore, we could show a good match between the theoretical free space path loss and empirical received power and evaluate the influence of multipath propagation on the distance estimate.

In the context of noisy source coding, contrary to the conventional Rate-Distortion theory, the so-called Information Bottleneck method formulates the existent fundamental complexity-precision trade-off in a symmetric and purely information-theoretic fashion. Since the pertinent optimization task to design the quantizer is quite demanding, a number of heuristics have been developed to provide practically feasible procedures at the expense of yielding suboptimal solutions. In this paper, we consider two pertinent routines originally appeared in totally different applications and set out to precisely prove their algorithmic equivalence by conducting a thorough analysis over the corresponding algorithmic steps. We further corroborate our theoretical investigation employing computer-based simulations.

Multi-carrier transmission schemes with general non-orthogonal waveforms provide a flexible time-frequency resource allocation and are bandwidth efficient. However, the interference inherently introduced by the non-orthogonal waveforms always requires a higher order equalizer at the receiver. Depending on the localization properties of the applied waveform, the structure and the complexity of this equalizer is adapted to consider channel influences, like carrier frequency and timing offsets. Especially for two-phase two-way relaying channels (TWRCs), where two users simultaneously transmit data on the same resources, a robust transmission scheme in presence of practical constraints such as asynchronicity is of utmost importance. This paper focuses on the utilization of general multi-carrier transmission schemes applied to TWRCs and the utilization of factor graph-based equalizers (FGEs) at the relay in order to mitigate the impacts of the physical channels, offsets, and the non-orthogonal waveforms. In combination with the subsequent physical-layer network coding detection/decoding scheme, this combination allows for a flexible design of the waveforms and the FGE to meet the complexity-performance trade-off at the relay. As demonstrated by numerical evaluation results, the proposed multi-carrier scheme with well-localized waveforms utilizing FGEs outperforms orthogonal frequency division multiplexing in TWRC for a wide range of practical impacts.

The sampling of neural signals plays an important role in modern neuroscience, especially for prosthetics. However, due to hardware and data rate constraints, only spike trains can get recovered reliably. State of the art prosthetics can still achieve impressive results, but to get higher resolutions the used data rate needs to be reduced. In this paper, this is done by expressing the data with exponential and sinusoidal splines. As these signals have a finite number of degrees of freedom per unit of time, they can be analyzed and reconstructed with the Finite Rate of Innovation (FRI) framework. We show, that we can reduce the needed data rate by 90% to achieve the same resolution as without compression. Additionally, we propose analytic boundaries for the reconstruction of these splines and present an algorithm that guarantees the reconstruction within these boundaries. Furthermore, we test the algorithm on real neural stimuli.

The general problem of quantizing observation signals appears in different aspects of data processing, from special code designs to realization of low-complexity receivers. To this end, a new framework, known as the Information Bottleneck method, has recently attracted a great deal of attention. In this paper, after introducing this framework and providing the Iterative Information Bottleneck algorithm as the primary pertinent solution, we also discuss three other heuristics aiming to solve the similar problem efficiently. Since the resultant solution of considered approaches is locally optimum, it strongly depends on the choice of initialization. The main contribution of this work is to prove the equivalence of these algorithms asymptotically, i.e., assuming an infinite run of algorithms for the extreme case of infinitely large trade-off parameter. We also substantiate this claim by means of computer-based simulations.

Compressive Sensing Multi-User Detection (CS-MUD) is a recently developed physical (PHY) layer technique [1] to support Massive Machine Communication (MMC) in the next generation of mobile communication (5G) [2], [3]. CS-MUD has been investigated in joint Medium Access Control (MAC) and PHY layer protocol design [4], [5] but the lack of analytical performance description makes the joint design cumbersome. To expand the joint MAC- and PHY-layer protocol design considering a larger parameter space and to gain insights in the cross-layer optimization, we present a performance abstraction for CS-MUD to avoid extensive numerical simulation. Within this work, we exploit a low-complexity approach to approximate the performance of CS-MUD through the Replica Symmetry in large system analysis [6].

This paper presents a new approach for distributed consensus-based estimation with low communication effort in cooperative networks, where a group of nodes cooperates to estimate source messages with information exchange achieving consensus among all nodes. A new distributed estimation algorithm is developed by adopting the novel approach of virtual clustering, in which the size of exchanged data is reduced during the distributed processing by the partial transmission after data clustering. Moreover, the required communication effort and estimation performance of the algorithm are evaluated for networks with different topology and varying system parameters. We show that communication cost is reduced considerably by the proposed algorithm while keeping the estimation performance.

The air interface for millimeter wave (mmWave) communications must be designed by properly taking into account the specific characteristics of the wireless channel at higher frequencies. In this work, we start by considering a channel model recently proposed in the literature for mmWave communications in outdoor urban scenarios. First, on top of this channel model we implement a sectorized beamforming model necessary to compensate the large path-loss at mmWave range and study how channel statistics, namely, delay spread and angle spread, are influenced by employing different beamwidths. Subsequently, adopting this beamforming model in the mmWave channel, orthogonal frequency division multiplexing (OFDM) and single carrier frequency domain equalization (SC-FDE) systems are compared. Extensive link level simulations are performed by considering different beamwidths, line-of-sight (LOS) coverage and channel coding. Numerical results show that SC-FDE using minimum mean square error (MMSE) equalization performs close to OFDM in coded systems. However, SC-FDE might be beneficial in practice due to much lower peak to average power ratio (PAPR) than OFDM.

We consider a device-to-device scenario in a fragmented spectrum band. Multiple devices transmit complex symbols with a single antenna on distributed, but disjoint OFDM resources. The devices select sufficient frequency resources to enable channel estimation, if the receiver has complete knowledge of the resource map. However, in this scenario the actual allocation map is unknown to the receiver. Therefore, a receiver observing the superposition of all transmitted signals is faced with two problems, channel estimation and identification of the correct resources allocation map. In our previous work [1], we identified the channel and the allocation map of the corresponding users by applying an objective function based on the ℓ1-norm. In this work, we show that successful recovery is possible under different practical ITU channel models. Furthermore, we apply basis pursuit denoising (BPDN) from the compressed sensing framework for channel estimation and show the superior performance in contrast to classical least square estimators. These results show, that identifying non-adjacent multiuser allocations is possible.

Massive Machine Type Communication is seen as one major driver for the research of new physical layer technologies for future communication systems. To handle massive access, the main challenges are avoiding control signaling overhead, low complexity data processing per sensor, supporting of diverse but rather low data rates and a flexible and scalable access. To address all these challenges, we propose a combination of compressed sensing based detection known as Compressed Sensing based Multi User Detection (CS-MUD) with multicarrier access schemes. We name this novel combination Multicarrier CS-MUD (MCSM). Previous investigations on CS-MUD facilitates massive direct random access by exploiting the signal sparsity caused by sporadic sensor activity. The new combined scheme MCSM with its flexibility in accessing time frequency resources additionally allows for either reducing the number of subcarriers or shortening the multicarrier symbol duration, i.e., we gain a high spectral efficiency. Simulation results are given to show the performance of the proposed scheme.

Sleep mode techniques can provide energy savings in Phantom Cell Concept (PCC) systems. However, the impact of the traffic profile on the performance of these energy savings schemes is not well understood. In this paper, we evaluate the influence of the size of transmitted files on the network performance. Contrary to previous findings, we show that for smaller file sizes the connection latencies associated with different energy savings schemes do have a non-negligible impact on system performance. However, as the file size increases, the influence of the connection delays associated with different schemes becomes negligible. The particular file size inflection point depends on the ratio of bandwidth resources on the small cell and the macro cell.

The performance of physical-layer network coding (PLNC) in two way relay channels (TWRCs) is significantly decreased by impairments like carrier frequency offsets or timing offsets. This mismatch cannot be completely compensated at the receiver side, even if the offsets are known. Multi-carrier systems with Gaussian waveforms for TWRC systems are more robust against the impact of these offsets. In comparison to rectangular multi-carrier systems, Gaussian waveforms have a better time-frequency shape and they provide improved spectral efficiency due to lower out of band radiations. In this paper, we introduce a multi-carrier TWRC system with Gaussian waveforms and develop an adapted soft interference cancellation (SIC) equalizer to consider the intrinsic interference of the Gaussian transmit/receive filters. The presented results show, that systems with Gaussian waveforms achieve bit error rates close to the rectangular waveforms in PLNC systems while being more robust against Doppler and delay spreads.

In slotted random access of many nodes, multi-user detection (MUD) can be applied to handle collisions. One novel PHY layer approach for jointly detecting activity and data in such a setting is Compressed Sensing based Multi-User Detection (CS-MUD). In this paper, we first summarize previous investigations on CS-MUD and subsequently propose two novel solutions for problems which have not yet been fully addressed: Firstly, we improve on previous results, by introducing a new approach which incorporates the channel decoder into the Compressed Sensing (CS) detector. Secondly, we analyze the resource efficiency of CS-MUD by adapting phase diagrams known from CS literature to the application of sporadic communication.

The emergence of Machine-to-Machine (M2M) communication requires new Medium Access Control (MAC) schemes and physical (PHY) layer concepts to support a massive number of access requests. The concept of coded random access, introduced recently, greatly outperforms other random access methods and is inherently capable to take advantage of the capture effect from the PHY layer. Furthermore, at the PHY layer, compressive sensing based multi-user detection (CS-MUD) is a novel technique that exploits sparsity in multi-user detection to achieve a joint activity and data detection. In this paper, we combine coded random access with CS-MUD on the PHY layer and show very promising results for the resulting protocol.

In this paper, a novel setpoint-based design approach for Irregular Repeat Accumulate (IRA) codes in iterative detection and decoding structures is presented. In contrast to conventional IRA code design in which the convolutional decoder is combined with the detector, the goal behind this approach is to keep the IRA decoding structure consisting of convolutional decoder and repetition decoder intact, i.e. to consider it as an inner loop of the overall detection structure. The outer loop is then composed of the IRA decoder and the system specific detector. This approach requires to adapt the irregular repetition code jointly to the convolutional decoder as well as to the detector which is achieved by formulating setpoints for the inner and outer code characteristic. As will be shown, the presented code design approach, although starting from a completely different viewpoint as the conventional approach, leads to an irregular repetition code with a very similar transfer characteristic and code rate than the conventional approach.

This paper discusses two approaches of reliability-based HARQ, adapting the packet size of a retransmission in a 3GPP Long Term Evolution (LTE) system. We focus on the adaptation of the retransmission size in terms of physical resources by using information 1) of the channel, namely the signal-to-noise ratio (SNR) or 2) reliability information from the decoder output, taking the overall transmission into account. Both approaches will be compared to the HARQ system used in LTE in terms of throughput performance. Link level simulations will be performed with single bit feedback and 2 bit multilevel ACK/NAK. This work takes realistic impairments such as channel estimation, signal-to-noise ratio (SNR) estimation and implementation of a Turbo en- and decoder into regard.

This paper studies potential energy savings that can be realized in dual connectivity heterogeneous networks (HetNets) with densely deployed small cells. Using the Phantom Cell Concept (PCC) as a reference architecture, a novel database-aided mechanism is introduced to provide macro cell-controlled sleep mode functionality to small cells. System level simulations show that a system with this capability can yield energy savings of up to 40% and throughput gains of about 25% in dense deployment scenarios compared to a system where no energy savings scheme is implemented.

Performing joint activity and data detection has recently gained attention for reducing signaling overhead in multi-user Machine-to-Machine Communication systems. In this context, Compressed Sensing has been identified as a good candidate for joint activity and data detection especially in scenarios where the activity probability is very low. This paper augments activity and data detection for frame based multi-user uplink scenarios where nodes are (in)active for the duration of a frame. We propose a two stage detector which first estimates the set of active nodes followed by a data detector. Our detector outperforms symbol-by-symbol Maximum a posteriori detection.

In this paper, distributed Interleave-Division- Multiplexing Space Time Codes (dIDM-STC) in Multi- User Decode-and-Forward Relay Systems are considered. Due to decoding errors at the relays, which are unavoidable in practical systems, error propagation to the destination occurs. In order to cope with this error propagation, recently a Reliability-Aware Iterative Detection Scheme (RAID) at the destination was proposed by the authors, which takes the decoding success at the relays, as well as the decoding reliability of the relays into account. This scheme requires a CRC check and also the estimation of the error probability at each relay. In this paper, a modification of RAID is presented, which only requires a CRC check at the relays, completely avoiding the estimation of the error probabilities at the relays and the signaling to the destination. Instead, the determination of the error probabilities is shifted to the destination reducing the complexity at the relays and the overall signaling overhead. As will be shown, the proposed complexity reduced RAID scheme (CR-RAID) allows for the same end-to-end performance in terms of frame-error-rates as the original RAID.

The application of Compresses Sensing is a promising physical layer technology for the joint activity and data detection of signals. Detecting the activity pattern correctly has severe impact on the system performance and is therefore of major concern. In contrast to previous work, in this paper we optimize joint activity and data detection in under-determined systems by minimizing the Bayes-Risk for erroneous activity detection. We formulate a new Compressed Sensing Bayes-Risk detector which directly allows to influence error rates at the activity detection dynamically by a parameter that can be controlled at higher layers. We derive the detector for a general linear system and show that our detector outperforms classical Compressed Sensing approaches by investigating an overloaded CDMA system.

In dense mobile network deployments, the cooperation of base stations in the uplink promises performance gains w.r.t. area throughput and power efficiency. In this paper, we propose the use of a distributed consensus-based estimation algorithm for the linear equalization of multiple user signals occupying the same resources. We will show that using an iterative process, the same estimation quality can be achieved as if a centralized joint detection of the signals was performed, and that with a limited number of iterations, a satisfactory bit error performance can be achieved.

The growing field of Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has been shown for a synchronous CDMA transmission that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). However, many practical applications show some degree of asynchronicity. In order to reduce transmitter complexity, we propose an enhanced CS MUD that detects the delay in addition to activity and data. This solves synchronicity issues for scenarios with a known maximum delay, without requiring signaling or pre-compensation of asynchronicity.

Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has already been shown that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in multi-user detection (MUD). For this detection we propose a new generalized Group Orthogonal Matching Pursuit algorithm that allows the use of additional side information regarding the sparsity structure. As a specific example, we exploit the information of a sparsity-aware Viterbi decoder in an iterative feedback loop to improve the activity detection. Here, a significant improvement of the activity detection is already achieved by executing only a single additional detection and decoding step.

This paper discusses retransmission approaches to improve the throughput performance of Hybrid-ARQ (HARQ) schemes in a point-to-point single user 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) uplink system. One goal of communication systems is to achieve a reliable transmission with a throughput performance as close as possible to channel capacity. For that, reducing the channel utilization will improve the throughput performance. Instead of sending two retransmission packets for two HARQ processes of one users, a previously published HARQ scheme uses the XOR combining of these packets to get only one retransmission packet with the same size. Similar to this idea, a new varied scheme performs XOR combining of parts of one conventional full retransmission to generate a smaller retransmission packet. Both approaches will reduce the channel utilization. They will be compared with a HARQ system in LTE uplink using a full size retransmission and a half size retransmission. The main focus of this work is the throughput performance evaluation of these schemes in an LTE link-level simulator.

In this paper physical layer network coding (PLNC) in two-phase two-way relaying networks using coded orthogonal frequency division multiplexing (OFDM) transmission is investigated. After receiving the superimposed signal from both sources, the relay estimates the XOR-based network coded signal, which is broadcast back to the sources. Assuming that the relay is equipped with multiple antennas, the uplink transmission forms a multiple-input multiple-output (MIMO) system, which allows the application of MIMO detection technologies. To this end, the impact of employing multiple antennas at the relay on different detection and decoding schemes under investigations is studied and compared with respect to mutual information (MI). Numerical simulations verify our theoretical analysis.

The standardization of the fourth generation of mobile communication systems was mainly driven by the demands for higher data-rates and improved Quality of Service. To reach these goals interference coordination has been identified as a promising research field for better exploitation of the time and frequency resources. This paradigm shift from interference avoidance to interference coordination is also reflected in the ongoing enhancement of the 4th generation of mobile communication systems such as 3GPP Long Term Evolution. In this context, numerous investigations have focused on the allocation of precoding matrices that are part of the link adaptation process by some form of base station (eNB) coordination. Within this work we develop a non-centralized interference coordination scheme by noticing that the re-allocation of a precoding matrix can lead to an uncontrolled change of the interference level at users located in neighboring cells, especially at the edge. To this end, we provide a fully closed form mathematical framework describing these changes. Based on this, we derive a simple metric that improves the precoding matrix selection process in the User Equipment with the result that interference changes can be reduced without having any standard impact. This novel scheme can also be seen as an extension to previous inter-cell interference coordination schemes without the need of base-station cooperation.

This paper is concerned with the application of distributed Interleave-Division-Multiplexing Space- Time Codes (dIDM-STCs) in relaying systems with error-prone relays applying Decode-and-Forward (DF). In case of erroneous decoding at the relays, error propagation occurs which is not considered by the original detection scheme for IDM-STCs. Hence, a new Reliability Aware Iterative Detection Scheme (RAID) is proposed which takes the decoding success of the relays as well as their decoding reliability into account. By optimally incorporating this knowledge in the detection process at the destination, substantial performance gains compared to the original detection scheme are achieved. The proposed RAID scheme even outperforms adaptive relaying as it explicitly exploits also erroneous relays, which is not the case for the adaptive scheme.

One challenging future application in digital communications is the wireless uplink transmission in sensor networks. This application is characterized by sporadic transmissions by a large number of sensors over a random multiple access channel. To reduce control signaling overhead, we propose that sensors do not transmit their activity states; instead sensor activity is detected at the receiver. As sensors have low activity probabilities, the multi-user vector is in general sparse. This enables Compressive Sensing (CS) detectors to perform joint Multi-User Detection (MUD) of activity and data, by exploiting the sparsity. Since sensors are either active or inactive for several symbol durations, block-wise CS detection can be applied to improve the activity detection. In this paper, we introduce blockwise greedy CS MUD, compare it to symbol-wise greedy CS MUD, and show that statistically independent channels for each symbol further improve the activity detection for block-wise CS detection. Herein, we use Code Division Multiple Access (CDMA) as a multiple access scheme.

In this paper, a novel HARQ transmission scheme based on network coding is proposed for wireless unicast scenarios. Instead of retransmitting erroneous packets individually, a network coded packet constructed by the XOR of two erroneous packets is transmitted similar to network coding. In order to fully exploit the network coded packet in combination with the previously received erroneous packets, soft combining methods with respect to Chase Combing (CC) and Incremental Redundancy (IR) are developed. The expected throughput gain of 33% for one retransmission for the proposed solution compared to common HARQ transmission is confirmed by LTE link-level simulations.

This paper investigates the optimal power routing problem in relay-based cooperative networks, where the relays are arbitrarily positioned. We generalize the standard shortest path routing algorithm (GSPRA) to find an minimum-power distributed MIMO multi-hop route from a source to a destination while satisfying a given e2e outage probability demand. The task of the proposed approach includes how to group relays to virtual antenna array (VAA) and discover the optimal multi-hop path. Instead of using per hop (or link) constraint, which is assumed by most of the existing routing algorithm, an e2e outage probability constraint is assumed for more relevance and freedom in practical systems. Under the concept of virtual node and virtual link, an efficient power allocation solution for general distributed MIMO multi-hop networks is used to calculate link costs for the shortest path algorithm. The proposed routing approach can fully exploit the merits of both cooperative communications and multi-hop transmissions. The significant power savings due to the proposed approach in comparison to the existing algorithms is demonstrated by numerical results.

In this paper we develop a power allocation scheme for single-relay systems applying Decode-and-Forward (DF) based on the resulting bit error rate (BER) at the destination. First, an analytical expression for the BER of M-QAM modulation considering estimation errors at the relay is derived. Based on this expression, the total transmit power is optimally assigned to the source and the relay in order to minimize the probability of errors at the destination. The preciseness of the derived closed form expression as well as the superior performance of the proposed DF-based relaying system are demonstrated by simulation results.

Coordinated Multipoint (CoMP) transmission technique is one method to improve performance of cellular wireless systems, e.g. LTE Advanced, by cooperation of cells for reducing interference and increasing SINR of users in weak radio conditions, e.g. located at cell edge. In this paper, we present an adaptive clustering algorithm to dynamically adjust the cooperation sets of a CoMP system to the UE perceived signal strength in order to maximize the overall system performance while avoiding major system architecture modifications. We show that additional gain in SINR could be achieved compared to non UE-aware fixed cluster with limited increase of system complexity, for a practical adaptive CoMP clustering scheme performing not far from an upper bound UE-specific scheme.

Efficiently transmitting data in wireless networks requires an integrated routing, scheduling, and power control strategy. As opposed to the universal dual decomposition we present a method that solve this optimization problem by fully exploiting its combinatorial structure. The method still maintains main requirements such as optimality, distributed implementation, multiple path routing, and per-hop error performance. The method represents a cross-layer approach where we include scheduling in the constraint set of a joint routing and power control optimization problem. Apart from the mathematical framework, the main contribution is a routing and power control decomposition (RPCD) algorithm. For verification, we compare the RPCD algorithm with state-of-art dual decomposition for wireless mesh backhaul networks. Impressive convergence results indicate that the RPCD algorithm calculates the optimum solution in one decomposition step only.

Efficiently transmitting data in wireless networks requires an integrated routing and radio resource allocation strategy. An initial observation that suggest dual decomposition to be a worthwhile approach is that the network flow variables for routing and the communication variables for resource allocation are only coupled through link capacities. With dual decomposition we split routing and resource allocation up in two separate subproblems and coordinate the solutions by solving a master dual problem. In this paper, we suggest to solve the master dual problem iteratively by using Aitken's method for an update of the dual variables. In contrast to former proposed subgradient methods, we accelerate the iteration process for achieving an optimum solution. This results in significant less complexity, but still facilitating distributed implementation. Beside describing the application of Aitken's method to simultaneous routing and resource allocation we also give necessary conditions for optimality, and show results for data transmission in a wireless mesh network being optimized in a proportional fair manner.

This paper addresses channel allocation for multi-user OFDM systems for real-time packet oriented data transmission. For high data rate wireless communication, optimum utilization of resources has become of utmost importance. The optimum problem we are facing is the minimization of the total transmitted power while meeting the quality of service (QoS) and rate requirements in a multi-user OFDM transmission. The idea is to utilize the knowledge of rate requirements not only as a constraint, but also in the channel allocation process. We propose to include the rate requirements in a two-fold manner: i) to perform pre-calculation of the maximum number of channels per user and, ii) to define the set of users competing for the available channels. Thereby obtaining a much balanced allocation policy, that results in reduced total power at low complexity

We present an accelerated power control algorithm applicable for CDMA based communications systems employing advanced uplink receiver techniques such as beamforming or multiuser detection. The proposed algorithm operates a fixed point power control algorithm accelerated by utilizing Aitken's /spl Delta//sub 2/-process (also known as Steffensen's method) that is merged with linear MMSE filtering. The linear MMSE filter either performs beamforming or multiuser detection. The proposed algorithm shows asymptotically quadratic convergence and is benchmarked against Newton's method. We further evaluate a lower bound on the Lipshitz constant which can be used to ensure convergence of the proposed algorithm. Numerical results are given for a fully deployed sectorized UMTS network with MMSE multiuser detection. Beside its employment within CDMA based communication systems, the proposed algorithm can also be utilized for the acceleration of computational intensive network simulations.

This paper addresses the evaluation of cross-layer metrics for wireless access systems employing hybrid automatic retransmission request (HARQ) processing combined with multiple input multiple output (MIMO) data transmission. The proposed metrics can be utilized by resource management algorithms to control resources in next generation networks. The objective is twofold. First, we evaluate a reliable throughput performance metric encompassing MIMO as well as HARQ protocol parameters in a single expression. In particular, we perform an information theoretic consideration by employing the conditional cutoff rate for MIMO transmission while the HARQ process is described by the renewal-theory. In a second step, we illustrate the flexibility of the metric evaluation approach by its application to two transmission schemes. Both schemes employ V-BLAST transmission but differ in HARQ processing. Results given for all schemes clearly indicate the suitability of the proposed metrics. With the metrics introduced we also get hints on designing wireless transmission schemes with multi antenna technologies and HARQ processing while avoiding intensive simulation studies

This paper addresses power controlled call admission for interference limited mobile communication networks that controls user classes with different priorities as well as service classes with different quality-of-service constraints. The idea is to mathematically formulate the power controlled admission as a homotopy method. This novel and generic approach enables network operators to flexible define a user/service-specific admission policy in order to handle users with different access priorities and/or different service classes. As an example, we can interactively control the access of high priority users by dynamically adjusting the service quality of already active users with low priority. On the other hand, the proposed approach also allows for safe and soft call admission. That is, to prevent a QoS deterioration in the active links and to assure acceptance of new users, if and only if all users can be supported at their required QoS. Due its generic character the proposed method gains new insight in power control and admission theory. Beside describing the admission approach we also study the existence of power solutions and show results for different pre-defined admission control functions when applied in a CDMA based cellular system.

This paper addresses aspects of channel coding in orthogonal frequency-division multiplexing-code-division multiple access (OFDM-CDMA) uplink systems where each user occupies a bandwidth much larger than the information bit rate. This inherent bandwidth expansion allows the application of powerful low-rate codes under the constraint of low decoding costs. Three different coding strategies are considered: the combination of convolutional and repetition codes, the code-spread system consisting of one single very low-rate convolutional code and a serial concatenation of convolutional, Walsh-Hadamard and repetition code. The latter scheme is improved by combining the Walsh-Hadamard codes with an additional M-phase-shift keying modulation resulting in complex-valued Walsh-Hadamard codes (CWCs). Analytical performance evaluations will be given for these codes for the first time. The application of CWCs as inner codes in a serial code concatenation is also addressed. We derive a symbol-by-symbol maximum a posteriori decoding algorithm in the complex signal space in order to enable iterative decoding for the entire code. A comprehensive performance analysis by simulation of all the proposed coding schemes shows that the Walsh-Hadamard-based schemes are the best choice for low-to-medium system load. Note that even for fully loaded OFDM-CDMA systems, the concatenation with an inner complex-valued Walsh-Hadamard code leads to a bit-error rate less than 10/sup -5/ for an E/sub b//N/sub 0/ of about 6 dB.

In the paper downlink beamforming for cellular radio systems with frequency division duplexing (FDD) is investigated. System level simulations clarify what gain can be achieved by applying circular arrays with omnidirectional antenna elements in contrast to linear arrays with sectorizing antenna elements.

We investigate the application of M-ary orthogonal modulation for multi-carrier spread spectrum (MCSS) uplink transmission over a Rayleigh fading indoor channel. Different coherent detection strategies with perfectly known channel coefficients are analyzed. Furthermore, we present a decision-directed estimation receiver to apply channel phase estimation. With decision-directed estimation no redundancy like training data is required to be transmitted. Simulation results show no performance loss for low E~/sub b//N/sub 0/ and only a moderate degradation for high E~/sub b//N/sub 0/ in comparison to ideal phase equalization. Moreover, the results are always contrasted with BPSK performance. Applying M-ary orthogonal modulation is revealed to outperform BPSK with respect to bit error rate and spectral efficiency, even if ideal equalization is considered for BPSK and decision-directed estimation for M-ary orthogonal modulation. The results are generally valid, whereas in this paper, they are based on the European HIPERLAN/2-standardization.

For a multi-carrier spread-spectrum (MC-SS) system, the inner spreading can be optimized by applying M-ary orthogonal modulation. We investigate the concatenation of maximum likelihood (ML) Viterbi (1995) decoding with M-ary orthogonal modulation in a MC-SS system. The system operates over a frequency-selective Rayleigh fading indoor channel in the uplink. We first evaluate the bit specific log-likelihood ratio for ML Viterbi decoding and present an estimation of the ratio exploiting the MC technique. Furthermore, the trade-off between channel coding, M-ary orthogonal modulation and simple spreading is considered by Monte-Carlo simulations. The results are always compared with BPSK performance and they emphasize for the concerned indoor transmission scenario that a moderate bit-error-rate can only be achieved if M-ary orthogonal modulation is employed. All simulations are related to the European Hiperlan/2 standardization whereas the results are generally valid.

In order to combat transmission errors in digital communication systems, forward error protection based on convolutional codes and interleaving in conjunction with soft decision maximum-likelihood decoding through the Viterbi (193) algorithm is often applied. The expected bit error rate for a given signal-to-noise ratio is generally difficult to foresee for a certain system. In this paper, we introduce a method which vividly treats the error behaviour on particular stages of the receiver, hereby allowing a coarse estimate of the overall bit error rate. Although no close mathematical results are given, several astonishing effects can easily be explained thus providing a better understanding of concatenated systems.