We focus on low-power spike-based sensor node communication where runlength-limited (RLL) encoding is applied to map the information to the timing of the spikes. In this type of sensor communication, often rare events need to be communicated such that spikes are transmitted rarely, and the detection of those spikes has to be performed in an energyefficient way. Generally, standard analog-to-digital converter (ADC) based detectors are employed at the receiver, which are always active even if there is no spike being transmitted, resulting in unnecessary power consumption. To mitigate this issue, this paper studies an integrate-and-fire (IF) circuit followed by a counter and pre-processor as an energy-efficient spike detector. It counts the number of fires in each symbol interval, which is further used by the pre-processing unit to map the fire count within each symbol interval into transmitted RLL symbols' loglikelihood ratios. We evaluate a lower bound on the mutual information (MI) rate and the bit error rate of the communication system using this spike detector. Numerical results indicate that the proposed spike detection enables to receive RLL encoded spike sequences at a significantly lower energy per communicated bit Eb than traditional ADC based spike detection and alternative low-power IF time encoding machine (IF-TEM) based detection methods. Moreover, the required Eb decreases when increasing the minimum runlength constraint jointly with the signaling rate. Furthermore, a comparative discussion on the power consumption between the IF circuit based analog-todigital conversions and standard ADCs is presented.

Semantic communication has emerged as a promising paradigm to address the challenges of next-generation communication networks. While some progress has been made in its conceptualization, fundamental questions remain unresolved. In this paper, we propose a probabilistic model for semantic communication that, unlike prior works primarily rooted in intuitions from human language, is grounded in a rigorous philosophical conception of information and its relationship with data as Constraining Affordances, mediated by Levels of Abstraction (LoA). This foundation not only enables the modeling of linguistic semantic communication but also provides a domain-independent definition of semantic content, extending its applicability beyond linguistic contexts. As the semantic communication problem involves a complex interplay of various factors, making it difficult to tackle in its entirety, we propose to orthogonalize it by classifying it into simpler sub-problems and approach the general problem step by step. Notably, we show that Shannon's framework constitutes a special case of semantic communication, in which each message conveys a single, unambiguous meaning. Consequently, the capacity in Shannon's model-defined as the maximum rate of reliably transmissible messages-coincides with the semantic capacity under this constrained scenario. In this paper, we specifically focus on the sub-problem where semantic ambiguity arises solely from physical channel noise and derive a lower bound for its semantic capacity, which reduces to Shannon's capacity in the corresponding special case. We also demonstrate that the achievable rate of all transmissible messages for reliable semantic communication, exceeds Shannon's capacity by the added term H(X|S).

In this paper, we investigate the pertinence of the angle of arrival (AoA) as a feature for robust physical layer authentication (PLA). While most of the existing approaches to PLA focus on amplitude-dependent features of the physical layer of communication channels, such as channel frequency response, channel impulse response, or received signal strength, the use of AoA in this domain has not yet been studied in depth, particularly regarding the ability to thwart spoofing (impersonation) attacks. In this work, we demonstrate that an impersonation attack targeting AoA-based PLA is only feasible under strict conditions on the attackers location, which highlights the AoA’s role as a strong feature for unspoofable PLA, especially when 2D AoA is employed.We extend previous works considering a single-antenna attacker to the case of a multiple-antenna attacker, and we develop a theoretical characterization of the conditions under which a successful impersonation attack can be mounted. Furthermore, we have performed extensive simulations in support of theoretical analyses, to validate the robustness of AoA-based PLA.

Extremely large-scale (XL) antenna arrays and wideband transmission are critical enablers for next-generation mobile communication networks. These technologies offer substantial improvements in angular resolution, spatial degrees of freedom, and spectral efficiency (SE). However, the combination of large antenna apertures and ultra-wide bandwidth induces a frequency-dependent beam misalignment, known as the beam squint effect, which severely degrades spatial directivity and SE. Traditional frequency-independent analog phase shifters (APSs) cannot compensate for this phenomenon, necessitating the use of true-time delays (TTDs). Despite their potential, most existing TTD-based architectures rely on the assumption of ideal hardware, ignoring practical constraints such as limited delay range and finite delay resolution. This paper analyzes the performance of existing architectures under these practical hardware limitations and proposes a novel hardware-aware multi-stage delay-phase-precoding (MSDPP) architecture. The proposed MSDPP is designed to comply with commercially available device specifications, effectively minimizing the impact of quantization and clipping errors inherent in practical TTDs. Extensive simulations considering realistic hardware constraints demonstrate that the proposed MSDPP outperforms state-of-the-art solutions by approximately 3 dB and 55 % in SE and energy efficiency (EE), respectively, making it a robust solution for energy-efficient ultra-wide bandwidth extremely large-scale XL antenna array systems.

Recent studies have explored the synergy between reconfigurable intelligent surfaces (RIS) and multiple-input multiple-output (MIMO) systems to overcome limitations in spectral efficiency and coverage. While RIS-aided MIMO systems promise transformative gains, their real-world deployment faces critical challenges rooted in hardware imperfections, in particular, the nonlinearity of power amplifiers (PAs) in the MIMO transmitters. To bridge this gap, this paper investigates a holistic design that jointly optimizes RIS phase shifts and base station transmit precoder for the RIS-aided MIMO systems with a practical PA model in real life. We propose a hardware-aware joint design framework that co-optimizes the base station precoder and RIS phase shifts to balance beamforming gain with PA distortion suppression. A closed-form solution for the optimal RIS phase-shifting matrix is derived as a function of the precoder, reducing the joint optimization to an equivalent precoder optimization problem. A low-complexity successive convex approximation-based algorithm is developed for efficient precoder design. Simulations demonstrate that the proposed method achieves up to 30.6% spectral efficiency improvement over benchmarks under PA nonlinearity, highlighting its practical significance in RIS-aided systems.

Future industrial shopfloors will feature a large number of autonomous, mobile devices that communicate wirelessly. In such controlled environments, collecting channel state information (CSI) by location and reusing it over time may reduce the overhead of channel sounding and CSI feedback that is required for channel-aware radio resource allocation. However, even if the trajectories of devices repeat over time, temporary variations of the environment put a question mark behind the usability of radio environment maps (REMs) from the past. To address this important question, we designed and carried out a channel measurement campaign in an industrial-like area, we evaluated the data regarding the consistency of the radio channel in a time-varying environment, and we conducted simulations of a multi-user communications scenario based on measured data to present a potential application of channel information reuse. The measurement results show a high similarity of the REM over time and a spatially limited impact of the temporary variations caused by a metallic object on the REM. Our simulation results show up to 75% savings in CSI feedback overhead while the reliability of the communications system remains almost unaffected. In the future, we will elaborate on the estimation of the REM in a time-varying environment.

In this paper, we focus on the nonlinear distortion in a massive MIMO system with nonlinear power amplifiers. It is known that the distortion generated by nonlinear power amplifiers is beamformed in the line-of-sight (LoS) channel. In particular, spurious emissions are caused by beamformed intermodulation (IM) distortion and form as IM beams. We propose a precoder for IM beam suppression in the MIMO system, which aims to suppress the intermodulation beams while maintaining beamforming for the intended receivers. First, we derive the direction of the IM beams under the free-space LoS channel condition. Then, the intermodulation suppression (IMS) precoder is proposed by maximizing the signal-to-leakage ratio. Moreover, we propose a generalized IMS precoder to achieve the trade-off between the intended signal coherent combination and IM beam suppression. Adjusting the weights of the precoder can suppress the IM beams while ensuring a coherent combination of signals at intended receivers. Simulation results show that the proposed IMS precoder can suppress the IM beams and reduce the power leakage in spurious directions. The power gain obtained by the proposed IMS precoder can approach to the maximum ratio transmission while achieving substantial suppression gain for the IM beams.

For very high bandwidth systems the use of a 1-bit analog-to-digital converter is a promising solution to keep the power consumption of terahertz receivers manageable. With 1-bit quantization being a highly non-linear operation, it is necessary to revise all signal processing algorithms, including synchronization algorithms. In this paper we focus on the estimation of an unknown deterministic frequency offset between the transmitter and the receiver. To this end, two different data-aided frequency offset estimators are presented which both operate with 1-bit quantization at the receiver and in the presence of additive white Gaussian noise, and which correspond to the existing weighted phase averager and the planar filtered estimator used in conventional receivers. Comparing the performance of the considered estimators, we find an interesting trade-off between the achievable estimation performance, the computational complexity, and the estimation range. The planar filtered estimator has a higher complexity and achieves theoretical performance bounds for low signal-to-noise ratios in comparison to the weighted phase averager. Performance at high signal-to-noise ratios is limited by an error floor for both estimators, which can be lowered by oversampling in the case of the planar filtered estimator.

Big generative models have significantly impacted several domains like natural language processing, computer vision, and drug discovery. These developments, large-scale generative foundation models exemplified by architectures such as GPT-4 have emerged to address a broad spectrum of generalized tasks. These models are trained to capture the underlying general correlations from a large training dataset. Although big generative AI techniques have been explored in various wireless communication applications, such as channel generation, the integration of generalized foundation models into this domain remains limited. A core component of these models is the masked autoencoder. This work investigates the suitability of masked autoencoders for massive multiple-input multiple-output (MIMO) systems, focusing on their capacity to capture spatial and temporal correlations in massive MIMO channels. To this end, a massive MIMO scenario with user mobility is considered, where the channel state information (CSI) varies with both spatial and temporal correlation. A masked autoencoder is trained in a self-supervised manner using channel state information (CSI) from multiple users. The trained model is then tested for its performance in tasks of feedback compression, channel interpolation, and channel prediction. Experimental results demonstrate that masked autoencoders effectively capture inherent correlations within massive MIMO channels, underscoring their potential to advance foundational model-based approaches in wireless communications.

In mobile communication multiple-input multiple-output (MIMO) systems, angle estimates are invaluable for positioning, initial beam acquisition and interference mitigation. In the context of true-time delay (TTD) systems, angle estimates can be obtained by established signal processing routines at the receiver. Similar signal processing approaches are applicable to frequency diverse array (FDA) systems. However, real-world implementations suffer from hardware impairments such as sample-time-offset (STO) and carrier frequency offset (CFO), which can degrade estimation accuracy.This work compares TTD and FDA arrays in regards of their resilience to STO and CFO impairments using the root mean square error (RMSE) of angle estimates as metric. Furthermore, the impact of implementing the time/frequency shifts in base-/pass-band is examined. The investigation shows that even under typical STO and CFO impairments, estimation errors of 1° can be achieved at 0 dB signal-to-noise ratio (SNR), highlighting the robustness of these techniques.

Routing plays a pivotal role in quantum communication as it directly impacts the efficiency, reliability, and scalability of quantum networks. While several studies in the literature have explored routing algorithms leveraging quantum memories, current quantum memory technologies are unable to simultaneously achieve high fidelity, extended storage durations, wide bandwidths, multimode capacity, and high efficiency. To address this limitation, our study focuses on fidelity-guaranteed entanglement routing within a memoryless network architecture, employing both distributed and centralized routing approaches. Utilizing our proposed routing algorithm, MEFID, we achieved a throughput of 72 qubits per second under a fidelity threshold of 0.8 and within three iterative rounds. By integrating a purification process to ensure that the final fidelity consistently exceeds the specified threshold, our algorithm facilitates the development of robust and high-performance quantum networks.

Runlength-limited (RLL) sequences have recently gained attention as a means to cope with the self-introduced intersymbol interference (ISI) in communications systems employing faster-than-Nyquist (FTN) signaling, in particular in systems using zero-crossing modulation (ZXM). However, previous research has revealed a significant gap between the achievable rates in ZXM systems using i) sequences generated by practical RLL codes and ii) maxentropic RLL sequences, which cannot solely be explained by the lower code rate of practical RLL codes. In this paper, we give evidence that the assignment between bit sequences and RLL sequences is essential for the performance of an RLL code when transmission over noisy channels is considered. We propose an RLL coding scheme with a high degree of freedom for the selection of the assignment and present a simple algorithm for its optimization. We show that our codes outperform RLL codes published in the literature in terms of the achievable rate in ZXM systems.

With the increasing adoption of orthogonal frequency division multiple access (OFDMA) technology in WiFi networks, achieving ultra-reliable low-latency communications (URLLC) presents new challenges in resource management. We propose implicit channel quality-driven resource allocation (ICQRA), a lightweight framework that avoids explicit channel sounding overhead while maintaining high reliability. ICQRA passively monitors channel quality during normal packet demodulation, proactively detects degradations, and switches to better frequency resources before packet losses occur. Our approach incorporates an adaptive threshold mechanism that learns from transmission outcomes while efficiently coordinating resources across multiple users. Evaluations using both simulations and industrial channel measurements demonstrate that ICQRA achieves near-optimal performance in reducing consecutive packet losses with significantly lower resource consumption compared to existing approaches.

Current orthogonal frequency division multiplexing (OFDM) standards specify limited options for cyclic prefix (CP) duration, regardless of the wireless channel characteristics. These fixed options can result in significant overhead when the channel delay spread is very short. To address this, a more flexible approach to CP selection is needed, allowing for CP lengths that may be shorter than the delay spread. In this paper, we revisit the classical issue of waveform optimization for channels with short delay spreads, and investigate the potential to reduce the CP duration in OFDM. Building on our prior work in [1], we extend the analysis to multi-antenna systems and assess the impact of number of antennas on multiple-input single-output (MISO)-OFDM system with reduced CP durations. We first derive closed-form expressions for the desired signal power and inter-symbol interference (ISI) power in MISO-OFDM where the CP duration is shorter than the length of channel impulse response (CIR). Then, conditioned on the radio link reliability, defined by the link outage probability, we formulate an optimization problem to jointly determine the minimum CP duration and SNR values required for the system to satisfy that reliability condition. To solve the optimization problem, we use a weighted-sum approach combined with the Bisection method. Our analysis demonstrates that energy efficiency comparable to conventional OFDM systems can be maintained, while achieving increased spectral efficiency (SE) due to the reduced CP duration.

In this paper, we study the performance of a practical secret key generation method under low-mobility scenarios. Instead of relying on traditional cryptographic methods or leveraging spatial diversity and reconfigurable intelligent surfaces to increase channel variations, we utilize locally generated pilots to add randomness to the system, thus in turn helping to increase the secret key rate. The results demonstrate significant improvements over the original channels, whose entropy source mainly relies on mobility and channel variations. More importantly, this scheme works well without extra helpers or multiple antennas, thus providing a potential for developing reliable, lightweight security solutions for resource-constrained devices in practice.

Wireless MIMO communication systems nowadays are driven by the requirement to minimize the hardware computational complexity of detection algorithms while preserving high detection performance. To address this challenge, traditional tree-based approaches like the K-best algorithm have been proposed, which employ a fixed complexity at each layer to manage computational demands. The K-best algorithm remains computationally intensive due to its complexity highly dependent on the QAM modulation size and is further followed by a sorting scheme applied at each layer. Another tree-based approach, called the box decoding, has been developed to overcome these limitations in small-scale MIMO systems. However, the complexity of this algorithm escalates significantly in MIMO systems with higher dimension as the number of candidates generated by the box decoding grows substantially. In this paper, we propose an innovative solution, called the K-box algorithm. In contrast to K-best, K-box decouples the candidate selection procedure and the size of constellation space similar to box decoding while restraining the candidates expansion in higher-dimensional MIMO by sorting only when the number of candidates exceed a certain limit. Testing on 4 × 4 and 8 × 8 64-QAM systems for 5G new radio (NR) link demonstrates SNR gains of 0.6 dB at a BER of 10−2compared to the K-best algorithm, while achieving 77% and 76% complexity reduction in partial Euclidean distance (PED) computations and sorting complexity reductions of 94% and 92% respectively.

Recent transition in mobile communication standards suggests massive multiple-input multiple-output (MIMO) to be an integral part of the foreseeable future. However, as antenna elements increase to hundreds in the fifth-generation (5G) and beyond, traditional signal processing methods become prone to significant hardware impairments compound from multiple chains, leading to a substantial performance degradation. This paper explores the effectiveness of generative artificial intelligence (AI) techniques in addressing these challenges within massive MIMO systems. For this purpose, the conditional generative adversarial network (CGAN), a special class of generative AI algorithms, is employed to enhance the accuracy of channel state information (CSI) estimation in a hardware-impaired transceiver setup. This problem is treated as an image-denoising task, where the noise is introduced by the hardware impairments and LS estimation error. Through simulations conducted across various antenna array sizes, the potential of generative AI to improve CSI estimation accuracy under hardware impairments is demon-strated. This highlights its capacity to address critical signal processing challenges in the next-generation wireless systems.

Energy efficiency is a critical challenge for next-generation mobile networks. Especially as traffic demand grows, the energy per bit must decrease significantly. One promising solution is the Gearbox-PHY, which adaptively switches between modulation schemes and tailored radio front ends to maximize energy efficiency while delivering required data rates. In this regard, high spectral efficiency needs can be addressed with standard quadrature amplitude modulation, while low-power alternatives like impulse radio are employed for lower data rate scenarios, significantly reducing front end power consumption. While we considered the energy optimization for such a Gearbox-PHY in prior work, the specific focus of this paper is the consideration of the trade-off between oscillator power consumption and its phase noise. Using literature-based models and measurements for hardware power consumption, we demonstrate that embracing hardware impairments can lead to substantial energy savings of up to three orders of magnitude.

The utilization of pure space-division multiple access (SDMA) in mobile communications systems relies on the assumption that the array propagation vectors of different user equipments (UEs) differ by a minimum angle to obtain reasonable propagation conditions. However, when serving several UEs, this hypothesis does not hold with probability one, especially for channels with a strong line-of-sight (LOS) component, and it is beneficial w.r.t. maximizing the sum rate to switch to a different, truly orthogonal, multiple access scheme like time-division multiple access (TDMA) or frequency-division multiple access (FDMA). In this work, we use the array propagation vector correlation of two UEs to decide whether the utilization of pure SDMA is advantageous. For this reason, we derive an optimal analytical decision threshold based on this correlation for a two UE scenario. In addition, we extend this criterion numerically to find a practical decision criterion for the assignment of transmission groups in multi UE scenarios for a hybrid SDMA/TDMA approach and apply a linear-model based estimation approach.

With the rise of the sixth generation (6G) and the use of frequency range 3 (7.125-24.25 GHz) (FR3) centimeter wave (cmWave) bands, interference-limited environments due to the coexistence of multiple incumbents are becoming increasingly common. This emphasizes the need for effective interference management without sacrificing time and frequency resources. In multi-cell, multi-user scenarios, beam selection is crucial in reducing interference, particularly in non-orthogonal space division multiple access (SDMA) systems. This paper explores interference mitigation for multi-panel user equipment (MPUE) in coexisting FR3 networks with the help of beam selection mechanism. Numerical simulations showcase the gains in spectral efficiency and fairness achieved through this approach.

With the increasing adoption of orthogonal frequency division multiple access (OFDMA) technology in WiFi networks, accurately modeling packet loss is crucial for enhancing communication reliability. This is particularly important for ultra-reliable low-latency communications (URLLC), where the complexity of channel models can be prohibitive with many realizations. This paper introduces two streamlined approaches: a correlation-based model and a multi-dimensional Markov chain model. Both models are engineered to capture the intricate interplay of time and frequency dependencies among resource units in OFDMA WiFi systems. By leveraging frequency diversity and employing selection combining techniques, this study validates the accuracy of the proposed models in analyzing packet loss. The paper presents efficient model approximations that establish a comprehensive framework to simulate and analyze packet loss dynamics, contributing to the enhancement of network reliability and the optimization of system performance.

The instruction set architecture of vector processors operates on vectors stored in the vector register file which needs to handle several concurrent accesses by functional units with multiple ports. When the vector processor is running with high utilization, access conflicts become a major source of performance degradation. With a software model of a vector processor, we take a deep dive into the runtime impact of conflicts and their characteristics, and on ways to manage them, i.e., avoidance, resolution, and mitigation. For conflict avoidance, we study the existing approaches of banking with different static bank layouts and propose a dynamic bank layout to overcome their shortcomings. Our approach assigns newly written registers a temporarily unique starting bank. For conflict resolution, we compare different arbitration algorithms and optimize round-robin arbitration for mixed-width arithmetics by prioritizing wide operands. For conflict mitigation, operand queues of varying depths are studied. Our inventions are likely to increase the area efficiency of vector processors, either because they allow to use shallower operand queues while keeping the same performance, or reduce the area even further by using less banks, albeit at a performance impairment of 10% or less. The insights of the study can further be applied to other shared memory systems.

Koopman operator approximation is becoming a leading trend for the identification and control of nonlinear systems, particularly with the use of Deep Neural Networks (DNNs). Although DNNs have shown potential to simultaneously identify Koopman observation functions and its lifted control dynamics, training these components jointly often leads to reduced model accuracy and robustness. This study proposes a novel learning approach called system-oriented DNN (soDNN), which improves the learning of Koopman observation functions by offering gradient information to update the lifted bilinear system dynamics. Unlike conventional learning strategies, soDNN achieves enhanced model precision for both short- and long-term predictions, as demonstrated in a quadrotor attitude control system in SO(3) using various datasets, including measurements collected from the AirSim simulator and the open-source NeuroBEM dataset. Furthermore, the control efficacy of the soDNN-trained system is evaluated using sequential linear model predictive control (sLMPC).

In this paper, we consider the problem of concurrent multipath routing and end-to-end entanglement distribution for online resource allocation in quantum networks. We propose a heuristic algorithm to solve this problem, considering quantum memory, decoherence time, entanglement distribution probability, and fidelity. A time-slotted quantum network operation model is considered based on the cut-off decoherence time of quantum memories. The proposed heuristic is designed for a quantum network with noisy intermediate scale quantum (NISQ) constraints, including fixed quantum memory decoherence time, and probabilistic entanglement generation and swapping. It considers integer linear programming (ILP)-based and heuristic approaches to select multiple paths and resource allocation in the network. Simulations are performed to evaluate the performance of ILP and heuristic based approaches in terms of requests using multipath approach, blocking ratio, and computation time in a small sized quantum network with few requests. Next, the performance of heuristic based approaches are evaluated for a large problem size. The obtained results ensure that performance of ILP and heuristic based approaches are comparable, and multipath routing outperforms single path routing in terms of blocking ratio.

In this paper, we investigate the secret key generation (SKG) for static scenarios utilizing the pilot randomization method. In fact, the majority have studied SKG under dynamic scenarios in which a high secret key rate is achievable due to sufficiently high randomness. In this study, we instead consider the static scenario which, though important, is not well-studied. More specifically, we utilize the pilot randomization method, which is known to prevent injection attacks effectively, to randomize the associated channels. More importantly, we derive the secret key rate of the system and demonstrate that the secret key rate of a static system can increase significantly due to the added randomness. The proposed approach is demonstrated against known methods to show its effectiveness in increasing the secret key rate in static environments.

Highly dynamic maneuvers pose a challenge to conventional state estimators of quadrotors in rapidly tracking the pose. This paper proposes a data-driven Koopman operator-based error-state Kalman filter (K-ESKF) to enhance pose estimation in agile flight. Our method uses the Koopman operator theory to transform the full-state nonlinear quadrotor dynamics into a lifted bilinear control system driven by accelerations and angular rates. A deep neural network (DNN) is used to represent the Koopman observable functions. Our proposed K-ESKF extends the propagation step of a standard error-state Kalman filter (ESKF) using the lifted bilinear control system. An open-source quadrotor dataset, NeuroBEM, is used for training and evaluating the DNN and for testing the K-ESKF. The learned Koopman bilinear system demonstrates a 60% less attitude errors compared to the first-order Euler method in terms of model accuracy. Using real trajectories from the dataset, our proposed K-ESKF can estimate the pose as accurately as the ESKF during normal flight. More importantly, our proposed approach outperforms the ESKF by achieving about 50% less attitude and velocity estimation errors in a highly agile flight. During drastic attitude and velocity changes, the K-ESKF can still estimate the pose while the ESKF loses tracking.

Modulation schemes that encode information in the time domain play an important role for energy-efficient communications at high and low spectral efficiencies. Verdú showed that a spectrally efficient operation of pulse-based modulation schemes with independent identically distributed symbols at low signal-to-noise ratios is only possible when the phase is used as an additional degree of freedom. In the present work, four maxentropic symbol sequences are considered that encode information in the time domain and make use of the phase to varying degrees. The spectral efficiency is evaluated for the transmission over a bandlimited additive white Gaussian noise channel where the output is quantized with the lowest resolution that allows the reconstruction of the input symbols in the noiseless case. Moreover, faster-than-Nyquist (FTN) signaling is considered. Our results show that using the phase only to provide redundancy already leads to a significantly increased spectral efficiency, a lower required energy per communicated bit, and allows to benefit from FTN signaling. This makes the previously proposed zero-crossing modulation and time-derived zero-crossing modulation, a specific kind of impulse radio, to energy-efficient contenders for wideband millimeter-wave communications and for the operation of lowpower wireless sensor nodes in the Internet of things, respectively.

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.

The operation of future radio systems will benefit from any available information about the radio environment, e.g., to better allocate radio resources, and to predict the radio conditions of users based on their locations. Thereby, radio environment maps (REMs), i.e., the information about the radio channel per location, can assist future radio systems. However, measuring large-area REMs with high spatial resolution results in enormous effort and it is more efficient to estimate REMs from sparse observations. In this work, we present a deep neural network (DNN)-based interpolation technique that is capable of recovering spatial fading patterns through interpolation by extracting position-dependent channel correlations. Our approach solely relies on the sparsely sampled REM that is to be interpolated. By systematically studying DNN structures and input features, we extract a favorable structure for the spatial interpolation of anisotropic environments. Based on a simulated indoor REM with varying fading structures, we demonstrate that our approach is superior to conventional methods in recovering spatial fading structures. Using reconstructed REMs for radio applications in future work will yield application-specific metrics to further assess the reconstruction quality.

For fifth-generation (5G) and 5G-Advanced networks, outage reduction within the context of reliability is a key objective since outage denotes the time period when a user equipment (UE) cannot communicate with the network. Earlier studies have shown that in the experimental high mobility scenario considered, outage is dominated by the interruption time that stems from the random access channel (RACH)-based handover process from the serving cell to the target cell. A handover by itself is a necessary mobility process to prevent mobility failures and their associated outage. This paper proposes a RACH-less handover signaling scheme for the 3rd Generation Partnership Project (3GPP) conditional handover (CHO) mechanism. The proposed scheme exploits the decoupling between the CHO preparation and execution phases to establish initial synchronization between the UE and the target cell through an early acquisition of the timing advance. This significantly curtails the RACH process and therefore the handover interruption time. Results based on a system-level simulation-based mobility study have shown that the proposed scheme significantly reduces the outage and its constituent handover interruption time relatively by 18.7% and 43.2%, respectively.

In the context of joint communication and sensing (JCAS), communication systems can be used for sensing, where parameters such as time delay can be determined from channel estimation. These time delays enable applications such as ranging and localization. Various studies show that environmental conditions significantly impact sensing performance. Thus practical measurements within the actual environment are essential to overcome the limitations of simulation models. To accurately evaluate the real-world performance of sensing algorithms, a software-defined radio (SDR)-based experimental platform was developed in previous work for indoor measurements with co-located user equipments (UEs). This paper introduces extensions to the platform to enable long-range measurements. Using the enhanced platform, two-way ranging (TWR) measurements are performed in a vehicle-to-vehicle (V2V) setup to evaluate the realistic accuracy using cross-correlation (CCR)-and superresolution path delay estimation (SPDE) algorithms for ranging applications. The measurement results confirm that SPDE can improve accuracy in scenarios where the signal-to-noise ratio (SNR) and signal integrity are assured.

Future wireless communication systems are foreseen to provide powerful enhancements such as high-precision localization, allowing the radio environment to be characterized with high spatial resolution. On the other hand, future industrial ultra-reliable low-latency communications (URLLC) scenarios will place extreme reliability requirements on communication systems. In this work, we present the idea of using high-resolution radio environment maps (REMs) that contain received power levels per location as well as information about spatially occurring fading patterns for the trajectory optimization of mobile robotics to proactively increase communications reliability. To find the optimal trajectory, we show how to apply the Dijkstra path-finding algorithm to a two-dimensional REM. Simulations using a high-resolution REM from a previous measurement campaign indicate that small trajectory adaptations may increase the minimum received power (and hence the reliability) by orders of magnitude. By penalizing trajectory adaptations during optimization, we analyze the tradeoff between the number of lane adaptations and the communications reliability. Appropriate penalty parameters enable to drastically reduce the number of path adaptations without compromising reliability. Similar results are found for a ray-tracing-based REM of the same environment which suggests the use of synthetic REMs for effective investigation of other environments. This encourages follow-up investigations on reliability gains for radio propagation conditions in other environments.

The hand blockage effect of the human hand around the user equipment (UE) is too considerable to be ignored in frequency range 2 (FR2). This adds another layer of complexity to the link budget design in FR2 for 5G networks, which already suffer from high path and diffraction loss. More recently, multi-panel UEs (MPUEs) have been proposed as a way to address this problem, whereby multiple distinct antenna panels are integrated into the UE body as a way to leverage gains from antenna directivity. MPUEs also enhance the Rx-beamforming gain because it is now subject to each individual antenna panel. In this paper, the mobility performance of hand blockage induced by three practical hand grips is analyzed in a system-level simulation, where in each grip both the UE orientation and the hand positioning around the UE is different. It is seen that each hand grip has a significant impact on mobility performance of the network, where in the worst case mobility failures increase by 43% compared to the non-hand blockage case. Moreover, a detailed analysis of the tradeoff between the mobility key performance indicators and the panel and Rx beam switching frequency is also studied. Results have shown that both the panel and Rx beam switches can be reduced considerably without compromising on the mobility performance. This is beneficial because it helps in reducing UE power consumption.

The conventional orthogonal frequency division multiplexing (OFDM) combats inter-symbol interference (ISI) by adding cyclic prefix (CP) at the beginning of OFDM block. Current standardization employs fixed CP duration irrespective of wireless channel characteristics, which is effective for channels with a long root mean-square (RMS) delay spread. However, this is impractical for industrial environments where the channel exhibits a shorter delay spread leading to a more pronounced CP overhead. Therefore, the classical problem of waveform design and optimization has to be revisited for industrial applications in the sixth generation (6G) of wireless communication systems. With the understanding of wave propagation characteristics in industrial environments, in this paper, we first analyze the impact of insufficient CP duration on OFDM and subsequently derive the closed-form expressions for ISI and desired power. Then, we design a multi-objective optimization problem (OP) which determines the minimum required signal-to-noise ratio (SNR) and CP duration conditioned on link reliability. To solve the proposed OP, we adopt the weighted-sum approach that transforms the multi-objective OP into a single-objective OP. We present numerical results for channels with various RMS delay spreads and confirm the spectral efficiency (SE) gains attainable by the proposed method with no need for additional equalization complexities.

ZuSE-KI-Mobil (ZuKIMo) is a nationally funded research project, currently in its intermediate stage. The goal of the ZuKIMo project is to develop a new System-on-Chip (SoC) platform and corresponding ecosystem to enable efficient Artificial Intelligence (AI) applications with specific requirements. With ZuKIMo, we specifically target applications from the mobility domain, i.e. autonomous vehicles and drones. The initial ecosystem is built by a consortium consisting of seven partners from German academia and industry. We develop the SoC platform and its ecosystem around a novel AI accelerator design. The customizable accelerator is conceived from scratch to fulfill the functional and non-functional requirements derived from the ambitious use cases. A tape-out in 22 nm FDX-technology is planned in 2023. Apart from the System-on-Chip hardware design itself, the ZuKIMo ecosystem has the objective of providing software tooling for easy deployment of new use cases and hardware-CNN co-design. Furthermore, AI accelerators in safety-critical applications like our mobility use cases, necessitate the fulfillment of safety requirements. Therefore, we investigate new design methodologies for fault analysis of Deep Neural Networks (DNNs) and introduce our new redundancy mechanism for AI accelerators.

Embedded systems usually integrate multiple Pro-cessing Elements (PEs) on a single chip. Various PEs are con-nected to the same Tightly Coupled Memory (TCM) to increase the area and energy efficiency. However, memory sharing comes at the cost of conflicts resulting in performance degradation. To counteract this issue, Access Interval Prediction (AIP) has been introduced in the literature to predict the interval between two memory accesses. State-of-the-art AIP units are based on predictors proposed for branch prediction, such as TAgged GEometric (TAGE). This work shows for the first time that several types of neural networks are suitable for AIP as well. By treating AIP as a classification problem, we can continue to decrease the error rate compared to the TAGE predictor. For example, Vision Transformer (ViT) networks reduce the average error rate by over one-third to 2.1 percent. Through our investigation, we demonstrate that offline training alone is sufficient since the memory access traces contain the same repetitive patterns independent from the input parameters of the program run.

Physical layer security (PLS) is seen as the means to enhance physical layer trustworthiness in 6G. This work provides a proof-of-concept for one of the most mature PLS technologies, i.e., secret key generation (SKG) from wireless fading coefficients during the channel's coherence time. As opposed to other works, where only specific parts of the protocol are typically investigated, here, we implement the full SKG chain in four indoor experimental campaigns. In detail, we consider two legitimate nodes, who use the wireless channel to extract secret keys and a malicious node placed in the immediate vicinity of one of them, who acts as a passive eavesdropper. To estimate the final SKG rate we evaluate the conditional min-entropy by taking into account all information available at the eavesdropper. Finally, we use this paper to announce the first ever physical layer security challenge, mirroring practices in cryptography. We call the community to scrutinize the presented results and try to “break” our SKG implementation. To this end, we provide, i) the full dataset observed by the eavesdroppers, ii) 20 blocks of 16 - byte long ciphertexts, encrypted using one-time pad with 20 distilled secret keys, and, iii) all codes and software used in our SKG implementation. An attack will be considered successful if any part(s) of the plaintext are successfully retrieved.

The massive deployment of low-end wireless Internet of things (IoT) devices opens the challenge of finding de-centralized and lightweight alternatives for secret key distribution. A possible solution, coming from the physical layer, is the secret key generation (SKG) from channel state information (CSI) during the channel's coherence time. This work acknowledges the fact that the CSI consists of deterministic (predictable) and stochastic (unpredictable) components, loosely captured through the terms large-scale and small-scale fading, respectively. Hence, keys must be generated using only the random and unpredictable part. To detrend CSI measurements from deterministic components, a simple and lightweight approach based on Kalman filters is proposed and is evaluated using an implementation of the complete SKG protocol (including privacy amplification that is typically missing in many published works). In our study we use a massive multiple input multiple output (mMIMO) orthogonal frequency division multiplexing outdoor measured CSI dataset. The threat model assumes a passive eavesdropper in the vicinity (at 1 meter distance or less) from one of the legitimate nodes and the Kalman filter is parameterized to maximize the achievable key rate.

In this paper, we study the use of the angle of arrival (AoA) as a feature for performing robust, machine learning (ML)-based physical layer authentication (PLA). In fact, whereas most previous research on PLA relies on physical properties such as channel frequency/impulse response or received signal strength, the use of the AoA in this context has not yet been studied in depth as a means of providing resistance to impersonation (spoofing) attacks. In this study, we first prove that an effective impersonation attack on AoA-based PLA can only succeed under very stringent conditions on the attacker in terms of location and hardware capabilities, and thus, the AoA can in many scenarios be used as a robust feature for PLA. In addition, we exploit machine learning in our study to perform lightweight, model-free, intelligent PLA. We show the effectiveness of the proposed AoA-based PLA solutions by testing them on experimental outdoor massive multiple input multiple output data.

Resource allocation has always been a challenging task in vehicular networks due to their dynamic nature. In this paper, we study the decentralized joint subchannel allocation and power control problem for a Cooperative Adaptive Cruise Control (CACC) system to satisfy string stability in a platoon of connected and autonomous vehicles. The developed optimization problem takes the string stability of the platoon as well as the reliability of all the Vehicle to Vehicle (V2V) links into account, aiming at maximizing the total ergodic capacity. We tackle the optimization problem from two different angles. The first approach is a centralized classical algorithm governed from the Base Station (BS) perspective, where we assume that the BS only knows the large-scale fading information of the V2V links due to the rapidly changing channel conditions in vehicular environments. In the second strategy, we devise a Federated Multi-Agent Reinforcement Learning (MARL) based algorithm where each transmitter vehicle in the platoon acts as an independent agent and tries to find an optimal policy to maximize its total expected reward. Finally, to better understand the policies each agent has learned, we also compare the performance of these algorithms in terms of per-link achievable capacity.

Frequency range 2 (FR2) has become an integral part of 5G networks to fulfill the ever-increasing demand for data hungry-applications. However, radio signals in FR2 experience high path and diffraction loss, which also pronounces the problem of inter and intra-cell interference. As a result, both the serving and target links are affected, leading to radio link failures (RLFs) and handover failures (HOFs), respectively. To address this issue, multi-panel user equipment (MPUE) is proposed for 5G-Advanced whereby multiple spatially distinct antenna panels are integrated into the UE to leverage gains from antenna directivity. It also opens the possibility of using UE-side Rx-beamforming for each panel. In this paper, three different Rx-beamforming approaches are proposed to improve the serving link, the target link, and the handover process for an MPUE equipped with three directional panels. Thereafter, the mobility performance is analyzed in a system-level simulation for a multi-beam FR2 network. Results have shown that the proposed schemes can help reduce RLFs by 53% and HOFs by 90%.

This paper presents a novel approach to scheduling resources in a multi-beam next-generation Node B (gNB) that enables efficient resource reuse across beams within a transmission time interval (TTI). Unlike traditional medium access control (MAC) scheduling, which focuses on resource allocation within a single beam, our approach considers the simultaneous scheduling of multiple beams. We leverage a recently introduced sparse model and propose an algorithm that avoids exhaustive Monte Carlo (MC) simulation while approximating signal-to-interference-plus-noise ratio (SINR) and achievable throughput parameters in snapshot-based simulations. This approximation significantly reduces computational complexity while maintaining negligible error. We validate our approach through extensive simulations, demonstrating its effectiveness in approximating SINR and achievable throughput.

The realization of ultra-reliable low latency communications (URLLC) is a highly relevant problem that remains unsolved. Multi-connectivity (MC) is regarded as one of the main enablers as it has the potential to boost reliability by orders of magnitude. Typically, selection combining (SC) is implemented due to its simplicity. However, it is not optimal regarding the effective signal-to-noise ratio (SNR), in contrast to maximal-ratio combining (MRC). Furthermore, research focus is typically led on the outage probability only, but for reliable communications also the temporal behavior is relevant, which is better reflected by metrics like level crossing rate (LCR) and average fade duration (AFD). In this paper, we introduce a transformation which facilitates such an analysis by decoupling the dependence of the envelope or SNR from their time derivatives in MRC. Thereby, we present a thorough comparison between the investigated schemes of not only the outage but also the temporal behavior.

Achieving high reliability in the presence of fading is particularly challenging under latency constraints, because the usual way of error mitigation by repetition becomes unfavorable. On the other hand, multi-connectivity does improve reliability without adding latency, but multiplies the required bandwidth per link and does not scale to a large number of users. There is hence a need for frequency diversity in a spectrum-efficient way. In this work, we investigate multi-user resource allocation schemes both without and with knowledge of each user’s channel state. We evaluate the allocation-dependent reliability in terms of outage rate and outage duration based on simulations of automated guided vehicles in an industrial environment. To increase validity and ensure real-world correlation between vehicles, we draw channel states from high-resolution channel measurements at a factory floor. For channel-aware allocation, we propose a near-optimal low-complexity algorithm using different quality functions based on channel state preference lists. Since accurate channel information per user and resource incurs signaling overhead, we also evaluate the algorithm’s sensitivity to the number and bandwidth of resources as well as to outdated channel information. In conclusion, channel-aware allocation offers significant reliability improvements over static allocation and emerges as a key enabler to realize ultra-reliable low-latency communications on a larger scale.

When employing wireless connectivity in industrial applications, reliability is indispensable. However, due to the real-time requirements of said scenarios, time diversity is not feasible. Instead, frequency diversity has to be utilized by transmitting on frequencies with different fading characteristics. But as increasing bandwidth requirements for the individual user does not scale well for many users in the presence of a limited system bandwidth, the available resources have to be allocated efficiently. In this work, we compare multi-user resource allocation algorithms utilizing each user’s channel state information. The performance of the allocators is evaluated with regard to the achievable outage rate for different scenarios and different sensitivity analyses are performed. We also evaluate the algorithmic complexity of the allocators, both empirically and analytically. For evaluation, we employ analytical, spectrally correlated Rayleigh fading in order to emulate different environments. For a number of existing allocators, we propose modifications to improve the performance in regard to reliability and algorithmic complexity. The simulation results show, that the utilized allocators can be employed for a multitude of scenarios as they are suited for a large variety of application areas. Additionally, our results show that the allocation complexity of existing allocators can be reduced by up to a factor of 10 in the investigated scenario with a modified approach without sacrificing reliability.

Long Range (LoRa) technology has emerged as a promising communication solution for low power wide area networks. However, its ALOHA-based medium access scheme is prone to collisions, leading to limited network scalability. In congested LoRa networks, simultaneous transmission by multiple users using different spreading factors (SFs) results in inter-SF multi-user interference (MUI), thereby increasing packet loss likelihood under low signal-to-interference ratio (SIR) conditions. In this paper, we show the impact of MUI and propose an iterative interference cancellation method based on signal segmentation to address this issue. Our approach incorporates an algorithm for detecting MUI, which effectively identifies multiple interference SFs without prior knowledge, enabling interference cancellation without synchronizing interfering signals. Our numerical analysis demonstrates that MUI significantly impacts LoRa performance, but the proposed interference cancellation method can significantly reduce the symbol error rate under low SIR conditions compared to conventional demodulation. Our work makes a contribution to the field of LoRa technology, offering a practical and effective solution to the challenges posed by MUI in congested networks.

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The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions from wireless-network components to sustain quality-of-service (QoS) and user experience. Moreover, new use cases in the area of vehicular and industrial communications will emerge. Specifically in the area of vehicle communication, vehicle-to-everything (V2X) schemes will benefit strongly from such advances. With this in mind, we have conducted a detailed measurement campaign that paves the way to a plethora of diverse ML-based studies. The resulting datasets offer GPS-located wireless measurements across diverse urban environments for both cellular (with two different operators) and sidelink radio access technologies, thus enabling a variety of different studies towards V2X. The datasets are labeled and sampled with a high time resolution. Furthermore, we make the data publicly available with all the necessary information to support the on-boarding of new researchers. We provide an initial analysis of the data showing some of the challenges that ML needs to overcome and the features that ML can leverage, as well as some hints at potential research studies.

5G sidelink technology recently presented its unique potential for precise positioning using time delay estimates, although environmental conditions and the reference signal used highly influence the performance. Given the complexity of simulating all potential environmental impacts in a particular scenario, carrying out measurements within the specific environment becomes necessary. This paper introduces a flexible experimental platform to support research in designing the protocol, waveform, and time delay estimation algorithms, offering configurable transmitting signals and radio frequency (RF) parameters. Furthermore, this platform can emulate potential timing errors due to hardware constrains, offering a more practical understanding of 5G sidelink ranging applications.

Addressing the key issues of WiFi OFDMA uplink transmissions, especially in terms of real-time responsiveness, is a vital concern for contemporary industrial scenarios. The challenges include rigid resource allocation and an inability to cater to varied user requirements effectively. This paper presents an advanced OFDMA-based WiFi uplink random access scheme, specifically tailored to enhance performance and optimize real-time responsiveness in industrial wireless networks. At the heart of our scheme is the implementation of unique association identifiers (AIDs) for real-time users, fostering quasi-scheduled access transmission for efficient data management. To complement this, we incorporate a state-aware resource allocation mechanism that dynamically refines resources in line with current network conditions. Furthermore, we leverage the maximal ratio combining (MRC) technique on resource units (RUs) transmitting identical data, an approach that significantly reduces consecutive frame loss and bolsters reliability. We validate our innovative approach via comprehensive simulations using the MATLAB WLAN toolbox, demonstrating its robustness and effectiveness. Our research aims to augment real-time responsiveness in industrial wireless networks.

This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.

Utilizing 1-bit quantization at the analog-to-digital converter (ADC) is a promising approach to reduce the problematically high power consumption of high resolution ADCs in millimeter-wave (mmWave) and sub-terahertz (THz) communications. However, as 1-bit quantization is a highly nonlinear operation standard channel estimation algorithms cannot be applied. Therefore, we study algorithms for channel estimation in receivers with 1-bit quantization under consideration of a two-wave with diffuse power (TWDP) fading channel model, which was shown to be a realistic model for indoor communications in the mmWave regime. We combine maximum-likelihood (ML) amplitude estimation with a least-squares (LS) phase estimation approach known from literature to estimate the fading channel based on blocks of pilot symbols periodically inserted into the transmit symbol sequence. Furthermore, we apply Wiener filtering for interpolation of the channel estimates at the data blocks. The estimation performance of the proposed algorithms is evaluated numerically in terms of the mean squared error (MSE) and the suitability of the approach is demonstrated by evaluating the coded block error rate (BLER) for an exemplary system in comparison to the case with perfect channel knowledge. Our results show that almost the same BLER can be achieved by utilizing the derived estimation approach as compared to a system with perfect channel knowledge.

A promising approach to reducing the energy consumption is to consider coarse quantization at the receiver. In this study, we investigate novel precoding techniques in space and time for bandlimited multiuser MIMO downlink channels with 1-bit quantization and oversampling at the receiver, considering zero-crossing modulation. The proposed time-instance zero-crossing modulation conveys the information into the time-instances of zero-crossings. Two design criteria for time-instance zero-crossing modulation are investigated, namely, the minimum distance to the decision threshold and the mean-square error between the received and the desired signal. The maximization of the minimum distance to the decision threshold can be formulated as a quadratically constraint quadratic program. As an alternative, an equivalent problem can be formulated based on power minimization, which reduces computational complexity. Departing from the conventional mean-square error based technique, a more sophisticated algorithm is developed, which implies active constellation extension in order to improve the performance at high SNR. The extended problem is solved with two approaches, namely by formulating the problem as a second-order cone program and by considering an alternating optimization algorithm. Numerical results show that the proposed time-instance zero-crossing precoding methods significantly improve the bit error rate compared to the state-of-the-art methods.

A surge of reduced capability and low-cost devices under the 5G framework for many Internet of Things (IoT) applications is foreseen. The new 3GPP standard, NR REDCAP, provides the means to develop such devices. However, keeping these inexpensive devices synchronized with the 5G network requires continuous synchronization algorithms running parallel with the receiver processing. Our analysis and literature agree that the acceleration of these algorithms on Single Instruction Multiple Data (SIMD) architectures is challenging because of the data dependencies in the recursive filter that is usually employed in synchronization algorithms and hence, requires a significant computing budget. The consequent need for activating extra hardware to perform continuous synchronization leads to rising device cost and higher power consumption which is undesirable. In this work, we address this problem by proposing an implementation that efficiently vectorizes the synchronization algorithm, including the recursive filter, on a wide SIMD vector DSP. The results show that the proposed implementation achieves a speedup of 9X over scalar processing. Subsequently, the synchronization kernel can run on a limited MHz budget of the vector DSP.

Sub-THz communications have been recently considered as an alternative to increase the data rate for the 6th generation (6G) of mobile systems. Since maintaining a reasonable link budget becomes more difficult in higher frequencies, the DFTs-OFDM waveform has been considered as a candidate for sub-THz transmissions, because it has low power-to-average peak ratio (PAPR) in comparison to waveforms with higher PAPR, e.g., orthogonal frequency division multiplexing (OFDM). Additionally, recent channel measurements at 140 GHz have demonstrated that the channel is frequency-selective. This fact motivated us to investigate the DFTs-OFDM link-level performance under an empirical sub-THz channel with the employment of an iterative receiver, since it is known that iterative equalization can mitigate the effects of inter-symbol interference. For this purpose, we consider the minimum mean squared error with parallel interference cancellation (MMSE-PIC) iterative receiver, with convolutional and low-density parity-check (LDPC) codes. The results show that for medium frequency selectivity, LDPC codes provide best performance in terms of frame error rate, but for high selectivity, the convolutional code system has the best performance.

Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.

Several waveforms have been recently proposed in the literature as alternatives to orthogonal frequency division multiplexing (OFDM) for frequency selective channels. However, in order to achieve a superior performance, it is necessary to employ iterative equalization. In this paper, we consider the sparse Walsh-Hadamard (SWH) waveform with maximum a-Posterior (MAP) equalization. We show that the inherent structure of the SWH matrix allows a significant reduction in the number of multiplications required for the MAP equalizer implementation. The proposed solutions is compared with the zero padding single carrier (ZP-SC) with MAP equalization. We show that SWH with MAP equalization achieves a good trade-off performance vs complexity compared with ZP-SC. In particular, for 16-QAM under the Proakis C channel, ZP-SC is not even feasible while SWH with MAP equalization has manageable complexity.

Knowledge about the current and future states of a radio channel takes the reliability of a communications system to a new level. A Radio Environment Map (REM) contains information about the channel state in the spatial domain for a given environment. Given a known user trajectory, this information can be used for channel prediction. In this work, we investigated the two primary limitations to this approach: the required spatio-temporal stationarity of the channel and the high localization accuracy of the user. The channel stationarity is quantified by repeated channel measurements. The high measurable consistency indicates the value of REMs in non-changing environments. Based on a high-resolution REM that we measured in an office environment, we quantify the impact of one- and two-dimensional localization errors on the resulting prediction error. With the results shown, localization accuracy requirements can be derived given the target channel prediction accuracy. Also, the results help to determine the required spatial resolution of REM measurements in practice.

Packet repetitions aim at increasing the communication reliability of vehicle-to-vehicle (V2V) communication by exploiting the wireless channel diversity. Congestion-awareness is essential to limit the channel load increase, leading to packet loss and rising access delays. Previously, channel busy ratio (CBR) thresholds have been derived under static load conditions, the investigation of time-varying load conditions remained open. This work provides the first investigation on threshold-based repetition allocation under time-varying load conditions, focusing on the accuracy and timeliness of the CBR observation. Therefore, we evaluate the impact on the achievable transmission range and end-to-end delay. The results show a trade-off between responsiveness to load changes achieved by short, and high accuracy achieved for long observation windows. While short observations allow to quickly adapt to load changes, which avoid performance degradation due to temporarily channel congestion, the observation underlies severe fluctuations leading misallocations causing performance loss. In contrast, long observations allow to avoid wrong allocations, instead they tend to oscillations. Based on the results, the dynamic adaptation of the observation time based on the load history is proposed. It shows that the adaptation using a simple threshold-based detection of load changes does not only quickly converges to the optimal allocation, but also reduces false and oscillating allocations.

The sixth generation of wireless networks (6G) is expected to support the deployment of Internet of things (IoT) devices in massive scales. Finding lightweight and decentralized secret-key distribution primitives is therefore a challenge. Secret-key generation (SKG) from wireless channel coefficients is seen as a possible solution. It allows the extraction of secret keys using the channel randomness observed at the physical layer, without a centralized key distribution server. In this work an SKG approach suitable for wideband IoT devices is proposed. We investigate a filterbank-based SKG method, in which secret bits are generated through power measurements over different frequencies. To minimize dependencies and correlation among frequencies the quantile and the Karhunen-Loeve transforms are used. Finally, we perform a numerical evaluation of the achievable SKG rates, in the form of mutual-information (MI) estimates, using 3GPP channel models. Our numerical evaluation shows that the achievable SKG rate depends on the channel statistics, and, hence, to optimally harvest the information, devices need to be channel-aware.

Digital receivers based on 1-bit quantization and temporal oversampling w. r. t. the transmit signal bandwidth are a promising solution for the design of energy-efficient communications systems in the millimeter-wave (mmWave) and sub-terahertz bands. However, off-the-shelf algorithms for channel estimation cannot be applied as 1-bit quantization is a highly non-linear operation. Phase noise (PN) in particular has a deteriorating effect on the communication performance at these high frequencies and, therefore, needs to be tracked and compensated at the receiver. In this context, we derive an analytical solution for a close approximation of the Bayesian Cramér-Rao bound for PN estimation in systems employing 1-bit quantization, which provides insights into the impact of various design parameters on the achievable estimation performance. Furthermore, we use the bound to benchmark the performance of two existing PN estimators, showing that one of these estimators performs close to the optimum.

In this work, we consider the acquisition of stationary signals using uniform analog-to-digital converters (ADCs), i.e., employing uniform sampling and scalar uniform quantization. We jointly optimize the pre-sampling and reconstruction filters to minimize the time-averaged mean-squared error (TMSE) in recovering the continuous-time input signal for a fixed sampling rate and quantizer resolution and obtain closed-form expressions for the minimal achievable TMSE. We show that the TMSE-minimizing pre-sampling filter omits aliasing and discards weak frequency components to resolve the remaining ones with higher resolution when the rate budget is small. In our numerical study, we validate our results and show that sub-Nyquist sampling often minimizes the TMSE under tight rate budgets at the output of the ADC.

Trustworthiness verification and integrity testing have been identified as key challenges for the sixth generation (6G) of mobile networks and its variety of envisioned features. In this paper, these issues are addressed from a fundamental, algorithmic point of view. For this purpose, the concept of Turing machines is used which provides the fundamental performance limits of digital computers. It is shown that, in general, trustworthiness and integrity cannot be verified by Turing machines and therewith by today’s digital computers. In addition, the trustworthiness problem is further shown to be non-Banach-Mazur computable which is the weakest form of computability. Neuromorphic computing has an enormous potential to overcome the limitations of today’s digital hardware and, accordingly, it is interesting to study the issues of trustworthiness verification and integrity testing also for such powerful computing models. In particular, as considerable progress in the hardware design for neuromorphic computing has been achieved.

A promising approach to avoid the bottleneck of the analog-to-digital converter’s (ADCs) high power consumption at high sampling frequencies is to use only 1-bit quantization resolution. By using temporal oversampling at the receiver, a high resolution in the time-domain can be achieved, which can partly recover the losses in terms of rate caused by a reduced amplitude resolution. However, channel estimation and synchronization has to be performed on 1-bit quantized receive samples, which poses a new challenge.This work is concerned with the phase estimation of a 1-bit quantized system with phase noise in the low signal-to-noise ratio (SNR) range. A block-based least squares (LS) estimator, whose output is interpolated by a Kalman filter is presented to track the phase noise. To enhance the performance, especially for the case of a large spacing between pilot blocks, we study the Rauch-Tung-Striebel (RTS) algorithm. Both algorithms are adjusted to the system characteristics and bounds for the steady state performance are derived. These bounds, as well as numerical results show that the RTS algorithm achieves a lower error variance than the Kalman filter at the price of increased latency, as it is non-causal.

Reliable V2V communications is key to enable energy-efficient and accident-free road mobility. Packet repetitions as proposed for IEEE 802.11bd allow to increase the transmission reliability by combining redundant messages at the cost of increased channel load, leading to packet loss and rising access delays. As so far no investigations on this reliability trade-off are available, we present the first comprehensive analysis on repetitions for the new technology. Therefore, we quantify the combining gain in link-level simulations, which show a benefit of frequency diversity. Next, we model the repetition reliability trade-off in dependency of the vehicle density on system-level. The results show a severe performance degradation with increasing density caused by the added transmissions. Based on the results, we elaborate on how to design an optimal distributed repetition algorithm. It shows that defining static channel load thresholds is not sufficient to maximize the transmission reliability, as it does not correctly considers the impact of the allocation. Finally, we propose to employ multi-channel repetitions to mitigate potential channel congestion, meanwhile maximizing the combining gain.

Multi-connectivity can increase the reliability of wireless communications by orders of magnitude due to an increased diversity. However, simply copying packets and sending them on multiple links in parallel, i. e., as standardized as packet duplication, will result in a wasteful scheme. For example, there is no benefit in receiving the same information more often than once if multiple links succeed. This drawback can be resolved by channel coding. In a previous work, we proposed packet transmission schemes based on rateless codes, which could already increase resource efficiency compared to packet duplication. Here, we extend our previous studies in two different ways. First, we replace the previously used random linear fountain code by a more sophisticated raptor code, namely the R10 code. Second, we investigate a scenario, where the rateless coding scheme, which typically does not need any feedback, has acknowledgement information available. Both proposals show significant benefits of up to 15 % compared with our previous work in our mathematical analysis and empirical simulations.

Teleoperated robots are believed to play an important role for future applications in industry, medicine and other domains. Examples for this are remote assembly and maintenance, surgery, diagnosis or deep-sea and space exploration. Such applications are made possible by state-of-the-art tactile manipulators, well-researched control schemes and novel communication technologies such as the fifth generation of mobile communication (5G). The achievable performance is highly dependent on the communication delay and thus on the distance between leader and follower station, as well as the potentially used wireless protocol. Specially in this regard, 5G is a promising technology compared to the other communication protocols for transferring tactile information. In this paper, we introduce our telepresence reference platform, which can be used for empirical evaluation of different algorithms and communications. Comparative analysis are conducted to capture the influence of wireless communication protocols on telepresence systems consisting of complex robotic arms. The experiment compares the influence of 5G, LTE and WiFi communication protocols with regard to the motion and force tracking performance of the system.

Flexible and scalable solutions will be needed for future communications processing systems. RISC-V processors enhanced with vector processing capabilities as specified by the soon-to-be ratified RISC-V vector extension (RVV) pose an interesting base for such systems. Vector processors provide an efficient means of exploiting data-level parallelism, which is heavily present in communications kernels. Furthermore, RVV code is by its design agnostic from the underlying hardware platform which enables scalability. On the exemplary basis of a generalized frequency division multiplexing (GFDM) implementation on a RVV processor, we investigate its baseband processing capabilities and guide through RVV's key features and peculiarities. Our vectorization achieves a speedup of up to 60 times compared to the scalar base case and a throughput of 784 symbols per second. The utilization of 77 % is slightly below more specialized solutions. Nevertheless, this work serves as a baseline for further investigations on flexible and scalable RISC-V vector communications processors.

Conditional handover (CHO) is a state-of-the-art 3GPP handover mechanism used in 5G networks. Although it improves mobility robustness by reducing mobility failures, the decoupling of the handover preparation and execution phases in CHO significantly increases the signaling overhead. For 5G-Advanced networks, fast CHO (FCHO) is a recent 3GPP proposal that offers a practical solution whereby the user equipment (UE) can reuse earlier target cell preparations after each handover to autonomously execute subsequent handovers. This saves the signaling overhead associated with the reconfiguration and repreparation of target cells after each handover. In this paper, a comprehensive study on the mobility performance of FCHO with respect to mobility failures and signaling overhead in frequency range 2 (FR2) is carried out. In particular, the performance of FCHO is compared with CHO for two different multi-panel UE (MPUE) schemes. Results show that FCHO substantially reduces the signaling overhead of CHO, while at the same time it also reduces mobility failures due to faster triggering of the handover that is achieved by saving the preparation delay.

With the use of millimeter waves (mmWaves) in high carrier frequencies, massive bandwidth is available in the fifth-generation (5G) of cellular networks and upcoming 6G. However, at such high frequencies, the radio propagation suffers from higher free space path loss, which is compensated by the use of beamforming at the transmitter and the receiver sides. With a high number of beams at the base station, the user equipment (UE) has multiple candidate serving cells to connect to, which in turn offers new opportunities to achieve high load balancing gains. In this paper we introduce an optimal load balancing approach with respect to conventional initial access (IA) based on maximum reference signal received power (RSRP). It is followed by simulation for urban micro (UMi) hexagonal scenario with multi-beam at next generation node base station (gNB). It is shown that on average 8.5% gain, not to mention over 40% gain for some realizations, is achievable for New Radio (NR) (single radio frequency (RF)-chain) while this gain is on average less than 0.4% for Long-Term Evolution (LTE).

Frequency Range 2 (FR2) has become an integral part of 5G networks to fulfill the ever increasing demand for user data throughput. However, radio signals in FR2 experience high path and diffraction loss in mobile environments. To address this issue, multi-panel user equipment (MPUE) is adopted for FR2 whereby multiple antenna panels are placed on the UE body to leverage gains from antenna directivity. In contrast to traditional UEs with isotropic radiation patterns, signal measurements of cells in the network may not be available on all panels simultaneously for MPUE, which may result in outdated signal measurements that affect the reliability of mobility decisions. In this paper, we investigate the mobility performance of two different MPUE schemes following different paradigms for signal measurement and compare their performance with traditional UEs. This performance evaluation is based in multi-beam 5G networks operating in FR2 where there are multiple simultaneously active beams per cell to realize the high throughput requirements. Furthermore, an in-depth analysis of the mobility performance is carried out to determine the best mobility parameter combinations for the different MPUE schemes. Results have shown that both MPUE schemes offer considerable mobility performance gains as compared to traditional UEs. Moreover, it is seen that the MPUE schemes require different mobility parameter settings for the best mobility performance.

The emergence of Internet of things (IoT) applications brings the challenge of finding lightweight schemes for secret key distribution. A promising solution coming from the physical layer is the so called secret key generation (SKG) from shared randomness. SKG allows two communicating parties to extract the shared randomness already present in wireless channels. This work investigates the achievable SKG rates using received signal strength (RSS) as a key generation parameter. A multi-path scenario is considered and the probability density function of the RSS in different channel conditions is evaluated. Next, through a numerical evaluation, the SKG rates are derived in the form of mutual information (MI) estimates, using a 3GPP standard channel model. The simulations are performed for different values of bandwidth and delay spread. We demonstrate that while both parameters have similar impact on how multi-path components are resolved, their effect on the MI is opposite.

In this paper, we consider different equalization techniques for chirp-based communications systems. Although chirps are more commonly used for radar systems, several studies show their potential for either low-rate communication or for high spectral efficiency using an overlapping technique. However, previous literature has dealt only with additive white Gaussian noise (AWGN) channels. In this paper, we investigate the overlapped chirps under multipath channel models. More specifically, we model the overlapped chirp-based communication system and then reformulate the receive signals to which common linear and non-linear equalizers are applicable. For the purpose of benchmarking, we demonstrate the effectiveness of those equalizers in a realistic 5G tapped delay line (TDL) channel model. Our simulation results show that, with proper equalization, overlapped chirps can also operate under multipath channels, and that non-linear equalization methods outperform linear ones.

This work proposes an architecture and platform for researching and demonstrating use-cases for connected cars based on small-scale vehicles. The proposal bridges the gap between a lab setup to test individual algorithms and deployments on real cars. It allows researchers to communicate their results with a small-scale indoor demonstrator. The platform employs ROS2 and MicroROS to allow for a modular and scalable hardware and software setup. Moreover, it allows running all control algorithms in a graphical simulation to ease development of complex scenarios. We successfully apply the platform to build a demonstrator for a platooning use-case and point out limitations such as lacking photorealism of the simulation and limited processing power of the platform. Our results indicate that using a well-designed platform and architecture can significantly reduce required effort for implementing connected cars use-cases.

Power-domain asynchronous non-orthogonal multiple access (ANOMA) is a novel radio access technique with non-orthogonal resource allocation that enables asynchronous transmissions and has an enhanced spectrum efficiency compared to orthogonal multiple access. In this work, an iterative receiver is derived for linearly modulated waveforms. Orthogonal frequency division multiplexing (OFDM), single-carrier (SC) and orthogonal chirp division multiplexing (OCDM) are investigated. The receiver is based on triangular successive interference cancellation (T-SIC) in combination with a minimum mean square error parallel interference cancellation (MMSE-PIC) detector. It is advantageous to utilize a waveform which spreads the data symbols in the frequency domain as OCDM or SC in order to exploit the multipath diversity in frequency-selective channels. However, it is numerically shown that OCDM performs the best due to its additional time-spreading property, which is desirable for the time-dependent interference that occurs in an ANOMA system. Furthermore, for the considered scenario of two users and four blocks, we show that all the studied waveforms achieve the best performance in terms of block error rate with the derived receiver when the blocks overlap halfway.

The usage of wireless networks for Networked Controlled Systems (NCS) impose constraints on the required radio resources to ensure the desired Quality-of-Control (QoC). Usually, control systems are designed regardless of the wireless system and could misuse current wireless technologies capabilities by utilizing more resources than required and limiting the number of users accessing the network. However, the goal of Communications-Control Co-Design (CoCoCo) is to enable better usage of the communications resources by exploiting the knowledge of the degrees of freedom of the control system. This paper proposes a methodology to find optimal rates for a random access scheme to ensure string stability in a platoon of vehicles.

Ultra-Reliable Low-Latency Communications (URLLC) is a prerequisite for advancing industrial automation. To verify whether communications systems meet these stringent requirements, physical layer simulations are a powerful tool. Such simulations rely on a large number of channel realizations to obtain statistically significant results. Using exclusively real-world measured channels is challenging, e.g., due to the measurement time needed. In this work, we propose how to derive a channel model that mimics not only the mean but equally the temporal behavior of data from an industrial channel measurement campaign. The approach considers time variation on a large scale, modeled through a Markov process, as well as fading of individual channel components, achieved through Doppler-filtered random processes based on empirical distributions. The model is validated in link-level simulations by comparing the synthesized channels to the original measured data by means of performance metrics relevant to URLLC, including mean and maximum outage durations. Our validations show that the model performs well and especially the outage durations, caused by consecutive packet losses, match the real channel characteristics very well. In the future, we can use the model to investigate how extensive measurements need to be in order to make statements about the performance of communications systems.

The channel impulse response (CIR)-based localization requires estimating the channel path delays at a certain position. Several state-of-the-art (SoA) approaches have been proposed to estimate the delays based on eigendecomposition. However, they are limited by the assumption of uncorrelated paths, which is not accurate in the context of localization. The attempt to mitigate the rank deficits problem by means of frequency smoothing (FS) requires a considerable amount of measurements on different center frequencies. Another solution with single snapshot multiple signal classification (MUSIC) lacks the accuracy in resolving fractional path delays and requires high computational complexity. In this paper, we propose a new approach to construct the eigendecomposition model by means of a simple arrangement of the frequency-domain estimated channel gains. Our method has lower computational complexity and is able to outperform the SoA techniques in terms of estimation accuracy and resolution. The performance is evaluated with numerical simulations and supported by measurements using realistic radio frequency (RF) hardware.

Power-domain non-orthogonal multiple access (NOMA) is a promising radio access technique with non-orthogonal resource allocation that provides a greater spectrum efficiency than the conventional orthogonal multiple access (OMA). In this paper, an iterative receiver is derived for NOMA. It is based on soft-information successive interference cancellation (SIC) combined with a minimum mean square error parallel interference cancellation (MMSE-PIC) detector. Orthogonal frequency division multiplexing (OFDM) is usually the typical waveform employed. However, with the proposed receiver design, any linear modulation can be used. In addition to OFDM, single-carrier (SC) and the recently proposed sparse Walsh-Hadamard (SWH) are investigated. The NOMA scheme is analysed in a multi-path fading channel, where two users have different power ratios and waveforms. Simulation results show that mixing OFDM and SWH for a two-user NOMA gives the best performance with low receiver complexity.

To realize the next generation of ultra-reliable low-latency communications (URLLC) with less radio resource consumption, methods relying solely on the addition of redundancy do not suffice. An alternative is to monitor the wireless channel and prevent outages due to small scale fading by allocating users to suitable radio resources based on the channel gain. To overcome monitoring delays, predicted channel information is utilized. In this paper we are analyzing the outage probability of a max-min radio resource allocation (RRA) approach when Gaussian errors are present in the channel state information (CSI). As a communications channel, a Rayleigh fast fading channel is assumed. The outage probability is lower and upper bounded to get analytical performance approximations. Furthermore, the performance is also empirically evaluated by means of extensive Monte-Carlo simulations. The analysis shows, that the achievable outage probability mostly depends on the size of the resource pool and the quality of the CSI.

The fifth-generation (5G) and beyond standards are being challenged by the diverse requirements of modern use cases. Multicarrier modulation techniques are one of the key components of the physical layer (PHY) design, which has immense potential to improve efficiency and reliability. In current state-of-the-art wireless technologies (i.e., NR and IEEE 802.11ax) orthogonal frequency division multiplexing (OFDM) is used which has many disadvantages such as peak-to-average power ratio (PAPR), out-of-band emission (OOBE), and sensitivity to carrier frequency offset (CFO). To overcome these drawbacks several alternate multicarrier modulation techniques are being considered, such as discrete Fourier transform-spread-OFDM (DFT-s-OFDM), generalized frequency division multiplexing (GFDM), and orthogonal time-frequency space (OTFS). In this paper, we develop the physical layer abstraction (PLA) of these candidate multicarrier techniques to evaluate their performance under various use cases and scenarios. The PLA is a commonly used technique to avoid time-consuming PHY simulations in system-level simulators. To improve the accuracy of PLA in different fading conditions, we derive a fitting parameter as a function of the received signal-to-interference-plus-noise ratio (SINR) variance. The validation results show that performance can be accurately estimated through the proposed multicarrier PLA. Moreover, PLA techniques are at least thousands of times faster compared to PHY simulations.

Digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth promise lower energy consumption. However, since 1-bit quantization is a highly non-linear operation, standard off the shelf receiver algorithms cannot be applied. In this paper we consider an unknown phase rotation and timing offset and a fully digital receiver. To reduce the non-linear behavior introduced by 1-bit quantization, we assume that the receiver applies uniform phase and sample dithering, which can be implemented by sampling at an irrational normalized intermediate frequency and with an irrational oversampling factor, respectively. Based on the least squares objective function we derive a typical digital matched filter receiver with a data- and timing-aided phase estimator and square time recovery based timing estimation. Our main contribution is to show that both estimators are consistent under very general assumptions, e.g., arbitrary colored noise and stationary ergodic transmit symbols. Performance evaluations are done via simulations and are compared against a numerically computable upper bound of the Cramér–Rao lower bound. For low signal-to-noise ratio the estimators perform well but for high signal-to-noise ratio they run into an error floor. The performance loss of the phase estimator due to decision-directed operation or estimated timing information is marginal.

The prototyping of embedded platforms demands rapid exploration of multi-dimensional parameter sets. Especially the design of the memory system is essential to guarantee high utilization while reducing conflicts at the same time. To aid the design process, several probabilistic models to estimate the throughput of interleaved memory systems have been proposed. While accurately estimating the average throughput of the system, these models fail to determine the impact on individual processing elements. To mitigate this divergence, we extend three known models to include non-uniform access probabilities and priorities.

The multi-connectivity is a key enabler for ultra-reliable low-latency communications. To evaluate its performance and suitability to various use cases system-level studies are essential, where the physical layer (PHY) plays an important role. Therefore, PHY modeling is required which is time-intensive and requires highly complex computations. For this purpose, the PHY performance is usually abstracted in terms of signal-to-interference-plus-noise ratio (SINR), also known as physical layer abstraction (PLA). However, due to fading, the symbols inside a packet could have different SINRs which require effective SINR mapping to compute an equivalent SINR. In the context of multi-connectivity, the received SINR depends on all connected links where each link experiences independent fading and on the used link combining technique. As a result, the computation of effective SINR also depends on the combining technique and fading experienced by individual links. To model PHY performance by considering all these effects, we develop PLA for multi-connectivity communications. This includes the computation of received symbols SINR for various link combining techniques and mapping them to effective SINR using enhanced exponential effective SINR mapping (eEESM). Furthermore, a new optimization method is introduced for eEESM to reduce its optimization complexity. Simulation results show that the proposed PLA accurately estimate the performance of different order of multi-connectivity communications under various fading conditions.

This work focuses on providing robust line-of-sight (LoS) spatial multiplexing at flexible communications distances and directions. Considering oblique LoS uniform linear arrays, we first derive the rank-deficient and orthogonal conditions for the LoS MIMO channel matrices. With this discovery, the topology of high spatial-resolution on one side of the link is shown with a wide full-rank-channel guarantee interval over distance and direction variations. Additionally, to reduce the implementation costs and power consumption, we propose to use low amplitude-resolution quantizers at the side of high spatial-resolution. With numerical evaluations on systems having few-bit analog-to-digital converters (ADCs), the proposed system design is shown to simultaneously achieve a higher spectrum efficiency and higher energy efficiency compared to a conventional single-stream high-amplitude-resolution over a wide signal-to-noise-ratio (SNR) range. Furthermore, we investigate channel equalization under the extreme case of using 1-bit ADCs. After providing a new viewpoint on the generalized approximate message passing (GAMP) algorithm from constrained Bethe free energy minimization, our simulations on bit-error-rates show that the GAMP algorithm can significantly reduce the performance degradation due to coarse quantization and can significantly outperform the Bussgang decomposition based linear minimum-mean-square-error estimator, especially at high SNRs.

With the advent of the fourth industrial revolution i.e. Industry 4.0, plants and factories are becoming smarter and interconnected. The transitions demand vertical integration and seamless connectivity. For this purpose, there is a need for semantic communication between various devices including the heavily resource-constrained field devices. To address this, a real-time capable hardware-based implementation of a well-established semantic communication protocol, i.e. OPC Unified Architecture was designed and developed. This chip-based implementation is power-efficient and compact, making it suitable for the field level. The chip was analyzed and incorporated in a demonstrator as a proof of concept of its integration at field level in a plant module of the process industry. Various opportunities are also examined where the chip could be utilized to deliver benefits to existing and future technologies.

This paper describes a blind transmitter localization technique based on the deep neural network (DNN) framework. Blind localization assumes no previous knowledge on the transmit signal. It is shown that DNN based location approaches the maximum likelihood solution with reduced computational complexity. Moreover, the maximum likelihood, least squares and radio environment map localization estimators are presented in order to compare the design and performance of the proposed DNN algorithm. The system model is built based on a wireless sensor network that collects received signal strength measurements assuming disturbances of distance dependent correlated shadowing noise. Performance evaluation using numerical simulations shows that the proposed DNN scheme achieves location accuracy similar to the optimum maximum likelihood estimator while presenting computational complexity reduction of more than 90%.

In the future, mobility use cases will depend on precise predictions, with Quality of Service (QoS) prediction being a prominent example. This paper presents realistic measurements from today’s vehicles to support robust QoS prediction in the future. Based on a dedicated and controlled measurement campaign, we highlight aspects of the wireless environment and the device characteristics, like the sampling rates, that influence the collected datasets. If not properly handled, such characteristics might hinder the performance of Artificial Intelligence-based algorithms for QoS prediction. Therefore, we also provide insights on dataset characteristics that should be further used to enable easier adoption of AI-based algorithms. New AI-based algorithms should be able to operate in very diverse radio environments with data captured from different devices. We provide several examples that highlight the importance of thoroughly understanding the datasets and their dynamics.

By scheduling users to resources that are operational rather than on a best effort basis, the overall resource consumption of ultra-reliable low-latency communications (URLLC) can be reduced while maintaining a desired quality of service (QoS). To overcome the time delay between monitoring the channel state and the actual payload transmission, predictive methods which are tailored to the needs of URLLC become indispensable. In this paper we extend our Wiener filter based Rayleigh fading outage predictor to the Rician fading case. Compared to Rayleigh fading, additional estimators for the line of sight (LOS) parameters are presented. Our results show that the overall outage prediction performance increases significantly with increasing power of the LOS component compared to the Rayleigh fading case. The resource utilization for a particular user equipment (UE) rises to more than 99% in the investigated scenario for small prediction horizons and a Rician K-factor of K = 10 while achieving effective outage probabilities of 10−5. By comparing with the case of perfect parameter estimation, we show that the influence of the introduced estimators on the outage prediction performance is within acceptable limits.

Communication using orbital angular momentum (OAM) modes has recently received a considerable interest in free space optical (FSO) communications. Propagating OAM modes through free space may be subject to atmospheric turbulence (AT) distortions that cause signal attenuation and crosstalk which degrades the system capacity and increases the error probability. In this paper, we propose to enhance the OAM FSO communications in terms of bit error rate and spectral efficiency, for different levels of AT regimes. The performance gain is achieved by introducing orthogonal frequency division multiplexing (OFDM) with index modulation technique to the OAM FSO system.

The ultra-high reliability is an essential requirement for enhanced vehicle-to-everything (eV2X) use cases. In order to ensure the desired reliability in a time-varying channel, link adaptation is required. Therefore, the current and upcoming technologies use adaptive modulation and coding (AMC) schemes. By using AM C, the reliability and data rates could be adapted according to the channel conditions. To further improve reliability, concepts like multi-connectivity could also be used. In multi-connectivity, redundant data can be transmitted using multiple simultaneous links and combined at the receiver to improve reliability. However, this requires link adaption in terms of both numbers of multiple links and AMC. In this paper, we evaluate different link adaptation schemes for IEEE 802.11bd based on single-link and multi-connectivity communications. For single-link communications, we generate channel quality indicators (CQI) based on various signal-to-interference-plus-noise ratio (SINR) mapping techniques, e.g., exponential effective SINR mapping (EESM), received bit information rate (RBIR), and recently proposed enhanced EESM (eEESM). The performance of these schemes is evaluated in terms of achieved reliability and data rates. Results show that eEESM achieves close to optimal performance. In the case of multi-connectivity, different MCS and link adaptation schemes are evaluated. It is shown that joint adaptation of MCS and the number of links deliver better performance in terms of data rates and link utilization.

The integration of functions into future communication systems that predict crucial Quality of Service (QoS) parameters is expected to enable many new or enhanced use cases, for example, in vehicular networks and Industry 4.0. Especially with high user mobility, QoS prediction is required in an End-to-End (E2E) fashion to guarantee uninterrupted connectivity and provisioning of real-time applications. In this paper, we present a concise list of mobility use cases, both from automotive and industrial production domains, that benefit from Artificial Intelligence-based QoS prediction. These applications are investigated in the publicly-funded research project AI4Mobile by a representative consortium of industry and academia. Based on a literature review, we identify the main challenges in realizing predictive QoS at high mobility, and we propose research directions to enable the envisioned E2E solutions.

Autocorrelation (AC) is one of the most frequently used methods for initial acquisition. Usually, algorithms that employ a metric based on AC for this purpose, focus on the estimation of synchronization parameters, ignoring the detection performance. This paper analyses the relationship between the structure of the reference sequence that serves to compute the AC and the corresponding attainable detection performance. The distributions of test statistics based on AC for frame detection are derived and validated through simulations in a frequency-flat fading channel. It is shown that the use of AC can outperform, in terms of detection performance, the more complex matched filtering in a certain signal-to-noise ratio region, at the cost of significantly longer required sequences. Besides, we show that a set of sequences, with different periodic structures, can provide the same target detection probability, allowing a flexible sequence design.

Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.

This paper presents a control architecture that relaxes the communication requirements for achieving a low inter-vehicle distance compared to Cooperative Adaptive Cruise Control (CACC). Previous work showed that CACC achieves a low inter-vehicle distance of a longitudinal platoon without amplifying oscillations throughout the platoon, i.e., the system is string stable. However, it requires the transmission of control information through a wireless link that discretizes the information, introduces delays, and could lose meaningful updates due to packet loss, imposing communications requirements to ensure the platoon's string stability. This work proposes an Estimator Enhanced CACC (EE-CACC) architecture that improves the Maximum Allowable Transmission Interval (MATI) by 20% compared to CACC. This control architecture uses the acceleration estimation and transmits the estimation's error to improve the receiver's reconstruction of the control signal, effectively reducing the communication requirements.

In this work we explore the problem of physical layer security key exchange between two communicating devices in wideband wireless networks. We propose to obtain a pair of symmetric keys through a general filtering procedure, which exploits the frequency-selective characteristics of the radio channel. We focus our analysis on the keys bits mismatch with respect to the signal-to-noise-ratio and the link distance, through simulations of realistic radio channels in both indoor and outdoor scenarios. We investigated the impact of a proximity-attack by a malicious third device, founding that it is capable to eavesdrop large parts of the generated key compromising the overall security.

Production processes coming up with Industry 4.0 will demand a high degree of flexibility since customers request more individual products. Employing wireless communication is a key enabler to meet these requirements. In order to deploy wireless systems for emerging industrial use cases in a way that both low latency as well as high reliability is guaranteed, knowledge about the radio channel is crucial. This requires representative channel measurements considering a specific use case. With this in mind, we propose a novel channel measurement approach representing industrial Device-to-Device (D2D) communication between moving Automated Guided Vehicles (AGVs), and present the obtained results. We study how obstacles affect the channel by modifying the test environment with metallic obstacles. For reproducibility, we automate the movement during the measurements using an AGV. We capture impulse responses each millisecond to resolve the time variation of the channel. It turns out that even under Non-Line-of-Sight (NLOS) conditions, the loss of receive power over the whole bandwidth is moderate due to a large number of reflections. We conclude that exploiting frequency and spatial diversity is a promising way to improve the communication reliability in industrial scenarios. We also infer that modeling the time-varying nature of channel parameters in industrial environments is feasible based on measurement data.

Wireless communications systems beyond 5G are expected to utilize large available bandwidths at frequencies above 100 GHz in order to achieve data rates above 100 Gbit/s. However, the power consumption of the analog-to-digital converters (ADCs) for such systems is becoming a major challenge. Trading a reduced amplitude resolution for an increased temporal resolution by employing temporal oversampling w.r.t. the Nyquist rate is a possible solution to this problem. In this work, we consider a wireless communications system employing zero-crossing modulation (ZXM) and 1-bit quantization in combination with temporal oversampling at the receiver, where ZXM is implemented by combining runlength-limited (RLL) transmit sequences with faster-than-Nyquist (FTN) signaling. We compare the performance and complexity of four different soft-output equalization algorithms, namely, two approximations of the linear minimum mean squared error (LMMSE) equalizer, a BCJR equalizer and a deep-learning based equalizer, for such systems. We consider the mutual information (MI) between the input bits of the RLL encoder and the output log-likelihood ratios (LLRs) of the RLL decoder as a performance measure and evaluate it numerically. Our results demonstrate that one of the proposed LMMSE equalizers outperforms the competing algorithms in the low and mid signal-to-noise ratio (SNR) range, despite having the lowest implementational complexity.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Smart manufacturing based on artificial intelligence and information communication technology will become the main contributor to the digital economy of the upcoming decades. In order to execute flexible production, smart manufacturing must holistically integrate wireless networking, computing, and automatic control technologies. This article discusses the challenges of this complex system engineering from a wireless networking perspective. Starting from enabling flexible reconfiguration of a smart factory, we discuss existing wireless technology and the trends of wireless networking evolution to facilitate multirobot smart factories. Furthermore, the special sequential decision-making of a multirobot manufacturing system is examined. Social learning can be used to extend the resilience of precision operation in a multirobot system by taking network topology into consideration, which also introduces a new vision for the cybersecurity of smart factories. A summary of highlights of technological opportunities for holistic facilitation of wireless networked multirobot smart factories rounds off this article.

Wireless closed-loop control is of major significance for future industrial manufacturing. However, control applications pose stringent quality of service requirements for reliable operation. Contrary to traditional ultra-reliable low-latency communications design goals such as low packet loss rates and low latency, research results in the domain of networked control systems (NCS) state that depending on the sampling period, control applications inherently tolerate a few consecutive packet losses. This translates into a better-suited metric to capture control application requirements and therefore a more conclusive design goal for wireless networks: ensuring a maximum age of information (AoI). With a Markov modeling approach, we propose to exploit the tolerance through a novel dynamic multi-connectivity scheme that we term state-aware resource allocation (SARA), which temporally negatively correlates packet losses, thus avoiding long packet loss sequences. Through statistical multiplexing, SARA enables a mean time to failure (MTTF) in the order of years while keeping the per-agent average channel usage close to one, also in a multi-agent setting with competition for resources. Compared with static dual-connectivity, the MTTF can be increased 100-fold whereas the number of required channels reduces by 40%. Our approach also statistically guarantees system-wide AoI distributions, which aid to ensure control performance.

Mobile networks have to cope with an ever increasing demand for video streaming, an application that imposes high quality requirements for user satisfaction. In this paper, we present an analytical model to calculate two important quality measures for streaming traffic, namely the video startup delay distribution and the buffer starvation probability. The queuing-theoretic model differs from related work by incorporating data flow dynamics of the considered cell, as well as the dynamics of fluctuating interference from neighboring cells, with the goal of accurately representing a multi-cellular environment. In this regard, we propose a finite-volume method to approximate the solution of the involved system of partial differential equations, where other approaches from comparable work were faced with numerical problems. We also evaluate two simplified versions of the model, where only the interference dynamics or all coupling terms are omitted, respectively. The model allows us to study the impact of different parameters on buffered video streaming performance. Additionally, we propose a user-centric metric to measure quality of experience (QoE). To the best of our knowledge, our approach is novel and has not been covered by comparable work. The presented results can help to design future cellular networks with enhanced video streaming experience.

In this paper, we investigate three different concepts for robust link-level performance under doubly-dispersive wireless channels, namely, i) channel estimation, ii) cyclic prefix (CP)-free transmission, and iii) waveform design. We employ a unique word-based channel estimation, where we decouple the channel related errors into channel estimation error (CEE) and Doppler error (DE). Then, we show that a trade-off between CEE and DE emerges in the frame design, where the system can be optimized to achieve the minimum composite channel error. Another strategy to improve the link-level performance is to suppress the CP of the sub-blocks. This allows for better channel estimation due to the reduced transmission time, with the penalty of requiring the CP-restoration processing at the receiver. Furthermore, we propose the waveform design based on the equal-reliability criterion (ERC), leading to the block multiplexing-orthogonal chirp division multiplexing (BM-OCDM). This waveform is advantageous in the CP-free transmission mode, where the data symbols have equally distributed interference from adjacent sub-blocks. Our framework is a generalization of the recently proposed orthogonal time frequency space (OTFS), which fails to achieve the ERC. The link-level simulations show that at high modulation and coding scheme, the proposed BM-OCDM provides superior link-level performance than OTFS.

Multi-connectivity (MC) is regarded as one of the key features that meet the requirements of ultra-reliable low-latency communications for 5th generation networks, as it provides multiple diversity branches. Recently, we evaluated the decoding reliability of various MC setups and different combining algorithms by analyzing the resulting outage probabilities. In this work, we are interested in how much the performance is affected by correlated fading. Specifically, we analyze the outage probability of MC systems with joint decoding reception operating over correlated quasi-static Rayleigh and Nakagami-m fading channels. Our main contributions are as follows: (i) deriving the exact outage probability in integral form and the asymptotic outage probability at high signal-to-noise ratio (SNR) in closed form; (ii) deriving the correlation loss, which quantifies the extra SNR required under correlated fading as compared with the independent scenario; and (iii) evaluating frame-error rates of quasi-cyclic low-density parity-check codes by Monte-Carlo simulations. Our results show that the correlation loss is marginal for a low to moderate level of correlation, whereas the diversity gain is not affected by correlation whatsoever. Furthermore, we find that the correlation loss is independent of the degrees of freedom encapsulated by the Nakagami-m fading parameter.

Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The overhead of additional pilot transmission at the price of spectral efficiency is shown necessary to achieve high resilience in operation. An efficient method to deal with high dynamics of UAVs applies machine learning, particularly Q-learning, to analog beam tracking. The proposed Q-learning-based beam tracking scheme uses current/past observations to design rewards from environments to facilitate prediction, which significantly increases the efficiency of data transmission and beam switching. Given the selected analog beams, the goal of digital beamforming is to maximize the SINR. The received pilot signals are utilized to approximate the desired signal and interference power values, which yield the SINR measurements as well as the optimal digital weights. Since the selected analog beams based on the received power do not guarantee the hybrid beamforming achieving the maximization SINR, we therefore reserve additional analog beams as candidates during the beam tracking. When the candidates include the ideal beams, the combination of analog beams with their digital weights achieving the maximum SINR consequently provides the optimal solution to the hybrid beamforming.

In this paper, we investigate a sparse channel estimation problem for broadband massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We propose a hidden Markov model to capture the structured sparsity and temporal dependency characteristic of massive MIMO-OFDM channels in the angle-delay domain, and this probability model exhibits extensive adaptability to different realistic propagation scenarios. Then we solve the channel estimation problem based on a novel optimization framework named constrained Bethe free energy (BFE) minimization, which is valid for a generic statistical model. Under this systematic theoretical framework, a hierarchical hybrid message passing (HHMP) algorithm is proposed to track dynamic channel parameters recursively. The proposed method can adaptively learn the sparse structure and temporal correlation of multiuser channels without requiring the knowledge of hidden Markov channel parameters. Numerical simulations demonstrate that the proposed HHMP algorithm can accurately estimate angle-delay domain channels with reduced iteration times and pilot overhead.

Due to the great flexibility and innate simplicity of wireless networks, many applications are now realized with wireless connectivity at their core. Countless novel applications are enabled this way and legacy applications are transferred to mobile networks to reap the same benefits. As the networks evolve, applications are becoming ever more demanding, particularly with respect to high reliability and low latency. A common way to achieve high reliability in wireless communications is adding redundant communication channels by means of multi-connectivity. This comes at the cost of additional radio resources that could have been used by other connections. However, multiple links are not always required to achieve the desired reliability. Thus, we propose to combine multi-connectivity with rateless coding as a strategy for efficient resource usage in reliable wireless communications. The proposed schemes consider an erasure channel, and can therefore be implemented on the application layer, making them suitable for use with different physical layer wireless technologies simultaneously. We start out with a single-user analysis and then extend our discussion to multi-user scenarios. Our approach is compared against standardized packet duplication with our analytical framework and by means of simulation. The results with rateless coding show significant gains with respect to the required resources and feedback transmissions.

Although the combination of the orthogonal time frequency space (OTFS) modulation and the massive multiple-input multiple-output (MIMO) technology can make communication systems perform better in high-mobility scenarios, there are still many challenges in downlink channel estimation owing to inaccurate modeling and high pilot overhead in practical systems. In this paper, we propose a channel state information (CSI) acquisition scheme for downlink massive MIMO-OTFS in presence of the fractional Doppler, including deterministic pilot design and channel estimation algorithm. First, we analyze the input-output relationship of the single-input single-output (SISO) OTFS based on the orthogonal frequency division multiplexing (OFDM) modem and extend it to massive MIMO-OTFS. Moreover, we formulate an accurate model for the practical system in which the fractional Doppler is considered and the influence of subpaths is revealed. A deterministic pilot design is then proposed based on the model and the structure of the pilot matrix to reduce pilot overhead and save memory consumption. Since channel geometry changes very slowly relative to the communication timescale, we put forward a modified sensing matrix based channel estimation (MSMCE) algorithm to acquire the downlink CSI. Simulation results demonstrate that the proposed downlink CSI acquisition scheme has significant advantages over traditional algorithms.

Variational message passing (VMP), belief propagation (BP) and expectation propagation (EP) have found their wide applications in complex statistical signal processing problems. In addition to viewing them as a class of algorithms operating on graphical models, this article unifies them under an optimization framework, namely, Bethe free energy minimization with differently and appropriately imposed constraints. This new perspective in terms of constraint manipulation can offer additional insights on the connection between different message passing algorithms and is valid for a generic statistical model. It also founds a theoretical framework to systematically derive message passing variants. Taking the sparse signal recovery (SSR) problem as an example, a low-complexity EP variant can be obtained by simple constraint reformulation, delivering better estimation performance with lower complexity than the standard EP algorithm. Furthermore, we can resort to the framework for the systematic derivation of hybrid message passing for complex inference tasks. Notably, a hybrid message passing algorithm is exemplarily derived for joint SSR and statistical model learning with near-optimal inference performance and scalable complexity.

This paper describes a coherent chirp spread spectrum (CSS) technique based on the Long-Range (LoRa) physical layer (PHY) framework. LoRa PHY employs CSS on top of a variant of frequency shift keying (FSK), and non-coherent detection is employed at the receiver for obtaining the transmitted data symbols. In this paper, we propose a scheme that encodes information bits on both in-phase and quadrature components of the chirp signal, and rather employs a coherent detector at the receiver. Hence, channel equalization is required for compensating the channel induced phase rotation on the transmit signal. Moreover, a simple channel estimation technique exploits the LoRa reference sequences used for synchronization to obtain the complex channel coefficient used in the equalizer. Performance evaluation using numerical simulation shows that the proposed scheme achieves approximately 1 dB gain in terms of energy efficiency, and it doubles the spectral efficiency when compared to the conventional LoRa PHY scheme. This is due to the fact that the coherent receiver is able to exploit the orthogonality between in-phase and quadrature components of the transmit signal.

In machine-type communication scenarios, sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) scheme owing to shaping gain by combining constellation modulation and spreading patterns together. In this paper, to fully exploit the channel knowledge contained in received data sequences, we propose the joint active user detection (AUD), channel estimation (CE), multi-user detection (MUD), and decoding receiver without knowing users' activity parameters in uplink grant-free massive multiple-input multiple-output (MIMO) SCMA systems. To avoid the permutation and scaling ambiguities of estimation results, the proposed receiver estimates channel based on both received short pilot and data sequences. We introduce auxiliary active state indicators in AUD to describe the sporadic transmission feature. The joint CE and MUD module is constructed as a bilinear inference problem with joint column-wise sparsity. Furthermore, we exploit the SCMA codewords sparsity feature and put Gaussian approximations on modulated symbols in joint CE and MUD module to reduce the receiver complexity. Simulation results show that the proposed unified receiver has substantial performance improvement and lower computational complexity than the conventional two-stage receiver and joint receiver in the literature.

Wireless closed-loop control is of major significance for different application areas, such as future industrial manufacturing, and ultra-reliable low-latency communications (URLLC) are designed to enable such systems. Static multi-connectivity, in which a number of independent parallel channels are allocated for each service, is a possible solution to achieve URLLC requirements, but this increases resource usage significantly, which becomes an issue particularly in multi-user systems. Building upon a control-communications codesign (CoCoCo) approach, a control-application optimized state-aware resource allocation (SARA) scheme was developed, which exploits the control cycle's inherent capability of tolerating a limited number of consecutive packet losses before ultimately failing. In essence, SARA negatively correlates packet losses through dynamic channel allocation in order to yield extraordinary availability values while keeping the average resource consumption low. This article develops a multi-user system representation of SARA with competition for limited resources using a Markov chain approach and subsequently evaluates the mean time to failure, demonstrating that SARA scales better than static multi-connectivity, fully supporting the maximum system availability at fewer channels per agent.

Control communications co-design enables robust and scalable wireless closed-loop control system design. We study the metric “control-communications availability” that allows consecutive errors until the control application is deemed dysfunctional. Multi-connectivity helps increasing the network availability, but there is a lack of dynamic and resource efficient link management. Thus, we propose the “state-aware resource allocation” scheme, whereby parallel links can be assigned adaptively to a given connection, depending on the number of previously, consecutively lost packets. We develop a Markov chain that captures the novel resource allocation approach suited for closed-loop wireless control applications. Our approach outperforms static dual connectivity by two orders of magnitude in terms of control-communications availability while reducing the amount of required resources to approximately half.

As communication systems require ever higher bandwidths, designing digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth can reduce circuit complexity and lower the energy consumption. This is due to the fact that high resolution in time domain is less difficult to achieve than high resolution in amplitude domain. However, as 1-bit quantization is a highly non-linear operation, standard channel parameter estimation algorithms for digital receivers cannot be applied. In this paper we consider an unknown phase rotation and an unknown timing offset at the receiver. Furthermore, we assume that the receiver applies uniform phase and sample dithering, which can be implemented by sampling at an irrational normalized intermediate frequency and with an irrational oversampling factor, respectively. Starting from the least squares objective function, we derive a phase independent, non-data aided (NDA) timing estimator. Considering sample dithering, we prove that the estimator is consistent, even if the noise is colored due to oversampling w.r.t. the signal bandwidth. Moreover, we numerically evaluate the variance of the estimator and compare it to the Cramér-Rao lower bound. We find that the qualitative behavior is similar to the Oerder&Meyr estimator that is often used in the unquantized case and that oversampling can significantly improve the performance.

Dynamic connection allocation in time-division multiplexed network-on-chip(NoC) is a promising approach to provide a guaranteed service in NoC. Most recently, the trellis-search algorithm demonstrated using an application-specific instruction-set processor (ASIP). The processor achieved a considerable performance improvement for searching optimum paths. However, there is still a scalability issue. As network size increases, the path search time is rapidly increased. The purpose of this work is to introduce a relative search algorithm and to investigate its effect on NoC performance. Our proposed algorithm optimizes path search region and position according to the actual source-destination position on the network grid. Consequently, even though the network size is increased, the path search time of our proposed algorithm is gradually raised. The simulation results using 16x16 2D-mesh showed up to 10 thousand times and 3.5 times decreases in average execution cycles against 32bits RISC and ASIP, respectively.

A robust and adaptive variable length beam selection strategy based on M-ary sequential competition was proposed. It was enhanced by the elimination of inauspicious beams during the ongoing competition to improve the efficiency and speed of the training. In this paper, we refine the elimination process by introducing a new elimination mechanism based on estimated winning probability i.e. probability of being the strongest candidate for each beam at each time step. These probabilities are calculated using sequentially estimated a posterirori PDFs of the unknown signal amplitudes after beamforming. This way least promising beams that fail to promise a minimum predefined winning probability can be eliminated from the remaining candidates as early as possible.

To alleviate the blockage effects involved in millimeter-wave propagation, we investigate network massive multiple-input multiple-output (MIMO) transmission where only statistical channel state information is available at base stations (BSs). We first establish a network massive MIMO transmission model over millimeter-wave bands using per-beam synchronization. We Figure out that the beam domain is in favor of performing transmission in this scenario. We also demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence is further proposed to solve the problem. Numerical results reveal that the proposed network massive MIMO transmission approach can effectively alleviate the blockage effects and provide substantial performance gains over the existing transmission approaches.

Wireless communications systems beyond 5G are foreseen to utilize the large available bandwidths above 100 GHz. However, the power consumption of analog-to-digital converters (ADCs) for such systems is expected to be prohibitively high, because it grows quadratically with the sampling rate for high amplitude resolutions. Shifting the resolution from the amplitude to the time domain, i.e., by reducing the amplitude resolution and by employing temporal oversampling w.r.t. the Nyquist rate, is expected to be more energy efficient. To this end, we propose a novel low-cost sub-terahertz system employing zero crossing modulation (ZXM) transmit signals in combination with 1-bit quantization and temporal oversampling at the receiver. We derive and evaluate new finite-state machines for efficient de-/modulation of ZXM transmit signals, i.e., for efficient bit sequence to symbol sequence de-/mapping. Furthermore, the coded performance of the system is evaluated for a wideband line-of-sight channel.

UAVs require reliable, cost-efficient onboard flight state estimation that achieves high accuracy and robustness to perturbation. We analyze a multi-sensor extended Kalman filter (EKF) based on the work by Leutenegger. The EKF uses measurements from a MEMS-based inertial system, static and dynamic pressure sensors as well as GPS. As opposed to other implementations we do not use a magnetic sensor because the weak magnetic field of the earth is subject to disturbances. Observability of the state is a necessary condition for the EKF to work. In this paper, we demonstrate that the system state is observable - which is in contrast to statements in the literature - if the random nature of the air mass is taken into account. Therefore, we carry out an in-depth observability analysis based on a singular value decomposition (SVD). The numerical SVD delivers a wealth of information regarding the observable (sub)spaces. We validated the theoretical findings based on sensor data recorded in test flights on a glider. Most importantly, we demonstrate that the EKF works. It is capable of absorbing large perturbations in the wind state variable converging to the undisturbed estimates.

In this work, we investigate the spectral efficiency (SE) of a system where the transmitter utilizes zero crossing modulation (ZXM), which was implemented by combining faster-than-Nyquist signaling with runlength-limited transmit sequences, and the receiver employs 1-bit quantization and temporal oversampling. The SE is evaluated numerically with respect to a fractional power containment bandwidth, which allows for a fractional amount of out-of-band emissions. In contrast to most existing works, the SE is evaluated for a practical transmitter and receiver implementation under a wideband line-of-sight channel model. The studied system achieves SEs above 3 bit/s/Hz, which corresponds to an increase of more than 50 % as compared to standard QPSK. Furthermore, the studied system outperforms a related system by Deng et al., which also employs 1-bit quantization and temporal oversampling, by approx. 15 dB at a SE of 3 bit/s/Hz.

Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions. This is due to the noise enhancement in the LS estimation process. Minimum mean square error (MMSE) takes into consideration the noise effect and achieves better performance than LS with higher complexity. This paper proposes to correct the LS estimation error using deep learning (DL). Simulation results show that the proposed DL-based schemes perform better than both LS and MMSE channel estimation scheme, with less complexity than accurate MMSE.

Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.

Various radio technologies address the diverse performance requirements of today's ITS applications and compete for deployment. Instead of choosing a specific technology, a heterogeneous mix of currently available as well as emerging V2X technologies is considered. Benefits are: the support of mixed deployments, flexible technology choice based on application requirements and reliability enhancements achieved by redundancy concepts. Besides presenting the highly heterogeneous architecture, we show the implementation design of its main components. Further we present a proof-of-concept implementation, which is validated in the Dresden test-bed for different use cases.

In future 5G systems, it is envisioned that the physical resources of a single network will be dynamically shared between the virtual end-to-end networks called “slices” and the network is “sliced”. The dynamic sharing of resources can bring about pooling gains, but different slices can easily influence each other. Focusing on slicing the radio access network, a slice management entity is required to steer the radio resource management (RRM) so that all of the slices are satisfied and negative inter-slice influences are minimized. The steering of RRM can be done by adjusting slice-specific control parameters in scheduler and admission controller mechanisms. We use a model-free reinforcement learning (RL) framework and train an agent as a slice manager. Simulation results show that such agents are capable of relatively quickly learning how to steer the RRM. Furthermore, a hybrid method of Jacobian-matrix approximation with RL approach has been devised and shown to be a practical and efficient solution.

In analog receivers, the use of matched filtering with linear frequency modulated signals constitutes an effective means for detecting the presence of an incoming frame as well as for estimating the symbol timing offset (STO). However, in digital receivers, the discrete nature of the corresponding polyphase codes leads, under certain conditions of carrier frequency offsets, to a significant degradation at the output of the matched filter, which, consequently, can result in erroneous STO estimations. Exploiting the symmetry of a reference sequence consisting of a polyphase code and its complex conjugate, this work proposes a new algorithm based on reversed autocorrelation (RC) to improve the accuracy of the STO estimation obtained by matched filtering. On the one hand, the theoretical analysis demonstrates the superiority of matched filtering over RC in terms of detection performance in the low signal-to-noise ratio regime. On the other hand, it is shown by means of numerical evaluation that the RC can efficiently resolve the STO estimation ambiguity.

Matched filtering based on linear frequency modulated (LFM) or chirp signals constitutes a reliable approach for frame detection even in the presence of high carrier frequency offsets (CFOs). However, the use of polyphase codes as discrete versions of LFM signals can lead to strong degradation at the output of the matched filter for certain CFO conditions and sequence lengths, yielding missed detection. This work proposes a new approach to improve the probability of detection achievable with matched filtering for these unfavorable situations. The proposed approach exploits the symmetry of a reference sequence that consists of a polyphase code followed by its complex conjugate with an algorithm based on reversed autocorrelation (RC) and successfully utilizes the robustness of RC against CFO for detection. It is shown that joint detection based on matched filtering and RC can enhance the detection performance significantly compared to conventional matched filtering.

In [1] the upper-bound on the communications interval required to achieve string stability for a Cooperative Autonomous Cruise Control (CACC) system, i.e. the Maximum Allowable Transmission Interval (MATI), was found for the one-vehicle look ahead topology (vehicles communicating only with their immediate predecessors). Obtaining the MATI permitted to exploit the CACC dynamics as degrees of freedom for radio resource allocation while guaranteeing the proper functioning of CACC [2]. By transmitting only at the necessary update rate dictated by the system dynamics, the demands on the communication network are relaxed compared to an agnostic static allocation. This paper considers additional information from N preceding vehicles for further relaxation of the communications requirements. The hypothesis is that by over-hearing transmissions of additional predecessors, the update rate between vehicles can be reduced while maintaining string stability. Since overhearing state information of additional previous predecessors requires to process their information coherently, the design of N feedforward filters is necessary when considering N previous vehicles. In this work, we propose a methodology to design the feedforward filters that optimizes the MATI within the platoon based on a frequency domain analysis by posing an optimization problem. We show that transmission intervals on the wireless links can be relaxed up to 8x when a two-vehicle look ahead (2VLA) network topology is considered and up to 9x for a three-vehicle look ahead (3VLA) topology.

5G and beyond communications systems need to cope with a high degree of heterogeneity in terms of services and requirements. Specially, vehicle-to-everything (V2X) use cases require ultra-reliable and low latency communications (URLLC) under harsh channel conditions. To design an optimal waveform and coding scheme for such use cases is a key challenge. Therefore, new waveforms and coding techniques are need to be investigated. In this paper, we present a comparison of several waveform candidates (orthogonal frequency-division multiplexing (OFDM), discrete Fourier transform-spread-OFDM (DFT-s-OFDM), generalized frequency division multiplexing (GFDM) and orthogonal time frequency space (OTFS)) and coding schemes (convolution, turbo, low-density priority-check (LDPC) and polar) under a common framework. We consider two metrics, i.e. maximum data rates and packet error rate, to evaluate their performance under various fading conditions. The simulation results show that OTFS outperforms all other waveforms in both frequency selective and doubly selective channels. Regarding the coding schemes, turbo codes outperforms all other coding schemes, even though difference with LDPC codes is marginal.

In conventional multicarrier systems, a cyclic prefix (CP) is added to the transmission block in order to protect it from multi-path propagation of the wireless channel. Nonetheless, due to the random nature of the CP, it is usually discarded at the receiver side, and from a synchronization perspective, this energy is wasted. Unique Word (UW) is a promising concept for CP replacement, because, in addition to protecting the signal from multi-path propagation, it allows per-block synchronization. Considering a multiple-input-multiple-output (MIMO) system, the state-of-the-art (SoA) data-aided synchronization approaches are mainly preamble based, while, on the other hand, the synchronization techniques for UW sequences are being applied to single-input-single-output systems in low mobility scenarios. In this paper, we investigate time and frequency synchronization of UW-based MIMO systems in high mobility conditions where the wireless channel is both frequency selective and fast fading. Through theoretical derivations as well as extensive simulations, we show that the proposed UW-based synchronization approach for MIMO outperforms the SoA MIMO synchronization techniques.

This work focuses on the initial beam acquisition/alignment of millimeter wave (mmWave) communication systems. To detect the angle of arrival (AoA) and/or angle of departure (AoD), we propose a training protocol which probes all beamformers from a given codebook simultaneously by exploiting the sparse nature of mmWave channels. By applying a frequency-selective beam probing network, we can map each beamformer from the codebook to different frequencies and a spectral analysis at the receiver allows us to deduce favorable beamformers or AoDs. For practical reasons, we elaborate this idea of steering direction to frequency mapping for an orthogonal frequency division multiplexing (OFDM) communication system, i.e., we map each beamformer to specific pilot subcarriers. Under two different hardware designs, we investigate the feasibility of building such beamformer to frequency mappings for one additional radio frequency (RF) chain next to an existing OFDM communication system. We show that parallel beam training is able to achieve better effective transmission rates than exhaustive search in fast-time varying environments due to high temporal efficiency. This is crucial for mmWave communication systems which have access to large beamforming codebooks but suffer from short coherence times due to mobility and high spatial resolution.

Millimeter-wave has become an integral part of 5G networks to meet the ever-increasing demand for user data throughput. Employing higher carrier frequencies introduces new challenges for the propagation channel such as higher path loss and rapid signal degradations. On the other hand, higher frequencies allow deployment of small-sized antenna elements that enable beamforming. To investigate user mobility under these new propagation conditions, a proper model is needed that captures spatial and temporal characteristics of the channel in beamformed networks. Current channel models that have been developed for 5G networks are computationally inefficient and lead to infeasible simulation time for most user mobility simulations. In this paper, we present a simplified channel model that captures the spatial and temporal characteristics of the 5G propagation channel and runs in feasible simulation time. To this end, coherence time and path diversity originating from fully fledged Geometry based Stochastic Channel Model (GSCM) are analyzed and adopted in Jake's channel model. Furthermore, the deviation of multipath beamforming gain from single ray beamforming gain is analyzed and a regression curve is obtained to be used in the system-level simulations. We show through simulations that the proposed simplified channel model leads to mobility results comparable to Jake's model for high path diversity. Moreover, the multi-path beamforming gain increases the interference in the system and in turn number of mobility failures.

Increased usage of wireless technologies in unlicensed frequency bands inevitably increases the co-channel interference. Hence, for applications such as ultra-reliable-low-latency-communications (URLLC) in factory automation, the interference should be avoided. An intelligent coexistence management entity, which dynamically distributes the time and frequency resources, has been shown to be greatly beneficial in boosting efficiency and avoiding crippling interruptions of the wireless medium. This entity also supports multi-connectivity schemes, which are crucial for industry-level reliability requirements. The proposed governing technique of the coexistence management is a deep reinforcement learning (DRL) method, which is a model-free framework and channel allocation decisions are learned merely by interactions with the environment. The simulation results have shown that the employed method can greatly increase the reliability of the wireless network, when compared with legacy methods.

Realizing wireless mission-critical applications in industry, such as closed-loop control, necessitates ultra-reliable low latency communications (URLLC) to achieve error-free message transmission with hard real-time requirements. Recently, multi-connectivity (MC) has been introduced as a promising scheme to ensure URLLC in Industry 4.0. However, implementing MC in mobile industrial communications rises multiple technical challenges, such as avoiding degradation in reliability due to fading and the shadowing effect, and managing multiple links in parallel which increases signaling overhead dramatically. To deal with these challenges, this paper investigates the gains and limitations of implementing MC in industrial wireless communications. It studies conflicting optimization problems using MC based on different radio parameters. Also, a link management scheme is introduced for MC to reduce the signaling overhead based on different radio parameters. The simulation results demonstrate gains and limitations of using MC and the selected parameters (frequency reuse factor, number of users, and frequency band) on the reliability and the signaling overhead in industrial communications.

The selection of the most appropriate equalization-detection-decoding algorithms in wireless receivers is a challenging task due to the diversity of application requirements, algorithm performance-complexity trade-offs, numerous transmission modes, and channel properties. Typically, the fixed receiver-chain is employed for specific application scenario that may support iterative processing for better adaptation to variable channel conditions. We propose a novel method for optimizing receiver efficiency in the sense of maximizing packet transmission reliability while minimizing receiver processing complexity. We achieve this by packet-wise dynamic selection of the least complex receiver that enables error-free packet reception out of set of available receivers. The scheme employs convolutional neural network (CNN) and supervised deep learning approach for packet classification and subsequent prediction of the optimum receiver using raw baseband signals. The proposed scheme aims to approach a packet error rate close to the rate of the most complex receiver architecture while using a combination of both low and high complexity architectures. This is achieved by employing the neural network based classifier to dynamically select packet-specific optimum architecture; i.e. instead of using the most complex receiver for all packets, the approach dynamically assigns the packet to the most appropriate receiver in terms of equalization-detection-decoding capability and the least possible complexity. We analyze the performance of the proposed scheme considering various channel scenarios. The system demonstrates excellent packet classification performance resulting in the significant performance increase and the reduction of the usage of the functional blocks that can go up to 96% of the time in different scenarios.

Achieving extremely high reliability is one of the key targets in the development of fifth generation mobile networks. To meet this ambitious aim, usually redundancy is introduced by simultaneously utilizing multiple links that are separated in frequency and/or space. However, current standards simply duplicate packets on these links, resulting in an inefficient high usage of resources that could have been used for other applications. To address this problem, rateless coding using multiple links is proposed for ultra-reliable communications, mathematically modeled, and evaluated in this paper. The benefits comprise efficient resource usage and a simplified feedback mechanism. Finally, they can be implemented in a technology-agnostic manner on application layer to easily exploit interface diversity.

Mobility and blockage are two critical challenges in wireless transmission over millimeter-wave (mmWave) and Terahertz (THz) bands. In this paper, we investigate network massive multiple-input multiple-output (MIMO) transmission for mmWave/THz downlink in the presence of mobility and blockage. Considering the mmWave/THz propagation characteristics, we first propose to apply per-beam synchronization for network massive MIMO to mitigate the channel Doppler and delay dispersion effects. Accordingly, we establish a transmission model. We then investigate network massive MIMO downlink transmission strategies with only the statistical channel state information (CSI) available at the base stations (BSs), formulating the strategy design as an optimization problem to maximize the network sum-rate. We show that the beam domain is favorable to perform transmission, and demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence performance is further proposed for beam domain power allocation. Numerical results reveal that the proposed network massive MIMO transmission approach with the statistical CSI can effectively alleviate the blockage effects and provide mobility enhancement over mmWave and THz bands.

Ultra-Reliable Low-Latency Communications (URLLC) are considered as one of the key services of the upcoming fifth generation (5G) of wireless communications systems. Enabling URLLC is especially challenging due to the strict requirements in terms of latency and reliability. Multi-connectivity is a powerful approach to increase reliability. However, most of the current research is restricted to single-user scenarios, neglecting the challenges of multi-cellular, multi-user systems, i.e., interference and the competition for limited resources. In this article, we develop analytic comparisons of different connectivity approaches, showing that multi-connectivity may not always be optimal in the considered scenario. Moreover, we propose and evaluate novel resource allocation approaches based on stable matching theory to enable wireless URLLC. We extend the pure many-to-one stable matching procedure by utilizing the optimal connectivity approach for each user, optimizing the maximum number of matched resources, and providing a resource reservation mechanism for users suffering from bad channel conditions. System-level simulations demonstrate that the proposed algorithm outperforms baseline resource allocation approaches in outage probability by up to three orders of magnitude. Even in a highly loaded system, an outage probability in the range of ${10^{-5}}$ is achieved.

Digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth promise lower energy consumption. However, since 1-bit quantization is a highly non-linear operation, standard receiver algorithms cannot be applied. Thus, we derive performance bounds for phase, timing, and frequency estimation in order to gain a deeper insight into the impact of 1-bit quantization and oversampling. We identify uniform phase and sample dithering as crucial to combat the effect of the non-linearity introduced by 1-bit quantization. Since oversampling results in noise correlation, a closed form of the likelihood function is not available. Thus, we study a system model with white noise by adapting the receive filter bandwidth to the sampling rate. Considering the aforementioned dithering, we obtain very tight closed form lower bounds on the Cramér-Rao lower bound (CRLB) in the large sample regime. We show that with uniform phase and sample dithering, all large sample properties of the CRLB of the unquantized receiver are preserved under 1-bit quantization, except for an signal-to-noise ratio (SNR) dependent performance loss that can be decreased by oversampling. Numerical computations show that the properties of the CRLB for white noise still hold for colored noise except that the performance loss due to 1-bit quantization is reduced.

ITS-G5 is a communication system for vehicle-to-everything communication for road safety and traffic efficiency applications. On the lower layers, it is based on IEEE 802.11 standard and uses a simple ad hoc mode with a random medium access control scheme. Ad hoc networking is realized by a geographical routing scheme that provides single- and multi-hop communication over ITS-G5 links for periodic and event-driven broadcast messages. Specifically, contention-based forwarding (CBF) allows for efficient and reliable multi-hop packet transport by overhearing and timer-based transmission control. To cope with the channel congestion, decentralized congestion control (DCC) adjusts the message rate and ensures that the network load keeps below a predefined threshold of the bandwidth. To enforce a node's message rate, a “ Gatekeeper” above the MAC and beneath CBF is added. Under high network load, this Gatekeeper introduces an additional queuing delay seen by CBF, which can cause its overhearing function to fail. This contribution studies multi-hop forwarding in the context of DCC with the Gatekeeper and LIMERIC as a rate adaptation algorithm. We propose a congestion-enabled forwarding scheme that, in comparison to existing approaches considering DCC and forwarding separately, restores the efficient operation of CBF, and improves the communication performance in terms of reliability and latency for mixed data traffic composed of single- and multi-hop packets with different priorities. The simulations show the performance improvements in a freeway scenario. An analysis assesses the boundary conditions for CBF and the Gatekeeper. Moreover, the analysis corroborates the simulation.

In this paper, we design a frame structure for unique word (UW) based transmission of multiple-input-multiple-output (MIMO) systems under doubly-dispersive wireless channel conditions. We elaborate an energy and spectral efficiency analysis of a MIMO UW-based system vs. a conventional MIMO cyclic prefix (CP)-based system. Considering the UW-based transmission for a MIMO multi-carrier, we derive its signal processing algorithms for channel estimation and joint channel-equalization-and-demodulation. Through theoretical derivations as well as extensive simulations, we show that the proposed MIMO UW-based system significantly outperforms the state-of-the-art approaches.

Sharing tightly coupled memory in a multiprocessor system-on-chip is a promising approach to improve the programming flexibility as well as to ease the constraints imposed by area and power. However, it poses a challenge in terms of access latency. In this paper, we present a queue based memory management unit which combines the low latency access of shared tightly coupled memory with the flexibility of a traditional memory management unit. Our passive conflict detection approach significantly reduces the critical path compared to previously proposed methods while preserving the flexibility associated with dynamic memory allocation and heterogeneous data widths.

Physical layer abstraction (PLA) is commonly used in system level evaluations to speed up simulations. The accuracy of such evaluations highly depends on the modelling of PLA. Therefore, the main objective of this paper is to model and evaluate the performance of 5G and beyond (5GB) waveforms using PLA techniques. In the existing literature, PLA techniques were mainly used for orthogonal frequency division multiplexing (OFDM), which is commonly used to improve the performance under frequency selective fading. However, it suffers from high peak to average power ratio (PAPR). Therefore, to overcome this, DFT-spread- OFDM (DFT-s-OFDM) is used for uplink communication in the LTE/NR. In future use cases such as vehicle-to-everything (V2X), where high mobility is involved, inter-carrier interference becomes a bottleneck for above waveforms. Therefore, new waveforms are being proposed such as orthogonal time frequency space (OTFS). In the OTFS, data symbols are localized in delay-Doppler domain, and hence delay-Doppler channel impairments (due to mobility) can be easily compensated. To evaluate and compare the performance of above waveforms, we proposed waveforms specific PLA techniques. The proposed PLA techniques are validated though full PHY simulations, and used to compare the performance of different waveforms under frequency selective and doubly selective channels.

Multi-connectivity (MC) is regarded as one of the key features that meet the requirements of ultra-reliable low-latency communications for 5th generation networks, as it provides multiple diversity branches. Recently, we evaluated the decoding reliability of various MC setups and different combining algorithms by analyzing the resulting outage probabilities. In this work, we are interested in how much the performance is affected by correlated fading. Specifically, we analyze the outage probability of MC systems with joint decoding reception operating over correlated quasi-static Rayleigh fading channels. Our main contributions are as follows: (i) deriving the exact outage probability in integral form and the asymptotic outage probability at high signal-to-noise ratio (SNR) in closed form; (ii) deriving the correlation loss, which quantifies the extra SNR required under correlated fading as compared with the independent scenario; and (iii) evaluating frame-error rates of quasi-cyclic low-density parity-check codes by Monte-Carlo simulations. Our results show that the correlation loss is marginal for a low to moderate level of correlation, whereas the diversity gain is not affected by correlation whatsoever.

With the fifth generation (5G) of mobile networks the challenging field of ultra-reliable low latency communications (URLLC) is envisioned. In this area of research, reliability requirements are commonly formulated only in terms of packet loss rates. However, sparse packet loss can usually be tolerated by an application. Furthermore, the terms reliability and availability are usually loosely defined and not sufficiently separated in the wireless communications field. This paper applies fundamental metrics from dependability theory to wireless communications, providing more powerful key performance indicators (KPIs) for URLLC. In particular, we derive analytic expressions which reflect the up- and downtime distributions as well as the mission availability in a wireless multi-connectivity scenario with selection combining and Rayleigh fading. Further, we propose an appropriate approximation for the computationally complex expression of the mission availability.

General frequency division multiplexing (GFDM) provides a proven approach for a flexible physical layer implementation in wireless communication systems. However, this flexibility requires additional processing steps within the critical system path, the hybrid automatic repeat request. This is especially critical for devices of the Internet of Things, which have a very small power footprint. To tackle this problem, we present a data mapping that allows efficient parallel computation of the GFDM algorithm by a standard Harvard CPU architecture. To utilize the new mapping, we derive semi-custom processor configurations based on fixed-point arithmetic, which achieve a throughput close to the theoretical bound. In comparison with a custom FPGA implementation, we deliver 9 percent of the throughput, while only consuming 0.1 percent of the power. Thus, we achieve 14 times higher energy efficiency.

We consider the channel estimation (CE) of a non-orthogonal multi-carrier system where the wireless channel is both frequency-selective and time-variant. In non-orthogonal multi-carriers e.g. generalized frequency division multiplexing (GFDM), the reference signals for channel estimation become contaminated by the data symbols, which consequently, limits the transceiver performance. On the other hand, the well time-localization of the pilot symbols in GFDM, allows a more efficient use of cyclic prefix (CP). Particularly, by localizing the energy of the pilot symbols to the end of block, it is possible to use the pilot's information also from CP for channel estimation. Moreover, since in a non-orthogonal waveform, the energy concentration of the pilots might not be uniform over the transmit block duration, the CE algorithm that relies solely on block-fading assumptions might have its best performance at a specific time sample within the block duration. The knowledge of such time sample is specifically important for deriving the channel autocorrelation for adaptive filtering in time-variant situations. In this paper, we first propose an approach to efficiently use the whole transmission block for channel estimation including its CP, and then, we derive the well-known adaptive Wiener-Hopf filters for CE of the non-orthogonal interference-limited GDFM system. From the simulation results, we observe that using CP information for channel estimation and applying the Wiener-Hopf filters achieves up to 1.45 dB smaller frame error rate in comparison to an orthogonal frequency division multiplexing system.

The beam selection problem is one of the crucial problems for achieving high spectral efficiency in millimeter wave (mmW) systems. Most of the previous works use heuristic algorithms with fixed training length to solve this problem, without considering the optimal length of the training sequence. Thus the training length is often over-designed. In this paper, we show that beam selection by exhaustive search can be interpreted as an M-ary hypothesis test, where the optimal training length can be found using the formula of the selection probability. Using this relation, we design two algorithms based on composite hypothesis test theory to determine the optimal training length. Simulations show the applicability of this approach.

In the context of 5G systems, the emergence of various use cases with diverse requirements has attracted great attention to network slicing. In a single physical network, several instances of logical end-to-end networks, i.e. slices, will be instantiated to fulfill these requirements. To this end, slices should share the resources of the physical network, which consist of Core Network (CN) and Radio Access Network (RAN) resources. Herein, we focus on the Radio Resource Management (RRM) in the context of network slicing. To maximize the pooling gains, dynamic resource sharing is preferred over static sharing. However, dynamic resource sharing can lead to undesirable inter-slice influences, in particular, the contention on radio resources. In this article, we show that, although the radio resources are dynamically shared among the users of different slices, proper slice management can realize slice protection. This is achieved by adjusting the fraction of radio resources allocated to the different slices by the Packet Scheduler (PS) and by limiting the number of users admitted to the network via Admission Control (AC). We propose an iterative algorithm to optimize the parameters of PS and AC in order to ensure that the service level agreements are satisfied. Extensive system-level simulations have shown that a central entity that tunes these control parameters can greatly increase the network's performance.

Designing digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth enables lower power consumption and a reduced circuit complexity compared to conventional amplitude quantization, since high resolution in time domain is less difficult to achieve than high resolution in amplitude domain. However, standard receiver synchronization algorithms cannot be applied, since 1-bit quantization is a highly non-linear function. This paper is a first step to understand the influence of 1-bit quantization on the estimation of the channel parameters (e.g., timing, phase, and frequency offset). We will derive the Fisher Information (FI) matrix of phase and frequency, considering a known timing error and white Gaussian noise. Moreover, we propose to apply a uniformly distributed phase dither at the receiver, prior to 1-bit quantization, in order to reduce the nonlinear effect. The same effect can be achieved in practice by sampling at a low intermediate frequency. We obtain analytical results for the FI matrix with uniform phase dithering at the receiver and derive tight closed form upper bounds for the low and high SNR case.

To study critical 5G mobility features like zero-millisecond interruption time and multi-connectivity, an average downlink Signal-to-Interference and Noise Ratio (SINR) is needed for radio link failure detection and throughput calculation. This paper presents an accurate approximation of the average downlink SINR with low computational complexity in 5G networks where the base station forms multiple beams simultaneously. To this end, geometry-based link budget is formulated first for both desired and interfering downlink signals. Then, a closed form expression of the average downlink SINR is derived for multi-beam scheduling system and approximated by Monte-Carlo experiment using beam scheduling probabilities. In addition, the SINR model is derived for both strict and opportunistic resource fair scheduler where the latter targets a higher utilization of radio resources. Results have revealed that with increasing number of scheduled beams, the average downlink SINR generally degrades while the network throughput improves. Moreover, it has been also shown that the opportunistic resource fair scheduler performs better than the strict in terms of utilizing available network resources.

Ultra-reliable communications enable various advanced use cases, such as autonomous driving and safety critical applications. However, state-of-the-art vehicular communications technologies, such as IEEE 802.11p and LTE-V2X, cannot meet the reliability requirement of all time-critical use cases. Therefore, the next generation of these technologies are being developed to enhance vehicular support for ultra-reliable use cases. In this paper, the reliability of these upcoming vehicular communications technologies (i.e., IEEE 802.11bd and NR-V2X) is analyzed. Even though physical layer standardizations are not yet available, proposed candidate settings are used for investigations. We use Monte Carlo simulations to evaluate the physical layer performance of these technologies in various vehicle-to-vehicle (V2V) scenarios. High Doppler shifts in V2V scenarios is one of the main challenges to enable ultra-reliable communications. It is shown that NR-V2X can be expected to outperform IEEE 802.11bd in terms of reliability due to better handling of Doppler shifts. In case of IEEE 802.11bd, high Doppler shifts cause packet errors even at high signal-to-noise ratios (SNRs). Therefore, different measures to improve the performance of IEEE 802.11bd are discussed and evaluated.

Latency is the one of the critical performance metrics for 5G and beyond mobile networks, particularly for ultra-reliable and low-latency communications (URLLC). In URLLC applications, it is required that the transmitted packets reach the destination within a certain time and the packets that are unable to meet this strict latency requirement will be discarded. In this paper, we compute the waiting time in the packetized fronthaul at the Ethernet switch and compute packet loss rate (PLR) incurred due to the inability of the transmitted packets to meet the FH latency threshold. In addition, we derive the tractable closed-form solution for the waiting time distribution and verify it with simulation results. Our results show that PLR is affected mainly by packet size, spectral efficiency, switch speed and arrival rate.

Cloud radio access network (C-RAN) and massive multiple-input-multiple-output (MIMO) are two key enabling technologies for 5G as they improve radio performance while lowering the cost of operation. In a C-RAN system with massive MIMO-based remote radio units (RRUs), fronthaul is often the bottleneck in practice due to its finite capacity. To reduce the capacity requirements on fronthaul, precoding is done at the RRUs. In this paper, we maximize the energy efficiency (EE) of such a system by optimizing the transmit powers while explicitly incorporating the capacity constraints on fronthaul. We develop a successive convex approximation (SCA) algorithm, which is guaranteed to converge to a local optimum. Towards this, we propose novel bounds on the user rate function, which facilitates a convex approximation of the EE maximization problem. The convex problem is solved in each SCA iteration through Dinkel-bach's algorithm and dual decomposition. Numerical results show that the proposed algorithm significantly improves EE compared to the case with no power control and outperforms the weighted minimum mean square error algorithm.

An analytical method for computing the channel access delay budget between consecutive kinematic updates among a vehicle pair in a cooperative adaptive cruise control (CACC) enabled platoon under string stability conditions is presented. It is shown that even first-order approximations of the complex exponential delay term yield good numerical results. The method also indicates when communication in CACC is not required. The calculated channel access delay budget is used to obtain the semipersistent scheduling period for string stable CACC under LTE-V2X Mode 3 communication. Furthermore, an efficient semi-persistent scheduling algorithm capable of allocating heterogeneous periods and supporting multiple platoons is presented and validated with two examples.

Wireless system design on the physical layer is usually evaluated using comprehensive channel models. However, there is still a lack of publicly available stochastic channel models tailored to industrial use cases, which are recently considered more frequently. This paper presents the derivation of such a channel model for the 5 GHz ISM band and its parametrization. The frequency-selective behaviour is modeled by the Saleh-Valenzuela model. Based on a measurement campaign, the parameters of this model for a factory environment are determined and published the first time for the 5 GHz ISM band. Spatial correlation is modeled by the Kronecker model. The temporal variation of the channel is based on a theoretically derived Doppler spectrum assuming Laplacian distributed angle of arrivals. In addition to the description of the model components, key issues and common mistakes while constructing a channel model for industrial applications are discussed in order to advance the design and the deployment of future wireless industrial communications systems. The derived channel model is used in IEEE 802.11ax link layer simulations. It is shown that for industrial use cases specially tailored channel models are needed.

5G is not one problem and one solution, but spans a breadth of applications with largely differing requirements. One solution for all seems therefore inadequate. We therefore present a modular signal processor MPSoC architecture which can be tiled into the size to address the requirement as needed. We name it “Kachel”, the German word for “tile”.

Sharing scratchpad memory improves memory utilization but incurs conflicts. Existing statistical models for throughput estimation of shared memory systems assume mainly online memory arbitration, i.e., the memory access and arbitration logic are integrated into a single path. However, these models are not suited for modeling memory systems deploying off-line arbitration as they do not reflect the additional latency of such arbiters. To cope with this problem, we extend the existing occupancy and Markov models by including appropriate weighing parameters. We show that our extension can reduce the error of the original model by 71 percent over a wide range of parameters.

Vehicular communications and connected cars have an eminence potential to improve road safety and reduce the number of accidents by sharing information with their surrounding. The state of the art technologies to enable vehicular communications are IEEE 802.11p and LTE-V2X. A number of studies and field trials are carried out to evaluate their performance and suitability in various scenarios. On one hand 3GPP (3rd Generation Partnership Project) is working on the next generation V2X technology 5G NR-V2X to address new use cases and improve the performance. On the other hand, an IEEE 802.11 study group NGV (next generation V2X) is identifying new use cases and requirements to define a possible amendment IEEE 802.11bd. In this paper, we evaluate and compare the PHY layer performance of these upcoming technologies for vehicle-to-vehicle (V2V) communications. The purpose of this study is to identify which technology is more suitable for V2V communications. Our results show that NR-V2V is expected to outperform all other standards (even IEEE 802.11bd) in terms of reliability, range, latency and data rates. However, IEEE 802.11bd is expected to be more reliable with improved range and throughput compared to IEEE 802.11p.

This paper addresses the energy consumption concern of LPWAN by proposing an extension for the LoRa modulation. Conventional LoRa encodes data in the frequency shift of a chirp, our extension consists in encoding additional data in the phase- shift using the PSK modulation, giving rise to the PSK-LoRa. Our motivation is to encode more data per unit of time without performance degradation, such that we have a more energy efficient system. In order to assess the performance of PSK-LoRa, we derive approximate bit error rate and packet error rate expressions, and then we compare against simulation. For instance, both analytical and numerical outcomes demonstrate that QPSK-LoRa has no performance loss in comparison to LoRa, indicating the feasibility of the new scheme.

Generalised frequency division multiplexing (GFDM) is a novel multicarrier waveform with reduced out- of-band emission and peak-to-average-power-ratio compared to orthogonal frequency division multiplexing. Due to these properties, GFDM is regarded a candidate waveform for future wireless communication. However, an implementation that addresses operating conditions of handheld devices has not yet been considered. We propose a software programmable GFDM solution for handheld devices. This paper presents necessary capabilities of a vector DSP implementation to efficiently process GFDM in the context of 5G use cases. We investigate structural properties and numerical precision of the GFDM algorithm, propose a scalable vectorised approach for single instruction multiple data processing, and carry out a performance-cost evaluation. We present our key findings to enable GFDM functionality on handheld user equipment.

Cell-free massive multiple-input multiple-output (MIMO) is a promising cellular technology in which a large number of distributed access points (APs) jointly serve a small number of user equipments (UEs). In this work, we investigate the impact of the spatial distribution of APs on the performance of a cell-free massive MIMO system, considering different downlink power control policies. Further, we analyze the performance for the case where only subsets of all APs serve the individual UEs: this scenario has lower backhaul requirements and associated CAPEX/OPEX costs. In this framework, we first propose a novel, tractable approximation for the average spectral efficiency (SE) of the transmission to a UE conditioned on the estimated channel gains. This approximation is then used to develop different downlink power control policies. Further, we extend the policies to the scenario when power control is coordinated only among subsets of APs and not across the subsets. Through extensive system-level simulations, we evaluate the improvement in SE by spreading out the APs and the SE loss when a subset of all APs serve a UE and coordination across subsets is absent.

Cooperative automated driving applications require reliable and low- latency exchange of periodic control information among vehicles in proximity. Performing well at low channel loads, vehicular ad-hoc technologies suffer from performance degradation caused by channel congestion. We propose to apply Multi-RAT diversity to improve the reliability of transmissions by increasing robustness against channel congestion. Multi-RAT diversity is achieved by the redundant use of multiple access technologies in parallel. In this paper, we investigate on the potential reliability improvement by combing IEEE 802.11p and LTE-D2D mode 2. Besides explaining the basic effects and main design aspects, we quantify the potential gain in an example highway platooning scenario based on simulations and concrete requirements. The results show a high potential in the redundant use of IEEE 802.11p and PC5-based ad-hoc technologies. Significant increases in transmission range of up to four times, especially at high vehicle densities and under strict reliability requirements, are achieved.

Mission-critical applications in future vehicular networks require highly reliable wireless communications. Multi-connectivity is a potential solution to meet the desired quality-of-services of these applications. In this work, we consider a highway scenario with multi-connectivity where a vehicle can combine packets transmitted by multiple remote-radio-heads. For given inter-site- distances, number of links, and frequency reuse factors, we derive the expression for outage probability considering communication links affected by shadowing. We investigate the effect of network deployment parameters on the achievable outage reliability under different radio parameters. The simulation results show trade-offs between inter-site distance and control parameters (frequency reuse factor and number of links), as well as network and radio channel parameters (number of satisfied users, effect of shadowing, and path loss exponent) on communications reliability.

To meet the requirements of new 5G use cases, two methodologies have been proposed. The first considers additional processing on orthogonal frequency division multiplexing (OFDM), e.g. windowing, filtering, and precoding. The other aims at the design of new modulation schemes. Essentially, any block-based linear modulation, such as generalized frequency division multiplexing (GFDM), can be seen as precoded-OFDM, where the precoded data result from the frequency domain modulation. This representation has a significant benefit for the implementation of new waveforms. Namely, the available OFDM transceiver techniques can be reused, whereas the required signal features can be fulfilled by the configuration of the GFDM modem. In this paper, we propose flexible OFDM precoding based on the GFDM framework. As a particular case, we focus on orthogonal precoding to enhance the performance in fading channels. By means of closed-form expressions, we show that all the sybsymbols within the same GFDM subcarrier attain the same signal-to-interference-plus-noise ratio (SINR) in frequency selective channel. In special precoding cases, all symbols achieve equal SINR. This feature is important to enhance the performance without a need for power allocation.

Multi-connectivity is a key enabler for realtime applications demanding high reliability such as connected vehicles. Employing macro-diversity with distributed transceivers has the advantage of mitigating large-scale losses such as shadowing, but may incur time offsets between packets, requiring the receiver to synchronize to each packet individually. Since packet detection is prerequisite for any downstream receiver processing, synchronization can become a bottleneck to achieving high reliability. In this paper, we propose a concept to improve receiver performance in macro-diversity multi-connectivity networks in case of time offsets between packets, for instance, due to loose synchronization of distributed transmitters. By buffering the inputs of parallel receiver paths and allowing for iterative synchronization, successfully detected packets can serve as extended correlation sequence to detect previously undetected packets which thereby become available to diversity combining. Taking link-level simulations of IEEE 802.11 (WLAN) as an example, we demonstrate the efficacy of such Joint Synchronization (JS) and provide first numerical results. We see an SNR gain of about 1 dB in the mid-SNR range, which is equivalent to a packet error rate reduction by an order of magnitude for four-fold diversity. With power consumption in mind, we consider the trade-off between implementation complexity and the gain of JS. We conclude that JS is a viable backwards-compatible approach to improve diversity combining of delayed packets in multi-connectivity networks.

Emerging wireless communication use-cases demand ultra-reliable and low-latency communications. In order to meet these requirements, multi-connectivity (MC) approaches are being considered as a possible solution. To enable multi-connectivity with efficient use of resources, link adaptation requires not only to adopt modulation and coding scheme but also the number of links. This can be achieved by using an effective link quality metric (LQM) and mapping it to packet error rate or throughput, a process known as physical layer abstraction (PLA). In this paper, a new PLA method enhanced received bit information rate (eRBIR) is presented for OFDM based MC networks. The performance of proposed method is evaluated and compared against existing methods such as exponential effective SINR mapping (EESM) and received bit information rate (RBIR). Simulation results show that the proposed method enable an accurate and reliable link adaptation as compared to state of the art PLAs. Finally, the application of PLAs, to adapt the MCS in varying channel conditions is illustrated to ensure a certain target quality of service.

With the emergence of cloud radio access network (C-RAN) architecture, latency in fronthaul (FH) network is a critical performance metric especially for ultra-reliable and low-latency communication applications. The stringent FH capacity and latency requirements of C-RAN can be relaxed by offloading some baseband functionalities to remote radio unit (RRU), referred to as functional splitting. This allows packetized FH network solutions such as ubiquitous Ethernet. In this paper, we calculate the FH latency in the uplink of a C-RAN system with massive MIMO-based RRUs and 3GPP functional Split 7, wherein MIMO equalization is done at the RRU. We derive tractable, closed-form expressions for the steady-state probabilities of queue length and sojourn time distribution at the output port of an Ethernet switch in the FH network. We first present these results for Poisson file arrivals from users in the network and exponential file size distribution. We then extend the results to general file size distribution. The numerical results show that the file size and spectral efficiency of the users are critical in determining the FH latency. Further, results show that switch speed can be decreased without incurring significant increase in FH latency revealing the possibility for statistical multiplexing gains.

A study on feasibility regarding enabling cooperative adaptive cruise control (CACC) through LTE-V2X Semi-Persistent Scheduling (SPS) is considered. A linear time invariant (LTI) CACC model, previously presented in [1] is revisited. The effect of communication delay on the platoon stability, i.e. string stability, in relation to the CACC parameters is then explained. We study the effects on the SPS period limitations imposed by the LTE-V2X standard by modelling it as a communication delay. The performance of CACC under such considerations is presented and further improved by considering multiple SPS parallel sessions. We show that by following simple conditions on the configuration of multiple SPS parallel sessions, the CACC performance can be further improved. Moreover, by considering multiple SPS parallel sessions following these conditions, the SPS period set is refined in granularity. A refined granularity in the SPS period leads to less redundant transmissions from a stability perspective, which effectively translates to a higher efficiency in radio resource usage when compared to single session SPS.

Fifth generation (5G) of mobile networks are expected to serve multiple heterogeneous use cases. These use cases are extremely diverse in terms of service requirements and bundling them in a single monolithic network is a challenge. Network slicing is identified as one of the main enablers of 5G systems, where multiple logical End-to-End (E2E) networks share the resources of a single physical network. Radio Resource Management (RRM) in a sliced network should be able to simultaneously fulfill the required services of slices, dynamically share the network and assure the independence of slices so that slices cannot affect each other negatively. In this paper, we study the existing mechanisms that provide similar features in legacy networks and demonstrate the contributions and shortcomings of such existing RRM mechanisms in a sliced network. Thereafter, we argue the need for a new entity that will complement the existing RRM mechanisms to be slice-aware. With the aid of system-level simulations, we compare different slicing schemes and illustrate the drawbacks of legacy networks in fulfilling the objectives of a fully sliced network. Moreover, we illustrate the capabilities of the slice-aware RRM in steering the network's Key Performance Indicators (KPIs).

Locating active users/transmitters and predicting interference patterns in real-time is a key challenge in next generation radio networks to enable robust wireless communication and highly dynamic radio resource allocation. We consider a network of collaborating spectrum sensing units (SUs) and propose a two-phase approach for localization and tracking of transmitters using received signal strength at SUs. The first phase involves estimation of locations of multiple transmitters using compressed sensing based method. In the second phase, the location estimates are improved and velocities of mobile transmitters are estimated using extended Kalman filter. The proposed approach is evaluated through simulations considering an industrial channel model with correlated shadowing. We investigate the effect of SU placement, transmitter density, mobility and fading on the localization performance.

The conventional receiver designs of generalized frequency division multiplexing (GFDM) system assume full subcarrier allocation. In this case, the optimal linear receivers can be implemented with low-complexity channel equalization followed by zero-forcing (ZF) demodulation. In some use cases, e.g. multiuser, only a subset of the subcarriers is active for data transmission. Therefore, the optimal receiver design needs to consider the effective joint channel and modulation matrix, which complicates the practical implementation. To maintain low-complexity realization in these cases, full allocation can still be assumed, however, the performance loss is remarkable. In this paper, we propose an efficient transceiver design for non-fully allocated GFDM system. In the proposed approach, the frequency-domain (FD) sparsity of GFDM is exploited to represent the transmitted signal by means of an effective small-size GFDM model with one non-active subcarrier. Therefore, the assumption of full allocation becomes more realistic. Moreover, the received signal can be further reformulated with fully allocated system, but at the cost of altering the effective channel gains. The proposed design significantly reduces the computation cost of the practical GFDM receiver, whereas the performance still approaches the counterpart optimal linear receiver.

With the explosion of the number of compute nodes, the bottleneck of future computing systems lies in the network architecture connecting the nodes. Addressing the bottleneck requires replacing current backplane-based network topologies. We propose to revolutionize computing electronics by realizing embedded optical waveguides for onboard networking and wireless chip-to-chip links at 200-GHz carrier frequency connecting neighboring boards in a rack. The control of novel rate-adaptive optical and mm-wave transceivers needs tight interlinking with the system software for runtime resource management.

Mobile communication continues to play an important role in the modern economy, including consumer, health, education, logistics, and other major industries. At the same time, the current Internet has created a key infrastructure component for our modern world, having an impact on almost every aspect of our daily lives. The Internet democratized access to information and has enabled emerging economies to participate in the modern global economy. We are now approaching the next big wave of the Internet innovation: the Tactile Internet. The widely used term “Tactile Internet” was coined and defined by the IEEE P1918.1 as: “A network or network of networks for remotely accessing, perceiving, manipulating or controlling real or virtual objects or processes in perceived real time by humans or machines.”

Wireless communication systems have been evolving since the first generation. With the fifth generation of wireless systems, not only the evolutionary aspect of increased data rates is tackled but also the revolutionary aspect. Here, emerging use cases such as massive machine-type communication and ultrareliable low-latency communication will play a crucial role. Within this context, applications with stringent latency and reliability requirements are emerging. Wireless reliability is understood as successfully transmitting the desired amount of data within a given time. Diversity techniques, such as multiconnectivity, are potential solutions to achieve stringent reliability requirements. However, in a multiuser scenario, in which resources are shared, this might not always be possible. In this paper, we discuss the feasibility of various multiconnectivity approaches and propose a matching theory-based algorithm together with a novel scheduler aiming to guarantee the reliability requirements of as many users as possible in a multicellular, multiuser system. System-level simulations demonstrate that the proposed approach achieves 100% reliability for the fifth-percentile users in a highly loaded system. The maximum gain of fifth-percentile user throughput as compared to a static multiconnectivity approach is 150%.

We present a novel M-ary sequential test for beam selection when knowledge about the SNR operating point is not available. The proposed sequential test adaptively changes the test length (the number of observations) according to the SNR operating point to achieve the desired performance. Moreover, to achieve the same performance in terms of captured signal power, the sequential competition test requires on average less observations (particularly in the lower SNR regime) in comparison to a perfectly tuned fixed length test assuming genie knowledge.

Full-duplex (FD) jamming transceivers recently have been shown to enhance the information security of wireless communication systems by simultaneously transmitting artificial noise (AN) while receiving information. In this paper, we investigate whether FD jamming can also improve the system's secrecy energy efficiency (SEE) in terms of securely communicated bits per Joule when considering the additional power used for jamming and self-interference (SI) cancellation. Moreover, the degrading effect of the residual SI is also taken into account. In this regard, we formulate a set of SEE maximization problems for a FD multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, considering both cases where exact or statistical channel state information (CSI) is available. Due to the intractable problem structure, we propose iterative solutions in each case with a proven convergence to a stationary point. Numerical simulations indicate only a marginal SEE gain, through the utilization of FD jamming, for a wide range of system conditions. However, when SI can efficiently be mitigated, the observed gain is considerable for scenarios with a small distance between the FD node and the eavesdropper, a high signal-to-noise ratio (SNR), or for a bidirectional FD communication setup.

Multi-connectivity (MCo) is considered to be a key strategy for enabling reliable transmissions and enhanced data rates in fifth-generation mobile networks, as it provides multiple links from source to destination. In this paper, we quantify the communication performance of MCo in terms of outage probability and throughput. For doing so, we establish a simple, yet accurate analytical framework at high signal-to-noise ratio (SNR), in which the number of links, the spectral efficiency, the path loss, and the SNR are incorporated, giving new insights into the potentials of MCo as compared with the single-connectivity (SCo). These are our main contributions: 1) finding the exact coding gain of the outage probability for parallel block-fading channels; 2) quantifying the performance improvement of MCo over SCo in terms of SNR gain; and 3) comparing optimal and suboptimal combining algorithms for MCo at the receiver side, namely joint decoding, selection combining, and maximal-ratio combining, also in terms of SNR gain. In addition, we apply our analytical framework to real field channel measurements and thereby illustrate the potential of MCo to achieve high reliability and high data rates in real cellular networks.

Motivated by 5G application requirements that challenge the use of orthogonal frequency division multiplexing (OFDM), non-orthogonal multi-carriers are being investigated. Unlike OFDM that takes advantage of orthogonal pilot observation, in non-orthogonal waveforms, pilots are contaminated by interference from multiple dimensions, i.e., inter-subsymbol-, inter-carrier-, and inter-antenna-interference, when multiple-input-multiple-output (MIMO) is also part of the transmission. Employing cyclic-prefix (CP) in multi-carrier systems not only protects the signal from inter-symbol-interference but also allows circular interpretations of the channel, which simplifies the estimation and equalization techniques. Nevertheless, the CP information is usually discarded at the receiver side. In this paper, by considering the fact that non-orthogonal waveforms suffer from multiple dimensions of interference, we derive a MIMO linear-minimum-mean-squared-error (LMMSE)-based parallel-interference-cancellation (PIC) method for joint channel estimation and equalization of non-orthogonal waveforms. Unlike the common practice, by properly localizing the pilots in time domain, we also use the pilots' information from CP. We apply our proposed algorithm to a flexible non-orthogonal waveform known as generalized frequency division multiplexing (GFDM). Taking advantage of block-circularity of GFDM, we investigate the complexity aspects for such CP-aided LMMSE-PIC channel estimation. Through simulation results, we show that using CP information of pilots for GFDM gains up to 2.4-dB better frame error rate performance than an OFDM signal.

In this paper, we show a novel algorithm for strong line-of-sight (LoS) multiple-input-multiple-output (MIMO) channel equalization. With optimally spaced antennas under specific arrangements, the LoS channels can be made spatially orthogonal. In practice, antenna displacements are expected. Conventional algorithms like zero-forcing (ZF) do not consider the special properties of the LoS MIMO channel and result in high complexity. We show that the LoS MIMO channel can be factorized into a product of three matrices. Thereby, the two diagonal matrices at the outer product positions are the most varying terms and should be compensated dynamically. Being a good tradeoff between complexity and robustness, the proposed sequential channel equalization is applied in a reverse order of the factorization. The algorithm can be applied to LoS MIMO systems with uniform linear or rectangular arrays, which are making use of digital or analog equalization. By considering the usage of the Winograd butterfly and Butler matrices, the number of multiplications in both digital and analog implementations of the proposed solution is increasing approximately linearly with respect to the number of antennas, while the complexity of the state-of-the-art designs grows quadratically. As found numerically and verified experimentally, the proposed method performs nearly as well as the ZF-based algorithms with near-optimal arrangements, while having significantly lower complexity.

The fifth-generation (5G) of mobile networks are supposed to address radically new types of applications that have not been possible with previous generations of wireless technologies. Ultra-Reliable Low-Latency Communication (URLLC) is one 5G use case category imposing strict requirements on the availability, reliability, and latency together with zero mobility interruption. Aiming for a common understanding, we put forward a comprehensive description of performance metrics based on reliability theory focusing on availability and reliability metrics as a function of time. We establish a link to wireless communications by studying Rayleigh fading channels in a multi-connectivity system. The reliability metric definitions and analytic models presented in this paper contribute to enable a systematic and accurate performance analysis for URLLC. Numerical evaluations demonstrate the importance of considering not only availability alone but combinations of availability and time-based reliability statistics jointly.

Orthogonal time frequency space modulation (OTFS) has been recently proposed to achieve time and frequency diversity, especially in linear time-variant (LTV) channels with large Doppler frequencies. The idea is based on the precoding of the data symbols using symplectic finite Fourier transform (SFFT) then transmitting them by mean of orthogonal frequency division multiplexing (OFDM) waveform. Consequently, the demodulation and channel equalization can be coupled in one processing step. As a distinguished feature, the demodulated data symbols have roughly equal gain independent of the channel selectivity. On the other hand, generalized frequency division multiplexing (GFDM) modulation also employs the spreading over the time and frequency domains using circular filtering. Accordingly, the data symbols are implicitly precoded in a similar way as applying SFFT in OTFS. In this paper, we present an extended representation of GFDM which shows that OTFS can be processed as a GFDM signal with simple permutation. Nevertheless, this permutation is the key factor behind the outstanding performance of OTFS in LTV channels, as demonstrated in this work. Furthermore, the representation of OTFS in the GFDM framework provides an efficient implementation, that has been intensively investigated for GFDM, and facilitates the understanding of the OTFS distinct features.

The advent of hardware Trojans is posing an increasing threat on complex integrated circuits. Network-on-Chip, the established communication infrastructure for many core systems-on-chip, are growing in complexity. Integration of third-party components, which are increasingly becoming valuable targets, exposes the surface for attacks through the injection of hardware Trojans. In this paper, we address active attacks on NoCs, and focus on the integrity of transmitted data. Basically, we use network coding for the transmission of data in order to increase efficiency and robustness.

Multi-beam selection is one of the crucial technologies in hybrid beamforming systems for frequency-selective fading channels. Addressing the problem in the frequency domain facilitates the procedure of acquiring observations for analog beam selection. However, it is difficult to improve the quality of the contaminated observations at low SNR. To this end, this paper uses an idea that the significant observations are sparse in the time domain to further enhance the quality of signals as well as the beam selection performance. By exploiting properties of channel impulse responses and circular convolutions in the time domain, we can reduce the size of a Toeplitz matrix in deconvolution to generate periodic true values of coupling coefficients plus random noise signals. An arithmetic mean of these signals yields refined observations with minor noise effects and provides more accurate sparse multipath delay information. As a result, only the refined observations associated with the estimated multipath delay indices have to be taken into account for the analog beam selection problem.

Hybrid beamforming is a promising concept to achieve high data rate transmission at millimeter waves. To implement it in a transceiver, many references optimally adapt to a high-dimensional multi-antenna channel but more or less ignore the complexity of the channel estimation or the effort for the subsequent step of computing the singular value decomposition of the channel matrix. Realizing that received coupling coefficients of the channel and pairs of possible analog beamforming vectors on both sides can be used for analog beam selection, we further propose a low-complexity scheme that exploits these implicit channel knowledge to implement hybrid beamforming. The estimates of these coupling coefficients yield alternatives of effective channel matrices of much lower dimension. After calculating the Frobenius norm of these effective channel matrices, it turns out that the effective channel having the largest value of the Frobenius norm provides the solution to hybrid beamforming problem.

The fifth generation (5G) of mobile communication will enable new use-cases such as self driving cars, smart automation and mission critical applications, which require ultra-reliable communications. Multi-Connectivity (MC) is a promising approach to achieve high reliability in wireless networks. In order to enable MC and efficiently utilize radio resources, dynamic link adaptation is required for choosing appropriate modulation schemes and number of links. This can be achieved by using an effective link quality metric such as effective signal-to-noise ratio (SNR) and mapping it to the packet error rate, a process referred to as physical layer abstraction (PLA). In this paper, we investigate and compare the performance of existing PLA methods especially exponential effective SNR mapping (EESM) and received bit information rate (RBIR), for OFDM-based MC systems. Furthermore, we propose a new robust PLA method, called enhanced EESM (eEESM). The eEESM minimizes the efforts of optimizing tuning parameter by fitting the variations in tuning parameter to known curves as a function of channel and diversity order. Simulation results show that eEESM outperforms the state of the art PLAs for different channel conditions, modulation and diversity orders.

Exploiting the full potential of automated driving systems requires reliable wireless communication enabling network connectivity and cooperation among vehicles. Multiple V2X technologies are addressing the requirements of connected autonomous driving applications. Recent investigations have shown that none of the technologies is flexible and reliable enough to serve the diverse requirements in terms of delay, reliability and throughput under the various circumstances observed by vehicles. Hybrid V2X communications enables the coordination of multiple communication technologies to efficiently adapt to the time-varying channel and road traffic conditions. Further it allows to increase reliability and throughput of transmissions by combining multiple RATs in parallel. This work gives an overview of the potential, challenges and main design aspects of hybrid V2X communications considering the latest technological developments.

In the context of 5G mobile networks, several new use cases with various requirements with respect to throughput, latency, coverage, etc., should be addressed. To avoid deployment of separate networks for each of the use cases, the concept of network slicing has been introduced, where several logical networks share a single physical network. However, the accommodation of networks with diverse requirements in a single physical network is a new challenge. In this work, we study the effects of a mapping layer, which supervises the network over a service area and manages the allocation of radio resources to slices to guarantee their target service requirements. To do so, we propose an adaptation algorithm based on minimizing deviations from slice requirements. The results show that by utilizing the mapping layer, the resources can be shared efficiently and fairly and the deviations of Key Performance Indicators (KPIs) from the Service Level Agreement (SLA) targets are reduced compared to distributed control methods that are typically used in legacy and current cellular systems.

In recent years, two candidates for vehicular communications have evolved for the support of road safety and traffic efficiency applications. On the one hand, ad-hoc networks exist based on the ITS-G5/802.11p protocol stack, and on the other hand, there are cellular network infrastructures based on an extended LTE stack, which we refer to as Cellular-based Vehicular Communication Systems (Cellular-VCS). The most important extension of the classical LTE stack is a direct link among vehicles which is also called Device-to-Device (D2D) communication. Both approaches meet the requirements on vehicular communications but show technology-inherent mechanisms that result in different performances. ITS-G5 features a small latency at a small network load whereas Cellular-VCS promises a highly reliable packet transmission. One of the main difference of both approaches lies in the channel access which is random-based for ITS-G5 and centrally scheduled for Cellular-VCS. This contribution studies the performance of the two named technologies in a real-world road traffic scenario through comprehensive simulations. The simulation scenario makes use of real road traffic density measurements for modeling the mobility of the vehicles. Mixed network data traffic of periodically and event-based messages is generated through particular generation rules. The results prove that both technologies work stable at moderate road traffic conditions but have significant differences in the quantified communication parameters.

Besides classical performance indicators such as throughput and capacity, other metrics like reliability, availability, and latency are becoming increasingly important for designing and analyzing wireless networks. This work provides novel insights into how high availability can be achieved. For doing so, a preexisting model for detailed analysis of the signal-to-interference- plus-noise ratio (SINR) is adapted and applied to various intra-frequency multi-connectivity architectures. In addition, the impact of different carrier frequencies, i.e., 2.5 GHz and 15 GHz, is investigated as well. The developed models accurately capture the system performance and reveal distinct advantages and disadvantages of the investigated architectures. It is demonstrated that, especially, dynamic clustering mechanisms in combination with single frequency network transmissions are suited to achieve extremely high availability while using resources efficiently.

The steadily increasing demand for higher data rate in mobile communications has recently attracted attention to frequencies above some 10 GHz also known as millimeter wave (mmW) communications. However, to compensate the higher path loss antenna arrays are mandatory so that efficient beam alignment algorithms are important to make mmW communication feasible in practice. Furthermore, to keep cost and complexity within acceptable limits, imperfections of analog beam forming have to be handled with appropriate signal processing in digital baseband. To address this scenario we describe a robust beam alignment algorithm and illustrate its performance both by simulation and experiments using a real-time prototype system operating at 60 GHz.

Millimeter-wave frequency bands enable the deployment of small-sized antenna arrays in the fifth generation of mobile networks, which provide high beamforming gain for both transmitter and receiver. In order to study user mobility, a computationally efficient simulator is needed that runs with a time step resolution that is higher than transmission-time-interval of cellular networks. In such simulators, typically the average downlink signal-to-interference-and-noise ratio (SINR) is used for radio link failure detection and throughput calculation. In this paper, models of desired and interfering signals are formulated first, by considering the impact of antenna beamforming at transmitter and receiver. Then, a closed-form expression of average downlink SINR is derived by taking into account the scheduling probabilities of the users. In addition, the closed form expression is approximated by Monte Carlo method. Simulations are performed to analyze the complexity and accuracy of Monte Carlo method along with the impact of user location and beamforming gain on average downlink SINR. Results confirm that the average downlink SINR is approximated accurately, and the computational complexity of the proposed method is tolerable. In addition, the results reveal that user location and beamforming have significant impact on the average downlink SINR.

In this paper we optimize spatially coupled protographs for window decoding (WD) and arbitrary rate. Previous studies found that the belief propagation (BP) threshold of spatially coupled code ensembles achieves the maximum a posteriori (MAP) threshold of their underlying block code ensemble. This property requires a large coupling length L and thus the window decoder is considered to reduce latency and complexity of the decoding. To approach the BP threshold fast in the size of the window W, it is well known that the code requires a special structure to avoid degree-1 variable nodes inside the window. We further require additional structure to construct a systematic code with low encoding complexity, which unfortunately forces degree-1 variable nodes inside the window. Thus, we formulate an optimization problem to maximize the WD threshold and solve it by applying a differential evolution (DE) based algorithm. Compared to the regular protographs obtained by edge spreading, our optimized irregular protographs show a significant improvement in terms of WD threshold and finite length performance for small window sizes, hence leads to small decoding latency and complexity. Furthermore, for high rates our codes can compete with the highly optimized LDPC block codes from the WiMAX standard.

Multi-connectivity is considered to be key for enabling reliable transmissions and enhancing data rates in future wireless networks. In this work, we quantify the communication performance by the outage probability and the system throughput. We establish a remarkably simple, yet accurate analytical framework based on joint decoding to describe the outage probability and the system throughput depending on the number of links, the modulation scheme, the code rate, the bandwidth, and the received signal-to-noise ratio. To investigate the tradeoff between the outage probability and the system throughput we define two modes to either achieve low outage probabilities or high system throughput which we refer to as the diversity and the multiplexing mode, respectively. We then establish a rate-reliability tradeoff analysis based on time sharing between both modes.

With large bandwidths, millimeter wave (mmWave) line-of-sight (LoS) multi-input multi-output (MIMO) technology employing parallel stream transmission is well suited for future wireless backhaul systems. However, hardware imperfections of mmWave transceivers cause additional signal processing challenges. Considering the channel and hardware properties, this paper proposes a novel frame structure with high temporal efficiency. The required MIMO processing can be decomposed into two steps. The first one removes the interantenna interference including the carrier frequency offsets. In the second step, the intersymbol interference brought by the frequency-selective components is canceled with a parallel structure. As a comparison, a more general decision-directed least-mean-square approach (DD-LMS) for joint processing of all effects is introduced. With a 60 GHz 2 × 2 LoS MIMO demonstrator, only the widely linear version of DD-LMS achieves performance of about 0.2-0.6 dB better than the proposed method but requires approximately four times more complexity. The proposed method with parallel processing chains is well suited especially for larger LoS MIMO systems, e.g., for a system with 16 streams, its required complexity is less than 7% of the widely linear DD-LMS.

Driven by 5G requirements, research on alternatives to the popular cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) waveform recently arose. In particular, non-orthogonal circularly filtered waveforms such as generalized frequency division multiplexing (GFDM) were proposed due to flexibility and robustness. Applying multiple-input multiple-output (MIMO) techniques for future wireless networks are unquestionable and thereby compulsory for any alternative waveform. Despite advancements in accurate MIMO detection algorithms for GFDM, compared with CP-OFDM their complexity still exhibited a higher order of magnitude, impeding an energy-efficient implementation. In this paper, we propose a low-complexity formulation for iterative minimum mean squared error with parallel interference cancellation (MMSE-PIC) detection for non-orthogonal waveforms with localized inter-carrier interference, where we focus on the application to MIMO-GFDM. The proposal achieves complexity similar to CP-OFDM and we evaluate its performance under realistic channel conditions with imperfect channel state information, where we obtain up to 2-dB gain of GFDM compared with OFDM. We confirm our findings by analyzing the measured extrinsic information transfer charts and show that the proposal achieves the performance of optimal maximum likelihood detection. The results point out the MMSE-PIC algorithm as a viable technique for iterative MIMO receiver implementations for non-orthogonal waveforms.

Analog-to-digital conversion with high resolution in amplitude has a relatively high energy consumption in communication systems. A promising alternative to reduce the energy consumption is 1-bit quantization. Considering such a receiver, we design and analyze continuous phase modulation (CPM) schemes, which are favorable because of their bandwidth efficiency and their constant envelope. In this context, oversampling with respect to the symbol duration is promising because CPM signals are not strictly bandlimited and because it reduces the loss in achievable rate caused by the quantization. The additional degrees of freedom brought by oversampling can be exploited by higher order modulation schemes. A lower bound on the achievable rate is computed based on an auxiliary channel law. In a further step, we optimize the input distribution with an optimization strategy based on a Markov source model. For a specific example, we give upper bounds on the achievable rate and present a state-machine representation for sequences which are reconstructible at the receiver. Finally, the proposed approach has the advantage of a constant envelope enabling an energy efficient transmitter design while achieving only a slightly lower 90% power containment bandwidth efficiency than existing methods with 1-bit quantization and oversampling.

Spatial multiplexing is an important factor usable for improving the throughput of future millimeter-wave (mm-wave) backhaul links. One conventional strategy in mm-wave multiple-input multiple-output (MIMO) systems uses densely packed antennas and exploits the spatial signature of multiple paths. Meanwhile, spatial multiplexing over a single line-of-sight (LoS) path, known as LoS MIMO communication, offers an alternative option with widely spaced antennas exploiting the phases of spherical waves. In this paper, we first show that those two conventional approaches exploit two different degrees in the channel matrices, which we denote as inter- and intra-path multiplexing, respectively. Then, we show that the two kinds of spatial multiplexing can be jointly exploited and identify their different requirements on system design. Fulfilling all requirements simultaneously, we propose a system with multiple widely spaced subarrays. With the help of analog beamforming, the intra-path multiplexing of conventional LoS MIMO systems can be introduced to other non-LoS paths owing to its robustness. Merging the two kinds of spatial multiplexing in a combined channel model and connecting it with a hybrid beamforming architecture, the proposed system achieves spatial multiplexing of a higher order than conventional ones. Simulation results for a backhaul scenario illustrate that the channel of the proposed method has higher ranks than that of conventional approaches.

Current and future applications impose high demands on software-defined radio (SDR) platforms in terms of latency, reliability, and flexibility. This paper presents a heterogeneous SDR MPSoC with a hexagonal network-on-chip to address these issues. It features four data processing modules and a baseband processing engine for iterative multiple-input multiple-output (MIMO) receiving. Integrated memory controllers enable dynamic data flow mapping and application isolation. In a 4 x 4 MIMO application scenario, the MPSoC achieves a throughput of 232 Mbit/s with a latency of 20 μs while consuming 414 mW. It outperforms state-of-the-art platforms in terms of throughput by a factor of 4.

Rapid scaling of transistor gate sizes has significantly increased the density of on-chip integrations and paved the way for many-core systems-on-chip with highly improved performances. The design of the interconnection network of these complex systems is a critical one and the network-on-chip is now the accepted efficient interconnect for such large core arrays. An unfortunate adverse effect of technology scaling is the increased susceptibility to failures resulting in failing links and routers in the network-on-chip. To keep the network connected, efficient fault adaptive routing algorithms are necessary to route around faults. To design and evaluate the fault resiliency of such adaptive routing algorithms, fast, accurate and flexible analytic models are required, especially in large networks for which simulations are extremely time costly. In this paper, we present an analytic approach to evaluate the reliability of adaptive routing algorithms based on algebraic manipulations of the channel dependency matrix. It allows also to evaluate the number of alternate paths between source-destination pairs, in the presence of any number of permanent faults in the network. The analytic model is general and can be adapted to evaluate network reliability for any network topology and with any adaptive routing algorithm based on the turn model. We present cycle-accurate simulations to compare the accuracy of the model for the 2-D mesh and the hexagonal networks. The model is able to estimate the network fault resilience with an accuracy of about 1% and more than 70 times faster than the cycle accurate simulation.

Future 5G and beyond mobile networks target at services with a high degree of heterogeneity in terms of their communication requirements. To meet these requirements, different PHY numerologies would provide a better performance; still, all services must be served by a single network technology. Generalized Frequency Division Multiplexing (GFDM) is a good candidate for PHY virtualization where the dimensions of the data block can be dynamically configured in time and frequency. Allocating these blocks in a common spectrum every scheduling period leads to a "packing" problem, in which the QoS demands of the data flows need to be acknowledged. In this paper we consider the optimization of the data block allocation as a Knapsack problem. We incorporate the flows' QoS demands by means of utility theory, where utility functions provide a metric of urgency for a flow to be scheduled and the data block to be allocated. For the resulting two-dimensional geometric Knapsack problem we propose a heuristic solution, assess different design options and evaluate the performance in terms of data rate and queuing delay.

This paper presents a framework for pursuing lowlatency communication among V2V networks underlaying V2I networks. To achieve low-latency communication, solely relying on the improvement of the air-interface may not be enough. To cope with the highly dynamic environment of vehicular networks, a time dynamic optimization approach is proposed that improves the latency performance through not only optimization of spectrum resources but also by constraining the network switching rate. To further decrease the complexity of the time dynamic optimization problem, we convert the original problem to a deterministic optimization problem through the Lyapunov Optimization Theory. The proposed algorithm becomes a more suitable scheme for the vehicular network. Analytical results show that the proposed scheme can approach the best tradeoff between the latency performance and the network switching rate. Simulation results are provided to verify the proposed algorithm.

Multi-connectivity is considered to be key for enabling reliable transmissions in future wireless networks. Transmission reliability depends on the used combining algorithm such as joint decoding (JD), maximum selection combining (MSC), and maximum ratio combining (MRC). To compare the performance of these combining algorithms we derive their outage probabilities based on distributed source coding. The outage probability is analytically described depending on the number of links, the modulation scheme, the code rate, and the received signal-to-noise-ratios (SNR). We show that JD requires less transmit power than MRC and MSC to achieve a given target outage probability.

In this paper, we propose a sequential hybrid beamforming design for multi-link transmission over mmwave frequency bands. As a starting point, a baseline data communication link is established via traditional analog beamforming at both the BS and UE. If an extra RF chain is available at the UE, it can continue to probe the propagation environment at the same frequencies. In case the environment is favorable and system resources allow, a secondary data communication link is established to enable multi-stream transmission. In principle, the secondary link could be served by the same BS and/or one or several other BS(s). To initialize the secondary data communication link, a parallel beam search scheme is proposed, which helps the UE/BS to find a suit-able beam pair with given optimization criteria without interrupting the baseline data communication. By applying the proposed two-step approach, hybrid beamforming becomes an add-on feature that can be easily switched on over an analog beamforming enabled system without interrupting its operation whenever system requires. Meanwhile, the information obtained by deploying the proposed parallel beam search scheme can also be used for deciding a back-up beam pair if signal blockage occurs to the baseline data communication link.

The centralized methods for connection allocation in a circuit-switched network-on-chip (NoC) based on time-division multiplexing (TDM) may pose serious performance and scalability issues in large-scale networks due to the 1) limited path search speed, 2) increasing allocation request rate at central unit and 3) the increasing communication cost between the central unit and NoC nodes. This paper tackles this problem by proposing a combined centralized-distributed approach that splits the original NoC into multiple non-overlapping logical partitions, each of them served by a dedicated NoC-Manager unit. The NoC-Manager employs fast trellis-search shortest path algorithm enabling local path search inside the associated NoC partition, while a set of NoC-Managers jointly combine the partial results in a distributed manner in order to find the most likely global path. This approach attempts to combine the benefits of distributed and centralized systems, whilst the experimental results demonstrate its high potential regarding performance and scalability improvement.

Combining analog with digital beamforming in the sense of hybrid beamforming is one of the promising solutions to maximize throughput for millimeter wave links. Due to a concern of complexity, codebooks used in the analog beamforming are limited to their size. This paper shows that if the limited codebooks are made up of orthogonal steering vectors, the analog beamforming can be implemented with low complexity by exploiting implicit knowledge of the channel and then the resulting effective channel should be estimated explicitly to determine the optimal weighting coefficients in the digital beamforming. The simulation results show that the proposed low-complexity beamforming method can achieve nearly the same data rates as the one with perfectly known channel state information.

In this paper we present a router that combines the circuit switching and packet switching in order to efficiently and separately handle the guaranteed-service and best-effort traffics. The main innovation consists in proposing a novel connection configuration mechanism, in which the source node first sends the connection request to manager via a pre-reserved request path, and the manager sends back the response message via guaranteed-service path. Hence, the additional dedicated configuration network that is widely used in previous works is avoided, which reduces the hardware cost while still guaranteeing the configuration latency. The synthesis results show our approach is more area and energy efficient. Compared to previous works, our approach can provide up to 260% better power efficiency and 2.4X to 5X better area efficiency. In terms of configuration time, our approach can provide 2.7X to 36X faster configuration speed.

We consider a continuous-time bandlimited additive white Gaussian noise channel with 1-bit output quantization. On such a channel the information is carried by the temporal distances of the zero-crossings of the transmit signal. The set of input signals is constrained by the bandwidth of the channel and an average power constraint. Under a set of assumptions, we derive a lower bound on the capacity by lower-bounding the achievable rate for a given set of waveforms with exponentially distributed zero-crossing distances. We focus on the behaviour in the high signal-to-noise ratio regime and characterize the achievable rate depending on the available bandwidth and the signal-to-noise ratio.

Vehicular communications have a great potential to improve intersection safety and traffic efficiency. Achieving a high application performance is challenging due to the specific propagation conditions caused by buildings and obstacles found at urban intersections. Relying on the state-of-the-art solution for vehicular communication based on IEEE 802.11, we extend contention-based forwarding to distribute data packets via multiple paths and apply joint decoding on erroneous received data packets. We study the gain of cooperative relaying with joint decoding on the performance of collision avoidance applications in an intersection scenario. We could show that with our algorithm the awareness distance and reliability is increased up to 25m and 55 %, respectively, under poor channel conditions.

Since Time Division Multiplexing (TDM) Circuit Switching (CS) has the advantage of fixed low communication latency by transmitting data over pre-established connection, it has been a popular approach to provide guaranteed service. The challenge of the CS is the fast and dynamic connection allocation particularly for networks with high connection request rates. In this paper, a high performance connection allocator for TDM CS is presented, which enables parallel multiple path search in all directions. To enhance the path search speed, the Register-Exchange technique is adopted that saves the entire survivor path sequences during search. Hence, since the backtrack is omitted, the path search time is reduced by half compared to previous forward-backtrack approaches, which can also contribute to the success rate. Our approach is compared to the state of the art centralized and distributed approaches under uniform random traffic as well as real-application benchmarks. The experiment results showed our approach can provide up to 22% higher success rate and 2X greater allocation speed against centralized approaches, and up to 33% higher success rate and 18X higher allocation speed against distributed approach.

Enhancing the connectivity reliability is one of the most challenging requirements for the design of future wireless communications systems. The scope of this paper is to leverage the existing tool set of reliability theory for enabling reliable communication in wireless systems. Definitions, concepts, and methods of reliability theory are applied and extended to wireless communications networks, which are modeled as a repairable system. The steady-state and transient system behaviour are considered. Two new key performance indicators (KPIs) for the reliability analysis of wireless communications systems are introduced, namely mean time to first failure (MTTFF) and interval reliability (IR), and a closed form expression is derived for the MTTFF. By evaluating an exemplary scenario, the trade-off between availability, reliability and throughput is discussed.

Multiprocessor systems-on-chip with distributed memories and task processing are promising architectures to tackle processing demands of edge-cloud applications for autonomous vehicles. We present a novel model which allows estimation of the speedup when memory pooling is combined with prefetching. Processing time and data transfer time are both taken into account. In our scenario, memory pooling enables utilization of remote memories and prefetching hides the additional latency. The model shows speedups of up to 200% for data-intensive processing scenarios. Our approach shows that reasonable performance gains can be achieved when increasing flexibility of the memory architecture.

This paper proposes a novel design for linear precoder in underlay Device-to-Device (D2D) communication using cellular network, specifically for a use case with vehicle-to-vehicle communication and platooning. To increase the communication reliability, we develop an optimization algorithm for precoder that takes into account the outage probability when channel state information (CSI) is only partially available at devices and transmission could be in outage. The algorithm aims at maximizing the sum throughput over each transmission link received at one device, while constraining the interference caused by the transmit power from each device to the cellular network. Due to the intractable form of outage probability, an extended Markov Inequality is used to transform the problem into an upper-bound expression. A two-step alternating algorithm is then adapted to solve the multi- variable optimization. The proposed algorithm is compared with other state-of-the-art technologies in the field of vehicular communication for achieving high throughput. Simulation results show that our proposed algorithm outperforms the current techniques and achieves higher average throughput with extremely low outage probability, thus enables reliable vehicle- to-vehicle communication for platooning in a D2D underlay setting.

Generalized Frequency Division Multiplexing (GFDM) is a flexible non-orthogonal waveform. Due to its flexibility it can be served as a framework to emulate diverse multi-carrier waveforms including orthogonal frequency division multiplexing (OFDM) and single-carrier frequency domain equalization (SC- FDE). Nevertheless, inter-symbol- and inter-carrier- interference may arise in GFDM if the filter roll-off factor is larger than zero. In multiple-input multiple-output (MIMO) scenarios, also inter-antenna- interference further challenges the receiver design. In this paper, we focus on pilot-aided channel estimation for GFDM. In contrast to our prior works, we propose a technique to insert the pilot symbols in a manner such that they are orthogonal to the data symbols in the frequency domain. Based on this design, frequency-domain channel estimation algorithms initially developed for OFDM become straightforwardly applicable. We also examine the impact of such pilot design on the signal properties, including power spectral density (PSD) and peak-to- average-power ratio (PAPR). At the end of the paper, the performance of a MIMO-GFDM system is investigated and compared with the conventional MIMO-OFDM systems.

Cellular network performance is predominantly driven by the spatial distribution of the data traffic demand. We investigate the spatio-temporal correlation between the spatial mobile data traffic and spatially resolved information obtained from Twitter. The data stems from the centers of two European cities and is largely independent of the user device type and the wireless technology. We observe a high temporal and a moderate to high spatial correlation. In order to assess the actual suitability of Twitter data as input for self-organizing networks, we make use of a queuing-theoretic network performance evaluation tool and quantify cell utilizations and average flow sojourn times within an example network. We find that both metrics obtained with the linearly scaled Tweet density strongly correlate with the ones obtained with the actual data traffic density for reasonably chosen inter-site distances. The insights presented can help network operators to plan their cellular networks in regions with little information about spatially resolved traffic demand but high social media activity, and to further develop Big Data- enabled self-organizing networks.

Decentralized congestion control (DCC) in ITS-G5 based vehicular ad hoc networks ensures that the requirements of safety and traffic efficiency applications are met even under high vehicle density and channel load conditions. In European standardization, a "gatekeeper" on top of the ITS-G5 MAC sub-layer is being considered that controls a node's packet rate as a function of the channel load. This paper studies the performance of the gatekeeper with packet prioritization and an adaptive linear control algorithm. The simulation results indicate that the gatekeeper with priority queuing (PQ) can effectively handle different packet priorities for multi-hop packets. Our gatekeeper-specific enhancements of the forwarding algorithm yield performance improvements in terms of reliability and latency compared to the plain DCC approach. Finally, we discuss the issue of packet starvation caused by the gatekeeper's PQ scheme that affects the performance of lower-priority packets.

The support of mission-critical use cases is an ambitious goal of the upcoming fifth generation of mobile networks. In addition to challenging requirements regarding reliability and latency, particular use cases need to be supported in high mobility scenarios as well. To avoid extensive system simulations and enable studies of very small outage probabilities, we integrate mobility effects into an existing signal-to-interference- plus-noise ratio (SINR) model by combining the model with results from a mobility simulation. An evaluation of the model corroborates that high mobility can severely deteriorate the reliability performance in traditional single-connectivity architectures. As potential countermeasures, inter- and intra-frequency multi-connectivity are identified. Results show that high reliability of 99.999% or greater becomes possible if a sufficient number of connections is utilized.

In cloud-based radio access networks, remote radio units and central baseband units are connected by fronthaul links, which are commonly assumed to be error-free. However, especially for wireless millimeter wave fronthaul links, this might be challenging to achieve, as they face a more unreliable environment than the conventionally used fiber links. In this paper, we hence aim to mitigate the impact of imperfect fronthaul links. For this, we propose the concept of joint radio access and fronthaul reception, which considers to recover the transmitted messages correctly at the centralized baseband unit, rather than to ensure a nearly perfect fronthaul transmission in between. Based on the Bayesian minimum mean square error criterion, we develop a joint access and fronthaul estimation scheme that can be utilized for various signals transported over the fronthaul, including in-phase/quadrature phase (I/Q) samples, soft-bits, synchronization, and reference signals. In addition, we develop an approximated variant of the scheme to reduced complexity, and an iterative extension to further improve the performance. We demonstrate that our scheme can operate under less reliable fronthaul than conventional approaches by numerical simulation for different signals, and show that our method can be implemented in a parallel architecture to achieve a reasonable computational complexity.

The so-called chief executive officer problem suggests that the source message can be recovered at the destination by merging a set of corrupted replicas forwarded by multiple relays, as long as these replicas are sufficiently correlated with the original message. In this paper, we build on Slepian-Wolf's correlated source coding theorem to design a simple, yet efficient power allocation scheme for a multirelay system, in which the direct link is unavailable to convey information. In such a system, the replicas forwarded by the relays are allowed to contain intra-link errors due to previous unreliable hops, and the destination is supposed to retrieve the source message by jointly decoding all received replicas. Importantly, the proposed power allocation is asymptotically optimal at high signal-to-noise ratio.

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

This paper studies the link level performance of orthogonal frequency division multiplexing (OFDM) and four other advanced waveforms, namely, filtered OFDM (F-OFDM), universal-filtered OFDM (UF-OFDM), filter bank multicarrier (FBMC) and generalized frequency division multiplexing (GFDM). Compared to OFDM, the two filtered variants achieve lower out-of-band (OOB) emissions and can mostly preserve the conventional OFDM-based transceiver design. For the latter two non-orthogonal waveforms, this paper proposes a low complexity implementation of minimum mean square error equalization to jointly tackle the channel and waveform-induced interference. On this basis, the benefits of FBMC and GFDM can be exploited with complexity comparable to the former (quasi-) orthogonal waveforms. The observed benefits include lower peak-to-average power ratio (PAPR) and smaller frame error rate (FER) under challenging doubly dispersive multiple-input multiple-output (MIMO) fading channels. Additionally, linear filtering of FBMC offers an ultra-low OOB emission, while a good compromise in the usage of time and frequency resources can be achieved by circular filtering of GFDM. In the comparison of offset quadrature amplitude modulation (OQAM) versus QAM for non-orthogonal waveforms, OQAM can offer lower PAPR, while smaller FERs can be achieved by QAM in rich multipath fading channels.

Capacity and ultra-reliable communication are some of the requirements for 5th generation (5G) networks. One of the candidate technologies to satisfy capacity requirement is standalone Ultra Dense Network (UDN). However, UDNs are characterized by fast change of received signal strength that creates mobility challenges in terms of increased handovers and connection failures. In this paper, a low layer multiconnectivity scheme is presented for standalone UDN aiming at ultra-reliable communication that is free of interruptions from handover procedures and connection failures. Furthermore, the problem in managing of the set of serving cells, that are involved in multiconnectivity for each user, is formulated. By using numerical method, feasible scheme for management of the set of serving cells is derived. Performance of the proposed multiconnectivity scheme is evaluated and compared against single connectivity. It is shown that the proposed multiconnectivity scheme outperforms single connectivity considerably in terms of connection failures and cell-edge throughput.

Compressed bitmap indices are heavily used in scientific and commercial database systems because they largely improve query performance for various workloads. Early research focused on finding tailor-made index compression schemes that are amenable for modern processors. Improving performance further typically comes at the expense of a lower compression rate, which is in many applications not acceptable because of memory limitations. Alternatively, tailor-made hardware allows to achieve a performance that can only hardly be reached with software running on general-purpose CPUs. In this paper, we will show how to create a custom instruction set framework for compressed bitmap processing that is generic enough to implement most of the major compressed bitmap indices. For evaluation, we implemented WAH, PLWAH, and COMPAX operations using our framework and compared the resulting implementation to multiple state-of-the-art processors. We show that the custom-made bitmap processor achieves speedups of up to one order of magnitude by also using two orders of magnitude less energy compared to a modern energy-efficient Intel processor. Finally, we discuss how to embed our processor with database-specific instruction sets into database system environments.

In the last decade, the number of available cores increased and heterogeneity grew. In this work, we ask the question whether the design of the current operating systems (OSes) is still appropriate if these trends continue and lead to abundantly available but heterogeneous cores, or whether it forces a fundamental rethinking of how systems are designed. We argue that: 1. hiding heterogeneity behind a common hardware interface unifies, to a large extent, the control and coordination of cores and accelerators in the OS, 2. isolating at the network-on-chip rather than with processor features (like privileged mode, memory management unit, ...), allows running untrusted code on arbitrary cores, and 3. providing OS services via protocols over the network-on-chip, instead of via system calls, makes them accessible to arbitrary types of cores as well.

This paper presents a heterogeneous database hardware accelerator MPSoC manufactured in 28 nm SLP CMOS. The 18 mm2 chip integrates a runtime task scheduling unit for energy-efficient query processing and hierarchical power management supported by an ultra-fast dynamic voltage and frequency scaling. Four processing elements, connected by a star-mesh network-on-chip, are accelerated by an instruction set extension tailored to fundamental data-intensive applications. We evaluate the MPSoC with typical database benchmarks focusing on scans and bitmap operations. When the processing elements operate on data stored in local memories, the chip consumes 250 mW and shows a 96x energy efficiency improvement compared to state-of-the-art platforms.

Technology scaling has allowed the integration of a large number of cores on a single chip, which significantly improves the speed of on-chip processing. Network-on-chip is the interconnection network which provides efficient and flexible communication between cores in such multi-processor systems-on-chip. However, the performance enhancements of technology scaling come at the cost of reliability as on-chip components particularly the network-on-chip become increasingly prone to faults. Redundancy is the basic approach to fault tolerance and in this paper we investigate the hexagonal on-chip network topology with redundant diagonal inter-router links, having approximately 1.5 times the number of links as the mesh topology. To evaluate the fault tolerance of the hexagonal network with wormhole-switched routing, we present deadlock-free fault tolerant routing algorithms obtained by applying the turn model and without the use of costly virtual channels. To circumvent the problem of finding the right selection of turns to prevent deadlock, we propose an approach based on the transitive closure of the channel dependency matrix. The results indicate that the hexagonal NoC with the proposed adaptive routing algorithms significantly improves NoC resilience by being able to tolerate two router faults, while the mesh NoC can tolerate only one router fault. Moreover, the proposed approach is general and can be adopted for developing adaptive routing algorithms for any regular network topology.

New waveform candidates are being investigated for the fifth generation wireless systems. Among the promising candidates, generalized frequency division multiplexing (GFDM) offers the flexibility to address a wide range of requirements (e.g. low latency, coarse synchronization, etc.). Due to the non- orthogonality of GFDM, the transmit signal subjects to inter-symbol and inter-carrier interference. In this paper, the problem of GFDM channel estimation with the aid of reference signals (pilots) is investigated. In GFDM, the receive signal is a combination of pilots, data and the noise part. Hence, when utilizing the conventional estimation techniques, degradation of channel estimation performance due to interference from data symbols further challenges the receiver design for GFDM. We show that if we employ multiple pilots per subcarrier within a single GFDM block, different pilot patterns have significant impact on the resulting interference term and thus, the quality of the channel estimation in GFDM. Such results are then compared with the performance of channel estimation in orthogonal frequency division multiplexing (OFDM) which takes advantage of clear pilot observation.

This paper proposes a centralized approach for connection allocation for TDM-based NoCs by making use of dedicated hardware unit called NoCManager that employs trellis-based search algorithm enabling dynamic parallel multi-path, multi-slot allocation. Be different to the previous unrolled trellis search algorithm, in this paper the folded architecture is employed to achieve efficiency. In comparison with previous TDM connection allocation methods, the proposed design has the following advantages: (1) hardware supported low-latency, high-throughput allocation mechanism, (2) improved success rate due to parallel multi-path search and (3) efficient NoCManager architecture.

Spatially-coupled low-density parity-check (SC-LDPC) codes have been shown to be superior in performance than LDPC block codes. In order to comply with the practical constraints on latency, SC-LDPC codes are decoded using a window decoder that reduces the decoder latency and complexity compared to traditional block-wise decoding. However, so far the literature only considers the structural decoding latency of window decoder, ignoring the processing latency. Note that the processing latency directly impacts the decoder's throughput and is an important parameter in any modern communication system. The throughput of an iterative decoder is directly influenced by the number of iterations and hence in this paper we propose a fully parallel window decoder architecture for SC-LDPC codes where the decoding iterations are performed in parallel. This guarantees a high throughout while fulfilling the low latency requirements. The overall decoding latency (structural and processing latency) of the proposed window decoder architecture is compared with the classical window decoder.

The combination of automated driving and Inter-Vehicle Communication (IVC) allows automated vehicles to drive cooperatively, thereby greatly enhancing their safety and traffic efficiency. Convoys are groups of automated vehicles which keep a multi-lane formation with decentralized control supported by IVC. The vehicle control algorithm of convoy vehicles requires up-to-date information about the neighbor vehicle dynamics; fast and efficient convoy communications enable the cooperative maneuvering of the automated vehicles. For this reason, we evaluate IVC in convoys of automated vehicles by defining performance metrics which quantify the reliability, latency and data age of convoy communications. Our results explore the trade-off between the convoy message frequency and the communication performance; whereas a high message frequency results in a higher number of lost messages and delay due to channel congestion, a low message frequency yields a higher data age of the information available to the vehicle controller. As a result, convoy algorithm designers should choose carefully the optimal value for the convoy message frequency as a function of the required communication performance and the convoy size.

Multi-connectivity is a promising concept for addressing challenging requirements in next generation wireless networks. We put forward a modeling framework for analyzing signal-to-interference-and-noise ratio (SINR) distributions in inter-frequency multi-connectivity scenarios. The most important features are a best server association based on random shadowing, multiple path loss models, and intra-/inter-frequency shadowing cross-correlation. Furthermore, we consider diverse antenna types, such as sectorized antennas and antenna arrays with beamforming, to accurately model the distinct properties of conventional as well as upcoming millimeter wave carrier frequencies. In the analysis, we focus on the lower tail of the SINR distributions in order to explore the availability performance. The modeling results, which are corroborated by simulations, show that certain combinations of carrier frequencies can significantly improve the availability as well as the throughput performance compared to single-frequency usage.

Turbo equalization has demonstrated to be a powerful approach for wireless transmission over frequency-selective channels introducing intersymbol interferences (ISI). Regarding multiple-input multiple-output (MIMO) systems, tree-search based MIMO detection techniques (e.g., sphere detection) are well suited for significantly reducing multi-antenna interferences. However, direct application of these detection techniques under the presence of ISI leads to vast processing complexity, increasing exponentially with the channel length and the number of transmitting antennas. In this paper, a low-complex two-stage approach is described splitting the interference reduction into a frequency-domain equalization stage and a time-domain sphere detection stage. Both are able to take a-priori information into account. Based on this, we derive a novel multi-iterative receiver, enabling powerful and adaptable processing with respect to the dominating interferences.

In this work we show that the analog equalizing networks are suitable for low resolution quantization with limited mutual information loss in strong line-of-sight MIMO communication. More specifically, a simplified analog equalizing network design in comparison with state-of-the-art works is proposed in this work. Additionally, the new network design works equally good for larger displacement ranges. Furthermore, by using analog equalizing networks in line-of-sight MIMO systems, it is shown that the drawbacks of low resolution quantization can be minimized. This increases the energy efficiency of the analog-to-digital converters via minimizing the required magnitude resolution. Generally, low resolution quantization causes low entropy on the receive vectors which will effectively reduce the mutual information of the desired transmission. The analog equalizing network reshapes the distribution and reduces the dynamics of the received signals in the complex constellation plane before quantization. Therefore, the entropy loss after low resolution quantization is reduced and the system performs essentially as good as a system with higher resolutions. Finally, an algorithm is proposed to remove ambiguities which arise after analog-to-digital conversion with a given low resolution.

In this work, we show a new design of analog equalizing network for N-stream strong Line-of-Sight MIMO communication, aiming at improved robustness. The design includes a core fixed equalizing network that equalizes ideal spatially orthogonal channels. Existing works show that the fixed equalizing network can equalize a spatially orthogonal MIMO system with parallel arrays. However, it is observed that such a fixed equalizing network is very sensitive to displacement errors. To make the system robust, state-of-the-art approaches use N2 fully controlled analog elements to perfectly equalize the channel via aligning the structured interferences. In this work, the terms causing the sensitiveness of fixed analog equalizing networks are identified. By compensating the tackled sensitive terms, the proposed design uses only 2N fully controlled analog elements and the robustness of the system is improved significantly. Meanwhile, by exploring the channel property, this work shows that if the spatially orthogonality is achieved by uniform rectangular arrays, the equalization can be applied with a new two-stage scheme. The scheme can be applied to spatially orthogonal MIMO systems with digital and/or analog equalization. Meanwhile, the computational complexity, required components number, as well as the complexity of the hardware design are significantly reduced.

mmWave (millimeter-Wave) is a very promising technology for the future wireless communication. To mitigate its high attenuation characteristics, mmWave communication frequently employs directional beamforming for both transmission and reception. Localization commonly takes advantage of directionality in RF frequencies in urban and indoor environments. In this paper, we use lessons learned from classical RF-based localization for discussing a set of feasible localization approaches in the context of mmWave bands. We further map the requirements of each discussed localization approach to design requirements for future mmWave devices and assess the expected accuracy of such approaches for a set of realistic scenarios. Our results show that mmWave-based localization is promising in both its availability and accuracy, even in the presence of a limited number of localization anchor nodes.

Efficiently estimating spatial channel properties, such as angles of arrival and departure, for a hybrid beamforming (HBF) architecture is one of the crucial challenges to overcome at millimeter wave (mmW) systems. To this end, we propose an algorithm variant to a recently proposed approach [8] based on ideas borrowed from the compressed sensing literature and /0-norm minimization, which exploit the fact that the number of significant channel echoes is rather small for limited beamwidth. Our modified algorithm eliminates high dimensional singular value decomposition (SVD) of the estimated channel matrix in the original method by employing the orthogonality between the selected array propagation vectors. It is demonstrated that HBF design without SVD of the estimated channel matrix can achieve essentially the same capacity as the one with SVD. Moreover, the feedback overhead required for the beamforming systems can be significantly reduced by the proposed method.

5thGeneration (5G) mobile networks are required to support transmission of capacity demanding services such as real-time remote computing without any interruption. One of the candidate solution for high capacity is Ultra Dense Networks (UDNs). However, UDNs are characterized by fast change of the received signal by a user. The fast change of the signal and high speed of users create too many handovers and connection failures such as handover failures and Radio Link Failures (RLFs). Consequently, conventional handovers and connection failures are the major sources of service interruption. To achieve ultra-reliable communication by tackling the service interruptions, this paper proposes a novel multi-connectivity scheme that uses fast selection of serving cell from a set of prepared cells. A similar feature in Long Term Evolution - Advanced (LTE-A) that is defined under Coordinated Multi-Point (CoMP) transmission is Dynamic Point Selection (DPS). However, in DPS a cell is selected dynamically for transmission of only data signals. Transmission of mobility related control signals is performed through one primary cell which is changed through a conventional handover. Unlike DPS, this paper proposes that the selected cell, from the set of prepared cells, is used for transmission of both data and control signals. Simulation results show that the connection failures due to RLFs are considerably resolved by the proposed scheme.

To achieve low delay for vehicular communication in cellular networks, the use of direct device-to-device (D2D) communication among vehicles is regarded as a key functional requirement. When D2D users share the spectrum with regular cellular users (D2D underlay), resource allocation schemes assure the coexistence between D2D and cellular users. Due to the high mobility of the vehicles, the resources need to be reallocated, but frequent updates cause high signaling overhead and degrade the delay performance. In this paper, we present a radio resource re-allocation scheme for vehicular D2D users in a platoon scenario. The scheme reduces the re-allocation rate and gives delay guarantees for each vehicle.With the help of Lyapunov optimization, a closed form of the upper delay bound and resource re-allocation rate is also derived. The simulation results show that the proposed scheme can provide an delay upper bound and simultaneously minimizes the resource re-allocation rate.

The large unlicensed bandwidths in mmW bands promise to satisfy the need for higher throughput in cellular systems. At these frequency bands, the free space path loss have to be mitigated by utilizing directional antennas. However, RF hardware face more difficulties compared to lower frequency bands due to implementation impairments and costs. Thus antenna arrays with one RF chain and limited beam-steering capabilities are commonly used in reality, limiting the degree of freedom for beam-alignment algorithms.Scalable beamwidth in order to use IEEE 802.11ad algorithms is not realizable in some hardware setups. Therefore, a beam searching algorithm with non-scalable beamwidth is presented. To the best of the author's knowledge, existing algorithm rarely consider these restrictions except the well-known exhaustive beam searching. This paper proposes a novel approach, which can reduce the amount of required iterations for the beam searching procedure significantly compared to exhaustive search.

This paper presents a routing algorithm for wireless multi-hop ad hoc networks, which applies distributed source coding (DSC) and multi-path transport of data packets. DSC enables the relay nodes to efficiently encode the packets and helps the destination node to correctly decode the information from multiple erroneous copies of the same packet received via different paths. While in conventional communication systems packets with bit errors are discarded, in our design the relays are allowed to forward erroneous packets. In order to exploit DSC for ad hoc routing, we extend contention-based geographical forwarding (CBGF) to support multi-path packet transport and enable the joint decoder to recover transmission errors. Under harsh wireless conditions, the approach promises performance gains compared to the case without joint decoding. In order to evaluate the performance of CBGF, we define an evaluation framework that models the erroneous forwarding and joint decoding by an abstraction of the physical transmission. We assess the performance by simulations for a simple scenario with static nodes in terms of packet success ratio, end-to-end delay, and data traffic overhead, and compare the results with the baseline CBGF scheme without DSC-specific extensions and joint decoding.

Upcoming fifth generation (5G) cellular networks will demand more from the physical layer (PHY) than current- generation Orthogonal Frequency Division Multiplexing (OFDM) can deliver. The 5G waveform candidate Universal Filtered OFDM (UF-OFDM) is designed to provide the flexibility required for future applications. However, the introduction of subband filters in UFMC can increase implementation complexity and low-complexity solutions need to be found. State-of-the-art technologies provide an algorithm that performs shorter-length FFTs that can reduce complexity to two to ten times that of OFDM (depending on the allocation sizes), at the cost of only approximating the exact UFMC signal. In this paper we propose a new approximation of the UFMC signal which bases on the similarity of adjacent subcarriers that can be implemented with reduced number of operations. Analysis show that the system can be implemented with only 20% more operations than standard OFDM when accepting some increase in the subband bandwidth. A more accurate solution can be implemented at roughly 3.6 times OFDM complexity. The results can reduce implementation costs for future mobile devices.

High reliability and low latency are key requirements for a number of upcoming use cases and applications in mobile communication networks. In order to address the requirements, we present statistical models which capture the most important radio propagation phenomena, such as path loss, shadowing, and small scale fading. The models enable the evaluation of trade-offs between reliability, achievable rate, latency, and bandwidth. Additionally, extensions for joint channel coding across multiple frequency layers are presented. Numerical evaluations reveal that the antenna configuration has a significant impact on the reliability performance. Further, considerable performance gains of joint coding across multiple frequency layers are observed.

Powered by next generation mobile networking capabilities, the Tactile Internet will be able to transport touch and actuation in real-time. Enabled by suitable robotics and haptics equipment at the edges, and an unprecedented communications network, the Tactile Internet will provide a true paradigm in creating skill-set delivery networks. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. Specifically, the paper presents some of the most important Tactile Internet applications, outlines the key technical requirements, and covers end-to-end architectural aspects of the Tactile Internet.

Nowadays telecommunication systems consume a huge amount of energy. While energy efficiency methods have already been studied for linear antennas, this paper focuses on 2D antenna arrays. An antenna array with many elements gives the network operator a high degree of flexibility in network optimization, which can be used not only for improving the quality of service, but also for increasing the energy efficiency. The energy efficiency can be improved by adapting the array shape, i.e., by switching off certain elements of the array. This work presents three novel solutions for the energy efficiency optimization, which are based on Q-learning, biological neural networks, and random drop, respectively. Simulation results demonstrate that significant improvements in terms of energy efficiency can be achieved, while the 5thpercentile of user throughput and coverage performances decrease only marginally.

Improved throughput and reliable communication that is free of mobility failures are some of the requirements for 5thGeneration (5G) mobile networks. One of the cost-effective solutions to meet capacity requirement is standalone ultra dense network that use the same spectrum and a cloud radio access. However, stand alone ultra dense networks are prone to mobility challenges. Previous work proposed and evaluated multi-connectivity that co-ordinates transmission on both the user plane and control plane; this enables improvement not only in throughput but also in mobility robustness. One of the major component of multi-connectivity is managing the “active set” which is the set of co-ordinating cells. Proper procedures for managing user-specific active set was defined for 3G Soft Handover. This paper revisits prior art schemes for active set management and proposes new adaptive one for 5G Networks. Performance evaluation is provided with the help of elaborated models for simulation. Among other things, it is shown that the adaptive active set management scheme reduces the signaling overhead associated to active set updates by around 19% compared to static active set management.

Beamforming is one of the most challenging problems for millimeter wave communication. With limited codebook size, how to design the steering angles to compensate angles of arrival and departure (AoAs/AoDs) is essential to beamforming performance. Typically, two categories of steering vector sets are commonly used. One is orthogonal steering vector set where the spatial frequency indices of the steering angles are uniformly distributed in spatial frequency domain. The other one is non-orthogonal steering vector set where the steering angles are uniformly distributed in angle domain. In this paper, analyses of these two designs are presented. Due to the fact that beamwidth are constant with respect to different spatial frequency indices in spatial frequency domain, if the spatial frequency indices are uniformly distributed, one has the smallest deviation of the beamforming gain. Since the orthogonal steering vectors satisfy this condition that spatial frequency indices are uniformly distributed, they can achieve higher data rates than the non-orthogonal ones when the AoAs are uniformly distributed over (-π/2, π/2).

Vehicular ad hoc networks based on IEEE 802.11 OCB suffer from channel congestion, which will be critical for safety applications when the channel becomes saturated. Therefore, decentralized congestion control (DCC) is needed that keeps the channel load under a pre-defined threshold. In the European system for vehicular communication, a DCC-gatekeeper has been inserted into the protocol stack that reduces the data traffic generated or forwarded by the vehicle. This DCC-gatekeeper affects the functionality of the contention-based forwarding (CBF) and degrades its performance. In this paper we evaluate the performance of CBF under congestion-free and saturated channel conditions for several protocol variants: DCF, EDCA and DCC-gatekeeper. The results show that the standard DCC-gatekeeper hampers the functionality of CBF even for small node densities. We enhance the DCC-gatekeeper-based approach to overcome this issue and evaluate the protocol variants in terms of reliability, latency and channel load in a freeway scenario with realistic trace-based mobility.

In order to cope with the wireless traffic demand explosion within the next decade, operators are underlying their macrocellular networks with low power base stations in a more dense manner. Such networks are typically referred to as heterogeneous or ultra-dense small cell networks, and their deployment entails a number of challenges in terms of backhauling, capacity provision, and dynamics in spatio-temporally fluctuating traffic load. Self-organizing network (SON) solutions have been defined to overcome these challenges. Since self-organization occurs in a plethora of biological systems, we identify the design principles of immune system self-regulation and draw analogies with respect to ultra-dense small cell networks. In particular, we develop a mathematical model of an artificial immune system (AIS) that autonomously activates or deactivates small cells in response to the local traffic demand. The main goal of the proposed AIS-based SON approach is the enhancement of energy efficiency and improvement of cell-edge throughput. As a proof of principle, system level simulations are carried out in which the bio-inspired algorithm is evaluated for various parameter settings, such as the speed of small cell activation and the delay of deactivation. Analysis using spatio-temporally varying traffic exhibiting uncertainty through geo-location demonstrates the robustness of the AIS-based SON approach proposed.

The long-term ambition of the Tactile Internet is to enable a democratization of skill, and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time, which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts, which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. The paper outlines the key technical requirements and architectural approaches for the Tactile Internet, pertaining to wireless access protocols, radio resource management aspects, next generation core networking capabilities, edge-cloud, and edge-AI capabilities. The paper also highlights the economic impact of the Tactile Internet as well as a major shift in business models for the traditional telecommunications ecosystem.

The coded modulation (CM) capacity represents the maximum achievable data rate for a CM scheme assuming optimal decoding at the receiver. It is an important analytical tool, providing theoretic limits for near-optimum transceiver design. Next generation wireless communications systems with the use of new technologies, such as massive antennas and nonorthogonal waveforms, tend to be large scale. However, the conventional ways developed for evaluating the CM capacity of small-scale systems expose exponential complexity with respect to the system's input dimension. Therefore, they become infeasible when the input dimension increases by one or two orders of magnitude in large-scale systems of interest. This letter resorts to a lower and upper bound of the CM capacity, allowing for a computationally efficient evaluation with polynomial complexity. In particular, two message passing algorithms, namely expectation propagation (EP) and variational message passing (VMP), are applied to evaluate the bounds. Two applications are examined in the end. The presented results bring valuable information about the system design, allowing one to evaluate the impact of suboptimal implementation in the overall system performance.

The application of the turbo principle allows to exploit the full potential of multiple-input multiple-output (MIMO) communications at the cost of increasing the computational effort at the receiver. In the context of soft-input soft-output tree search detection, the computation of metric values and the optimal node order represents two of the most computationally demanding operations. Heuristic approaches may be applied to reduce the complexity, but their accuracy is compromised by the effect that the input soft information has on the node ordering. The design of adaptive, good-performing, and cost-effective tree search detectors for iterative receivers represents, hence, a challenging task. To alleviate these complexity and performance loss drawbacks, an efficient MIMO sphere detector realization is proposed in this paper. A novel smart-sorting enumeration approach offers a significant gain in terms of throughput (from 40% up to a factor 5) and energy efficiency (up to 80% energy saving in the low SNR regime) with regard to preceding implementations. Owing to the additional low delay and area cost reported, the proposed design represents a very promising candidate toward a fast, accurate, and efficient MIMO detector.

In regard to the continuing network densification as a part of the solution to the mobile data traffic demand explosion, managing future 5G ultra-dense networks is becoming increasingly complex. Moreover, the problem of (partly) limited capacity in time and space requires the joint treatment of spatio-temporal data traffic and intercell interference dynamics. Concerning this matter, we propose a performance evaluation framework, which is capable of estimating various cell-specific and user-specific key performance metrics considering the complex spatio-temporal interaction of traffic and interference dynamics. We provide methods for obtaining these metrics with low complexity, making the framework attractive to self-organizing network solutions for future (ultra)dense networks. We stress the framework's broad applicability and demonstrate the effects of internal flow and external interference dynamics on network performance under various conditions. In particular, we highlight the dominance of these dynamics over the impact of the speed of the variation of intercell interference, the scheduler, the file size distribution, and fast fading.

Generalized frequency division multiplexing (GFDM) as a nonorthogonal waveform aims at diverse applications in future mobile networks. To evaluate its performance, its capacity limits are of particular importance. Therefore, this paper analyzes its constellation-constrained capacities for cases where the channel state information (CSI) is unknown at the transmitter and perfectly known at the receiver. In frequency selective channels, GFDM may provide advantage over the conventional orthogonal frequency division multiplexing (OFDM) scheme. In order to achieve near-capacity performance, the interaction of data symbols in time and frequency combined with multiple antennas (MIMO) challenges the design of GFDM receivers. This paper, therefore, applies expectation propagation (EP) for systematic receiver design. It is shown that the resulting iterative MIMO-GFDM receiver with affordable complexity can approach optimum decoding performance and outperform MIMO-OFDM in a rich multipath environment. Simulations are also used to illustrate the impact of channel delay spread on the constellation-constrained capacities and on the performance of the novel receiver algorithm.

Cyber-physical systems (CPSs), due to their direct influence on the physical world, have to meet extended security and dependability requirements. This is particularly true for CPS that operate in close proximity to humans or that control resources that, when tampered with, put all our lives at stake. In this paper, we review the challenges and some early solutions that arise at the architectural and operating-system level when we require cyber-physical systems and CPS infrastructure to withstand advanced and persistent threats. We found that although some of the challenges we identified are already matched by rudimentary solutions, further research is required to ensure sustainable and dependable operation of physically exposed CPS infrastructure and, more importantly, to guarantee graceful degradation in case of malfunction or attack.

Coordinated multi-point (CoMP) is a well-known approach to mitigate interference in cellular systems and, thus, to improve spectral efficiency. On the downside, implementing CoMP comes at a high cost, e.g., regarding backhaul and signal processing. These costs can be reduced when only BSs at the same site cooperate, referred to as intra-site CoMP. However, the capacity gains of intra-site CoMP are also lower, because intra-site clusters cover only a subset of cell edges. In conventional cellular networks, the deployment of 3 non-overlapping sectors is known to be a good compromise between spatial multiplexing and interference. Using intra-site CoMP, earlier simulation-based work has shown that a higher number of sectors that are largely overlapping are beneficial. In this work, we evaluate multi-cell propagation in an urban testbed, where one site is deployed either with 3 non-overlapping or with 6 overlapping sectors. Moreover, we compare measurements from field trials with simulation results for reference.

Analytical models enable accurate and quick assessment of performance metrics in network planning and system design. In wireless networks, the signal-to-interference-and-noise ratio (SINR) is of key importance since other metrics, such as throughput and capacity, strongly depend on the SINR. In this work, we characterize the SINR by a composition of log-normal random variables describing shadow fading and propose a comprehensive framework for modeling SINR distributions at specific user locations. In contrast to existing works, we include shadowing cross-correlation, noise power, and the best server policy in a single framework. Especially, the best server policy, which captures the influence of shadowing on the selection of the serving base station, is frequently neglected in analytical models. Moreover, we put forward SINR models for non-coherent joint transmission in dynamic multi-point networks. Finally, numerical evaluations show the applicability of the models but also reveal the limits of them.

In this work, a two-level hierarchical MIMO system is proposed to combine the spatial multiplexing gain and beamforming gain in a strong LOS channel. The superior is a MIMO system that consists of specially arranged sub-arrays to fully exploit the spatial multiplexing gain in deterministic channels. Additionally, a deterministic spherical-wave channel model is introduced. This channel model includes the radiation patterns of the sub-arrays, orthogonal phase relations introduced by the specific sub-array arrangement and the path loss considering deployment in practical scenarios. The attenuation includes the free space path loss, the oxygen absorption, the rain attenuation in bad weather and the front-end loss. The regulations for the maximum radiated power and the available bandwidth at 60 GHz were also investigated. Furthermore, the maximum transmission rate and upper bound of the energy efficiency are modeled and calculated for the proposed system operating at 60 GHz compliant to those regulations, as well for a constraint of the maximum available transmit power on-board. The result shows that the proposed system architecture is promising to achieve over 100 Gbps for macro-cell backhaul links with reasonable antenna sizes and high energy efficiency.

For next-generation wireless networks, massive multiple-input multiple-output (MIMO) promises significant performance gains compared to today's wireless communication standards. In this paper, we address the challenge of how to synchronize the carrier signals of all antenna units in a large network. We show that mutually coupled digital phase-locked loops (DPLLs) can enable in-phase synchronous clocking in largescale systems with transmission delay. Using a phase model of coupled DPLLs including signal filtering and signal transmission delays, we show how the collective frequency and the time scales of synchronization depend on system specifications. To test our theoretical predictions, we designed and carried out experiments, thereby providing a proof-of-principle that mutually delay-coupled DPLLs can provide self-organized synchronous clocking.

Enhanced Inter-Cell Interference Co-ordination (eICIC) enables expanding the range of small cells (and thereby better Macro cell offloading) by protecting users in the expanded area through blanking subframes in the Macro cells. Range expansion is achieved by modifying the handover parameters. However, those handover parameters are already modified by the existing feature Mobility Robustness Optimization (MRO). The interaction between MRO and eICIC is particularly challenging since real deployments face a mixture of Rel. 10/11 users (which are able to perfectly exploit eICIC) and legacy Rel. 8/9 users. Furthermore, the details of the eICIC impact on mobility (failures) is not very well explored, in particular due to the lack of simulation tools capable of investigating both eICIC and MRO at the same time. In Release 11, there was a proposal for a new feature which enables proper interaction between MRO and eICIC in the presence of both Rel. 8/9 and Rel. 10/11 users. The feature allows setting up separate MRO statistics for Rel. 8/9 users and for Rel. 10/11 users. The paper presents comparison of this new feature with simpler workarounds. The results were created with a system level simulator which has elaborated mobility and eICIC model. The main conclusions are that mobility problems also exist for Rel. 10/11 users, and simple workaround solutions cannot solve the interaction problem.

Due to technology scaling, the number of processor cores on a chip constantly increases. Already today, we reach the domain of so called many-core systems-on-chip. However, this advance comes at the cost of reliability, which especially affects the communication performance of the underlying network-on-chip. Today's resiliency concepts for network-on-chip like automatic repeat request with retransmission are not feasible anymore and lead to long latencies and high network load. In this paper, we propose an on-chip transmission concept based on random linear network coding to provide high resiliency and an efficient communication in many-core processors at the same time. The concept offers a flexible and efficient computable coding scheme, which is well suited for on-chip communication and allows to exploit the path diversity of large networks. First, we use a flit-level cycle-accurate simulation model to investigate the performance potential of the proposed transmission scheme on a network-on-chip with 64 cores. Second, we propose an analytic model for random linear network coding in network-on-chip with retransmission, which is able to provide a very accurate performance estimation close to the cycle-accurate simulation. Finally, we apply the analytic model to investigate the performance potential on the large-scale, assuming a processor with 1024 cores.

Spatially-coupled regular LDPC code ensembles have outstanding performance with belief propagation decoding and can perform arbitrarily close to the Shannon limit without requiring irregular graph structures. In this paper, we are concerned with the performance and complexity of spatially-coupled ensembles with a rate-compatibility constraint. Spatially-coupled regular ensembles that support rate-compatibility through extension have been proposed before and show very good performance if the node degrees and the coupling width are chosen appropriately. But due to the strict constraint of maintaining a regular degree, there exist certain unfavorable rates that exhibit bad performance and high decoding complexity. We introduce an altered LDPC ensemble construction that changes the evolution of degrees over subsequent incremental redundancy steps in such a way, that the degrees can be kept low to achieve outstanding performance close to Shannon limit for all rates. These ensembles always outperform their regular counterparts at small coupling width.

Cooperation and information exchange will allow autonomous vehicles to increase their sensing range and maneuver coordinately, thereby greatly enhancing their safety and efficiency. The combination of autonomous driving and vehicular communications will enable Cooperative Autonomous Driving Systems (C-ADS) with stringent requirements. We present two new multi-hop forwarding algorithms specially suited to the dissemination of information in C-ADS: (i) Greedy Broadcast Forwarding, aimed to the distribution of packets within a geographical area, and (ii) Greedy Multicast Forwarding, which delivers packets only to the members of a vehicle group. A performance comparison of the proposed schemes with the broadcast forwarding algorithms defined in the ETSI GeoNetworking standard shows that Greedy Broadcast Forwarding achieves the highest reliability, while Greedy Multicast Forwarding yields the lowest traffic overhead in the considered scenario. These results indicate that the proposed forwarding algorithms are promising candidates to enable multi-hop communications in future C-ADS.

Revolutionary use cases for 5G, e.g., autonomous traffic or industrial automation, confront wireless network engineering with unprecedented challenges in terms of throughput, latency, and resilience. Especially, high resilience requires solutions that offer outage probabilities around 10-6 or less, which is close to carrier-grade qualities but far below what is currently possible in 3G and 4G networks. In this context, multi-connectivity is understood as a promising architecture for achieving such high resilience in 5G. In this paper, we analyze an elementary multi-connectivity solution, which utilizes macro-as well as microdiversity, and evaluate trade-offs between power consumption, link usage, and outage probability. To elaborate, we consider exponential path loss, log-normal shadowing, shadowing cross-correlation, and Nakagami-m small scale fading, and derive analytical models for the outage probability. An evaluation of the multi-connectivity system in a hexagonal cellular deployment reveals that optimal operating points with respect to the number of links and resources exist. Moreover, typical 5G aspects, e.g., frequent line of sight in dense networks and multiple antenna branches, are shown to have a beneficial impact (fewer links needed, more power saved) on ideal operating points and overall utility of multi-connectivity.

Cooperative multi-cell MIMO techniques are well known for their outstanding capability to mitigate inter-cell interference by allowing user data to be jointly processed by several interfering base stations for the improvement of the system performance. This paper proposes an iterative algorithm that designs the precoding matrix for cooperative multi-cell MIMO downlink communications. As the demand for higher throughput and higher system resilience is envisioned for future wireless communication systems, the proposed algorithm takes the potential outage into consideration when channel state information is only partially available at the transmitter side. The aim is to maximize the sum user throughput considering outage with subject to power limitation at each base station. The joint optimization of the precoding matrix and the assigned transmission rate is solved via a 2-step alternating algorithm. The performance of the proposed method is evaluated by Monte Carlo simulations and compared with existing methods. Simulation results show that our proposed algorithm achieves performance gain than other referenced methods over the entire compared SNR region. Performance of all methods are also compared and analyzed when inter-cluster interference is present.

Within the framework of Monte Carlo simulation, this paper derives a Markov chain Monte Carlo (MCMC) algorithm for efficient detection in multiple-input multiple-output (MIMO) systems using the non-orthogonal multi-carrier waveform termed generalized frequency division multiplexing (GFDM). The proposed MCMC algorithm performs the detection task in frequency domain. Its adopted proposal distribution and Gibbs sampler are tailored under the consideration of complexity and latency for tackling the three-dimensional interference involved in the received signal, i.e., inter-carrier, inter-symbol and inter-antenna interference. By means of simulation, its decoding performance is compared with that achieved by employing the sphere decoding algorithm in a conventional orthogonal frequency division multiplexing (OFDM) based MIMO system. For multi-path fading channels with strong frequency selectivity, the MCMC algorithm proposed for the MIMO-GFDM system can deliver superior performance.

Innovative 5G applications will challenge future cellular systems with new requirements. The OFDM based 4G standard will not be able to address all of them. Generalized frequency division multiplexing is a flexible multicarrier waveform with additional degrees of freedom. This paper presents a strategy towards a flexible FPGA implementation of GFDM, which is reconfigurable at run-time.

This paper explores using the first subsymbol in the structure of a GFDM symbol as a pseudo circular preamble. As data and training sequence overlap in the GFDM structure, an initial estimation approach for isolating the preamble information from the data is presented. The concept allows for adaptation of state-of-the-art techniques developed for OFDM in order to estimate time offset at every GFDM symbol. The paper studies the proposal in the context of vehicular communication scenarios and assesses the performance of single-shot estimation of time offset. Both, line-of-sight and non-line-of-sight scenarios with doubly-dispersive wireless channels are considered and compared with the CP-based method from the IEEE 802.11p standard.

The enormous demand for mobile video streaming in the future necessitates an accurate estimation of video quality of experience. In this paper, we give insights on how buffered video streaming performance metrics can be modeled in a multi-cellular context by a joint treatment of data flow dynamics seen by a streaming flow in its own cell and dynamic inter-cell interference dynamics seen in neighboring cells. In particular, we show that dynamic inter-cell interference strongly affects video streaming startup delay distributions, not only for users at the cell edge, but also in the cell center. The model provided can help designing and optimizing cellular networks in the future.

The GeoNetworking protocol provides single-hop and multi-hop communication in vehicular ad hoc networks based on IEEE 802.11p/ITS-G5. It has been standardized by the ETSI Technical Committee ITS as part of its Release 1 set of specifications and is expected to be deployed in the next years. This paper presents a performance evaluation of the GeoNetworking protocol in its recently published version. Our study assesses the performance of the broadcast forwarding algorithms for multi-hop packet transport that are used to disseminate information in geographical areas for road safety and traffic efficiency applications. From the algorithms specification in the standard, we derive six variants with different combinations of protocol mechanisms with increasing complexity and assess their performance in terms of reliability, latency, and overhead. The algorithms are evaluated in a reference freeway scenario with bidirectional road traffic and a realistic trace-based mobility model with varying vehicle density. The obtained results indicate that the combination of contention-based and greedy forwarding shows the best overall performance; further functional improvements have a limited performance gain in the studied scenario.

For upcoming 5G networks, new challenges are posed on the physical layer, which go beyond increased data rate. Generalized Frequency Division Multiplexing (GFDM) is proposed as a candidate waveform to combat these challenges. However, inherent self-interference between subcarriers of GFDM hinders the application of standard spatial multiplexing (SM) detection algorithms. We present an algorithm that combines maximum likelihood and successive interference cancellation detection techniques that allows to exploit the inherent frequency diversity of GFDM coming from self-interference. Computer simulations reveal that the proposal outperforms OFDM in terms of symbol error rate in fading multipath channels. These findings prove self- interference to be beneficial and that SM can be successfully applied to GFDM.

A low-complexity algorithm for calculation of LMMSE filter coefficients for Generalized Frequency Division Multiplexing (GFDM) in a fading multipath environment is derived. The simplification is based on the block circularity of the involved matrices. The proposal reduces complexity from cubic to squared order. The proposed approach can be generalized to other waveforms with circular pulse shaping.

High resilience is expected to be a key component of next generation wireless networks enabling new services and applications in, e.g., vehicular communication, smart grids, and industrial automation. In this work, we analyze diversity concepts with a focus on the joint availability of power-controlled Nakagami-m fading links. For various fading environments, we investigate whether an optimal number of combined links in terms of total power consumption can be identified. Results show that, indeed, optimal operating points exist and huge power savings are possible when multiple lower power links instead of a single powerful link are used. The savings decrease with increasing fading parameter and decreasing outage probability. Furthermore, we present an optimization method based on min-plus convolution for determining the optimal power allocation among several selection combined Nakagami-m fading links with unequal fading parameters.

Resilience is a major cornerstone in the development of next generation wireless networks. Important aspects of resilience are the availability and survivability of wireless connections. In order to characterize these properties, we present elementary models for Rayleigh-fading links. An important part of the survivability model is an approximation of the fade duration distribution by an exponential distribution. We utilize this approximation to derive the minimum duration outage of multiple selection combined links, a performance metric for survivability which also captures the channel correlation of time-varying channels. Since the findings are given in closed form, they are much simpler than existing expressions containing infinite series and Bessel functions. Finally, numerical evaluation confirms the accuracy of the approximation and the expressions derived.

Mobile networks of the future are predicted to be much denser than today's networks to cater to increasing user demands. In this context, cloud-based radio access networks have garnered significant interest as a cost-effective solution to the problem of coping with denser networks and providing higher data rates. However, to the best of the authors' knowledge, a quantitative analysis of the cost of such networks is yet to be undertaken. This paper develops a theoretic framework that enables computation of the deployment cost of a network (modeled using various spatial point processes) to answer the question posed by the paper's title. Then, the framework obtained is used along with a complexity model, which enables computing the information processing costs of a network, to compare the deployment cost of a cloud-based network against that of a traditional LTE network, and to analyze why they are more economical. Using this framework and an exemplary budget, this paper shows that cloud-based radio access networks require approximately 10% to 15% less capital expenditure per square kilometer than traditional LTE networks. It also demonstrates that the cost savings depend largely on the costs of base stations and the mix of backhaul technologies used to connect base stations with data centers.

This paper presents a solution for achieving transmit diversity with generalized frequency division multiplexing (GFDM). Compared to previous works, the proposed solution significantly improves symbol error rate (SER) performance and latency, where both aspects are crucial for future 5G cellular networks. It is shown that widely linear estimation at the receiver side can jointly equalize and demodulate the space-time encoded GFDM signal. Moreover, maximum ratio combining can further increase the SER performance with multiple receive antennas. SER performance is evaluated in Rayleigh fading multipath channels.

In wireless fading channels, multi-user scheduling has the potential to boost the spectral efficiency by exploiting diversity gains. In this regard, proportional fair (PF) scheduling provides a solution for increasing the users' quality of experience by finding a balance between system throughput maximization and user fairness. For this purpose, precise instantaneous channel state information (CSI) needs to be available at the transmitter side to perform rate adaptation and scheduling. However, in practical setups, CSI is impaired by, e.g., channel estimation errors, quantization and feedback delays. Especially in centralized cloud based communication systems, where main parts of the lower layer processing is shifted to a central entity, high backhaul latency can cause substantial CSI imperfections, resulting in significant performance degradations. In this work, robust rate adaptation as well as robust PF scheduling are presented, which account for CSI impairments. The proposed rate adaptation solution guarantees a fixed target outage probability, which is of interest for delay critical and data intensive applications, such as, video conference systems. In addition to CSI imperfections, the proposed scheduler is able to account for delayed decoding acknowledgements from the receiver.

The architecture of cellular systems is currently revolutionized in order to remove the capacity limitation that is enforced by intercell interference. Instead of considering signals that propagate across cell boundaries as intercell interference, novel coordinated multipoint (CoMP) techniques exploit multicell propagation through joint signal processing of multiple base stations (BSs). Extensive research on these techniques should guarantee that ultimately the most effective forms of CoMP are standardized and implemented and that operators can evaluate the costs and benefits of introducing CoMP into their networks before deployment. CoMP algorithms are typically evaluated using system simulations. However, an open challenge is to see if life measurements in a field trial can demonstrate the performance characteristic. In particular as, in the past, system simulations failed to meet their purpose of accurate performance assessment. Thus, in order to promote innovations, performance needs to be assessed under real-world conditions, and simulation studies have to be accompanied by field trials that prove the maturity of a concept and provide reference data. This paper compares multicell propagation in field trials to that obtained through simulation and thus shows the accuracy of the approach taken for system-level simulations. It provides a basis for enhancements of joint detection algorithms and cellular system design in general.

Future mobile networks are visualized as networks that consist of more than one type of base station to cope with rising user demands. Such networks are referred to as heterogeneous networks. There have been various attempts at modeling and optimization of such networks using spatial point processes, some of which are alluded to (later) in this paper. We model a heterogeneous network consisting of two types of base stations by using a particular Poisson cluster process model. The main contributions are two-fold. First, a complete description of the interference in heterogeneous networks is derived in the form of its Laplace functional. Second, using an asymptotic convergence result which was shown in our previous work, we derive the expressions for the mean and variance of the distribution to which the interference converges. The utility of this framework is discussed for both the contributions.

The uncertainty of the sampling time is of major concern for bandpass signal reception. It reduces the achievable signal-to-noise ratio of the bandpass sampling receiver. Traditionally, only the absolute sampling time is considered to be subject to timing errors, which then result in corresponding amplitude errors. But jitter also has an impact on the integration duration of the sample-and-hold circuit. In this paper we investigate the impact of jitter on the signal-to-noise ratio performance of the bandpass sampling receiver utilizing a sample-and-hold sampling circuit. In addition, a bound on the acceptable standard deviation of the jitter for different receiver setups is given.

Nonlinear distortions in analog frontends are becoming a growing problem which is not limited to power amplifiers. Modern modulation methods such as OFDM and next generation standards have high linearity requirements on all components in the signal path. A radio system that can tolerate a certain degree of nonlinear distortion without substantial loss of performance could enable high cost savings in development and production. In this paper we present a novel iterative blind estimator for nonlinear distortions. It complements existing mitigation algorithms by providing them with accurate estimates of the nonlinearity characteristic. It is shown that there is a negligible performance gap between perfect and estimated knowledge. The method is designed to be computationally inexpensive and can be readily implemented on today's digital signal processing systems.

Typically, wireless network performance decreases in high traffic regimes, e. g., at traffic hot spots and peak hours, since a large number of active users have to share limited radio resources. From a network perspective, even congestion may occur in some cells, which may lead to drastic deterioration of user throughputs. As a consequence, network operators employ admission control in their base stations in order to prevent congestion and enhance quality of experience. In this paper, we develop an effective network planning and optimization tool set, which considers the dynamic behavior of mobile traffic. The tool set allows for a more accurate prediction of data request blocking probabilities and data throughputs under admission control, since it explicitly considers the inter-cell interference coupled nature of frequency reuse one networks. This enables more reliable planning and (self-) optimization of wireless networks. We prove utility by applying the tool set to a traffic-adaptive admission control scheme and compare the resulting network performance with that of static admission control schemes under demands of high mobile data traffic. We find that the adaptive algorithm is able to exploit the trade-offs between blocking of requests, reduced interference, and guaranteed resources for individual data transmissions in each of the cells. Under high traffic conditions, it yields better performance compared to any other static scheme investigated.

Spatially coupled regular LDPC code ensembles have outstanding performance with belief propagation decoding and can perform close to the Shannon limit. In this paper we investigate the suitability of coupled regular LDPC code ensembles with respect to rate-flexibility. Regular ensembles with good performance and low complexity exist for a variety of specific code rates. On the other hand it can be observed that outside this set of favorable rational rates the complexity and performance penalty become unreasonably high. We therefore propose ensembles with slight irregularity that allow us to smoothly cover the complete range of rational rates. Our simple construction allows a performance with negligible gap to the Shannon limit while maintaining complexity as low as for the best regular code ensembles. At the same time the construction guarantees that asymptotically the minimum distance grows linearly with the length of the coupled blocks.

A flexible and effectual back-haul infrastructure is required for managing extremely dense future mobile networks proficiently, and economically. This paper aims to establish a framework which can estimate the cost of a network, and then use the framework to minimize the deployment cost of the network by optimizing the number of back-haul nodes required to help the base stations satisfy user demands. The two main contributions of this work are: the derivation of a framework that gives the cost of deploying a back-haul node in a 3-layer model which consists of users, base stations, and back-haul nodes; and the generalization of the framework to obtain the cost of deploying a node in a particular layer of interest in a k-layer model. The utility of this framework is illustrated for the 3-layer model by showing that there exist a range of back-haul node intensity values for which deployment costs can be minimized while satisfying users' rate requirements.

Spatially coupled low-density parity-check (SC-LDPC) codes are considered for transmission over the block-fading channel. The diversity order of the SC-LDPC codes is studied using density evolution and simulation results. We demonstrate that the diversity order of the code can be increased, without lowering the code rate, by simply increasing the coupling parameter (memory) of a SC-LDPC code. For a (3,6)-regular SC-LDPC code with rate R = 1=2 and memory mcc= 4 a remarkable diversity of d = 10 is achieved without the need for any specific code structure. The memory of the SC-LDPC codes makes them robust against a non-stationary mobile-radio environment. The decoding of SC-LDPC codes using a latency constrained sliding window decoder is also considered.

Analytical modeling of Network-on-Chip mainly focuses on steady-state conditions although traffic patterns and the behavior of applications are frequently non-stationary. Hence, in such scenarios, it is likely that a system seldom reaches stationarity, and steady-state models are inapplicable. In this work, we propose queuing-theoretic models for the transient analysis of output contention in Network-on-Chip. Contention occurs when multiple input queues intend to forward to the same output, and it is one of the main reasons for increased latencies and blocking probabilities in input-buffered routers. In Network-on-Chip, the end-to-end latency of a single packet can be determined by adding the latencies along its path. Therefore, understanding interactions within a single router are beneficial for system and parameter design of Network-on-Chip. Furthermore, we validate the models proposed by numerical evaluations which confirm the accuracy and practicality of the queuing models.

Proportional fair (PF) scheduling is known to provide a balance between overall throughput maximization and fairness, by ensuring that users are served at one point in time, even if they experience poor channel conditions. In this regard, the PF scheduler accounts for the current channel state as well as for the throughput a user previously obtained. However, in slow fading scenarios with feedback delays the channel state information (CSI) known to the transmitter is outdated. Consequently, the transmission rates supported by the actual channel are only known imperfectly, which affects the scheduling as well as the rate adaptation, resulting in potential outages. While current schedulers typically ignore the CSI impairment, this work proposes a scheme which accounts for the variance of the CSI impairment and targets to achieve a fixed outage probability, referring to average delay constraints. Simulation results validate the advantages of the robust PF scheduling scheme when it is compared to non-robust solutions.

The key task of database systems is to efficiently manage large amounts of data. A high query throughput and a low query latency are essential for the success of a database system. Lately, research focused on exploiting hardware features like superscalar execution units, SIMD, or multiple cores to speed up processing. Apart from these software optimizations for given hardware, even tailor-made processing circuits running on FPGAs are built to run mostly stateless query plans with incredibly high throughput. A similar idea, which was already considered three decades ago, is to build tailor-made hardware like a database processor. Despite their superior performance, such application-specific processors were not considered to be beneficial because general-purpose processors eventually always caught up so that the high development costs did not pay off. In this paper, we show that the development of a database processor is much more feasible nowadays through the availability of customizable processors. We illustrate exemplarily how to create an instruction set extension for set-oriented database primitives. The resulting application-specific processor provides not only a high performance but it also enables very energy-efficient processing. Our processor requires in various configurations more than 960x less energy than a high-end x86 processor while providing the same performance.

Receiver front-ends, enabling multi-mode multi-band operation, are essential for future efficient mobile communications and require a proper parametrization to achieve certain performance requirements. A key component in the receive chain is the analog-to-digital converter (ADC). To determine feasible configurations of the ADC, an abstract model is investigated in order to evaluate the performance in terms of the signal-to-noise ratio (SNR) of bandpass sampling receivers. It models the available types of sampling circuits, the impact of stationary and non-stationary jitter processes, as well as limited quantization resolution. The derived ADC model is used to determine the dominating jitter effect, either aperture or clock jitter, depending on the receiver setup. Furthermore, required root mean square jitter values are derived analytically for a predefined receiver noise figure. A properly designed bandpass sampling receiver, matching the proposed maximum jitter requirements, avoids significant SNR performance losses and can be employed in mobile communications.

Self-organizing networks (SONs) can carry out their optimization procedures in an on- or off-line manner. On one hand, an online SON solution optimizes network parameters during operation. On the other hand, an offline SON solution employs a simulation environment of the network to be optimized in order to perform an offline parameter optimization before applying changes to the network. Thus far, researchers have not yet compared the characteristics of on- and offline SON solutions and they typically do not comment their SON solution's operational mode. However, specifying the SONs operational mode is crucial because it determines the type and number of measurements to be performed, and it decides whether it is required to accurately model the network to be optimized or not. In this work, we compare the general properties of on and off-line SON solutions qualitatively and compare an on and an off-line algorithm for coverage and capacity optimization quantitatively, using a realistic LTE simulation scenario. Based on the results obtained, we can conclude that offline SON solutions should be preferred as long as the required inputs are available. However, online SON solutions provide an adequate alternative to offline SON solutions if some of the inputs required are missing.

Generalized frequency division multiplexing (GFDM) is a new candidate waveform for 5G applications. With flexible pulse shaping filtering and tail-biting cyclic prefix, GFDM has lower out of band leakage and hence is more suitable as an opportunistic cognitive radio waveform. Improved adjacent channel leakage ratio (ACLR) of GFDM makes the coexistence of secondary signals easier, with lower adjacent channel interference to legacy users. This paper details the experimental validation of the coexistence study of this 5G waveform in an LTE system. The paper also highlights the improved sensing performance of GFDM- CR waveform compared to traditional OFDM. OFDM with implicit rectangular pulse shaping has higher out of band leakage and increases the probability of false alarm; while sharper GFDM waveform demonstrates substantial spectral efficiency and produces lesser number of false alarms.

Generalized Frequency Division Multiplexing (GFDM) is a recent multicarrier modulation technique with low out-of-band radiation that makes it an attractive choice for the PHY layer of cognitive radio. In orthogonal frequency division multiplexing (OFDM) the out of band leakage is around -13 dB; with raised cosine or root raised cosine pulses, the out of band leakage of a GFDM system is -35 dB. To improve the adjacent channel leakage ratio (ACLR) even further, a technique of inserting cancellation carriers is implemented. Cancellation carriers are inserted at the vicinity of interference avoidance notch and are designed such that these cancellation carriers mitigate the interference from other subcarriers to the adjacent band. With this technique the out of band leakage is lowered to around -65 dB. This stunning improvement in the GFDM adjacent channel leakage ratio satisfies stringent FCC requirements for cognitive radio transmissions in TV white space scenarios.

To relax power consumption requirements in multi- gigabit/s communications systems low resolution quantization can be used. Information-theoretic results have shown that systems employing 1-bit quantization and oversampling are a viable option for this. This work investigates such a structure under the influence of additive Gaussian noise and two matched pulse shaping filters. It is described how a BCJR algorithm, based on a finite-state channel assumption, can be used to reconstruct symbols of higher order modulation schemes that allow the transmission of more than one bit per symbol. Furthermore, it is shown how symbol sources that are fitted to the 1-bit constraint can significantly improve the error rate performance of the system.

Oversampling combined with low quantization resolutions has been shown to be a viable option when aiming for energy efficiency in multigigabit/s communications systems. This work considers the case of 1-bit quantization combined with oversampling and shows how the performance of such a system can be improved by using matched pulse shaping filters and faster than Nyquist signaling. The channel is considered with additive Gaussian noise and the performance of the system is evaluated in terms of achievable information rate under symbol-by-symbol detection.

Existing non data-aided synchronization schemes for OFDM in particular exploit the redundancy implemented by the CP to perform a time and frequency offset estimation. In general this is more challenging to exploit in non-orthogonal multicarrier systems. This paper gives a short insight of the impact of frequency offset of the performance of the non-orthogonal scheme GFDM and proposes a new estimator for it, which makes use of the data repetition implemented by the sidelobes of the prototype filter and is not feasible in OFDM. It allows a coarse CFO estimation in a wide range, even in a tough multipath environment, which can be improved by using a larger number of data.

In recent years, research on flexible algorithms for self-organizing networks has been a significant part of the development of wireless network technologies, such as Long Term Evolution. In this paper, we focus on modeling specific error types related to input data, i. e., to mobile data traffic maps, and investigate their impact on self- organizing network algorithm and network performance. We develop traffic map error patterns, which are able to reproduce erroneous geolocation of mobile users, e. g., by methods like (Enhanced) Cell-ID, Observed Time Difference of Arrival, or Global Positioning System. Furthermore, we model errors that occur when call traces and performance measurement counters are used to determine user locations. We find that self-organizing network performance may be robust to systematic distortions of user and traffic distributions, provided that 1) the overall traffic in the network is estimated accurately, i. e., regions without traffic information are avoided, 2) performance measurement counters in the base stations provide additional information on imperfect geolocation data, and 3) a certain degree of correlation between original heterogeneous user distributions and distorted maps is maintained to identify traffic hot spots.

Typically, mobile users cluster around points of interest in dense urban environments such as city centers forming so-called data traffic hot spots and hot zones. To provide capacity to such users efficiently, mobile operators deploy small cells. However, the deployment of heterogeneous networks, which consist of overlaying macro cells and many co-channel small cells, entails many problems. One typical problem is that, more often than not, hot spot users are not covered by the small cells due to the spatially fluctuating nature of the traffic demand. Data offloading, meaning actively shifting macro cell users to small cells, is a promising approach to address this issue. In this paper, we extend a queuing- theoretic model based on the notion of elastic data flows in order to model data offloading, or more specifically, cell range expansion along with inter-cell interference coordination. The model explicitly considers mutual co-channel interference and enables predicting the performance of networks consisting of hundreds of cells with very low computational effort. Based on this model, we present a heuristic centralized data offloading algorithm, which, for a certain traffic demand, is able to increase the 5thpercentile of the data flow throughput by a factor of 4.5 and to halve the probability of service unavailability. Moreover, we show that the network capacity can be increased by about 41.3% if data offloading is performed.

Single-carrier frequency-division multiplexing access (SC-FDMA) has been adopted as transmission scheme in Long Term Evolution (LTE) uplink to ensure low peak-to-average power ratio (PAPR). SC-FDMA inherently creates significant intersymbol interference (ISI), especially in large bandwidth scenarios. Additionally, inter-antenna interference (IAI) caused by transmitting spatially-multiplexed data streams in multiple-input multiple-output (MIMO) antenna systems further corrupts the received signal, making reliable estimation of the transmitted signal a computationally complex task. In this paper we propose an iterative receiver including a frequency-domain equalizer that mitigates both ISI and MAI jointly. The equalizer takes soft-input values in addition to the received SC-FDMA symbols into account to perform parallel interference cancellation (PIC). This soft-input is determined from decoders output values obtained in the previous receiving process, leading to equalization-decoding iterations. Simulation results for the LTE-Advanced uplink show that the proposed approach achieves considerable performance gains compared to non-iterative receivers.

Mobility load balancing alone does not improve the performance of intra-frequency heterogeneous networks because at higher offloading, users in the range extended region suffer from low throughput and radio link failures. Enhanced Inter-Cell Interference Coordination (eICIC) improves mobility load balancing by protecting users in the range extended region. However, setting optimal blanking pattern configuration and range extension is not an easy task since the algorithm needs to take into account among other things user measurement capabilities, traffic variation due to mobility, mobility-associated characteristics such as radio link failures, etc. Moreover, eICIC is used in HetNets along with other SON features such as mobility robustness optimization and carrier aggregation. Coexistence studies cannot be undertaken by using a radio resource management model that have simulation step equivalent to a transmission time interval because of unfeasible computation time. Thus, an abstract radio resource management model is needed. This paper contributes the comparison of an abstract radio resource management with a real radio resource management based on proportional-fair scheduler. The abstract model uses ideal proportional fair scheduler based on utility maximization whereas the real radio resource management model use metric based proportional fair in the presence of link adaptation, packet error and retransmission. Despite the differences between the two models due to approximations and resource sharing behavior, the comparison shows that both models result in the same trend and order of gain in terms of 5-% user throughput.

Coordinated Multi-Point (CoMP) Multiple Input Multiple Output (MIMO) transmission improves user's coverage and data throughput particularly on the cell edges. To make full advantage of CoMP, radio planning tools need very accurate models that fully capture the MIMO channels characteristics. This paper presents detailed modeling and analysis of an uplink CoMP system using Ray-Tracing (RT)-based channel modeling. The contribution of this paper is to show how close RT simulations can predict end-to-end system performance compared to the real-world measured performance. Thorough drive test measurements and RT simulations were performed. CoMP and Conventional MIMO systems performances are evaluated and compared for measured and RT-simulated channels. The results of several scenarios show that the RT matches the measurements in terms of rates and geometrical properties. The CoMP gain resulting from the measurements is almost double the gain of RT simulations. The differences come from the hardware and the RT 3D models impairments.

Mobile traffic demand varies significantly in time and space. Hence, wireless radio resources in hotspot areas and at peak traffic hours may be scarce. Consequently, special attention has to be paid to effects induced by admission control, i. e., blocking of data requests by base stations in case of high utilization or overload. Moreover, rising traffic demand requires denser deployments and frequency reuse one. Due to the resulting inter-cell interference, the base stations' utilizations have to be considered mutually dependent, which affects the admission control performance. In this paper, we extend a flow level model for elastic traffic, which explicitly takes into account the dynamic mutual inter-cell interference among base stations, by admission control. The model presented allows computing exact values for the average base station resource utilization, flow throughputs, and blocking probabilities. To analyze large networks containing many cells, we extend two approximation techniques, a state aggregation and an average interference approach, and compare them with the exact solution. Both techniques require far less computational effort and show remarkable accuracy. We believe that the extended flow level model is a positive step towards a more accurate, flexible, and holistic framework for network analysis and planning, and self-organizing network techniques.

Future mobile networks are forecast as being increasingly heterogeneous and dense. An aspect crucial to managing such networks is the existence of a flexible and effective backhaul infrastructure. Since backhaul infrastructure is essential, it becomes important to analyze the cost of its implementation. This paper aims to realize a framework to estimate deployment costs in a network which consists of users, two types of base stations, and backhaul nodes that could either be microwave or fiber optic backhaul nodes. The main contribution of this work is the derivation of a framework using spatial point processes that helps estimate the cost of deploying a backhaul node based on the number of users and base stations connected to it. The framework, along with assumptions of typical costs of various network components, is utilized to examine whether there exist an optimal number of backhaul nodes that can minimize the overall deployment cost of a network while catering to a given number of users in the area.

Increasing the spatial reuse of frequency spectrum by deploying more access points has historically been the most effective means to improve the capacity of any cellular communication network. Today's mobile networks face a proliferation of data services and overall demand for data traffic that has been strongly increasing over several years. As a result, increasing network capacity through the deployment of small lower power nodes is of key importance for mobile network operators. Although such small access points are conceptually equivalent to conventional cellular base stations in many ways, the expected large number of small cells as well as their much more dynamic unplanned deployment raise a variety of challenges in the area of network management. This paper discusses such challenges and reviews state-of-the-art modeling as well as selected network management techniques.

Cellular systems of the fourth generation (4G) have been optimized to provide high data rates and reliable coverage to mobile users. Cellular systems of the next generation will face more diverse application requirements: the demand for higher data rates exceeds 4G capabilities; battery-driven communication sensors need ultra-low power consumption; and control applications require very short response times. We envision a unified physical layer waveform, referred to as generalized frequency division multiplexing (GFDM), to address these requirements. In this paper, we analyze the main characteristics of the proposed waveform and highlight relevant features. After introducing the principles of GFDM, this paper contributes to the following areas: 1) the means for engineering the waveform's spectral properties; 2) analytical analysis of symbol error performance over different channel models; 3) concepts for MIMO-GFDM to achieve diversity; 4) preamble-based synchronization that preserves the excellent spectral properties of the waveform; 5) bit error rate performance for channel coded GFDM transmission using iterative receivers; 6) relevant application scenarios and suitable GFDM parameterizations; and 7) GFDM proof-of-concept and implementation aspects of the prototype using hardware platforms available today. In summary, the flexible nature of GFDM makes this waveform a suitable candidate for future 5G networks.

Heterogeneity and parallelism in MPSoCs for 4G (and beyond) communications signal processing are inevitable in order to meet stringent power constraints and performance requirements. The question arises on how to cope with the problem of system programmability and runtime management incurred by the statically or even dynamically varying number and type of processing elements. This work addresses this challenge by proposing the concept of a heterogeneous many-core platform called Tomahawk. Apart from the definition of the system architecture, in this approach a unified framework including a model of computation, a programming interface and a dedicated runtime management unit called CoreManager is proposed. The increase of system complexity in terms of application parallelism and number of resources may lead to a dramatic increase of the management costs, hence causing performance degradation. For this reason, the efficient implementation of the CoreManager becomes a major issue in system design. This work compares the performance and capabilities of various CoreManager HW/SW solutions, based on ASIC, RISC and ASIP paradigms. The results demonstrate that the proposed ASIP-based solution approaches the performance of the ASIC realization, while preserving the full flexibility of the software (RISC-based) implementation.

Electronic systems of the future require a very high bandwidth communications infrastructure within the system. This way the massive amount of compute power which will be available can be inter-connected to realize future powerful advanced electronic systems. Today, electronic inter-connects between 3D chip-stacks, as well as intra-connects within 3D chip-stacks are approaching data rates of 100 Gbit/s soon. Hence, the question to be answered is how to efficiently design the communications infrastructure which will be within electronic systems. Within this paper approaches and results for building this infrastructure for future electronics are addressed.

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

In this work we discuss the aspect of channel prediction for cooperative multi-cell downlink transmission, where channel state information (CSI) of all users need to be available at all cooperating base stations (BSs). We assume that users feed CSI back to its local BS which forwards it to the other cooperating BSs using backhaul connections. In case of feedback and backhaul latency, CSI of a single user equipment (UE) is affected by multiple delays. Compensating for the delay via channel prediction raises the question of where to place the predictor. Prediction at the UE before the channel observations are quantized allows to compensate only for a single delay. Prediction at the BS side keeps the flexibility to compensate for the actual delay at each base station, at the drawback that less accurate information is available due to feedback quantization. This paper extends previous work from a transmission over uncorrelated subcarriers to the more realistic transmission over correlated subcarriers. Previously, we have shown that prediction before and after quantization results in the same channel uncertainty. As a consequence, prediction at the BS is always preferable if multiple delays need to be compensated. This paper shows that this result remains valid also for correlated subcarriers.

The increasing demand for multi-mode multi-band operation in mobile communications requires flexible radio frontends. Direct sampling receivers are very promising for this purpose. For this class of receivers, a proper parameterization of the analog-to-digital converter, in terms of input bandwidth, sampling frequency, and quantization resolution, is essential for its operation. This paper evaluates prospects and challenges of direct sampling receivers analytically. The impact of the sampling frequency and the quantization resolution on the signal-to-noise ratio of the received bandpass signal is approximated with a closed-form analytical expression. This is used to compare the performance of direct sampling receivers to the performance of traditional homodyne receiver concepts. Furthermore, an optimal choice and the trade-off between sampling rate and quantization resolution are discussed for direct sampling receivers that are intended for the reception of LTE signals.

Due to continuously increasing performance requirements of embedded applications, today's multi-processor system-on-chips will evolve towards many-core system-on-chips with thousands of processors on a single chip. Accurate, fast and flexible (i.e., parameterizable) simulation models are necessary to be able to analyze and optimize these large systems. Network-on-chip is a common solution for the interconnection of large processor arrays. Existing analytic models for the performance analysis of network-on-chip often possess a lack of accuracy, if applied for the popular round-robin arbitration scheme. It turns out to be challenging to find an appropriate analytic representation for this apparently simple scheme. In this paper, we propose an accurate service time estimation model that is designed for round-robin arbiters. It is further employed to a queueing model for network-on-chip. The comparison with cycle-accurate simulation proves the accuracy of the proposed service time model, which is essential for predicting key performance indicators, such as network throughput or latencies.

We consider the distributed pricing (DP) algorithm for adapting transmit beamforming vectors in a multi-user multiple-input multiple-output interference channel. Our objective is to maximize the sum of all user utilities. Each user announces an interference price which reflects the marginal cost of interference from other users. A particular user then updates its beamforming vector to maximize his utility minus the interference cost to other users. By local linearization of all user utilities, the beamformer update has a closed-form solution which is given by the dominant eigenvector of a matrix. We show that if the sum utility is quasiconvex with respect to the power gain vectors of the individual users, then the simplified DP algorithm must monotonically converge to a stationary point, where the power gain vector of a particular user is defined as the set of interference powers originating from that user's transmitter. We then demonstrate for the sum rate utility, that the quasiconvexity condition is only satisfied for the two user case but not for the general setting.

Employing direct sampling receivers for multi-mode multi-band operation in mobile communications is advantageous due to their high flexibility and programmability. But this type of receiver needs to be properly adjusted regarding the parameters of the sampling and the quantization stage of the analog- to-digital converter to comply with certain performance requirements. This paper studies the impact of the ideal, the track-and-hold and the sample-and-hold sampling circuit on the effective in-band signal-to-noise ratio of the receive signal. It also proposes sets of valid parameters of the complete analog-to- digital converter in terms of the duty cycle, the sampling rate, and the quantization resolution for a given band-pass input signal to limit the maximum performance loss. Moreover, the investigations for the different samplers and the overall analog-to- digital converter are used to trade-off the individual parameters in case of an exemplary LTE signal reception.

One major topic of research into self-organizing network technology is the coordination of SON use cases. Network operators expect a coordinated handling of the parameter and configuration changes submitted to the operating network by closed-loop SON use case implementations. There are currently two basic approaches for SON use case coordination discussed in the literature: A so-called heading or tailing use case external coordination and the combination of separate use cases into one joint algorithm. In this paper, we extend a verified framework to combine mobility load balancing and inter-cell interference coordination use cases, especially for a heterogeneous network environment. Our approach results in a coordinated set of cell range expansion offsets, an efficient bandwidth allocation to support the (enhanced) inter-cell interference coordination use case, and an energy-efficient smart cell switching of the small capacity cells in a heterogeneous networks environment for a varying traffic demand during the course of a day, resulting in significant capacity enhancements while saving energy at the same time.

To meet the increasing capacity demands of future mobile networks, dense deployment of radio access nodes in combination with partly centralized processing by means of a cloud-based architecture is a promising option. In such an architecture, the design and optimization of the backhaul plays a crucial role. In this paper, we review different backhaul technologies available and discuss their characteristics for use in cloud-based networks. We point out how a heterogeneous backhaul network and a flexible centralization enables the proposed architecture and give an outlook on how a joint design of access and backhaul can help in meeting the increased demands.

Nonlinear distortions by analog frontend components are becoming a growing problem. Not only do stringent linearity requirements that are crucial for modern modulation schemes like OFDM boost the costs in development and fabrication, but highly linear amplifiers are usually very energy inefficient. After looking at the theoretical and practical aspects of estimation of memoryless AM/AM nonlinearities, the focus of this paper is on AM/PM nonlinearities. First, we will derive the maximum likelihood estimator for a parameterized AM/PM nonlinearity. Then, we point out the complexity issues in practical implementations, propose suboptimal methods and compare them in terms of complexity and estimation performance.

Generalized frequency division multiplexing (GFDM) is a multi-carrier modulation scheme. In contrast to the traditional orthogonal frequency division multiplexing (OFDM), it can benefit from transmitting multiple symbols per sub-carrier. GFDM targets block based transmission which is enabled by circular pulse shaping of the individual sub- carriers. In this paper we propose a low complexity design for demodulating GFDM signals based on a sparse representation of the pulse-shaping filter in frequency domain. The proposed scheme is compared to receiver concepts from previous work and the performance is assessed in terms of bit error rates for AWGN and Rayleigh multipath fading channels. The results show, that for high-order QAM signaling, the error performance can be significantly improved with interference cancellation at reasonable computational cost.

Turbo equalization is a technique to enable wireless transmissions by means of an iterative exchange of information between several components at the receiver incorporating a soft equalizer. The performance achievable can be improved by adjusting the components related to one another. The focus of this paper lies on the study of the possible adjustments to terminated protograph-based Low- Density Parity Check convolutional codes. Some of the parameters (syndrome former memory ms, termination length L) are related to the underlying terminated convolutional protograph, while others (like permutation size P) come into play during the actual code construction process. Compared to our previous work where the design parameter of the protograph is the only focus of the study, this work will also focus on the final constructed codes and their performance. In order to evaluate the code's performance, the length of the code is an important property. Along with the permutation size P and the protograph parameters (ms, L), it is possible to make a fair comparison between the codes and to decide in favor of the code used to achieve the desired requirements of code length, performance, and code rate.

Self-organizing networks are considered to be the next generation technology for network management and, therefore, also treated as one possible way to tackle economical and ecological challenges in the future. In this paper, we extend our framework for integrated self-organizing networks by the energy saving management self-organizing network use case. In contrast to coordination among multiple use cases, this approach targets at an integrated solution, where the coordination is inherent in the optimization enabling managing multiple use cases concurrently. Moreover, it considers dynamic traffic and interference situations, and predicts cell loads for various network configurations based on measurements of receiving and traffic conditions. Using the cell load as a proxy for evaluating network performance and base station energy consumption, we show that a joint optimization of cell individual offsets and antenna downtilts is superior to the adjustment of cell individual offsets only. Throughout a day, we observe remarkable improvements in user throughputs and reduction in network energy consumption further enhanced by switching off base stations.

We present an approximate rate adaptation algorithm for MIMO spatial multiplexing transmission over multiple time-variant subchannels, based on delayed inaccurate feedback. The algorithm exploits an approximation to the mutual-information-averaged post-equalization signal-to-interference-and-noise- ratio statistics. It is suited for linear and successive interference cancelation MIMO receivers. We demonstrate that the algorithm successfully exploits frequency diversity in order to mitigate the rate loss at increasing velocities. In addition, the interference cancelation receiver gain, as compared to linear receivers, can be maintained even with error propagation.

The field of machine-to-machine (M2M) communications has gained wide popularity and is steadily growing. This paper studies the feasibility of using the Global System for Mobile Communications and in particular the General Packet Radio Service (GPRS) for a low data rate long- lasting battery-powered operation of M2M devices. A model is introduced to estimate the power consumption of a GPRS connection. It allows the identification and evaluation of optimizations of the data transmission procedures. Two M2M modes of GPRS operation are introduced. For applications with frequent transmissions, an Always- on-mode turns out to be most reasonable. For infrequent transmissions, e.g., one transmission every 2 hours, an On/off-mode reduces the power consumption of M2M devices by 93% as compared to the Always-on-mode. With a 3-cell battery providing 25.9 Wh of energy and considering only the power consumption of the communication module, a battery lifetime of up to 5 years is feasible. Measurements show that usually 40% of the energy spent for a short data transmission is wasted by one particular GPRS procedure called non-DRX period. Avoiding this saves up to 35% of total average power, depending on the rate of data transmissions.

Modern cellular communication systems facilitate fine-grained resource allocation that can opportunistically exploit the time variations and frequency selectivity of mobile channels by exploiting multi-user diversity. However, extracting such benefits requires the scheduler to have accurate channel state information (CSI). Channel estimation in the uplink direction requires all users to send unique reference signals. For resource allocation purposes, current standards have thus included sounding reference symbols (SRS), which allow simultaneous estimation, but are very sensitive to the channels' frequency selectivity and the number of users. Also demodulation reference symbols (DMRS) are used which are always multiplexed with the data during transmission and yield more accurate CSI. However, the information that can be obtained in this way directly depends on the scheduling decisions. In this paper, we propose two new scheduling algorithms that are aware of the channel estimation quality and are thus able to make best use of the available estimation types. Furthermore, through their decisions, the algorithms inherently ensure the right CSI amount to achieve high spectral efficiency. We compare our approaches to the conventional scheduling algorithm that rely only on estimates from channel sounding based on SRS.

LTE and LTE-Advanced have been optimized to deliver high bandwidth pipes to wireless users. The transport mechanisms have been tailored to maximize single cell performance by enforcing strict synchronism and orthogonality within a single cell and within a single contiguous frequency band. Various emerging trends reveal major shortcomings of those design criteria: (1) The fraction of machine-type-communications (MTC) is growing fast. Transmissions of this kind are suffering from the bulky procedures necessary to ensure strict synchronism. (2) Collaborative schemes have been introduced to boost capacity and coverage (CoMP), and wireless networks are becoming more and more heterogeneous following the non-uniform distribution of users. Tremendous efforts must be spent to collect the gains and to manage such systems under the premise of strict synchronism and orthogonality. (3) The advent of the Digital Agenda and the introduction of carrier aggregation are forcing the transmission systems to deal with fragmented spectrum. 5GNOW will question the design targets of LTE and LTE-Advanced having these shortcomings in mind. The obedience of LTE and LTE-Advanced to strict synchronism and orthogonality will be challenged. It will develop new PHY and MAC layer concepts being better suited to meet the upcoming needs with respect to service variety and heterogeneous transmission setups. A demonstrator will be built as Proof-of-Concept relying upon continuously growing capabilities of silicon based processing. Wireless transmission networks following the outcomes of 5GNOW will be better suited to meet the manifoldness of services, device classes and transmission setups being present in envisioned future scenarios like smart cities. The integration of systems relying heavily on MTC, e.g. sensor networks, into the communication network will be eased. The per-user experience will be more uniform and satisfying. To ensure this 5GNOW will contribute to upcoming 5G standardization.

Coordinated multi-point transmission/reception (CoMP) enables the communication between different base stations, which can improve the cell edge user performance as well as the system capacity. So far, the most studies focused on evaluating the performance of CoMP in regular scenarios with uniform traffic. Since these scenarios differ substantially from realistic conditions, it is doubtful whether the performance results obtained in these studies can be applied to real-word scenarios. That is why in this work we evaluate the performance of various CoMP methods for a European city. As we use the existing network topology, employing a high resolution ray-tracing path loss prediction and adopting a traffic map from live 3G network, we examine a highly realistic scenario. The results show moderate overall capacity gains but large reduction in network outage of advanced data services.

This paper studies linear precoding designed for the multi-cell multi-user multiple-input-multiple-output (MU-MIMO) downlink. The objective is to maximize the weighted sum rate (WSR) under imperfect channel state information (CSI) conditions and per base station (BS) transmit power constraints. The expectation of the WSR over the CSI error can be lower bounded by minimizing the expected weighted sum mean square error (MSE) assuming minimum MSE (MMSE) receive filters. The problem can be solved by an iterative algorithm which alternately calculates the MMSE receive filters given a fixed precoding matrix and vice versa. The algorithm converges to a local optimum. For the optimization of the precoding matrix under per BS power constraints, we present two robust solutions. The first one is based on the transmit Wiener filter solution under a sum power constraint combined with a consistent scaling to satisfy each per BS power constraint. In the second solution the problem is transformed into a second order cone program (SOCP) where per BS power constraints can be directly included. Simulation results show performance gains compared to robust and non-robust state of the art schemes.

In this paper, we address the problem of an efficient mapping of intellectual property (IP) cores onto a multiprocessor system-on-chip (MPSoC). The MPSoC is statically scalable in terms of number of IP cores and an 1-ary n-mesh network-on-chip (NoC). The approach places more affine IP cores closer to each other and affinity is based on an amount of exchanged communication and administration data. Assuming ideal network conditions, accounting for execution latency, separate affinity value matrices for communication and administration are extracted from the application mappings. Aiming at better system performance, the goal is to find a reasonable tradeoff between communication and administration affinity. Hence, both matrices are merged into a single affinity value matrix based on linear weighting. A genetic algorithm (GA) and a mixed-integer linear programming (MILP) solution use the weighted affinity value matrix to efficiently map IP cores onto a NoC. A scalability analysis shows that the GA generates results faster and with a satisfactory quality relative to the found MILP solutions. Realistic benchmark results demonstrate that a tradeoff between administration and communication affinity significantly reduces administration latency improving application performance. As network size and system adaptability increase, the growing influence of administration becomes more evident.

In the near future, embedded systems containing hundreds of processing elements running multiple concurrent applications will become a reality. The heterogeneous multicluster architecture enables to cope with the challenging hardware/software requirements presented by such systems. This paper shows principles and optimization of multicluster dimensioning aiming at an appropriate distribution of applications onto clusters containing different types of processing elements. The approach represents an initial exploration phase efficiently finding a suitable multicluster configuration in the large design space. Hence, results should be further refined by more accurate but less time-efficient simulation-based techniques. As the starting point, a parallelism value matrix is analytically extracted describing application mappings independently on the architecture and scheduling. A genetic algorithm (GA) and a mixed-integer linear programming (MILP) approach solving the dimensioning problem are introduced and compared. Both solutions use the parallelism value matrix as input. Scalability results show that the GA generates results faster and with a satisfactory quality relative to the found MILP solutions. Finally, the dimensioning approach is demonstrated for a realistic benchmark scenario.

The Special Session on “Pathways to Servers of the Future” outlines a new research program set up at Technische Universität Dresden addressing the increasing energy demand of global internet usage and the resulting ecological impact of it. The program pursues a novel holistic approach that considers hardware as well as software adaptivity to significantly increase energy efficiency, while suitably addressing application demands. The session presents the research challenges and industry perspective.

Wireless data transmission has become an integral part of modern society and plays an increasingly important role in health care. Technology scaling is continuously increasing wireless data rates, thus allowing for more flexible high-speed interfaces, e.g., between medical imaging equipment and mass storage devices. However, one issue remains: The power consumption of high-speed wireless transceivers and non-volatile memory grows with the data rate. This prevents from innovations using these high-speed wireless interfaces in ultra-low power (or even energy-passive) medical equipment that can be used by patients without a heavy power source. Clear efforts are required to close this gap, i.e., to provide high-speed wireless solutions with reduced energy consumption per transmitted bit. As a very example, this work presents the concept of a wireless medical smart card that combines near field communication for authentication and low-speed signaling together with a 60GHz interface for fast wireless memory access in a single patient-owned ID card. The basic architecture, functionality and prospects of the concept are discussed. A power budget is calculated based on state-of-the-art technologies. To put the concept into practice, some necessary developments for a reduction of the power consumption are outlined.

Cooperative joint detection in the uplink of cellular systems is a promising means to combat inter-cell interference. In fact, interference from other users in the same cooperation cluster is turned into useful signal energy in such systems. While cooperation is generally beneficial, the system behavior largely depends on the power control and scheduling strategies employed, as demonstrated in this paper. We investigate different combinations of these two mechanisms and provide insights into the system behavior considering spectral efficiency, fairness and energy efficiency.

This paper aims to discuss the influence of adjustable base station (BS) power parameters, such as power consumed during active mode and sleep modes, on the overall energy consumption of a network and highlight potential energy savings that can be achieved by the introduction of sleep modes. A BS density that can satisfy the demands of a given user density - defined by a daily traffic profile - can be found using the relationship between spatially averaged rate, user density, and BS density established here. The underlying framework for this relationship assumes users and BSs to be independently marked point processes in ℝ2. A power model that is an affine function of the BS density is used to determine the overall energy consumption of the network at full load, and these values are compared to those that incorporate sleep modes to utilize the minimum number of BSs that satiate the demands of a user density that varies during the course of the day. The relationship established between spatially averaged rate, user density, and BS density forms the main result of this paper, based on which it can be inferred that the introduction of sleep modes results in substantial energy savings when the load is seldom full.

In order to meet increasing traffic demands, future generations of cellular networks are characterized by decreasing cell sizes at full frequency reuse. Due to inevitable inter-cell interference, load conditions in neighboring cells can no longer be considered independent. Unfortunately, the adequate flow level model for such a setup is analytically intractable. Utilizing aggregation techniques, which were originally proposed to analyze large state models in economics, we propose a framework to compute the average base station loads based on an approximation of the joint stationary distribution of the number of active flows in all cells. The technique proposed requires solving a system of linear equations whose dimension increases exponentially with the number of cells. Since such a system is essentially intractable for large networks, we propose a fixed point algorithm to compute approximate base station loads based on the notion of average interference. Numerical results validate the accuracy of both modeling techniques. The modeling approach presented in this paper is essential for accurate characterization of cell throughput as well as base station energy consumption under varying load conditions.

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

Spectrum sensing, in particular, detecting the presence of incumbent users is no longer required for the operation of White Space Devices (WSD). However, detection of other opportunistic users using the same spectrum is still an open challenge. Generalized Frequency Division Multiplexing (GFDM) is a recent multicarrier modulation technique with extremely low out-of-band radiation that makes it an attractive choice for the PHY layer of cognitive radio. In this paper, we consider the problem of spectrum sensing by detecting GFDM based opportunistic users. Despite the fact that there is no incumbent transmission, an opportunistic user first senses the channel to make sure that no other opportunistic user is using the channel. In this work, the sensing characteristics of the opportunistic GFDM signal are obtained and analyzed in detail.

Inter-cell Interference (ICI) in the uplink of modern cellular communication systems with high frequency reuse is hard to predict, as fast scheduling and link adaptation lead to a high interference fluctuation. This fluctuation poses a big challenge for link adaptation algorithms that need an accurate SINR estimate to assign suitable modulation and coding schemes for transmission. To enable ICI prediction it has been proposed to exchange scheduling decisions amongst base stations. However, if this exchange is subject to delays, the system performance decreases as scheduling decisions become suboptimal. In this paper we propose a new scheme that maintains scheduling optimality at the cost of reduced link adaptation accuracy when faced with backhaul delays. We compare both schemes and provide insights into the multi-user diversity / link adaptation accuracy trade-off that arises for non-stationary users if information exchange on the backhaul is subject to delays.

As it is widely known, cooperation among base stations can improve spectral efficiency, particularly at cell edges. In theory, the problem is well understood, but the application of those advanced transmission techniques in practical deployments issues multiple challenges, some of which are very difficult to model in simulation studies. In this paper we investigate the properties of intra and inter-site cooperation in the cellular uplink doing practical field measurements in an urban environment. At first we concentrate on the characterization of important channel parameters which affect the performance in cooperative networks. In particular, this includes the achievable signal-to-noise ratio as well as the synchronization accuracy. Although the results show that intra-site cooperation is less complex, especially in terms of backhaul requirements and synchronization, intersite cooperation achieves (with higher probability) more balanced SNR levels at clustered base stations and, thus, higher spectral efficiency as well. This property was veryfied in the present field trial as well.

1-bit analog-to-digital conversion is very attractive for low-complexity communications receivers. A major drawback is, however, the small spectral efficiency when sampling at symbol rate. This can be improved through oversampling by exploiting the signal distortion caused by the transmission channel. This paper analyzes the achievable data rate of band-limited communications channels that are subject to additive noise and inter-symbol-interference with 1-bit quantization and oversampling at the receiver. It is shown that not only the channel noise but also the inter-symbol-interference can be exploited to benefit from oversampling.

The low peak-to-average power ratio of SC-FDMA, employed in the 3GPP-LTE-Advanced uplink, comes at the cost of tremendously increased receiver complexity as compared to OFDM. In this work, we investigate the achievable rate of four low-complexity, mismatched receiver designs based on our recently developed generalized mutual information framework. The receiver performance is analyzed depending on the channel length, subcarrier mapping and spatial correlation. Results may guide the system designer to choose a receiver, matched to the expected operation conditions. Numerical results stress that receivers which treat a MIMO channel with memory as if it was memoryless may be well suited for narrow bandwidths while additional frequency domain interference cancellation may be required when using large bandwidths.

With opening up of the TV white spaces for opportunistic access, designing a flexible physical layer (PHY) scheme has become an important focus for cognitive radio (CR) systems. Two possible PHY design solutions proposed are: generalized frequency division multiplexing (GFDM) and interference avoidance by partitioned frequency and time domain transmission (IA-PFT). Both of these methods extend the orthogonal frequency division multiplexing (OFDM) scheme to be applicable as a flexible CR PHY solution in a fragmented spectrum. In GFDM, introduction of pulse shaping filters reduces the out-of-band radiation of the opportunistic signals into the frequency band of the incumbent users; while in IA-PFT, simultaneous cancellation carrier insertion and time windowing technique suppress spectral leakage into the incumbent band of operation. In this paper, these two different approaches are compared in terms of interference suppression, transmission performance and processing complexity.

Generalized frequency division multiplexing (GFDM) is a new digital multicarrier concept. The GFDM modulation technique is extremely attractive for applications in a fragmented spectrum, as it provides the flexibility to choose a pulse shape and thus allows reduction of the out-of-band leakage of opportunistic cognitive radio signals into incumbent frequency space. However, this degree of freedom is obtained at the cost of loss of subcarrier orthogonality, which leads to self-inter-carrier-interference. This paper will explain how self-interference can be reduced by a basic and a double-sided serial interference cancellation technique and show that these interference cancellation techniques improve the GFDM bit error rate to match the theoretical performance of the well studied orthogonal frequency division multiplexing (OFDM).

Generalized frequency division multiplexing is a non-orthogonal, digital multicarrier transmission scheme with attractive features that address the requirements of emerging applications of wireless communications systems in areas like cognitive radio and machine-to-machine communication. In this paper, first a linear system description is obtained for the transmitter by ordering data in a time-frequency block structure and representing the processing steps upconversion, pulse shaping and upsampling as matrix operations. Based on the transmitter, three standard ways of detecting the signal are derived and compared in terms of bit error performance in AWGN and Rayleigh multipath fading channels.

Network planning and optimization becomes more and more important in cellular mobile communications due to the growing complexity of the networks. Besides taking new key performance indicators into account such as energy efficiency, the augmented heterogeneity, caused by a variety of radio access technologies (e.g., 3G and beyond as well as WiFi) and network node types (e.g., micro and femto cells), leads to an exploding dimension of the planning process. On the other hand, the degrees of freedom increase as well, giving rise for new optimization techniques. In this paper a novel approach for optimizing cellular deployments is presented. The model is based on characterizing the interrelations (among base stations and between base stations and the environment) by force fields, motivated by the physics of multiple particles in a closed system. Further, an algorithm is proposed which tracks the trajectory of base station locations under the presence of forces, focusing on finding a balanced state with minimal net force. Also, it is elaborated on how to combine different force types in order to capture different quality aspects of a network.

This paper tries to analyze the energy saving capabilities of two common power saving techniques being suggested - introduction of sleep modes and bandwidth (BW) variation. The framework for this analysis assumes users and base stations (BSs) to be independently marked point processes in R2. The relationship between spatially averaged rate, user density, and base station density, which is an extension of findings in [1], is used in an affine power model to estimate the energy that can be saved by the two methods under consideration. The primary contribution of this paper constitutes an analytic relationship between spatially averaged rate, user density, BS density, transmit power, and the noise power. Another key contribution is a proof that shows that power saved by using sleep modes (or turning off BSs) is always greater than the power saved by varying the BW, for a system model implementing an affine power model (described here) when traffic densities below full load are considered.

We investigate simplified, yet mismatched, receivers for SC-FDMA MIMO transmission currently employed in the 3GPP LTE-Advanced uplink. The receivers are based on frequency domain equalization and time domain detection. Rates achievable by those mismatched receivers with arbitrary input alphabets are derived in terms of generalized mutual information. The results allow to compare the impact of certain simplifying receiver assumptions under various conditions, such as spatial correlation or channel length. In addition, the results could be used to adjust the transmission rate according to the receiver detection metric.

A cognitive radio should be able to detect unused spectrum band and to change its transmission parameters in order to transmit within these free bands. To achieve this, reliable detection of incumbent signals as well as of other opportunistic signals that are using the said spectrum, is necessary. Generalized Frequency Division Multiplexing (GFDM) is a recent multicarrier modulation technique with extremely low out-of-band radiation that makes it an attractive choice for the PHY layer of cognitive radio. GFDM has an innovative tail biting cyclic prefix which shows unique circular detection properties. In this paper, we consider the cyclostationarity properties of GFDM and compare this with well studied OFDM. Detection of GFDM based opportunistic signal by cyclostationary detection is shown and compared to OFDM detection by the same method.

Public transportation vehicles are natural hotspots of wireless communication demand. Potentially, multiple users in the vehicle will compete for scarce spectral resources. At the same time, the direct link of users in the vehicle to the base stations might be rather week due to a high indoor-outdoor penetration loss. A potential solution to these this is the use of multi-antenna relays with antennas outside the vehicle for communication with the base stations and inside the vehicle for communication with the users. In order to increase the throughput on the base station - relay link, especially at cell edges, coordinated signal processing of multiple base stations could be used. In this work, we explore the performance of this approach in an uplink large-scale field trial of a multi-antenna transmitter carried on a measurement bus in an urban cellular environment. For this setup we show achievable data rates using linear and non-linear detection and explore the gain of joint detection in cooperation clusters of up to three base stations.

The dramatic increase of network infrastructure comes at the cost of rapidly increasing energy consumption, which makes optimization of energy efficiency (EE) an important topic. Since EE is often modeled as the ratio of rate to power, we present a mathematical framework called fractional programming that provides insight into this class of optimization problems, as well as algorithms for computing the solution. The main idea is that the objective function is transformed to a weighted sum of rate and power. A generic problem formulation for systems dissipating transmit-independent circuit power in addition to transmit-dependent power is presented. We show that a broad class of EE maximization problems can be solved efficiently, provided the rate is a concave function of the transmit power. We elaborate examples of various system models including time-varying parallel channels. Rate functions with an arbitrary discrete modulation scheme are also treated. The examples considered lead to water-filling solutions, but these are different from the dual problems of power minimization under rate constraints and rate maximization under power constraints, respectively, because the constraints need not be active. We also demonstrate that if the solution to a rate maximization problem is known, it can be utilized to reduce the EE problem into a one-dimensional convex problem.

In this paper, relaying and multi-cell MIMO transmission are investigated as approaches for improving resource reuse and more flexible organization of cellular networks. The analysis focuses on approaches for future cellular systems, which jointly exploit relaying and multi-cell MIMO transmission. Possible candidate approaches are identified, simplified for practical applications and evaluated using a system-level model from the European research project WINNER. Their achievable throughput is analyzed under practical constraints using three different normalization approaches: cost-normalization, energy-normalization, and joint cost-energy-normalization. It can be shown that the combined approach of relaying and multi-cell MIMO provides significant gains for the uplink communication. The selected approach exploits cooperative multi-cell MIMO processing between base stations and relay nodes, and uses a resource coordination technique on the links between relay nodes and user terminals. If a relay-based deployment is subject to a cost- and energy-normalization, multi-cell MIMO outperforms relaying with respect to achievable downlink throughput.

Wireless communications has been a hot area of technology advancement for the past two decades. As long as memory sizes increase, the demand for higher data rates of communications increases on the same scale. This means that one must understand today's high-end 10 Gbit/s wireless technology to get prepared for 100 Gbit/s and 1 Tbit/s data rates of tomorrow. This paper presents key boundary conditions learned by understanding leading edge wireless links of today to prepare for the Tbit/s technology of the year 2020.

Quality-of-Service becomes a vital requirement in MPSoCs with NoCs. In order to serve them NoCs provide guarantees for latency, jitter and bandwidth by virtual channels. But the allocation of these guaranteed service channels is still an important question. In this paper we present and evaluate different realizations of a central hardware unit which allocates at run-time guaranteed service virtual channels providing QoS in packet-switched NoCs. We evaluate their performance in terms of allocation success, compare it to distributed channel setup techniques for different NoC sizes and traffic scenarios and analyze the required hardware area consumption. We find centralized channel allocation to be very suitable for our run-time task scheduling programming model.

With the opening up of white spaces, efficient use of the fragmented spectrum - TV white space in particular - has become an extremely important focus of research. Apart from efficient spectrum usage, special care needs to be taken to maintain low out-of-band radiation to avoid harmful interference to incumbent services like TV signals and wireless microphones. For this reason, a flexible digital radio with multicarrier modulation scheme is the only solution. In this paper, we show the performance of two innovative multicarrier systems, Filter Bank Multi Carrier (FBMC) and Generalized Frequency Division Multiplexing (GFDM). A simple interference cancellation technique called serial inter-carrier interference cancellation scheme has been used to improve performance of the GFDM system. Channel equalization techniques have been implemented for FBMC.

Mobile internet devices are among the fastest growing markets in consumer electronics. A big issue with these devices is the low battery capacity that requires the user to recharge the device regularly. This paper aims to reduce the energy consumption of LTE modems used in future devices. For this, the LTE HARQ process is modeled as a Markov chain and a simple generic model for energy consumption is introduced that divides energy consumption in a static and a dynamic part. Using these and the results of link-level simulations it is found that throughput maximization will also maximize the energy efficiency if the static energy consumption dominates. However, if the energy consumption also contains a dynamic part, the energy efficiency will severely degrade unless another optimization approach is used.

Energy-efficient link adaptation is studied on a slowly varying Rayleigh fading channel assuming perfect CSI at the receiver but no CSI at the transmitter, except for the statistical characterization. Both circuit power and power dissipated in the power amplifier are considered and the energy consumption per bit is minimized over bandwidth, transmission power, and transmission rate. It is shown that the joint optimization of power and rate leads to a point on a curve that connects the two extreme situations that correspond to ignoring the power dissipated in the power amplifier and ignoring the circuit power dissipation, respectively. The first case leads to throughput maximization whereas the second case leads to transmit power minimization. The global optimum depends on the ratio of the power dissipated in the power amplifier to the total power dissipation.

Coordinated multi-point (CoMP) has been selected as a key technology feature of LTE-Advanced, as it enables the exploitation of inter-cell interference in order to significantly increase spectral efficiency, especially at the cell-edge. While first field trials on CoMP schemes have delivered the proof-of-concept and shown that a moderate extent of theoretically predicated CoMP gains can indeed be achieved in practical systems, the implementation of these schemes has revealed many practical challenges. One central question is, for example, how small cooperation clusters can be extracted from large cellular systems, such that major portions of potential CoMP gains can be obtained at minimum signaling overhead. This paper deals with static clustering concepts, and shows that both in a hexagonal cell layout and under a realistic deployment and signal propagation scenario, static clustering concepts can perform close to optimal UE-specific clustering, while being easy to use and requiring negligible signaling overhead.

Message passing schedules that reduce the decoding complexity of terminated LDPC convolutional code ensembles are analyzed. Considering the AWGN channel, various schedules are compared by means of density evolution. The results of the analysis together with computer simulations for some (3,6)-regular codes confirm that sliding window decoding is an attractive practical solution for low-latency and low-complexity decoding.

We analyze the convergence behavior of iteratively decoded coupled LDPC codes from a complexity point of view. It can be observed that the thresholds of coupled regular LDPC codes approach capacity as the node degrees and the number L of coupled blocks tend to infinity. The absence of degree two variable nodes in these capacity achieving ensembles implies for any fixed L a doubly exponential decrease of the error probability with the number of decoding iterations I, which guarantees a vanishing block error probability as the overall length n of the coupled codes tends to infinity at a complexity of O(n log n). On the other hand, an initial number of iterations Ibris required until this doubly exponential decrease can be guaranteed, which for the standard flooding schedule increases linearly with L. This dependence of the decoding complexity on L can be avoided by means of efficient message passing schedules that account for the special structure of the coupled ensembles.

Nonlinear distortions in the analog frontend are becoming a growing problem. With rising requirements it becomes more expensive to provide components with sufficient linearity. Especially in the receiver, where out-of-band radiation is not an issue, relaxing the requirements on the analog components could significantly reduce development and production costs as well as increase the energy efficiency. There are numerous algorithms to mitigate the effects caused by nonlinearities. This paper presents a method for the blind estimation of nonlinearity parameters which can be applied to a large class of nonlinearities. The estimator is integrated in an OFDM system with nonlinearity mitigation and it shows that there is negligible performance degradation compared to perfect knowledge. The Cramer-Rao lower bound is derived and it is shown that the estimator reaches it in the high SNR region. Special care was taken to provide a solution with low computational complexity that can be readily implemented on current platforms.

Inter-cell interference in the cellular uplink can be combated effectively by joint detection (JD) of multiple users at cooperative base stations, a concept known as network MIMO or more generally as coordinated multi-point (CoMP). Field trials verify large improvements in spectral efficiency and fairness which were proven theoretically. On the downside, JD requires a vast amount of data traffic to be exchanged over the backhaul. However, recent studies promise great performance of JD even under stringent backhaul constraints provided that the exchanged signals are compressed. The present work investigates the limits and potentials of this approach in a practical setting using large scale field trials.

Joint detection in the uplink of a cellular network involving several non-colocated base stations is a promising means to turn inter-cell interference into useful signal energy and hence dramatically increase spectral efficiency of reuse one networks. To maximize the benefits of the new degrees of freedom that come with base station cooperation, cluster centric schedulers need to be aware of the interference situation in all participating cells. Since the required exchange of information on the backhaul infrastructure can be subject to significant delays, scheduling decisions are potentially based on outdated channel state information and will thus lead to suboptimal system performance. In this contribution we extend our framework for studying the impact of these signaling delays on the system performance by allowing bigger cooperation clusters. By comparing new algorithms and introducing the possibility of channel prediction, we provide further insight into the system behavior.

Interference mitigation by applying Joint Processing (JP) in cooperative cellular networks boasts cell-edge performance compared to non-cooperative systems. However, downlink JP requires knowledge of Channel State Information (CSI) at all collaborating Base Stations (BSs), where interference received at the User Equipments (UEs) is markedly affected by the level of CSI quality influenced amongst others by the method of distributing CSI to the BSs. In this contribution, we compare CSI Distribution over the Air (CD-A) with CSI Distribution over the Backhaul (CD-B) considering a Frequency Devision Duplex (FDD) system. For CD-A, CSI is directly fed back from the UEs to the collaborating BSs via the uplink channel (assuming link adaptation is performed according to one master BS to which a UE has been assigned). Adopting CD-B, CSI is foremost transmitted only to the master BS and then forwarded to the other BSs using backhaul connections. This method typically introduces additional latency due to routing issues. In this contribution, we show that CD-A outperforms CD-B in the largest part of the cooperation area, even when pedestrian user velocities and small backhaul latencies are considered.

Coordinated Multi-Point (CoMP) appears to be an effective option to combat inter-cell interference in mobile communications. Previous field trials for uplink CoMP have shown that large improvements in spectral efficiency and fairness that are promised by theoretical work can also be achieved in real-world scenarios. However, these results only consider systems with single antenna base stations. We extend this work by presenting field trial results for a system with multi antenna base stations, and we show that this change of the system setup has a strong impact not only on the throughput but also on the relative performance of a cooperative compared to a non-cooperative system. Based on the presented results suggestions for further research and field trials are derived.

Energy-efficient link adaptation is studied for a channel exhibiting log-normal shadow fading in addition to path loss. Both circuit power and power dissipated in the power amplifier are considered. The effective throughput per Joule of energy is maximized over transmit power and a margin for shadowing. It is demonstrated that the optimization problem can be transformed to a pair of concave maximization problems over transmit power and hadowing margin, respectively. Effective algorithms with superlinear convergence are proposed to solve these concave problems. The effects of varying the circuit power, the power amplifier inefficiency parameter or the distance between transmitter and receiver are discussed. It is shown that neglecting the shadowing component by setting the shadowing margin to zero may result in large losses in energy efficiency.

The energy consumption of wideband communications receivers depends highly on the parametrization of the A/D conversion stage. The design of energy-efficient receivers requires an optimal parametrization. This paper investigates the power dissipation of state-of-the-art A/D converters and studies the optimal parametrization from an information theoretic perspective. The results show that a large sampling rate and very low quantization resolution will usually be most energy-efficient.

In this paper we investigate MIMO spatial multiplexing transmission over slowly time-variant, block-static fading channels with linear receivers. The system can adapt the transmission rate per spatial layer based on outdated channel state information. Two approximate low complexity approaches, based on the log-transformed post equalization SINR, are presented to adjust the transmission rate adaptively on a per block basis. Both approaches can be applied in order to maintain a certain target outage probability or to maximize the mutual information with outage. The methodology is suited for arbitrary input alphabets.

We present a simple but efficientalgorithm that iteratively refines the channel estimate in a turbo equalizer exploiting soft feedback (a-posteriori likelihoods) from the decoder. This algorithm, denoted as 'Soft Interference Cancelation and Correlation' (SICC), repeatedly performs an interference cancelation step followed by correlation with symbol estimates derived from the output of the decoder. The novelty lies in two facts: first, the moments of the likelihood ratio density at the decoder output are used to remove the bias of the estimate. Second, an iterative version (I-SICC) is proposed where a data block is evaluated repeatedly without activation of the channel decoder. It is shown that this variant significantly outperforms its non iterative version.

Soft-In Soft-Out (SISO) MIMO detection algorithms providing soft information to subsequent channel decoder are computationally high complex. Realizations based on depth-first search e.g. the Tuple Search (TS) algorithm enables near full MaxLogAPP optimal detection at much reduced but still high complexity. This paper presents a novel method for the complexity reduction of SISO MIMO detection algorithms. This method is based on the pruning of tree nodes and the corresponding subtrees. The pruning is decided based on the absolute value of a priori information of bits greater than or equal to a threshold value. Simulation results for the TS algorithm show that up to 25% reduction in complexity can be achieved without any BER performance degradation.

Cooperative detection involving neighboring base stations is a promising means to increase spectral efficiency in the uplink of cellular systems. To fully utilize the new degrees of freedom that come with base station cooperation, it is essential that a joint scheduler is aware of the interference conditions in all participating cells in order to perform efficient resource and rate allocation. However, joint scheduling can introduce significant delays because it requires the exchange of channel estimates and scheduling grants over a backhaul infrastructure. Due to these delays, channel information that is used for scheduling decisions is potentially outdated. In this contribution, we explore the impact of signaling delays on the performance of joint scheduling for a small cooperative cellular system. We introduce and compare different scheduling approaches inlcuding scheduling metrics that rely solely on statistical channel information and show benefits for high user mobility.

Energy-efficient link adaptation is studied based on minimizing the total energy consumption per transmitted bit in a mobile terminal. It is shown that the optimal power allocation is water-filling; the optimal energy consumption per bit is a function of the power amplifier efficiency, the circuit power rate dependence, and a cutoff channel to noise ratio (CNR). For a given transceiver architecture, low energy consumption per bit corresponds to a high cutoff CNR and vice versa. The optimization of the energy consumed per bit is carried out for flat fading and frequency-selective fading channels. In the former, the optimization problem is solved analytically, resulting in optimal rate, power and energy consumption per bit being expressed in terms of the cutoff CNR. In the latter, the optimal power allocation is water-filling in frequency, depending on the subcarrier noise levels. Finally, highly efficient algorithms with superlinear convergence are proposed, which are based on the Dinkelbach method for solving nonlinear fractional programs.

Coordinated Multi-Point (CoMP) concepts such as multi-cell joint detection and transmission, promising large improvements in spectral efficiency and fairness, appears to be an effective option to combat inter-cell interference in mobile communications. One major drawback of uplink joint detection is the large additional backhaul required when compared to a non-cooperative system. Theoretical work has demonstrated how distributed interference subtraction can be used as a low backhaul option providing moderate CoMP gain. While a large amount of theoretical work has been carried out on this topic, and previous publications have shown that these schemes work in principle, the scenarios of urban deployment and the extent of capacity gains that can be achieved are still unclear. To this end, we compare potential rate gains through linear and non-linear uplink CoMP schemes for a large scale field trial setup wherein two mobile terminals have been moved through an urban test bed of 12 base stations located at UMTS sites in downtown Dresden.

Coordinated Multi-Point (CoMP) is known to be a key technology for next generation mobile communications systems, as it allows to overcome the burden of inter-cell interference. Especially in the uplink, it is likely that interference exploitation schemes will be used in the near future, as they can be used with legacy terminals and be based on operator-proprietary signal processing concepts, hence requiring no or little changes in standardization. Major drawbacks, however, are the extent of additional backhaul infrastructure needed, and the sensitivity to imperfect channel knowledge. This paper jointly addresses both issues in a new framework incorporating a multitude of proposed theoretical uplink CoMP concepts, which are then put into perspective with practical CoMP algorithms. This comprehensive analysis provides new insight into the potential value of different uplink CoMP concepts in next generation wireless communications systems, and reveals the subset of schemes that are most likely to be used in practice.

This paper suggests a new approach for bitstream processing of embedded systems, using a combination of C++ metaprogramming combined with architecture extensions of an customizable embedded processor. Firstly, by using C++ metaprogramming techniques, we are able to code application software that needs to manipulate bitstreams in a very compact manner. Secondly, by using the architecture extensions of the Tensilica embedded processor indirectly via C++ operator overloading, the application code can seamlessly exploit custom architecture extensions. The intention is to do bitstream related processing with low programming effort, while generating runtime efficient code. Compared to other bitstream processing approaches we require no compiler modifications to exploit custom architecture features. Rather we put the bitstream related manipulation functionality into an active library, generated by a C++ metaprogram.

Energy-efficient link adaptation is studied for transmission on a frequency-selective parallel AWGN channel. The total power dissipation model includes a circuit power that varies with the sum rate and a power amplifier efficiency that varies with the bandwidth used. The mathematical analysis provides insight into how the subcarrier rates should be chosen for optimal energy efficiency and suggests a simple fixed-point algorithm that finds the solution in few iterations. Moreover, ways of improving the energy efficiency are discussed based on the dependence on bandwidth and distance between transmitter and receiver.

Coordinated multi-point (CoMP) in the cellular uplink appears to be an effective option to combat inter-cell interference, offering large improvements in spectral efficiency and fairness. However, one major drawback of these schemes is that they typically require a large extent of additional backhaul infrastructure compared to a non-cooperative system. A large amount of theoretical work has been published on this topic, emphasizing the benefit of adapting between different CoMP strategies depending on the channel realization in order to optimize the rate/backhaul trade-off. This paper complements previous publications through field trial results obtained in a representative urban setup. The results yield an insight into practical issues connected to some schemes, while being fairly correlated to theoretical predictions and in fact further emphasizing the gain of adaptive CoMP.

In this paper, results on the mutual information of MIMO transmission over spatially correlated channels in slow fading environments are presented. Different receiver techniques such as optimal maximum likelihood detection, parallel layer detection, successive interference cancellation detection and linear detection are compared. The transmit signals are derived from finite signal alphabets such as M-QAM. Different degrees of adapting the transmit signals to the channel state are taken into account. Results show that the gap between successive interference cancelation and maximum likelihood detection almost vanishes in a 2×2 MIMO transmission setup if link adaptation can be applied, regardless of spatial correlation.

Most mobile communication systems deployed use 3-fold sectorization with directed antennas to avoid inter-sector interference and to spatially reuse available radio resources. However, innovations in next generation networks such as multiple antenna systems and coordinated multi-point techniques allow mitigation or even exploitation of interference. In this paper, we discuss how sectorization and antenna directivity can be adjusted to intentionally avoid or create interference in order to maximize the throughput of non-cooperative and cooperative transmission concepts. A key result is: the introduction of cooperative transmission renders higher sectorization using a larger number of inexpensive antennas with low directivity per site attractive.

Coordinated Multi-Point and relaying are two likely candidates for the upcoming LTE-Advanced standard, as both are able to satisfy the ever increasing demands for ubiquitous services with higher data rates. In this paper, we present field trial results for both techniques and discuss the most challenging problems during the implementation process in the EASY-C testbed in downtown Dresden.

The achievable rate of digital communications systems can strongly depend on the analog-to-digital conversion at the receiver. It is hence important to adjust the gain control at the receiver in such a way that the performance degradation due to the analog-to-digital conversion is as small as possible. This paper studies the concept of an optimal gain control to maximize the achievable rate of single-carrier systems that employ analog-to-digital conversion with uniform quantization. Transmission of complex-valued symbols is considered, and a phase offset between transmitter and receiver is taken into account. The optimal gain control derives from the average mutual information between the transmitted symbols and quantized received samples. Allowing for a small tolerance of the achievable rate, it is possible to adequately adjust the gain control even with limited accuracy.

Base station cooperation is a promising solution to the interference limitation of today's cellular networks. The capacity and fairness gains shown by information theoretical analysis of base station cooperation in cellular networks are huge. However, a major downside of base station cooperation is the additional information exchange among base stations which is the motivation for recent work on backhaul efficient cooperation schemes. In this paper, we show how imperfect channel knowledge which is caused by a scheduling delay affects the performance of a backhaul constrained base station cooperation scheme. Using the example of a distributed antenna system, we investigate the benefits of a cooperation scheme which takes the varying channel state into account to make ad-hoc decisions about the required accuracy of exchanged information. Ad hoc cooperation is shown to give very good results, and is thus an effective means to counteract the influence of outdated channel information of the scheduler.

Energy efficiency in information and communications technology, and in cellular mobile radio networks in particular, is gaining in importance not only with regard to the ecological assessment. Reducing the power consumption of mobile radio systems has recently attracted attention of network operators as energy costs make up a vast portion of today's operational expenditure. In this regard, it is often talked of deploying small, low power base stations to significantly increase energy efficiency of cellular radio networks. In this paper we study the efficiency of deployment layouts featuring micro base stations in comparison with conventional pure macro systems by means of area power consumption and system throughput. We further introduce the notion of measuring energy efficiency by evaluating the ratio of achievable system throughput to power spent in the network.

A threshold analysis of terminated generalized LDPC convolutional codes (GLDPC CCs) is presented for the binary erasure channel. Different ensembles of protograph-based GLDPC CCs are considered, including braided block codes (BBCs). It is shown that the terminated PG-GLDPC CCs have better thresholds than their block code counterparts. Surprisingly, our numerical analysis suggests that for large termination factors the belief propagation decoding thresholds of PG-GLDPC CCs coincide with the ML decoding thresholds of the corresponding PG-GLDPC block codes.

The achievable rate of communications systems depends on the quantization resolution at the receiver. Earlier work has shown that the capacity of real-valued AWGN channels with 1-bit output quantization is achieved with BPSK. This paper studies optimal modulation schemes, the ergodic capacity and the outage probability for complex-valued fading channels with 1-bit output quantization, assuming full channel knowledge at the receiver. It is shown that circular symmetry with at most one amplitude per phase is a necessary condition for optimal modulation. Circular-symmetric PSK achieves the ergodic capacity in case of Rayleigh fading. Considering the outage probability for Rayleigh fading, L-PSK with large L shows the best performance among conventional modulation schemes.

Single-carrier modulation is currently regaining attractiveness for high-speed communications standards. Because of its robustness towards analog hardware impairments, single-carrier modulation can get along with low transceiver accuracy as compared to multi-carrier modulation. One hardware impairment that can, however, still seriously degrade the performance of single-carrier systems is amplifier non-linearity. The degradation is usually not as high as for multi-carrier modulation but can still not be neglected in practice. The pulse shaping at the transmitter, which is required for spectral mask compliance, will lead to power peaks in the transmitted signal that are clipped by the non-linearity. This paper presents an analytical approach for evaluating the performance degradation caused by non-linear amplifiers in single-carrier systems. The distortion due to the clipping is characterized by means of a discrete noise distribution. Numerical results show that this approximation allows for accurately predicting the performance degradation in terms of BER curves without extensive simulations.

This work presents results for wireless outdoor MIMO transmission experiments at 2.68 GHz, which were conducted in an urban residential area, using realistic handset and base station antennas. Characteristic parameters of MIMO systems, obtained from measurements, were compared to theoretical results. From the comparison it could be concluded that the transmission of two spatial streams was well supported in the investigated 2 × 2 MIMO system, even with receive antenna element spacings as small as a quarter of the wave length. Comparing different receive antennas, the MIMO performance was found to be dominated by the received signal power rather than the spatial correlation.

Optimization of the energy efficiency is considered not only to positively contribute to the ecological assessment, but gains in importance from operator's point of view as well, since energy costs for running a mobile radio network have an increasing share of the operational expenditure. From this perspective, the utilization of small, low power base stations is regarded as a promising strategy to enhance a network's throughput and to increase the energy efficiency. In this paper we investigate on the efficiency of homogeneous and heterogeneous networks consisting of a varying number of micro sites with regard to traffic load conditions.

We discuss a novel pilot design for multi-user channel estimation in the OFDM uplink with localized block-type pilot placement. Due to this placement, multiple users' channels interfere with each other. By analyzing the properties of this multi-user interference, a criterium of choosing an appropriate DFT phase shift in frequency domain is derived. With this optimized shift, the multi-user interference can be mitigated to improve the channel estimation performance significantly. Both analytical and simulative results confirm that the Mean Square Error (MSE) performance of the proposed channel estimation scheme is close to the upper bound of the single-user case.

3GPP LTE has become a hot topic in recent years. One of its main challenges is the computationally intense task of MIMO detection. This paper investigates MIMO detection methods based on Sphere detection for 3GPP LTE system. A 3GPP LTE simulation chain has been developed for the evaluation. The results show that TS algorithm in combination with complexity reduction techniques of SSD and ME achieves a good tradeoff between performance and complexity for three different user scenarios. Furthermore, the results show the performance improvement achieved with SISO detection at low complexity. To the best of our knowledge, this is the first reported work about SISO detection for 3GPP LTE.

It is well known that symbol timing offsets larger than the cyclic prefix as well as carrier frequency offsets between transmitter and receiver stations destroy the orthogonality among OFDM subcarriers and induce additional interference. In conjunction with MIMO transmission on frequency selective fading channels where different users interfere with each other, these effects strongly degrades the signal detection performance. In this paper we consider fully asynchronous spatially multiplexed transmission with different symbol timing and carrier frequency offsets on each transmitter-receiver link which appear in distributed MIMO systems with multiple users and base stations. We derive a factorized system model for signal transmission in frequency domain where the different effects of inter-carrier, inter-symbol and inter-block interference are separated and analyzed in terms of signal-to-interference-noise-ratio degradation. Finally, we evaluate the interference levels at a receiver station for different link-level as well as system-level simulation setups.

Precoding at the transmitter side facilitates the use of linear MIMO detection schemes, which are attractive for low-complexity receiver implementations. However, their performance remains sensitive to the impact of spatial correlation caused by mutual coupling between antenna elements and the spatial propagation conditions of the wireless channel. We discuss how to apply non-linear detection, which can help mitigating the impact of spatial correlation, in a closed-loop MIMO-OFDM system and compare the average rates achieved with both detection schemes. Results show that significant gains can be obtained in the high SNR regime and in a strong spatially correlated propagation environment by applying a non-linear detector while in the low to medium SNR regime a comparable performance is achieved with both detection schemes.

As semiconductor technology is rapidly approaching physical barriers that restrict the speed of digital signal processing, knowledge about physical limits to data transmission and processing is gaining more and more importance. This paper addresses a fundamental physical limit that derives from energy-time uncertainty. By relating the physical limitation to the basics of signal processing and information theory it turns out that trivalent signaling might become optimal at the frontiers of physics.

In embedded computing we face a continuously growing algorithm complexity combined with a constantly rising number of applications running on a single system. Multi-core systems are becoming popular to cope with these requirements. Growing computational complexity is handled by increasing the number of cores and core types within one system - leading to heterogeneous many-core MPSoCs in the near future. One key challenge in designing such systems is to determine the number of cores required to meet performance, power and area constraints. In this paper we present a methodology that helps dimensioning these systems via a novel parallelism analysis methodology within seconds. The presented methodology has an average performance estimation error of less than 4% compared to transaction level simulation.

Today, it is well understood that interference poses the main capacity limitation and thus challenge for future cellular networks. A promising concept that addresses interference is multi-cell cooperative signal processing, often referred to as Network MIMO. While in recent publications, it is often assumed that the required exchange of information among the base stations can be done with unlimited capacity, current network infrastructures do not necessarily support very high data rates and backhaul has been identified as a major cost driver. For the case of unlimited backhaul availability, it has been shown that large gains can be achieved through intelligent resource assignment (scheduling). In this paper, we introduce a low-complexity algorithm for uplink scheduling in cooperative cellular networks under the assumption of a capacity constrained backhaul with the target of maximizing the tradeoff between backhaul and sum rate.