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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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 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.

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.

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.

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.

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.

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.

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.

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.

We propose an appearance-based along-route localization algorithm that relies on robust place recognition by matching image sequences instead of individual frames. Our approach extends state of the art place recognition framework SeqSLAM in several aspects to realize real-time localization along routes for autonomous navigation. First, our method is online in that we only rely on the recently observed image frames. Second, we provide a homing mechanism based on rotations computed from frame matches. And third, we use a more flexible mechanism to search for matching locations, not restricting the search to straight lines in the cost matrix as in SeqSLAM, but allowing for a wide variety of route traversal conditions such as varying velocities or rotational and translational viewpoint differences. We investigate different image preprocessing steps as well as image similarity metrics wrt. their influence on illumination and viewpoint invariance for a more robust place recognition. On a new challenging dataset, recorded in real world experiments with a planetary rover, in the course of a Moon-analogue mission on Sicily's Mount Etna, we show the feasibility of our direct, sequence-based approach to along-route localization.

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.

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.

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.