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.

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.

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.

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 work, 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 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. Additionally, 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.

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.

With the emergence of the fifth generation (5G) wireless networks, not only is the increase in mobile broadband targeted, but also the support of various novel use cases, such as industrial automation, autonomous vehicles, e-health, and Internet of Things together with their requirements leading to highly heterogeneous wireless networks. This requires a re-design of the network architecture to ensure the coexistence of these use cases and guarantee user experience and service requirements. Therefore, 5G networks will be highly flexible and support online learning and autonomous decision making capabilities in a centralized and distributed manner to ensure highly efficient management of wireless and network resources. In this paper, the main features enabling flexibility and autonomy in 5G networks are discussed together with potential applications in different layers of the wireless network.

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.

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.

The signal-to-interference-and-noise ratio (SINR) is of key importance for the analysis and design of wireless networks. For addressing new requirements imposed on wireless communication, in particular high availability, a highly accurate modeling of the SINR is needed. We propose a stochastic model of the SINR distribution where shadow fading is characterized by random variables. Therein, the impact of shadow fading on the user association is incorporated by modification of the distributions involved. The SINR model is capable of describing all parts of the SINR distribution in detail, especially the left tail, which is of interest for studies of high availability.

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.

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.

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 lognormal 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.

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.

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 minplus convolution for determining the optimal power allocation among several selection combined Nakagami-m fading links with unequal fading parameters.

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.

Most of today's advanced cellular network models consider time-stationary behavior neglecting that networks are frequently subjected to transient effects due to, e.g., fluctuating traffic demand. In addition, modern concepts such as small cells and heterogeneous networks might introduce short-term dynamics that have to be taken into account as well. For instance, microcells can be switched on and off dynamically in order to adapt the energy consumption and frequency usage to varying input loads. Supplementary to existing steady-state flow level models, we present techniques enabling a transient analysis of system startup and non-stationary traffic scenarios. In detail, we propose analytical and numerical techniques based on queueing theory and differential equations to model the transient behavior of interference-coupled cellular networks exactly, and suggest further approximations for the same. Numerical studies reveal the need for transient models and show that the approximation techniques offer a reasonable trade-off between accuracy and fast computation.

Future cellular networks have to meet enormous, unprecedented, and multifaceted requirements, such as high availability and low latency, in order to provide service to new applications in, e.g., vehicular communication, smart grids, and industrial automation. Such applications often demand a temporal availability of six nines or higher. In this work, we investigate how high availability can be achieved in wireless networks. To elaborate, we focus on the joint availability of power-controlled Rayleigh-fading links while using selection combining. By applying a basic availability model for uncorrelated links, we determine whether it is more efficient in terms of power to utilize multiple links in parallel rather than boosting the power of a stand-alone link. The results reveal that, for high availability, it can actually be beneficial to use multiple links in parallel. For instance, an availability of 1-1012 is achieved with 100 dB less power when power is shared among multiple links. Depending upon the availability desired, an optimal number of parallel links in terms of power consumption exists. Additionally, we extend the availability model to correlated links and investigate the performance degradation due to correlation.

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.

Modern concepts for cellular networks, e.g., heterogeneous networks and small cells, increase base station densities to satisfy the capacity demand and, hence, lead to a highly dynamic, interference-limited regime. For instance, base stations can be turned on and off dynamically in order to adapt the energy consumption, and frequency usage, to fluctuating traffic demand. The time scale of such operations depends on hardware and system capabilities, but it can be in the range of seconds or minutes. Moreover, due to frequency reuse, user data rates are mutually coupled via inter-cell interference. In order to manage and optimize such dynamic networks, intelligent algorithms and sophisticated system models are needed. In this paper, we focus on flow level models based on queueing theory. Existing models often assume stationary conditions (i.e., fixed traffic rates and steady-state), which may be inadequate for dynamic systems with time-varying arrival intensities. Addressing this issue, we derive transient flow level models that consider time-dependent, dynamic network behavior. Since the flow level model of interference-coupled queues renders analytically intractable, we propose different approximation techniques, e.g., aggregation of variables and average interference, and determine first and second order bounds as well. Numerical studies compare the accuracies of the different approaches, and confirm that transient effects are not negligible in interference-coupled cellular networks.