Federated systems enable collaborative training on highly heterogeneous, non-i.i.d. data through model personalization, which can be facilitated by employing multi-task learning. However, multi-task learning algorithms are often implemented using methods like stochastic gradient descent, which may suffer from slow convergence in a multi-task federated setting. To accelerate the training procedure, we design an efficient iterative distributed method based on the alternating direction method of multipliers (ADMM) for support vector machines (SVMs), which tackles federated classification and regression. The proposed method utilizes efficient computations and model exchange in a network of heterogeneous nodes and allows personalization of the learning model in the presence of non-i.i.d. data. To ensure data privacy, we introduce a randomization algorithm that helps avoid data inversion. Finally, we analyze the impact of the proposed privacy mechanisms and participant hardware and data heterogeneity on the system performance. Our experiments confirm the advantages of the proposed ADMM-based personalized federated multi-task learning.

Integrated Access and Backhaul (IAB) represents a fast and cost-efficient network deployment technology that enhances the coverage of millimeter-wave (mmWave) 5G networks. In addition to the conventional challenges of wireless multi-hop relaying such as, e.g., increased interference and packet delays, traffic asymmetry can lead to significant delay degradation. While centralized coordination can mitigate these challenges, it may also lead to unnecessary overheads. In this paper, we propose an effective delay-aware distributed solution for joint access and backhaul link scheduling and route selection designed to function with limited information, which relies only on the knowledge collected from immediate neighbors. We formulate the joint upstream and downstream routing and scheduling problem, which is solved in a distributed manner for the IAB system with diverse delay requirements. To effectively tackle this problem, we employ deep reinforcement learning (DRL) algorithms. Our numerical results demonstrate that the proposed distributed solution provides improved scalability as compared to the centralized approach without a significant performance loss.

The use of millimeter-wave (mmWave) frequencies by 5G/5G+ technology results in increased signal attenuation naturally requiring dense network deployments. However, traditional fiber-based backhauling proves costly for network operators. To address this issue, 3GPP proposed the Integrated Access and Backhaul (IAB) concept to enable wireless backhaul and reduce deployment costs. However, system dynamics such as user mobility and traffic variations challenge system optimization and may shift the performance from its optimized state. On top of this, in-band mmWave IAB networks are subject to the half-duplex constraint, which prevents simultaneous transmission and reception. These limitations present challenges in optimizing the IAB network. Therefore, the goal of this study is to provide a computationally-efficient methodology for resource allocation and user scheduling in mm Wave IAB networks considering the aforementioned system limitations and constraints. Moreover, we evaluate the influence of system-specific factors and dynamics on the optimization of IAB networks and the time that it takes for the system to deviate from its optimized state. Our results show that by employing an optimally-parametrized scheduler, the throughput gain is 55% as compared to the baseline, where the radio resources are split equally among the users. The cell size is the primary parameter affecting the optimization gain, i.e., smaller cell sizes result in diminishing benefits when utilizing optimized algorithms.

Recently proposed by 3GPP, Integrated Access and Backhaul (IAB) technology promises to deliver a cost-efficient and flexible solution for network densification in 5G/6G systems. Since IAB architecture is based on multi-hop topology and advanced functionalities, such as multi-connectivity transmission and multi-routing, the potential utilization of IAB systems raises an issue of efficient system design. In this paper, we develop an optimization framework capable of jointly selecting transmission paths and allocating radio resources in compliance with half-duplexing and interference constraints. The presented numerical results illustrate that directional mm Wave beams employed at the wireless backhaul are essential for capacity boosting, thus allowing to fully exploit the radio resources in self-backhauled systems. We also establish that the multi-hop IAB topology provides advantages in terms of end-to-end user throughput as compared to single-hop systems.

Today, numerous machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of wireless networks. Distributed real-time ML solutions are highly susceptible to the so-called straggler effect caused by resource heterogeneity, which can be mitigated by various computation offloading mechanisms that severely impact communication efficiency, especially in large-scale scenarios. To reduce the communication overhead, we leverage device-to-device (D2D) connectivity, which enhances spectrum utilization and allows for efficient data exchange between proximate devices. In particular, we design a novel D2D-aided coded distributed learning method named D2D-CDL for efficient load balancing across devices. The proposed solution captures system dynamics, includingdata(time-varying learning model, irregular intensity of data arrivals),device(diverse computational resources and volume of training data), anddeployment(different locations and D2D graph connectivity). To decrease the number of communication rounds, we derive an optimal compression rate, which minimizes the processing time. The resulting optimization problem provides suboptimal compression parameters that improve the total training time. Our proposed method is particularly beneficial for real-time collaborative applications, where users continuously generate training data thus yielding a model drift.

In this paper, we propose an intelligent analog beam selection strategy in a terahertz (THz) band beamspace multiple-input multiple-output (MIMO) system. First inspired by transfer learning, we fine-tune the pre-trained off-the-shelf GoogleNet classifier to learn analog beam selection as a multi-class mapping problem. Simulation results show 83% accuracy for the analog beam selection, which subsequently results in 12% spectral efficiency (SE) gain over the existing counterparts. For a more accurate classifier, we replace the conventional rectified linear unit (ReLU) activation function of the GoogleNet with the recently proposed Swish and retrain the fine-tuned GoogleNet to learn analog beam selection. It is numerically indicated that the fine-tuned Swish-driven GoogleNet achieves 86% accuracy, as well as 18% improvement in achievable SE, over the similar schemes. Eventually, a strong ensembled classifier is developed to learn analog beam selection by sequentially training multiple fine-tuned Swish-driven GoogleNet classifiers. According to the simulations, the strong ensembled model is 90% accurate and yields 27% gain in achievable SE in comparison with prior methods.

Fifth-generation (5G) mobile communications are expected to integrate multiple radio access technologies (RATs) within a unified access network by allowing the user equipment (UE) to utilize them concurrently. As a consequence, mobile users face even more heterogeneous connectivity options, which creates challenges for efficient decision-making when selecting a network dynamically. In this work, with the tools of queuing theory, integral geometry, and optimization theory, we develop a novel mobility-centric analytical methodology for multi-RAT deployments. Particularly, we first contribute a framework for optimal data rate allocation in the network-assisted regime. Then, we characterize the convergence time of the distributed optimization algorithms based on reinforcement learning to reduce the signaling overheads. Our findings suggest that network-assisted strategies may improve the UE throughput by up to 60% depending on the considered deployment, where the gains increase with a higher density of millimeter-wave New Radio (NR) base stations. A user-centric solution based on reinforcement learning mechanisms is capable of approaching the performance of the network-assisted scheme. However, the associated convergence time may be prohibitive, on the order of several minutes. To improve the latter, we further propose and evaluate a transfer learning-based algorithm that allows to decrease the convergence time by up to 10 times, thus becoming a simple solution for rate-optimized operation in future 5G NR deployments.

An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multibase-station (multi-BS) network deployment and maintenance. It can be conducted by a network operator or its served consumers. However, the number of measurement points and their locations may not be known in advance for an efficient and accurate evaluation. The main goal of this study is to propose a new methodology for understanding the selection of measurement points during coverage and signal quality assessment. It is particularly tailored to multi-BS low-power wide-area network (LPWAN) deployments without explicit knowledge of BS locations. To this aim, we first conduct a large-scale measurement campaign for three popular LPWAN technologies, namely, narrowband IoT (NB-IoT), Sigfox, and LoRaWAN. Utilizing this baseline data, we develop a procedure for identifying the minimum set of measurement points for the coverage assessment with a given accuracy as well as study which interpolation algorithms produce the lowest approximation error. Our results demonstrate that a random choice of measurement points is on par with their deterministic selection. Out of the candidate interpolation algorithms, the Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. In contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as the natural neighbor, linear interpolation, or inverse-distance weighting, can achieve comparable (and occasionally even better) accuracy levels.

The adoption of abundant millimeter-wave (mmWave) spectrum offers higher capacity for short-range connectivity in various Unmanned Aerial Vehicle (UAV)-centric communications scenarios. In contrast to the conventional cellular paradigm, where the coordination of connected nodes is highly centralized, the distributed deployments, such as those operating over unlicensed frequency bands, maintain robust interactions in the absence of central control. These agile decentralized systems are being naturally created by dynamic UAV swarms that form a temporary 3D structure without reliance on remote management or pre-established network infrastructures. While much effort has been invested in the performance assessment of distributed, directional, and 3D systems individually, a combination of these three angles allows capturing more realistic UAV swarm scenarios and produces a novel research perspective. This work addresses one of the fundamental challenges in mmWave-based 3D networks– directional deafness– which is known to adversely affect the overall system performance. Particularly, we develop a mathematical framework by taking into account the peculiarities of 3D directional and distributed deployments. We provide a holistic analytical assessment of directional deafness and propose several powerful approximations that capture realistic antenna patterns.

The high propagation losses and sensitivity to link blockage naturally require dense deployments of millimeter-wave (mmWave) 5G New Radio (NR) systems. One of the inherent challenges in these deployments is cost-efficient backhauling. Addressing this issue, 3GPP has recently proposed the concept of integrated access and backhaul (IAB) to reduce the deployment costs by enabling wireless backhaul. The efficient utilization of spectrum in these systems is conditional on the ability of IAB nodes to simultaneously receive signals on their sectoral antennas. In this paper, we investigate the interference caused by this functionality and identify countermeasures including angular and spatial diversities. Our numerical results demonstrate that the angular distance of 25° between the user equipment (UE) served by adjacent sectoral antennas is sufficient to efficiently mitigate interference. A comparable reduction in the interference level can also be achieved by utilizing spatial diversity with antenna separation of at least 20 m. By combining these methods, one can identify the target levels of angular and spatial diversities suitable for the particular deployment restrictions.

Extended Reality (XR) is a disruptive technology that will play an essential role in future society by creating an immersive human–machine interface. For their mass adoption, XR head–mounted devices have to be made light and sleek in design, which may require distributed computing capabilities, where high-end devices wirelessly offload computational tasks to the accompanying processing units. To satisfy demanding wireless connectivity requirements of the emerging XR applications, the devices are expected to rely on radio technologies that operate in millimeter-wave (mmWave) frequency range and require directional transmission. In this paper, we evaluate a dynamic system of distributed 3D wearable networks operating in the mmWave band. We provide closed-form expressions for the session drop probability, the mean number of sessions that can run simultaneously, and its lower bound, which aid in understanding the impact of different parameters on the coexistence of dense directional wearable networks in the 3D space.

Autonomous vehicles rely on sophisticated hardware and software technologies for acquiring holistic awareness of their immediate surroundings. Deep learning methods have effectively equipped modern self-driving cars with high levels of such awareness. However, their application requires high-end computational hardware, which makes utilization infeasible for the legacy vehicles that constitute most of today's automotive industry. Hence, it becomes inherently challenging to achieve high performance while at the same time maintaining adequate computational complexity. In this paper, a monocular vision and scalar sensor-based model car is designed and implemented to accomplish autonomous driving on a specified track by employing a lightweight deep learning model. It can identify various traffic signs based on a vision sensor as well as avoid obstacles by using an ultrasonic sensor. The developed car utilizes a single Raspberry Pi as its computational unit. In addition, our work investigates the behavior of economical hardware used to deploy deep learning models. In particular, we herein propose a novel, computationally efficient, and cost-effective approach. The designed system can serve as a platform to facilitate the development of economical technologies for autonomous vehicles that can be used as part of intelligent transportation or advanced driver assistance systems. The experimental results indicate that this model can achieve real-time response on a resource-constrained device without significant overheads, thus making it a suitable candidate for autonomous driving in current intelligent transportation systems.

Blockage of millimeter-wave (mmWave) radio propagation paths in dense mobile scenarios requires advanced techniques to preserve session continuity in 5G New Radio (NR) systems. In this work, we employ the tools of stochastic geometry and queuing theory as well as rely on 3GPP cluster-based propagation modeling to formulate a mathematical framework, which captures session-level service dynamics of user equipment (UE) that supports multi-band operation. Accordingly, the sub-6 GHz NR base station (BS) is used to temporarily serve the sessions with strict throughput requirements that experience an outage at the mmWave NR BSs. We derive user- and system-centric key performance indicators, including new and ongoing session drop probabilities as well as radio resource utilization. Our numerical results confirm that the use of microwave BSs to serve mmWave sessions is only feasible in light traffic conditions. Particularly, the presence of throughput-hungry mmWave traffic increases the session drop probability at the sub-6 GHz band as well as decreases its utilization. Further, the support of sub-6 GHz radio may not improve the mmWave BS resource utilization, as many mmWave sessions are dropped during their service as a consequence of frequent blockage-induced outage situations.

The network operators will soon be accommodating a new type of users: unmanned aerial vehicles (UAVs). 5G New Radio (NR) technology operating in the millimeter-wave (mmWave) frequency bands can support the emerging bandwidth-hungry applications facilitated by such aerial devices. To reliably integrate UAVs into the NR-based network infrastructure, new system models that capture the features of UAVs in urban environments are required. As city building blocks constitute one of the primary sources of blockage on the links from the UAV to its serving base station (BS), the corresponding line-of-sight (LoS) probability models are essential for accurate performance evaluation in realistic scenarios. We propose a LoS probability model in UAV communication setups over regular urban grid deployments, which is based on a Manhattan Poisson line process. Our approach captures different building height distributions as well as their dimensions and densities. Under certain characteristic distributions, closed-form expressions for the LoS probability are offered. Our numerical results demonstrate the importance of accounting for the building height distribution type as well as the orientation of the UAV with respect to its BS. By comparing our model with the standard ITU and 3GPP formulations, we establish that the latter provide an overly optimistic approximation for various deployments.

In this paper, we study real-world performance of Sigfox, which is one of the most mature Low-Power Wide-Area Network (LPWAN) technologies that operate in unlicensed frequency bands. During an extensive measurement campaign conducted over three months in the city of Brno, Czech Republic, we assessed the communication performance and the radio channel properties in 311 different test locations. We observed that despite the challenging natural landscape and urban environment of the test area, more than 94% of the packets sent were received successfully, with at least one packet delivered from 297 out of 311 tested locations. Our results also reported experiment-based radio channel and signal-to-noise characterization as well as provided insights into the efficiency of two crucial mechanisms used by Sigfox to improve the packet delivery - packet repetition and multi-gateway reception. Finally, we employed our experimental data to understand the efficiency of two non-fingerprint localization methods based on received signal strength indicator in a practical Sigfox network.

To improve the quality of experience (QoE) and prolong the battery life, high-end wearable devices may offload their computations - partially or fully - to a paired computing device. One of the promising connectivity solutions, due to heavy load, is millimeter-wave (mmWave) technologies, which offer wide bandwidth and promise to provide extreme throughput and low latency. The features of the mmWave access and the use of sophisticated beamforming techniques have posed a whole new set of problem formulations related to directionality. Over the past decade, stochastic geometry has been extensively used to study directional mmWave connectivity in static deployments; however, there remains a research gap of employing directionality in highly dynamic scenarios. To bridge this gap, in this paper, we analyze the effects of mmWave directionality for non-static device-to-device (D2D) links, typical for high-end wearable applications. We propose a queueing-theoretical approach to capturing the dynamics of the representative mmWave D2D scenario and derive approximations for the key system-level metrics of interest. Our numerical results yield important insights on the role that the directivity has in changing the interference footprint in dynamic D2D systems.

While optimizing the system-level performance in a network of advanced high-end wearables, millimeter-wave (mmWave) medium access protocols may benefit from leveraging the information on the spatial and temporal dynamics of the radio channel. In this paper, we aim to bridge the existing gap in mmWave on-body propagation studies by analyzing the channel structure based on an extensive shooting-and-bouncing ray simulations. Particularly, we model a set of on-body trajectories and illustrate the evolution of the core channel parameters as well as address the specifics of their dynamics, which can be exploited in subsequent protocol development. As a key contribution of this study, we propose a methodology for processing and applying the simulation data as well as illustrate our approach through an example of estimating the effects of directionality and antenna beam misalignment. Our results and data, also available online, facilitate the system-level analysis and performance evaluation of various on-body mmWave systems.

A remarkable progress in the Low Power Wide Area Network (LPWAN) technologies over the recent years opens new opportunities for developing versatile massive Internet of Things (IoT) applications. In this paper, we focus on one of the most popular LPWAN technologies operating in the license-exempt frequency bands, named LoRaWAN. The key contribution of this study is our unique set of results obtained during an extensive measurement campaign conducted in the city of Brno, Czech Republic. During a three-months-period, the connectivity of a public Long Range Wide Area Network (LoRaWAN) with more than 20 gateways (GWs) was assessed at 231 test locations. This paper presents an analysis of the obtained results, aimed at capturing the effects related to the spatial diversity of the GW locations and the real-life multi-GW network operation with all its practical features. One of our findings is the fact that only for 47% tested locations the GW featuring the minimum geographical distance demonstrated the highest received signal strength and signal-to-noise ratio (SNR). Also, our results captured and characterized the variations in the received signal strength indicator (RSSI) and SNR as a function of the communication distance in an urban environment, and illustrated the distribution of the spreading factors (SFs) as a result of the adaptive data rate (ADR) algorithm operation in a real-life multi-GW deployment.

This article aims to put forward the utilization of onshore narrowband IoT (NB-IoT) infrastructure for tracking of containers transported by marine cargo vessels while operating near the coastline. We introduce and evaluate three connectivity strategies, including direct sensor-to-onshore base station (BS) transmission as well as two relay-aided schemes using dedicated vessel-BS or unmanned-aerial-vehicle (UAV)-mounted BS as intermediate nodes. To assess and compare the proposed schemes in terms of the message loss and delay metrics as well as sensor lifetimes, we first employ stochastic geometry to characterize the connectivity process with the onshore deployment and then resort to system-level simulations. Our results indicate that the direct access option suffers from the poorest performance. The relay-based alternatives allow to dramatically improve the system operation by effectively distributing the transmission requests over time at the relay side and thus mitigating contention. Furthermore, gains enabled with UAV relaying are due to extended coverage that increases the available BS density. The considered relaying operation may help tolerate intermittent connectivity across a broad range of system parameters.

Spatial correlation has been recognized by 3GPP as one of the key elements in millimeter-wave (mmWave) channel modeling. Correlated channel behavior is induced by macro objects, such as buildings, as well as by micro objects, including humans around the mmWave receivers. The 3GPP's three-dimensional (3D) spatially consistent channel model designed to capture these phenomena assumes a-priori knowledge of the correlation distance between the receivers. In this paper, we propose a novel spatially-consistent human body blockage state generation procedure, which extends the standardized 3D channel model by 3GPP to capture the correlation between the line-of-sight (LoS) links and the reflected cluster states affected by human body blockage. The proposed model is based on analytical expressions for the conditional link state probability, thus permitting the parametrization of the spatial field of receivers. It also does not require any a-priori information on the correlation distance as the latter is identified explicitly based on the environmental parameters. We compare the results for the proposed model with those obtained with the uncorrelated blockage model and conclude that in many special cases correlation manifests itself in quantitatively different propagation conditions experienced at the nearby receivers.

The use of highly directional antenna radiation patterns for both the access point (AP) and the user equipment (UE) in the emerging millimeter-wave (mmWave)-based New Radio (NR) systems is inherently beneficial for unicast transmissions by providing an extension of the coverage range and eventually resulting in lower required NR AP densities. On the other hand, efficient resource utilization for serving multicast sessions demands narrower antenna directivities, which yields a trade-off between these two types of traffic that eventually affects the system deployment choices. In this work, with the tools from queuing theory and stochastic geometry, we develop an analytical framework capturing both the distance- and traffic-related aspects of the NR AP serving a mixture of multicast and unicast traffic. Our numerical results indicate that the service process of unicast sessions is severely compromised when (i) the fraction of unicast sessions is significant, (ii) the spatial session arrival intensity is high, or (iii) the service time of the multicast sessions is longer than that of the unicast sessions. To balance the multicast and unicast session drop probabilities, an explicit prioritization is required. Furthermore, for a given fraction of multicast sessions, lower antenna directivity at the NR AP characterized by a smaller NR AP inter-site distance (ISD) leads to a better performance in terms of multicast and unicast session drop probabilities. Aiming to increase the ISD, while also maintaining the drop probability at the target level, the serving of multicast sessions is possible over the unicast mechanisms, but it results in worse performance for the practical NR AP antenna configurations. However, this approach may become feasible as arrays with higher numbers of antenna elements begin to be available. Our developed mathematical framework can be employed to estimate the parameters of the NR AP when handling a mixture of multicast and unicast sessions as well as drive a lower bound on the density of the NR APs, which is needed to serve a certain mixture of multicast and unicast traffic types with their target performance requirements.

In this paper, we address a problem of 3D directional deafness, which may arise for millimeter-wave (mmWave) devices, e.g., in the contention-based access period of the IEEE 802.11ad/ay protocols. To evaluate the probability of 3D deafness, we develop an analytical framework based on stochastic geometry methods. In particular, we study a minimal feasible set of devices equipped with highly directional antennas with an arbitrary antenna pattern and provide an analytical expression for the distance-dependent 3D directional deafness probability.To abstract away from particular antenna patterns, we propose an analytically tractable model of an antenna pattern that is given by a piece-wise linear function of the beamwidth. Using this tractable equation, we derive a corresponding closed-form lower bound for the deafness probability that serves as an approximation for an arbitrary antenna with the same half-power beamwidth. Finally, we study the effects of antenna settings on the deafness probability and derive a scaling law for its lower values.

Providing sufficient mobile coverage during mass public events or critical situations is a highly challenging task for the network operators. To fulfill the extreme capacity and coverage demands within a limited area, several augmenting solutions might be used. Among them, novel technologies like a fleet of compact base stations mounted on Unmanned Aerial Vehicles (UAVs) are gaining momentum because of their time- and cost- efficient deployment. Despite the fact that the concept of aerial wireless access networks has been investigated recently in many research studies, there are still numerous practical aspects that require further understanding and extensive evaluation. Taking this as a motivation, in this paper, we develop the concept of continuous wireless coverage provisioning by the means of UAVs and assess its usability in mass scenarios with thousands of users. With our system-level simulations as well as a measurement campaign, we take into account a set of important parameters including weather conditions, UAV speed, weight, power consumption, and millimeter- wave (mmWave) antenna configuration. As a result, we provide more realistic data about the performance of the access and backhaul links together with the practical lessons learned about the design and real-world applicability of the UAV-enabled wireless access networks.

The capability of smarter networked devices to dynamically select appropriate radio connectivity options is especially important in the emerging millimeter-wave (mmWave) systems to mitigate abrupt link blockage in complex environments. To enrich the levels of diversity, mobile mmWave relays can be employed for improved connection reliability. These are considered by 3GPP for on-demand densification on top of the static mmWave infrastructure. However, performance dynamics of mobile mmWave relaying is not nearly well explored, especially in realistic conditions, such as urban vehicular scenarios. In this paper, we develop a mathematical framework for the performance evaluation of mmWave vehicular relaying in a typical street deployment. We analyze and compare alternative connectivity strategies by quantifying the performance gains made available to smart devices in the presence of mmWave relays. We identify situations where the use of mmWave vehicular relaying is particularly beneficial. Our methodology and results can support further standardization and deployment of mmWave relaying in more intelligent 5G+ "all-mmWave" cellular networks.

As the Internet of Vehicles matures and acquires its social flavor, novel wireless connectivity enablers are being demanded for reliable data transfer in high-rate applications. The recently ratified New Radio communications technology operates in millimeter-wave (mmWave) spectrum bands and offers sufficient capacity for bandwidth-hungry services. However, seamless operation over mmWave is difficult to maintain on the move, since such extremely high frequency radio links are susceptible to unexpected blockage by various obstacles, including vehicle bodies. As a result, proactive mode selection, that is, migration from infrastructure- to vehicle-based connections and back, is becoming vital to avoid blockage situations. Fortunately, the very social structure of interactions between the neighboring smart cars and their passengers may be leveraged to improve session continuity by relaying data via proximate vehicles. This paper conceptualizes the socially inspired relaying scenarios, conducts underlying mathematical analysis, continues with a detailed 3-D modeling to facilitate proactive mode selection, and concludes by discussing a practical prototype of a vehicular mmWave platform.

Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performing data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms.

Today’s dominant design for the Internet of Things (IoT) is a Cloud-based system, where devices transfer their data to a back-end and in return receive instructions on how to act. This view is challenged when delays caused by communication with the back-end become an obstacle for IoT applications with, for example, stringent timing constraints. In contrast, Fog Computing approaches, where devices communicate and orchestrate their operations collectively and closer to the origin of data, lack adequate tools for programming secure interactions between humans and their proximate devices at the network edge. This paper fills the gap by applying Action-Oriented Programming (AcOP) model for this task. While originally the AcOP model was proposed for Cloud-based infrastructures, presently it is re-designed around the notion of coalescence and disintegration, which enable the devices to collectively and autonomously execute their operations in the Fog by serving humans in a peer-to-peer fashion. The Cloud’s role has been minimized—it is being leveraged as a development and deployment platform.

Recently, new opportunities for utilizing extremely high frequencies have become instrumental to designing the fifth-generation mobile technology. The use of highly directional antennas in millimeter-wave (mm-wave) bands poses an important question of whether 2D modeling suffices to capture the resulting system performance accurately. In this paper, we develop a novel mathematical framework for performance assessment of the emerging 3D mm-wave communication scenarios, which takes into account vertical and planar directivities at both ends of a radio link, blockage effects in three dimensions, and random heights of the communicating entities. We also formulate models having different levels of details and verify their accuracy for a wide range of system parameters. We show that capturing the randomness of both the transmitting and receiving heights as well as the vertical antenna directivities becomes crucial for accurate system characterization. The conventional planar models provide overly optimistic results that overestimate performance. For instance, the model with fixed heights that disregards the effect of vertical exposure is utterly pessimistic. The other two models, one having random heights and neglecting vertical exposure and another one characterized by fixed heights and capturing vertical exposure are less computationally expensive and can be used as feasible approximations for certain ranges of input parameters.

The intermittent millimeter-wave radio links as a result of human-body blockage are an inherent feature of the 5G New Radio (NR) technology by 3GPP. To improve session continuity in these emerging systems, two mechanisms have recently been proposed, namely, multi-connectivity and guard bandwidth. The former allows to establish multiple spatially-diverse connections and switch between them dynamically, while the latter reserves a fraction of system bandwidth for sessions changing their state from non-blocked to blocked, which ensures that the ongoing sessions have priority over the new ones. In this paper, we assess the joint performance of these two schemes for the user- and system-centric metrics of interest. Our numerical results reveal that the multi-connectivity operation alone may not suffice to increase the ongoing session drop probability considerably. On the other hand, the use of guard bandwidth significantly improves session continuity by somewhat compromising new session drop probability and system resource utilization. Surprisingly, the 5G NR system implementing both these techniques inherits their drawbacks. However, complementing it with an initial AP selection procedure effectively alleviates these limitations by maximizing the system resource utilization, while still providing sufficient flexibility to enable the desired trade-off between new and ongoing session drop probabilities.

The landscape of the contemporary IoT radio access technologies (RATs) is excessively diverse, especially when it comes to such a complex environment as Smart City. On the one hand, this diversity offers operators sufficient flexibility to select the most appropriate RAT for their target application. On the other hand, it becomes a severe limiting factor leading to high level of uncertainty for the IoT device vendors, who need to decide, which technology to support in their hardware. In this paper, we consider the provisioning of the low-power wide area network (LPWAN) devices supporting multiple RATs. First, we briefly discuss the parameters of several potential radio technologies as well as analyze the pros and cons of combining them in a single device. Next, we prototype a real-life device capable of communicating via two perspective LPWAN technologies, namely, LoRaWAN and NB-IoT, and report on the initial results of its performance evaluation. These confirm the feasibility of instrumenting dual-mode devices as well as reveal several important aspects related to the development of multi-radio IoT equipment and its performance. In our view, due to their higher flexibility, reliability, and dependability, the devices such as the one developed can be beneficial for various Smart City applications, with smart energy grids and road traffic control being only two of many examples.

Future automotive systems are expected to significantly benefit from a range of diverse mechanisms and capabilities that will be offered by the emerging fifth-generation (5G) cellular technology. In particular, one of the most prominent 5G use cases is represented by the Vehicle-to-Everything (V2X) context, which aims to enhance people's driving experience with collective safety and infotainment applications (e.g., autonomous driving, driver assistance, and contextual information). To achieve the requirements of better reliability, lower latency, and higher data rate, the use of extremely high frequencies (known as millimeter-wave, mmWave) is envisioned as an efficient solution. In fact, very high numbers of sensors deployed on vehicles introduce a serious challenge for the initial access procedure due to likely collisions in case of massive connection attempts. For that reason, the goal of this work is to offer improvements to the reliability of the initial access procedure for 5G mmWave cellular in massive V2X communications scenarios. In doing so, we propose to exploit redundant preamble transmissions in order to faster acquire a data transmission opportunity. Our obtained results indicate that by sending multiple replicas of a random access preamble the success probability to transmit at the first attempt is at least twice higher than that with the legacy approaches where a single random access preamble is being sent.

Dense deployments of millimeter-wave (mmWave) base stations (BSs) are being considered as the most feasible solution to meet the steadily growing data rate demands of mobile users. Accordingly, the achievable performance gains of mmWave-based dense networks in real deployments have to be studied carefully, since mmWave radio technology features specific transceiver, antenna, and propagation properties. In this paper, we contribute an accurate performance evaluation of single- versus dual-polarized MIMO systems operating over the mmWave channel in typical urban scenarios as well as address the impact of device- and network-centric parameters on the performance gains enabled by MIMO in dense to ultra-dense BS deployments. This study relies on our in-house ray-based modeler and takes into account the key mmWave system effects, such as multi-path propagation, utilization of dual-polarized antennas, and characteristic interference models. Our results show that the benefit of using mmWave- MIMO grows with increasing BS density, thus encouraging a further study of this technology especially for (ultra-)dense setups. We also demonstrate that non-coherent non-polarized diffuse scattering component may reduce the capacity gain of dual-polarized vs. single- polarized MIMO.

Internet access has become commonplace in the modern world. As the number of users and the amount of data traffic in the Internet keep rising exponentially, while the requirements of novel applications are becoming more stringent, there is a clear need for new networking solutions. Therefore, one of the key concepts in resolving the challenges of the upcoming 5G era of communications will be represented by multi-radio heterogeneous networks, where the users can gain benefits by either being connected to multiple different radio technologies simultaneously or seamlessly changing from one network to another based on their needs. In this work, we propose a multi-purpose automated vehicular platform prototype equipped with multiple radio access technologies, which was constructed to demonstrate the potential performance gains provided by the use of multi-radio heterogeneous networks in terms of network throughput, latency, and reliability. We discuss the potential drawbacks of using multiple radio interfaces at the same time. The constructed vehicular platform prototype constitutes a flexible research framework for communications technology within heterogeneous networks and becomes helpful for supporting future use cases of industrial IoT applications.

Future cellular systems are expected to revolutionize today's industrial ecosystem by satisfying the stringent requirements of ultra-high reliability and extremely low latency. Along these lines, the core technology to support the next-generation factory automation deployments is the use of millimeter-wave (mmWave) communication that operates at extremely high frequencies (i.e., from 10 to 100 GHz). However, characterizing the radio propagation behavior in realistic factory environments is challenging due to shorter mmWave wavelengths, which make channel properties be sensitive to the actual topology and size of the surrounding objects. For these reasons, this paper studies the important mmWave channel properties for two distinct types of factories, namely, light industry and heavy industry. These represent the extreme cases of factory classification based on the level of technology, the density and the size of the equipment, and the goods produced. Accordingly, we assess the candidate mmWave frequencies of 28 and 60 GHz for licensed-and unlicensed-band communication, respectively. After analyzing the signal propagation (e.g., in terms of path loss) and the line-of-sight (LoS) probability, our understanding is that in a factory automation environment the presence of metallic equipment and various objects produces many dissimilarities in the mmWave channel properties, thus making them difficult to describe with conventional empirical or stochastic models. Our findings suggest that the deployment of the practical mmWave systems in indoor industrial environments should not therefore rely on past propagation studies available in the literature blindly but might take into account more accurate and reliable evaluation of the environment that is possible with ray-based simulations.

The Internet of Things (IoT) undergoes a fundamental transformation by augmenting its conventional sensor network deployments with more advanced and mobile devices, such as connected and self-driving cars. This fusion of embedded and automotive domains promises to deliver unprecedented mutual benefits, where vehicles will receive timely updates from their proximate sensors while assisting them in delivering their sensory data to the remote network infrastructure. In this paper, we put forward the vision of opportunistic crowdsensing applications, in which the ubiquitous deployments of low-cost and battery-constrained IoT sensors take advantage of more capable and energy-abundant vehicle-mounted mobile relays. In particular, we consider the use of the emerging narrowband IoT radio technology recently ratified by 3GPP and offering efficient means for underlying wireless connectivity. Our rigorous mathematical analysis supported with comprehensive system-level evaluations reveals the effects of vehicle-based relays on the important metrics of interest, such as connection reliability, transmission latency, and communication energy efficiency. These systematic findings advocate for an extensive utilization of vehicular relays as part of the next-generation IoT ecosystem.

To satisfy the stringent capacity and scalability requirements in the fifth generation (5G) mobile networks, both wireless access and backhaul links are envisioned to exploit millimeter wave (mmWave) spectrum. Here, similar to the design of access links, mmWave backhaul connections must also address many challenges such as multipath propagation and dynamic link blockage, which calls for advanced solutions to improve their reliability. To address these challenges, 3GPP New Radio technology is considering a flexible and reconfigurable backhaul architecture, which includes dynamic link rerouting to alternative paths. In this paper, we investigate the use of aerial relay nodes carried by e.g., unmanned aerial vehicles (UAVs) to allow for such dynamic routing, while mitigating the impact of occlusions on the terrestrial links. This novel concept requires an understanding of mmWave backhaul dynamics that accounts for: 1) realistic 3-D multipath mmWave propagation; 2) dynamic blockage of mmWave backhaul links; and 3) heterogeneous mobility of blockers and UAV-based assisting relays. We contribute the required mathematical framework that captures these phenomena to analyze the mmWave backhaul operation in characteristic urban environments. We also utilize this framework for a new assessment of mmWave backhaul performance by studying its spatial and temporal characteristics. We finally quantify the benefits of utilizing UAV assistance for more reliable mmWave backhaul. The numerical results are confirmed with 3GPP-calibrated simulations, while the framework itself can aid in the design of robust UAV-assisted backhaul infrastructures in future 5G mmWave cellular.

Network softwarization is a major paradigm shift, which enables programmable and flexible system operation in challenging use cases. In the fifth-generation (5G) mobile networks, the more advanced scenarios envision transfer of high-rate mission-critical traffic. Achieving end-to-end reliability of these stringent sessions requires support from multiple radio access technologies and calls for dynamic orchestration of resources across both radio access and core network segments. Emerging 5G systems can already offer network slicing, multi-connectivity, and end-to-end quality provisioning mechanisms for critical data transfers within a single software-controlled network. Whereas these individual enablers are already in active development, a holistic perspective on how to construct a unified, service-ready system as well as understand the implications of critical traffic on serving other user sessions is not yet available. Against this background, this paper first introduces a softwarized 5G architecture for end-to-end reliability of the mission-critical traffic. Then, a mathematical framework is contributed to model the process of critical session transfers in a softwarized 5G access network, and the corresponding impact on other user sessions is quantified. Finally, a prototype hardware implementation is completed to investigate the practical effects of supporting mission-critical data in a softwarized 5G core network, as well as substantiate the key system design choices.

Today, the number of interconnected Internet of Things (IoT) devices is growing tremendously followed by an increase in the density of cellular base stations. This trend has an adverse effect on the power efficiency of communication, since each new infrastructure node requires a significant amount of energy. Numerous enablers are already in place to offload the scarce cellular spectrum, thus allowing utilization of more energy‐efficient short‐range radio technologies for user content dissemination, such as moving relay stations and network‐assisted direct connectivity. In this work, we contribute a new mathematical framework aimed at analyzing the impact of network offloading on the probabilistic characteristics related to the quality of service and thus helping relieve the energy burden on infrastructure network deployments.

A diversity of wireless technologies will collaborate to support the fifth‐generation (5G) communication networks with their demanding applications and services. Despite decisive progress in many enabling solutions, next‐generation cellular deployments may still suffer from a glaring lack of bandwidth due to inefficient utilization of radio spectrum, which calls for immediate action. To this end, several capable frameworks have recently emerged to all help the mobile network operators (MNOs) leverage the abundant frequency bands that are utilized lightly by other incumbents. Along these lines, the recent Licensed Shared Access (LSA) regulatory framework allows for controlled sharing of spectrum between an incumbent and a licensee, such as the MNO, which coexist geographically. This powerful concept has been subject to several early technology demonstrations that confirm its implementation feasibility. However, the full potential of LSA‐based spectrum management can only become available if it is empowered to operate dynamically and at high space‐time‐frequency granularity. Complementing the prior efforts, we in this work outline the functionality that is required by the LSA system to achieve the much needed flexible operation as well as report on the results of our respective live trial that employs a full‐fledged commercial‐grade cellular network deployment. Our practical results become instrumental to facilitate more dynamic bandwidth sharing and thus promise to advance on the degrees of spectrum utilization in future 5G systems without compromising the service quality of their users.

Owing to a steadily increasing demand for efficient spectrum utilization as part of the fifth‐generation (5G) cellular concept, it becomes crucial to revise the existing radio spectrum management techniques and provide more flexible solutions for the corresponding challenges. A new wave of spectrum policy reforms can thus be envisaged by producing a paradigm shift from static to dynamic orchestration of shared resources. The emerging Licensed Shared Access (LSA) regulatory framework enables flexible spectrum sharing between a limited number of users that access the same frequency bands, while guaranteeing better interference mitigation. In this work, an advanced user satisfaction‐aware spectrum management strategy for dynamic LSA management in 5G networks is proposed to balance both the connected user satisfaction and the Mobile Network Operator (MNO) resource utilization. The approach is based on the MNO decision policy that combines both pricing and rejection rules in the implemented processes. Our study offers a classification built over several types of users, different corresponding attributes, and a number of MNO’s decision scenarios. Our investigations are built on Criteria‐Based Resource Management (CBRM) framework, which has been specifically designed to facilitate dynamic LSA management in 5G mobile networks. To verify the proposed model, the results (spectrum utilization, estimated Secondary User price for the future connection, and user selection methodology in case of user rejection process) are validated numerically as we yield important conclusions on the applicability of our approach, which may offer valuable guidelines for efficient radio spectrum management in highly dynamic and heterogeneous 5G environments.

Recently, new opportunities for utilizing extremely high frequencies have become instrumental in developing fifth-generation (5G) mobile technology. The use of highly directional antennas in millimeter-wave (mmWave) bands poses an important question of whether two-dimensional modeling suffices to capture the resulting system performance. Accounting for the effects of human body blockage by mmWave transmissions, in this work we compare the performance of the conventional two-dimensional and the proposed three- dimensional modeling. With our stochastic geometry based approach, we consider the aggregate interference and signal-to-interference ratio (SIR) to be the main metrics of interest. Both counterpart models attempt to capture the inherent behavior of 5G mmWave systems by incorporating the effects of human body blockage and antenna directivity. We thus deliver a realistic numerical assessment by comparing the three-dimensional modeling with its two-dimensional projection to reveal the resulting discrepancy.

This paper1considers a class of multi-channel random access algorithms, where contending devices may send multiple copies (replicas) of their messages to the central base station. We first develop a hypothetical algorithm that delivers a lower estimate for the access delay performance within this class. Further, we propose a feasible access control algorithm achieving low access delay by sending multiple message replicas, which approaches the performance of the hypothetical algorithm. The resulting performance is readily approximated by a simple lower bound, which is derived for a large number of channels.

The Internet undergoes a fundamental transformation as billions of connected ”things” surround us and embed themselves into the fabric of our everyday lives. However, this is only the beginning of true convergence between the realm of humans and that of machines, which materializes with the advent of connected machines worn by humans, or wearables. The resulting shift from the Internet of Things to the Internet of Wearable Things (IoWT) brings along a truly personalized user experience by capitalizing on the rich contextual information, which wearables produce more than any other today’s technology. The abundance of personally identifiable information handled by wearables creates an unprecedented risk of its unauthorized exposure by the IoWT devices, which fuels novel privacy challenges. In this paper1, after reviewing the relevant contemporary background, we propose efficient means for the delegation of use applicable to a wide variety of constrained wearable devices, so that to guarantee privacy and integrity of their data. Our efficient solutions facilitate contexts when one would like to offer their personal device for temporary use (delegate it) to another person in a secure and reliable manner. In connection to the proposed protocol suite for the delegation of use, we also review the possible attack surfaces related to advanced wearables.

Leveraging multiple simultaneous small cell connections is an emerging and promising solution to enhance session continuity in millimeter-wave (mmWave) cellular systems that suffer from frequent link interruptions due to blockage in ultra-dense urban deployments. However, the available performance benefits of feasible multi-connectivity strategies as well as the tentative service quality gains that they promise remain an open research question. Addressing it requires the development of a novel performance evaluation methodology, which should consider: 1) the intricacies of mmWave radio propagation in realistic urban environments; 2) the dynamic mmWave link blockage due to human mobility; and 3) the multi-connectivity network behavior to preserve session continuity. In this paper, we construct this much needed methodology by combining the methods from queuing theory, stochastic geometry, as well as ray-based and system-level simulations. With this integrated framework, both user- and network-centric performance indicators together with their underlying scaling laws can be quantified in representative mmWave scenarios. To ensure modeling accuracy, the components of our methodology are carefully cross verified and calibrated against the current considerations in the standards. Building on this, a thorough comparison of alternative multi-connectivity strategies is conducted, as this paper reveals that even simpler multi-connectivity schemes bring notable improvements to session-level mmWave operation in realistic environments. These findings may become an important reference point for subsequent standardization in this area.

Motivated by the unprecedented penetration of mobile communications technology, this work carefully brings into perspective the challenges related to heterogeneous communications and offloaded computation operating in cases of fault-tolerant computation, computing, and caching. We specifically focus on the emerging augmented reality applications that require reliable delegation of the computing and caching functionality to proximate resource-rich devices. The corresponding mathematical model proposed in this work becomes of value to assess system-level reliability in cases where one or more nearby collaborating nodes become temporarily unavailable. Our produced analytical and simulation results corroborate the asymptotic insensitivity of the stationary reliability of the system in question (under the “fast” recovery of its elements) to the type of the “repair” time distribution, thus supporting the fault-tolerant system operation.

As next-generation mobile networks are rapidly taking shape driven by the target standardization requirements and initial trial implementations, a range of accompanying technologies prepare to support them with more reliable wireless access and improved service provisioning. Among these are more advanced spectrum sharing options enabled by the emerging Licensed Shared Access (LSA) regulatory framework, which aims to efficiently employ the capacity of underutilized frequency bands in a controlled manner. The concept of LSA promises to equip network operators with the much needed additional spectrum on the secondary basis and thus brings changes to the existing cellular network management. Hence, additional research is in prompt demand to determine the required levels of Quality of Service (QoS) and service provisioning reliability, especially in cases of dynamic geographical and temporal LSA sharing. Motivated by this recent urge and having at our disposal a fully-functional 3GPP LTE cellular deployment, we have committed to implement and trial the principles of dynamic LSA-compatible spectrum management. This paper is our first disclosure on the comprehensive experimental evaluation of this promising technology. We expect that these unprecedented practical results together with the key lessons learned will become a valuable reference point for the subsequent integration of flexible LSA-based services, suitable for inter-operator and multi-tenant spectrum sharing.

In this paper, we propose a simple and practical method to analyze multi-channel random-access systems with a large number of machine-to-machine (M2M) devices, which helps determine the average data access latency, as well as characterize the system stability region. The proposed methodology can be applied to random-access 3GPP LTE channels and delivers the system-optimal retransmission probability for M2M data access, which is a key parameter for highly-populated industry-grade systems with stringent data access latency requirements. The presented numerical results compare our proposed retransmission control solution against a heuristic device-centric retransmission control procedure and quantify the respective performance gains.

The use of extremely high frequency (EHF) or millimeter-wave (mmWave) band has attracted significant attention for the next generation wireless access networks. As demonstrated by recent measurements, mmWave frequencies render themselves quite sensitive to “blocking” caused by obstacles like foliage, humans, vehicles, etc. However, there is a dearth of analytical models for characterizing such blocking and the consequent effect on the signal reliability. In this paper, we propose a novel, general, and tractable model for characterizing the blocking caused by humans (assuming them to be randomly located in the environment) to mmWave propagation as a function of system parameters like transmitter-receiver locations and dimensions, as well as density and dimensions of humans. Moreover, the proposed model is validated using a ray-launcher tool. Utilizing the proposed model, the blockage probability is shown to increase with human density and separation between the transmitter-receiver pair. Furthermore, the developed analysis is shown to demonstrate the existence of a transmitter antenna height that maximizes the received signal strength, which in turn is a function of the transmitter-receiver distance and their dimensions.

The stochastic channel models typically abstract away the details of the paths that carry energy in the radio channel. While these have been universally acceptable for decades due to their ease of use and reasonable accuracy in most practical cases, the appearance of steerable, narrow-beam antennas in mmWave bands makes the exact path information very valuable, primarily for beam tracking algorithms. Currently, only deterministic channel modeling (e.g. ray tracing) provides the required level of details, but at prohibitive computing cost. This limits the study and design environments for such algorithms to the confines of existing ray tracing data, which is bulky and rarely available for free. In this paper, we consider an approach to stochastic channel modeling that allows to achieve the level of details equivalent to ray tracing, but at a fraction of the computing costs. The proposed approach may be immediately applied to any system operating at 20-100 GHz. It allows the researchers and engineers to perform quick testing of elaborate mmWave MAC and PHY algorithms with a system-level simulation, without having to obtain exhaustive measurement or ray tracing data.

The emerging fifth-generation (5G) wireless technology will need to harness the massively unused millimeter-wave (mmWave) spectrum to meet the projected acceleration in mobile traffic demand. Today, the available range of mmWave-based solutions is already represented by IEEE 802.11ad (WiGig), IEEE 802.15.3c, WirelessHD, and ECMA-387 standards, with more to come in the following years. As the key performance-related aspects of these enabling technologies are rapidly taking shape, the primary research challenge shifts to characterizing network energy efficiency, among other system-level parameters. This is particularly important in scenarios that are not handled by current 4G communication networks, including congested public places, homes, and offices. In these dense deployments, wireless wearable devices are increasingly proliferating to assist in diverse user needs. However, mmWave operation in crowded environments, and especially for multiple neighboring personal networks, is not nearly well-understood. Bridging this gap, we conduct a full-fledged energy efficiency assessment of mmWave-based “high-end” wearables that employ advanced antenna beamforming techniques. Our rigorous analytical results shed light on the underlying scaling laws for the interacting mmWave-based networks based on IEEE 802.11ad and quantify the impact of beamforming quality on system energy efficiency under various conditions. Furthermore, we look at the system optimization potential subject to realistic hardware capabilities.

Maintaining multiple wireless connections is a promising solution to boost capacity in fifth-generation (5G) networks, where user equipment is able to consume radio resources of several serving cells simultaneously and potentially aggregate bandwidth across all of them. The emerging dual connectivity paradigm can be regarded as an attractive access mechanism in dense heterogeneous 5G networks, where bandwidth sharing and cooperative techniques are evolving to meet the increased capacity requirements. Dual connectivity in the uplink remained highly controversial, since the user device has a limited power budget to share between two different access points, especially when located close to the cell edge. On the other hand, in an attempt to enhance the uplink communications performance, the concept of uplink and downlink decoupling has recently been introduced. Leveraging these latest developments, this paper significantly advances prior art by proposing and investigating the concept of flexible cell association in dual connectivity scenarios, where users are able to aggregate resources from more than one serving cell. In this setup, the preferred association policies for the uplink may differ from those for the downlink, thereby allowing for a truly decoupled access. With the use of stochastic geometry, the dual connectivity association regions for decoupled access are derived, and the resultant performance is evaluated in terms of capacity gains over the conventional downlink received power access policies.

Given the importance of multi-radio heterogeneous networks (HetNets) in delivering more throughput and better connectivity experience to today's wireless users, we investigate the prioritized centrally-controlled resource allocation mechanisms in such systems. First, we theoretically formulate the problem of assisted rate allocation across multiple radio access technologies (RATs) as a special case of relative max-min fairness problem with bifurcated (splittable) traffic flows, which can then be solved by employing the standard linear optimization techniques. Our proposed solution delivers certain minimum guarantees to all the network users, while the rest of system resources are divided proportionally to the preset priority of the users. The priorities in our system may correspond to different subscription/pricing plans of a network operator. Finally, we demonstrate the practical benefits of the proposed resource allocation scheme with system-level simulations, as well as discuss its implementation within a testbed prototype based on the OpenFlow architecture.

The IEEE 802.11 standard defines access categories (AC) and differentiated medium access control mechanisms for wireless local area networks. The preferential or deferral treatment of frames is achieved using configurable Arbitration Inter-Frame Spaces (AIFS) and customizable Contention Window (CW) sizes. In this paper, we address the problem of determining when a station, being a part of wireless communication, will access the medium. We present an algorithm calculating the probability of winning the contention by a given station, characterized by its AIFS and CW values. The probability of collision is calculated by similar means. The results were verified by simulations in Matlab and OPNET Modeler tools. We also introduce a web applet implementing and interactively demonstrating the results.

For the past years, the analysts have been predicting a tremendous and continuous increase in mobile traffic, causing much of industry and academia to seek out any and all methods to increase wireless network capacity. In this paper, we investigate one such method, cellular data offloading onto direct connections between proximate user devices, which has been shown to provide significant wireless capacity gains. To do so, we formulate a new system model that couples a cellular network in licensed bands and a device-to-device (D2D) network in unlicensed bands. We propose that devices be continually associated with the cellular base station and use this connectivity to help manage their direct connections in unlicensed spectrum. In particular, we demonstrate that assisted offloading of cellular user sessions onto the D2D links improves the degree of spatial reuse and reduces the impact of interference. In this study, a session is a real-time flow of data from one user to another, which adheres to a Poisson point process (PPP). By contrast to a throughput- or capacity-centric system view, the application of PPP enables formulations where entire user sessions, rather than singular data packets, are arriving at random and leaving the system after being served. The proposed methodology is flexible enough to accommodate practical offloading scenarios, network selection algorithms, quality of service measures, and advanced wireless technologies. In this study, we are primarily interested in evaluating the data session blocking probability in dynamically loaded cellular and D2D networks, but given the importance of energy efficiency for mobile devices, we are also interested in characterizing the energy expenditure of a typical data session in these different networks. First with our advanced analytical methodology and then with our detailed system-level simulator, we evaluate the performance of network-assisted data session offloading from cellular to D2D connections under a variety of conditions. This analysis represents a useful tool in the development of practical offloading schemes and ongoing standardization efforts.

The ongoing densification of small cells yields an unprecedented paradigm shift in user experience and network design. The most notable change comes from cellular rates being comparable to next-generation WiFi systems. Cellular-to-WiFi offloading, the standard modus operandi of recent years, is therefore shifting towards a true integration of both technology families. Users in future 5G systems will thus likely be able to use 3GPP, IEEE, and other technologies simultaneously, so as to maximize their quality of experience. To advance this high-level vision, we perform a novel performance analysis specifically taking the system-level dynamics into account and thus giving a true account on the uplink performance gains of an integrated multi radio access technology (RAT) solution versus legacy approaches. Further, we advocate for an enabling architecture that embodies the tight interaction between the different RATs, as we lay out a standardization roadmap able to materialize the envisaged design. 3GPP-compliant simulations have also been carried out to corroborate the rigorous mathematical analysis and the superiority of the proposed approach.

In next-generation wireless networks, device-to-device (D2D) communication represents a feasible way for mobile users to offload their cellular traffic demand without extra costs for deploying additional infrastructure from the network operators. Cellular (e.g., 3GPP LTE) network assistance can automate user/service discovery and connection establishment procedures, as well as enable secure D2D connectivity between proximate users. Currently, assisted direct connectivity is only available in the form of unlicensed-band protocols (e.g., WiFi Direct), which motivates research on understanding its practical limitations with realistic distributions of users and content. Whereas there are concerns that D2D communication alone may not be efficient due to limited content availability, in this paper, we advocate the use of network coding to upgrade assisted proximate communication and make it realize its full potential. In particular, we demonstrate that even simpler network coding techniques are capable to significantly improve the degrees of content availability for communicating users and thus enhance offloading performance under realistic constraints. Inspired by the recent popularity of wireless content distribution systems over D2D caches, we contribute a practical methodology for assisted data caching and distribution, mindful of the state-of-the-art D2D technology.

Intensive competition between network operators as well as steady increase in mobile traffic call for additional investments into the networking infrastructure. Keeping current mobile networks profitable, the following criteria should be satisfied: end-user quality expectations need to be fulfilled on the one hand and service quality overprovisioning should be eliminated on the other. This generates growing demand for adequate QoE estimation models accounting for dominant mobile data services. Moreover, novel models have to fulfill requirements of good applicability and high accuracy. Our approach in this paper details an advanced QoE estimation model, extensively verified with appropriate statistical tools, for the most popular mobile web services: browsing, download, and upload. The proposed model follows from a recent extensive QoE assessment and utilizes the bitrate together with the initial loading delay as well-measurable input parameters. Targeted to estimate the mean opinion score, our proposed model demonstrates an excellent convergence across the considered practical scenarios.

In this paper, we address the emerging 5G heterogeneous networks, which comprise several tiers of small cells of various sizes and by different radio access technologies. As industry has recently been targeting efficient mechanisms for tighter coordination between the tiers of such heterogeneous network, where multiple radios may be utilized simultaneously, we argue that the corresponding problem of assisted resource allocation has not been as widely investigated analytically. To this end, we propose a novel mathematical methodology allowing to deliver the optimal rate distribution for users across the next-generation N-tier wireless network. In particular, we demonstrate that the considered problem of assisted rate allocation can be formulated as a special case of a network flow optimization problem and solved by employing linear programming routines. We also discuss a range of important practical extensions to our baseline model.

Currently, cellular operators are struggling to relieve congestion on their networks in the face of rapidly growing mobile data traffic. While deploying an increasing number of base stations is expected to mitigate the disproportion between user demand and available radio resources, this solution is costly and plagued with many practical challenges. An attractive alternative is to enable cellular traffic offloading onto device-to-device (D2D) connections in the unlicensed bands, as current multi-radio user devices are already capable of establishing concurrent LTE and WiFi links. However, WiFi lacks a fast, efficient method of device/service discovery, and it is not equipped to efficiently manage numerous D2D connections. In our research, we have found that a limited amount of network assistance for D2D communications can overcome these limitations; and in this paper we describe our network-assisted D2D technology prototype. Specifically, we outline a complete standards-compliant solution that provides a seamless D2D connectivity experience to the end user. Our solution utilizes WiFi Direct as the link-layer technology for proximal D2D connections. However, the challenges faced during the design phase are universal to all D2D link-layer protocols, thus the proposed solutions are applicable to other potential D2D technologies.

In this paper, we revisit the modeling assumptions of some of the most widely accepted models in existing literature on the IEEE 802.11 distributed coordination function (DCF) with respect to their accuracy. Ultimately, our objective is to verify the precision of the analytical models with simulation results. First, we outline a number of issues and criticalities in underlying assumptions of existing approaches to backoff process modeling for unsaturated and saturated scenarios. Then, by revisiting the assumptions in existing backoff process models, we reassess them for the more complicated unsaturated case under more realistic assumptions. We argue that under unsaturated load conditions the backoff process exhibits a ρ-persistent behavior. Our claims are supported by observing the simulated behavior of a wireless local area network (WLAN) under varying load conditions. Also, the principle of ‘measures of uncertainty’ provides theoretical basis for our arguments. Comparison with known models in different simulation environments confirms the increased accuracy of the proposed model over a wide range of settings.

As fourth generation communications technology is already being deployed, research efforts are now being shifted to what comes beyond state-of-the-art wireless systems. Driven by the anticipated acceleration in mobile traffic demand, the wireless industry is specifically focused on improving capacity and coverage of current networks through aggressive reuse of the cellular spectrum. Together with deploying an increasingly dense overlay tier of smaller cells, mobile network operators are beginning to rely on unlicensed-band WLAN technologies to leverage additional spectrum and relieve congestion on their networks. Consequently, the emerging vision of heterogeneous networks exploits the potential of a diverse range of devices requiring connectivity at different scales to augment available system capacity and improve the user connectivity experience. In this paper, we seek to meet this important trend with our novel integrated methodology for assisted (managed) radio network selection capturing spatial randomness of converged cellular/WLAN deployments together with dynamic uplink traffic from their users. To this end, we employ tools coming from stochastic geometry to characterize performance of macro and pico cellular networks, as well as WLAN, mindful of user experience and targeting intelligent network selection/assignment. We complement our analysis with system-level simulations providing deeper insights into the behavior of future heterogeneous deployments.

In this paper, we address the emerging concept of Machine-Type Communications (MTC), where unattended wireless devices send their data over the Long Term Evolution (LTE) cellular network. In particular, we emphasize that future MTC deployments are expected to feature a very large number of devices, whereas the data from a particular device may be infrequent and small. Currently, LTE is not optimized for such traffic and its data transmission schemes are not MTC-specific. To improve the efficiency of small data access, we propose a novel contention-based LTE transmission (COBALT) mechanism and evaluate its performance with both analysis and protocol-level simulations. When compared against existing alternatives, our data access scheme is demonstrated to improve network resource consumption, device energy efficiency, and mean data access delay. We conclude that COBALT has the potential for supporting massive MTC deployments based on the future releases of the LTE technology.

In this paper, we consider a wireless cellular system with an unbounded population of machine-type users. The system provides a number of non-interfering slotted-time channels which users contend for when sending their uplink data packets. We propose a provably stable control procedure for the channel access probability in the sense that it maintains a finite number of unserviced users in the system. We also compare the proposed algorithm against the optimal multi-channel slotted Aloha to conclude that our solution demonstrates near-optimum performance.

Analysts predict explosive growth in traffic demand on mobile broadband systems over the coming years due to the popularity of streaming video, gaming, and other social media services. While 4G wireless technologies are making a significant effort to keep up with this demand, the expectation is that cellular deployments will fall short of the required capacity unless there is a dramatic shift towards smaller cells. There is already significant interest in femto- and pico-cell deployments for this reason. However, there is another method of creating small cells that the wireless industry has yet to capitalize on, namely direct connectivity between clients in close proximity. 3GPP is currently working to enable device-to-device (D2D) communications within Release 12 of LTE-Advanced. By comparison, IEEE has already defined a D2D communications protocol, termed WiFi Direct, which is based on the 802.11 standards. WiFi Direct not only serves to offload user data onto direct links, but does so using the unlicensed bands. To benefit users further, WiFi Direct can be enhanced by enabling the LTE network to assist during peer discovery and direct connection establishment. In this paper, we discuss the network/client requirements and performance benefits of network-assisted WiFi Direct. We assume that clients are continuously under management by the LTE network, which assists them with service/peer discovery and direct connection establishment. We show that network-assisted WiFi Direct can significantly improve the performance of proximal applications and reduce the power consumed by the clients involved, while also improving capacity of the LTE network.

In this paper, we consider a wireless cellular network capable of supporting Machine-to-Machine (M2M) applications. According to the recent IEEE 802.16p proposals, a wireless M2M device may act as an aggregation point and communicate data packets on behalf of the other M2M devices, which may lack a cellular interface or have a poor communication link to the network. We propose a client relay scheme to improve the link reliability and energy efficiency for devices with weak links. Performance of the proposed scheme is evaluated through analysis and simulation across several metrics covering client throughput, latency, and energy consumption. Our analytical approach is a novel queueing model that captures realistic traffic arrival patterns borrowed from the evaluation methodology. It is shown that the obtained analytical results demonstrate excellent agreement with simulation. We also conclude that the proposed client relay scheme may save power for devices with poor communication link.

Growing demand for bandwidth dictates the use of smaller wireless cells, which results in increased inter-cell interference. In most contemporary cellular systems, the clients at the cell edge typically use higher transmission power to compensate for increasing path loss and fading and thus generate the most interference. Client relay is believed to be a promising technique to enhance the performance of cell-edge users by allowing them to exploit other users as relay nodes and thus transmit with less power. In this paper, authors conduct in-depth system-level evaluation of client relay technique in state-of-the-art wireless cellular networks. Several important scenarios are considered, including opportunistic client relay behavior and various channel models. It is demonstrated that client cooperation may considerably improve system performance in terms of cell-edge spectral efficiency for the cost of some increase in cell-center energy consumption.

In this paper we extend our recent work by analyzing the behavior of mobility scenario in saturation conditions for evaluating packet delays. For the first time we have shown that the normalized measure of dispersion for service time intervals and offered load dependency holds for multi-hop IEEE 802.11 wireless networks with mobile nodes, as well. Thus this paper not only extends and validates our previous results but also establishes that wireless mobile networks have the similar dependency pattern. We further extend this work by showing that the service time intervals do not follow exponential distribution but instead follow gamma distribution. The patterns for the shape parameters are also established for accurately computing average end-to-end packet delay for mobile networks. The analytical model presented in this paper has been verified through extensive simulations.

In vehicular ad-hoc networks (VANETs) beaconing is one of the core communication modes, which is designed to advertise the presence of a car to its neighborhood. For practical applications the delivery of beacons containing the speed, the direction and the position of a car should be organized both timely and successfully. IEEE 802.11p is the most recent developing international standard, which specifies the physical (PHY) and the medium access control (MAC) protocols for car-to-car and car-to-infrastructure communication and is expected to lay the foundation for safety-related and infotainment applications in future VANETs. In previous works, it has been shown that the requirements of safety-related applications for the mean beacon transmission delay could be met for typical cases, but the corresponding probability of a successful beacon reception does not attain the required threshold. In this paper, we present a novel analytical method based on Markov chain for car-to-car communication analysis and investigate the influence of the beacon generation rate on the probability of a successful beacon reception in an IEEE 802.11p-based network.