Future 5G/6G cellular Internet of Things (CIoT) technologies need to be able to multiplex a number of traffic types having different arrival patterns, resource requirements, and directions. To this aim, the random access (RA) and data transmission (DT) phases in such technologies should be appropriately optimized. In this paper, we develop a computationally efficient and accurate model accounting for both RA and DT phases and the different nature of traffic types in uplink and downlink directions. We establish the stability criterion for the considered system and then utilize this model to optimize resources allocated to RA and DT phases under different traffic loads in uplink and downlink directions. We show that the proposed model provides accurate assessment of the delay at both RA and DT phases with an error of less than 1%, while its fully analytical structure makes it computationally efficient. Our results also indicate that modern CIoT system are not optimized for different load conditions and even a 5 % decrease in the amount of resources allocated to RA or DT phase changes the supported packet arrival rate by up to 80 %. The model developed in this paper allows optimal tuning RA and DT phases for any offered load conditions in the uplink and downlink directions.

The increasing demand for power distribution systems in terms of control with nearly immediate response requires deploying a new type of user equipment (UE) that demands permanent connectivity. In Narrowband Internet of Things (NB-IoT) systems, the traffic generated by such UEs may constitute a large part of the overall load. In this article, we first propose a detailed 2-D Markov chain model to capture the system’s behavior with the mixture of conventional stochastic and regular traffic types. To provide a computationally efficient solution, we then apply the state aggregation technique to reduce it to a 1-D model and develop approximations and associated numerical algorithms for assessing the mean delay when transmitting the considered traffic. Our results show that a single NB-IoT cell remains stable for up to$72\times {}10^{4}$conventional UEs and$9\times {}10^{3}$UEs demanding permanent connectivity. The presence of the latter UEs type has a linear effect on their delay, but affects conventional UEs more drastically. A delay bound of 10 s specified in ITU-R M.2410 is met for the conventional UEs, even under a high number of permanently connected UEs$({10^{3}})$. However, the delay on the side of the latter UEs is violated even for 100 permanently connected UEs requiring redesigning the NB-IoT channel access mechanism or expanding resources.

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.

DECT-2020 is a recently standardized enabling technology for 5G massive machine-type communications operating in the unlicensed band. By relying upon multi-hop communications, DECT-2020 allows for cost-controlled flexible deployments, it requires an efficient solution for topology design, including the relay selection and next-hop selection mechanisms. The goal of this paper is to evaluate the impact of these algorithms on the fraction of successfully delivered packets for DECT-2020 technology. To this aim, we utilize the system-level simulation tool to capture the functionality of the physical and data-link DECT-2020 solutions. Our numerical results illustrate that the system is susceptible to the choice of the energy conservation parameters with the difference in the fraction of delivered packets reaching 10% and may effectively lead to non-operational conditions. Without simple analytical models, these parameters must be set with caution. The relay-selection mechanism procedure is generally insensitive to the parameters of the relay selection procedure, resulting in a deviation of at most 3%. Finally, the power control feature gives an additional gain of around 2-4% compared to the system without power control.

This work opens a new chapter in the 100, 000 year-old concept of paint, by leveraging innovations in nano-technology in the sub-THz frequency range. More specifically, the groundbreaking concept of Internet of Paint (IoP) is introduced along with a comprehensive channel model and a capacity analysis for nano-scale radios embedded in paint and communicating through paint. Nano-network devices, integrated within a paint medium, communicate via a multipath strategy, encompassing direct waves, reflections from interfaces, and lateral wave propagation. The evaluation incorporates three distinct paint types to assess path losses, received powers, and channel capacity. Analysis of path loss indicates a slight non-linear increase with both frequency and Line of Sight (LoS) distance between transceivers. Notably, paints with high refractive indexes result in the highest path loss. Moreover, burying transceivers at similar depths near the Air-Paint interface showcases promising performance of lateral waves with increasing LoS distance. Increasing paint layer depth leads to amplified attenuation, while total received power exhibits promising results when in close proximity to the Air-Paint interface but steeply declines with burial depth. Additionally, a substantial reduction in channel capacity is observed with LoS distance and burial depth, so transceivers need to be close together and in proximity of the A-P interface to communicate effectively. Comparing paint and air mediums, IoP demonstrates approximately two orders of magnitude reduction in channel capacity compared to air-based communication channels. This paper provides valuable insights into the potential of IoP communication within paint mediums and offers a foundation for further advancements in this emerging field.

The swarms of unmanned aerial vehicles (UAV) are nowadays finding numerous applications in different fields. While performing their missions, UAVs have to rely on external positioning information to maintain connectivity and communications between units in a swarm. However, some of the critical applications such as rescue missions are performed in locations, where this information is partially or fully not available, e.g., deep woods, mountains, indoors. In this paper, we propose a method for dynamic topology organization and maintenance in UAV swarms. In addition to the baseline functionality, we also design advanced features required for dynamic swarms merging and disjoining, making it suitable for practical applications. Specifically, the proposal is based on the virtual coordinates system allowing for the utilization of conventional geographical routing algorithms. We test the proposed algorithm in different swarm conditions to illustrate that: (i) it is insensitive to distance estimates up to at least 30% allowing for simple estimation techniques, (ii) the accuracy of the topology inference is at least 90% even under impairments caused by mobility and temporal loss of connectivity, and (iii) the impact of the developed merging algorithm for swarms lasts for multiple tens of time steps that correspond to just few seconds in practice. The set of developed algorithms can be utilized to ensure always connected topology in conditions where positioning information is partially or fully unavailable.

The 5G millimeter wave (mmWave) New Radio (NR) systems are prone to blockage and micromobility effects. To improve service reliability, 3GPP proposed a dual-connectivity allowing UE to maintain links to two base stations (BS). However, this functionality is power-hungry resulting in a trade-off between performance and power efficiency. In this paper, we compare user equipment (UE) power efficiency, consumption, and battery lifetime for different intra- and inter-radio access technologies single- and dual-connectivity under different UE usage scenarios, micromobility, and blockage impairments. We evaluate five schemes: (i) BSs in FR1 and FR2 bands (NR-DC FR1/FR2), (ii) BSs in FR2 band (NR-DC FR2/FR2), and (iii) LTE BS and NR BS in FR2 band (EN-DC LTE/FR2), (iv) NR FR1, and (iv) LTE. Our results show that the LTE-only scheme provides three times longer battery lifetime as compared to its nearest rival-NR FR1 single-connectivity option. For dual-connectivity options, the best lifetime is observed for EN-DC FR2/LTE, and the worst – for NR-DC FR2/FR1. The difference between NR-DC FR2/FR1 and NR-DC FR2/FR2 schemes is negligible. Densification negatively affects all the dual-connectivity schemes as it forces UEs to switch between master and backup technologies more often. We recommend EN-DC FR2/LTE for low-traffic outage-sensitive applications, while NR-DC FR2/FR2-for heavy-traffic outage non-sensitive applications.

Network slicing is a technique introduced by 3GPP to enable multi-tenant operation in 5 G systems. However, the support of slicing at the air interface requires not only efficient optimization algorithms operating in real time but also its tight integration into the 5 G control plane. In this paper, we first present a priority-based mechanism enabling defined performance isolation among slices competing for resources. Then, to speed up the resource arbitration process, we propose and compare several supervised machine learning (ML) techniques. We show how to embed the proposed approach into the ITU-T standardized ML architecture. The proposed ML enhancement is evaluated under realistic traffic conditions with respect to the performance criteria defined by GSMA while explicitly accounting for 5 G millimeter wave channel conditions. Our results show that ML techniques are able to provide suitable approximations for the resource allocation process ensuring slice performance isolation, efficient resource use, and fairness. Among the considered algorithms, polynomial regressions show the best results outperforming the exact solution algorithm by 5–6 orders of magnitude in terms of execution time and both neural network and random forest algorithms in terms of accuracy (by 20–40 %), sensitiveness to workload variations and training sample size. Finally, ML algorithms are generally prone to service level agreements (SLA) violation under high load and time-varying channel conditions, implying that an SLA enforcement system is needed in ITU-T's 5 G ML framework.

5G New Radio (NR) millimeter wave (mmWave) and future sub-terahertz (sub-THz) 6G systems are designed for rate-greedy multimedia applications. Such applications often utilize rate adaptation allowing to vary the rate according to the network conditions. Unreliable nature of these bands subject to blockage may induce persistent user behavior resulting in repeated attempts (retrials) to continue the service. These effects may lead to the loss of sessions during the service and service provisioning at reduced rate. In this paper, by using the stochastic geometry and queuing theory, we characterize performance of adaptive rate-greedy applications at mmWave/sub-THz base stations. We consider user- and system-centric performance including new and ongoing session drop probabilities, resource utilization, and the amount of wasted resources. Our results show that the user persistence improves the new and ongoing session drop probabilities: making two retrials brings both down by$20-70$% while for fully persistent behavior the gain is 300%. The impact is more pronounced in underloaded system conditions. User persistence also increases the usage of system resources by$20-40$% and decreases the percentage of wasted resources by two/three times. However, these gains come at the expense of decreased successful session completion probability without retrials negatively affecting the overall user experience.

The recently standardized ETSI DECT-2020 New Radio (NR) technology promises to enable operator-independent Internet-of-Things (IoT) services. One of the supported operating bands is the 1880 − 1900 MHz band where IoT devices use one or more 1.728 MHz wide channels for multi-hop communications. It has been shown that such systems scale well to satisfy the requirements of 5G massive machine-type communications (mMTC). During the standardization process, the coexistence with classic DECT technology was addressed but the benefits of different options have not been deeply explored. In this paper, by utilizing system-level simulation techniques we evaluate several coexistence solutions for DECT-2020 and classic DECT systems including conventional listen-before-talk access (LBT), last-minute scan, and scheduling-based mechanisms. Our numerical results illustrate that the standard LBT operation ensures excellent performance with no more than 2% of packet drops for classic DECT, and still results in satisfactory DECT-2020 operation under 25% of resources allocated to classic DECT. The use of last-minute-scan leads to drastic performance degradation of DECT-2020 (by more than 10% in terms of packet drops) as compared to the standard LBT-only operation. Finally, a scheduling-based mechanism allows to improve the LBT performance for DECT-2020 devices by 2-5%.

Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by$\approx 7$% with respect to fingerprinting with the traditional clustering models.

The support of multicast communications in the fifth-generation (5G) New Radio (NR) system poses unique challenges to system designers. Particularly, the highly directional antennas do not allow to serve all the user equipment devices (UEs) that belong to the same multicast session in a single transmission. The capability of modern antenna arrays to utilize multiple beams simultaneously, with potentially varying half-power beamwidth, adds a new degree of freedom to the UE scheduling. This work addresses the challenge of optimal multicasting in 5G millimeter wave (mmWave) systems by presenting a globally optimal solution for multi-beam antenna operation. The optimization problem is formulated as a special case of multi-period variable cost and size bin packing problem that allows to not impose any constraints on the number of the beams and their configurations. We also propose heuristic solutions having polynomial time complexity. Our results show that for small cell radii of up to 100 meters, a single beam is always utilized. For higher cell coverage and practical ranges of the number of users (5-50), the optimal number of beams is upper bounded by 3.

Sub-terahertz (sub-THz, 100–300 GHz) communications promise to bring extraordinary rates in future 6G systems. High path loss and blockage effects will limit the coverage of base stations (BS) to a few hundred meters making deployment of such systems along the roads expensive. As a way to decrease the BS density, relaying has been proposed. However, vehicle-to-vehicle (V2V) propagation is characterized by different sets of communications paths depending on the antenna locations raising the question of their optimal positions. In this paper, by utilizing IEEE 802.15.3d parameters and 300 GHz propagation measurements, we develop a mathematical framework for comparison of multi-hop relaying systems with different antenna locations. We utilize coverage, BS availability, and the data rate over a multi-hop path as metrics of interest. Our results show that the windshield location is characterized by lower data rates and larger coverage while bumper and engine levels are similar in terms of these metrics. For the windshield location, the coverage is extended by 50% with BS availability 0.95. The windshield location is recommended as it is less sensitive to the technology penetration rate and is characterized by larger coverage. The proposed approach shows gains of up to 32% in terms of required BS density for the range of vehicles density (10-40 units/km).

Energy efficiency and service reliability are two critical requirements for 5G New Radio cellular access. To address the latter, 3GPP has proposed multiconnectivity operation allowing user equipment (UE) to maintain active links to more than a single base station. However, the use of this technique compromises the energy efficiency of UE. In this paper, we develop a mathematical model capturing key energy and performance indicators as a function of system and environmental conditions. Then, we apply it to investigate the trade-offs between user performance and energy efficiency as well as the effect of scaling of discontinuous reception (DRX) timers. For a considered set of system parameters, our results reveal that for low micromobility speed$\leq 0.1^{\circ}$/s and blockers density,$\leq 0.1$bl./$\text{m}^{2}$two simultaneously supported links with minimal DRX timers lead to optimal performance. For higher blockers density more than two links are needed to optimize energy efficiency while for high micromobility speed multiconnectivity does not allow to improve energy efficiency at all. Thus, the optimal degree of multiconnectivity and DRX timer scaling coefficients depend on the environmental characteristics including both micromobility speed and density of blockers and need to be dynamically updated during UE operation.

Millimetre wave (mmWave) communication is a promising technology which can fulfil the growing demands for spectrum for future wireless networks. One of the key areas for the development of the mmWave networks is the Internet of Things (IoT) communications within fifth generation (5G) and beyond 5G networks. For significant analysis and development of the compliant IoT systems through testbed implementation, current mmWave spectrum transceivers are too expensive when substantial number of the nodes is required by the IoT applications. Considering all the above, it is suggested to use Software Defined Radio (SDR) transceivers with a lower frequency band and with an increased distance between the nodes. The idea is to scale observation time and distance to emulate mmWave radio without actual mmWave hardware. Using scaling factors for the certain system parameters to keep the signal characteristics in accordance with the mmWave band makes it possible. This approach allows to develop mmWave IoT testbeds with significant improvement in the system scalability and cost-effectiveness without the need to transmit and receive the signal in the mmWave band. In this paper, the concept of SDR-based Hardware-in-the-loop (HIL) system combined with the observation time and distance scaling approach is proposed. As an example, a testbed with a simple Wireless Physical Network Coding scheme is implemented and demonstrated.

Indoor Positioning based on Machine Learning has drawn increasing attention both in the academy and the industry as meaningful information from the reference data can be extracted. Many researchers are using supervised, semi-supervised, and unsupervised Machine Learning models to reduce the positioning error and offer reliable solutions to the end-users. In this article, we propose a new architecture by combining Convolutional Neural Network (CNN), Long short-term memory (LSTM) and Generative Adversarial Network (GAN) in order to increase the training data and thus improve the position accuracy. The proposed combination of supervised and unsupervised models was tested in 17 public datasets, providing an extensive analysis of its performance. As a result, the positioning error has been reduced in more than 70% of them.

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.

Modern mobile handheld devices, such as smartphones and tablets, feature multiple wireless interfaces, some of which can support device-to-device communications, which enable mesh networks on even when the infrastructure is unavailable. One of the key technological challenges hampering the use of multi-hop mesh networks is the extremely high communication overhead of route discovery and maintenance algorithms. The problem is especially pronounced under dynamic network conditions caused by user mobility and nodes joining and leaving the network. In this paper, we propose a fully distributed algorithm for constructing a virtual coordinate system used for geo-like routing by approximating the physical network nodes coordinate. The proposed algorithm, called gradient assisted routing (GAR), builds upon two-hop neighbors’ information exchanged in beacons in contrast to conventional geographic routing protocols which rely on external positioning information. We evaluate the proposed solution using algorithmic, topological, and routing-related metrics of interest. We further numerically quantify how the node mobility increases the time needed for topology stabilization, and how network size affects the route discovery success rate. Our comparison also shows that for small to mid-size mesh networks (up to 60 nodes), the performance of the proposed routing procedure is similar to the conventional geographic routing protocols that exploit external positioning information. The proposed solution may efficiently supplement the traditional on-demand routing in small to mid-size mesh systems by independently establishing 50 to 70% of paths and thereby reducing the discovery overheads.

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.

Considering the dynamically changing nature of the radio propagation environment, the envisioned battery lifetime of the end device (ED) for massive machine-type communication (mMTC) stands for a critical challenge. As the selected radio technology bounds the battery lifetime, the possibility of choosing among several low-power wide-area network (LPWAN) technologies integrated at a single ED may dramatically improve its lifetime. In this article, we propose a novel approach of battery lifetime extension utilizing reinforcement learning (RL) policies. Notably, the system assesses the radio environment conditions and assigns the appropriate rewards to minimize the overall power consumption and increase reliability. To this aim, we carry out extensive propagation and power measurements campaigns at the city-scale level and then utilize these results for composing real-life use cases for static and mobile deployments. Our numerical results show that RL-based techniques allow for a noticeable increase in EDs’ battery lifetime when operating in the multi-RAT mode. Furthermore, out of all considered schemes, the performance of the weighted average policy shows the most consistent results for both considered deployments. Specifically, all RL policies can achieve 90% of their maximum gain during the initialization phase for the stationary EDs while utilizing less than 50 messages. Considering the mobile deployment, the improvements in battery lifetime could reach 200%.

Terahertz (THz) communications systems are expected to become a major enabling technology at the air interface in sixth-generation (6G) cellular systems. However, utilizing extremely narrow antenna radiation patterns at both base station (BS) and user equipment (UE) sides, these systems are affected by not only dynamic blockage but micromobility of UEs. To alleviate the impact of both factors one may utilize the multiconnectivity mechanism allowing for UE to remain connected to several BSs simultaneously and switch between them in case the active connection is lost. In this work, we develop a mathematical framework to characterize the outage probability and spectral efficiency associated with different degrees of multiconnectivity in dynamic blockage and micromobility environment for different beamsearching design options. Our results demonstrate that the presence of UE micromobility may have a positive impact on system performance. Particularly, multiconnectivity allows improving outage and spectral efficiency for small and medium blockers density (up to 0.5 bl./m2) up to that of an ideal system with zero beamsearching times. Furthermore, higher gains are observed for higher degrees of multiconnectivity (e.g., greater than two) as compared to the system with only blockage impairments. For higher blockers densities, however, the reverse effect is observed.

To alleviate the impact of outages caused by dynamic blockage of propagation paths in millimeter wave (mmWave) systems, 3GPP has proposed multi-connectivity operation, where user equipment (UE) maintains simultaneous links to both sub-6 GHz and mmWave BSs. We address one of the most challenging prospective deployments – multi-lane street deployment with intra- and inter-RAT multi-connectivity. By utilizing the mixture of analytical and simulation tools, we first characterize the outage characteristics at mmWave layer. These characteristics further serve as an input to the queuing model characterizing service performance of sub-6 GHz BS serving sub-6 GHz and temporarily offloaded mmWave sessions and implementing traffic protection strategies. Our numerical results show that the use of inter-RAT multi-connectivity to offload mmWave sessions decreases sub-6 GHz session rate multiple times while utilizing multi-connectivity at the mmWave layer allows to partially compensate it. Traffic “jam” road conditions affect the sub-6 GHz session rate reducing it by approximately 20% compared to “normal” road traffic conditions. Furthermore, reserving shares of resources to sub-6 GHz and mmWave traffic favors “heavy-weight” mmWave sessions. On the other hand, individual session resource allocation leads to fair performance. However, this option induces the trade-off between the attained rate and the session drop probability.

Terahertz (THz) band is considered as the main candidate for new radio access technology in sixth-generation (6G) cellular systems. However, the performance of these systems will be severely affected by not only blockage but user equipment (UE) micromobility in hands of a user. The negative effects of these phenomena can be alleviated by utilizing the multi-connectivity functionality that allows UE to maintain two or more links to nearby base stations (BS) and use them when the currently active link is lost. By accounting for THz specific propagation, antenna and beamsearching design, the density of THz BS deployment, and multi-connectivity operation, we investigate the successful session completion probability under both types of impairments. Our results indicate that the gains of multi-connectivity are observed up to 5 simultaneously supported links and heavily depend on the application outage tolerance time and is mostly affected by micromobility. To improve it, one needs to ensure that the application may tolerate outage caused by beamsearching time which is on the order of milliseconds.

Nowadays, several indoor positioning solutions sup-port Wi-Fi and use this technology to estimate the user position. It is characterized by its low cost, availability in indoor and outdoor environments, and a wide variety of devices support Wi-Fi technology. However, this technique suffers from scalability problems when the radio map has a large number of reference fingerprints because this might increase the time response in the operational phase. In order to minimize the time response, many solutions have been proposed along the time. The most common solution is to divide the data set into clusters. Thus, the incoming fingerprint will be compared with a specific number of samples grouped by, for instance similarity (clusters). Many of the current studies have proposed a variety of solutions based on the modification of traditional clustering algorithms in order to provide a better distribution of samples and reduce the computational load. This work proposes a new clustering method based on the maximum Received Signal Strength (RSS) values to join similar fingerprints. As a result, the proposed fingerprinting clustering method outperforms three of the most well-known clustering algorithms in terms of processing time at the operational phase of fingerprinting.

Comparing to cloud computing, fog computing performs computation and services at the edge of networks, thus relieving the computation burden of the data center and reducing the task latency of end devices. Computation latency is a crucial performance metric in fog computing, especially for real-time applications. In this article, we study a peer computation offloading problem for a fog network with unknown dynamics. In this scenario, each fog node (FN) can offload its computation tasks to neighboring FNs in a time slot manner. The offloading latency, however, could not be fed back to the task dispatcher instantaneously due to the uncertainty of the processing time in peer FNs. Besides, peer competition occurs when different FNs offload tasks to one FN at the same time. To tackle the above difficulties, we model the computation offloading problem as a sequential FN selection problem with delayed information feedback. Using the adversarial multiarm bandit framework, we construct an online learning policy to deal with delayed information feedback. Different contention resolution approaches are considered to resolve peer competition. Performance analysis shows that the regret of the proposed algorithm, or the performance loss with suboptimal FN selections, achieves a sublinear order, suggesting an optimal FN selection policy. Besides, we prove that the proposed strategy can result in a Nash equilibrium (NE) with all FNs playing the same policy. Simulation results validate the effectiveness of the proposed policy.

Fog computing provides computation and services to the edge of networks to support real-time applications. The latency performance is a crucial metric in fog computing. In this article, we consider a computation offloading problem in a fog network with unknown dynamics. In this network, mobile users can offload their computational tasks to neighborhood fog nodes (FNs) in each time slot. The queue of arrival tasks at each FN follows a Markov model with unknown statistics. In order to provide a satisfactory quality of experience, the network latency needs to be minimized. In this article, we construct an offloading policy with interleaved exploration and exploitation epochs to solve the sequential FN selection problem. An upper bound of regret is derived to show the effectiveness of the proposed method. The proposed policy is optimal in the sense that it achieves a regret with sublinear order. In addition, the proposed policy can be applied to both single-user setting and multiuser setting. Simulation results show that when compared with the existing offloading algorithms, the proposed algorithm can reduce the average latency by 7%–47% in the single-user setting, and 91% in the multiuser setting.

Wireless communications in the low terahertz band (0.1 THz-1 THz) is a promising candidate to enable ultra-high-rate vehicular networks beyond 5G. The successful design and adoption of such systems require a deep understanding of the low THz channel specifics in complex vehicular scenarios. In this paper, a comprehensive measurement campaign is reported with the aim of analyzing the wave propagation at 300 GHz in typical vehicular deployments. Following a modular approach, the generic vehicular scenario is decomposed into basic propagation setups that are further analyzed in detail. The obtained measurement data are then applied to derive the mathematical approximations that characterize the low THz band channel properties for each scenario. Finally, the combination of measurement and modeling results is used to identify the critical propagation effects that has to be accounted for in the applied studies. The presented approach, raw and processed data, as well as the contributed analysis, serve as building blocks for future analytical and simulation tools to model prospective vehicular communication systems in the low THz band.

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.

Fog computing brings computation and services to the edge of networks enabling real time applications. In order to provide satisfactory quality of experience, the latency of fog networks needs to be minimized. In this paper, we consider a peer computation offloading problem for a fog network with unknown dynamics. Peer competition occurs when different fog nodes offload tasks to the same peer FN. In this paper, the computation offloading problem is modeled as a sequential FN selection problem with delayed feedback. We construct an online learning policy based on the adversary multi-arm bandit framework to deal with peer competition and delayed feedback. Simulation results validate the effectiveness of the proposed policy.

5G millimeter wave (mmWave) New Radio (NR) base stations (BS) are expected to be deployed in areas with extremely high and drastically fluctuating traffic demands resulting in frequent quality-of-service violations in terms of the provided rate at the access interface, especially, during busy hour conditions. As a cost-efficient countermeasure we consider NR unlicensed (NRU) technology encompassing both NR and WiGiG at a single NR-U BS. To ultimate goal of this study is to determine the required density of these NR-U BSs in an area characterized by a certain density of NR and WiGiG users, where NR users may utilize WiGiG technology as long as their rate requirements are met. Joining the tools of stochastic geometry, queuing theory and Markov chains we characterize the sought metric of interest. We then report the dependency of eventual NR UE session loss probability that can be used to deduce the sought density of NR-U BSs as a function of system parameters. Among other conclusions, we reveal that the effect of the antenna array at NR part of NR-U BS is non-uniform and needs to be taken into account planning NR-U deployments.

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.

Recent Brain-Machine Interfaces have moved towards miniature devices that can be seamlessly integrated into the cortex. In this paper, we propose communication between miniature devices using light. A number of challenges exist using nanoscale light-based communication and this includes diffraction, scattering, and absorption, where these properties result from the tissue medium as well as the cell's geometry. Under these effects, the paper analyses the propagation path loss and geometrical gain, channel impulse and frequency response through a line of neurons with different shapes. Our study found that the light attenuation depends on the propagation path loss and geometrical gain, while the channel response is highly dependent on the quantity of cells along the path. Additionally, the optical properties of the medium impact the time delay at the receiver and the width and the location of the detectors. Simulations were conducted for cells that are lined horizontally up to a distance of 450 μm using light wavelength of 456 nm and different neuron densities (men's neocortex (25924(±15110) /mm3) and women's (27589(±16854) /mm3)). Based on the simulations, we found that spherical cells attenuate approximately 20% of the transmitted power compared to the fusiform and pyramidal cells (35% and 65%, respectively).

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.

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.

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.

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.

The fifth generation wireless systems are expected to rely on a large number of small cells to massively offload traffic from the cellular and even from the wireless local area networks. To enable this functionality, mm-wave (EHF) and Terahertz (THF) bands are being actively explored. These bands are characterized by unique propagation properties compared with microwave systems. As a result, the interference structure in these systems could be principally different to what we observed so far at lower frequencies. In this paper, using the tools of stochastic geometry, we study the systems operating in the EHF/THF bands by explicitly capturing three phenomena inherent for these frequencies: (1) high directivity of the transmit and receive antennas;( 2) molecular absorption; and (3) blocking of high-frequency radiation. We also define and compare two different antenna radiation pattern models. The metrics of interest are the mean interference and the signal-to-interference-plus-noise (SINR) ratio at the receiver. Our results reveal that: (1) for the same total emitted energy by a Poisson field of interferers, both the interference and SINR significantly increase when simultaneously both transmit and receive antennas are directive and (2) blocking has a profound impact on the interference and SINR creating much more favorable conditions for communications compared with no blocking case.

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.

Significant increase in multimedia traffic challenges 5G networks in terms of capacity and correspondent QoS parameters. Device-to-device communication paradigm has already become an integral part of 3GPP standards; nevertheless it has not yet been widely deployed due to many different reasons. D2D is expected to leverage implementation of many qualitatively new services and to efficiently accomplish it D2D devices are supposed to form clusters. Due to practical limitations, current D2D implementations are mostly out-of-band and use Wi-Fi Direct. In this paper, we propose a novel model for throughput optimization in out-of-band D2D clusters. We delivered numerical results for different typical cluster member distributions and revealed key functional dependencies. Further, for the first time we compare clustering algorithms for out-of-band D2D and identify effective clustering algorithm that increases network resource utilization rate.

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.

The fifth generation wireless systems are expected to rely on a large number of wideband small cells to offload traffic from the cellular and wireless local area networks. To create such small cells, EHF and THF bands are actively explored. These frequencies are characterized by fundamentally different propagation characteristics resulting in different interference structure at the receiver compared to, e.g., microwave systems. In this paper, we study the interference structure for systems operating in EHF/THF bands by explicitly capturing three major phenomena: (i) extreme directivity of the transmit and/or receive antennas, (ii) pass loss component caused by molecular absorption and (iii) blocking of high-frequency radiation. The metric of interest is the mean interference at the receiver. Our results reveal that (i) for the same emitted energy in a Poisson field of interferers, the interference increases with the directivity of the transmit or receive antennas, (ii) blocking has a profound impact on the interference creating much more favorable conditions for communications compared to lower frequencies and (iii) the choice of the antenna model is of crucial importance for accurate performance assessment.

In recent years, numerous research efforts have been dedicated towards developing efficient implantable devices for Deep Brain Stimulation (DBS). However, there are limitations and challenges with the current technologies. Firstly, the stimulation of neurons currently is only possible through implantable electrodes which target a population of neurons. This results in challenges in the event that stimulation at the single neuron level is required. Secondly, a major hurdle still lies in developing miniature devices that can last for a lifetime in the patient's brain. Recently, the concept of neural dust has been introduced as a way to achieve single neuron monitoring and potentially actuation. In parallel to this, the field of optogenetics has emerged where the aim is to stimulate neurons using light, usually by means of optical fibers inserted through the skull. Obviously, this introduces many challenges in terms of user friendliness and biocompatibility. We address this shortcoming by proposing the wireless optogenetic neural dust (wi-opt neural dust). The wiopt neural dust is equipped with a miniature LED that is able to stimulate the genetically engineered neurons, and at the same time harvest energy from ultrasonic vibrations. The simulation results presented in the paper investigates the behaviour of the light propagation in the brain tissue, as well as the performance of designed circuitry for the energy harvesting process. The results demonstrates the feasibility of utilizing wi-opt neural dust for long term implantation in the brain, and a new direction towards precise stimulation of neurons in the cortex.

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.

To efficiently utilize ultra-short range wireless technologies providing extreme data rates at the air interface, the concept of “information shower” (IS) has been proposed to complement typical small cell deployments. Although the concept was introduced several years ago, the benefits of ISs is still an open question. We propose an analytical framework for performance evaluation of ISs operating in the terahertz (THz) frequency band. Taking into account different THz bandwidth allocations resulting in different IS coverages and capacities and assuming applications capable of data prefetching, we estimate both user- and network-centric metrics as a function of input parameters including mobility of users. Our study reveals that initiating heavy traffic sessions upon entering THz ISs allows to offload up to 95% of traffic from long-range networks even for low IS sizes and high user densities as the THz IS data rates efficiently compensates for large inter-IS visit times. Such massive offloading also results in considerable improvements in mobile terminal energy efficiency as the fraction of time the radio interfaces have to be active drastically decreases.

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.

Utilizing bacteria as information carriers is a promising technique for molecular communication to establish connections and networking capabilities between micro- and nanoscale devices. A particular process, that is of interest in this paper, and vital to achieve reliable networking performance in bacterial nanonetwork, is conjugation. Conjugation is a process where bacteria come within close range to form physical connections to allow plasmids to be transferred. However, there are a number of processes that could cause loss or damage during the transfer process, such as external vibrations. Message encoding techniques are envisioned as a promising technique to mitigate these effects. In this study we first define the concept of optimal message encoding for bacterial nanonetworks providing the upper bound on the message delivery time and the probability of message delivery within the specified time. We then investigate the effect of encoding on the performance of these metrics by proposing and numerically comparing several feasible encoding techniques. The performance comparison has been done using a specifically developed simulation environment capturing bacteria movement and interactions. Numerical results demonstrate that even the simple encoding strategies allow to significantly improve the performance compared to the baseline system.

The six papers in this special section focus on the Internet of nanotechnology things. While researchers are currently investigating these challenges to develop fully functional nano communication systems, a question remains as to whether they can represent an extended communication network that is part of the broader Internet. These papers address new solutions for the Internet of Nano Things. The Internet of Things paradigm has transformed the way we operate our personal and professional lives, it is driving our economy and will continue to enable many new opportunities in broad research areas. As this pervasive and ubiquitous interconnection of our everyday life appliances continues into the future, new types of devices enabled by nano and biotechnology promise to push engineering to previously unexplored application domains, where the exchange of information and access from/to the broader Internet for their monitoring and control are even more essential. The research on nanoscale communication and networks aims to develop systems for interconnecting these novel devices at the nanoscale, i.e., the Internet of Nano Things.

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.

Wireless communications in the terahertz (THz) frequency band, 0.1-10THz, promises a rapid increase of channel capacity in next- generation networks. However, the advantages of this technology depend not only on the single link performance, but also on the possibility of several THz links to coexist in the same area. Thus, the efficiency of spatial channel reuse has to be studied. Due to the presence of specific effects in the THz band, such as molecular absorption and molecular noise, existing performance evaluation techniques are not straightforwardly applicable. In this paper, the approach for network-level analysis of THz wireless communications is proposed. This approach is based on the tools of stochastic geometry and takes into account the specific signal propagation features of the THz frequency band. The presented technique is used to derive the distribution of SINR and spectral efficiency as well as to estimate the optimal distance between receiving nodes and maximize the area capacity.

The recent interest in the use of THz band, 0:1-10THz, for communications purposes stimulates development of new models for performance assessment of prospective technologies. One of the unique properties of this band is molecular noise generated by water vapor in response to absorption of electromagnetic radiation. In this paper we specify a model capturing the behavior of the molecular noise in the frequency domain and propose the associated parameters fitting algorithm. The model has a simple structure and can be used as a building block for bit error rate (BER) performance assessment.

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.

Over the last decade short-range communications in the terahertz band have been extensively studied as a technology-enabler for dense and ultra-dense wireless networks. Recent advances in miniaturized terahertz transceivers design promise wireless connectivity and simultaneous interaction between thousands of devices. However, the feasibility of network-wide communications is still an open issue due to specific features of the terahertz band and inherent properties of dense deployments. We address this issue developing an analytical model for interference and SINR assessment in dense terahertz networks obtaining the first two moments and density functions for both metrics. Our results demonstrate that the presence of molecular noise does not qualitatively affect the behavior of SINR, while its quantitative effect is of secondary importance compared to interference. The presented approach provides the so-far missing building block for performance analysis of prospective dense terahertz networks.

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.

The adaptive modulation and coding AMC technique, which has been adopted by advanced mobile telecommunication systems, supports a flexible response to the random radio behaviour. As a result, the attained transmission rate over a wireless link is time varying. Hence, resource demands are not deterministic but fluctuating even for calls with constant bit rate service requirements. Consequently, constant bit rate calls are susceptible to a forced call termination because of insufficient resources not only in a target cell during inter-cell handoffs but also in a serving cell during radio link deterioration. Furthermore, call blocking and dropping probabilities depend on radio propagation conditions among other factors and therefore they are dissimilar throughout a service area. The latter leads to unfairness problems. We analytically measure the impact of AMC on fixed-rate service with hard delay constraints such as voice for different signal, mobility and traffic conditions. We consider a reference case call requests are admitted into the system provided there are enough free resources and two classes of admission control approaches: traditional only inter-cell handoffs are prioritised and modified all ongoing calls are prioritised. The reported results reveal conditions for which AMC affects voice call performance and can serve as guidelines on admission control design. Copyright © 2013 John Wiley & Sons, Ltd.

We propose a model for computing the optimal message length in delay tolerant networks with long propagation delays. As a protocol of interest we use CCSDS File Delivery Protocol operating in differed NAK mode. The proposed model is based on the absorbing Markov chain framework allowing to get not only the mean performance metrics but the probability mass function of time it takes to transmit a message. The latter is due to the special structure of the absorbing Markov chain allowing for simple estimation of the first passage time till absorbtion. Using the pmf one can determine the maximum message length that can be delivered with a certain reliability when two nodes remain in coverage range of each other for a given time.

One form of molecular communications for short range transmission between nanomachines is Calcium Signaling. This form of signaling is commonly found in cellular tissues, which consist of tightly packed cells, whereby Ca2+ions propagate and diffuse between the cells. However, the natural flexible structure of cells usually leads to them dynamically changing shapes under certain strains and forces. Since the interconnected cells form a tissue, changes in the shape of one cell will change the shape of neighboring cells and the tissue as a whole. This may in turn significantly impair the communication channel between the nanomachines (which we assume to be embedded within the cells). In order to counter this problem, we propose an adaptive transmission protocol for Ca2+signaling based molecular communications in cellular tissues. The protocol operates in two phases. The first phase utilizes information metrics to infer the state of the tissue; second phase then involves the determination of the most appropriate time-slot for bit transmission. In this way, we aim to improve the information rate by using a time slot length that is appropriate for the prevailing type of tissue deformation. Through simulation studies we show that, for two types of deformation and two different topologies, our protocol can improve the information rate performance by 15%.

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.

As throughput of wireless technologies had improved rapidly over the last decade, handheld devices became more attractive to the end users enabling multimedia applications "on-the-move". The uptime of mobile devices depends on their battery power that is not growing at the pace of communication technologies. In this study we describe the trade-offs between the power consumption, compression and the quality of video streaming in wireless environment. We show that using scalable baseline profile of H.264 codec the careful choice of encoding parameters may result in significant energy and bitrate savings at the expense of only slight degradation in quality, if at all. The amount of motion in a sequence plays a crucial role for energy savings when packet loss probability is non-negligible.

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.

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 the lightweight many-to-many authentication protocol, that uses Near Field Communications as a carrier technology is proposed. The solution works without any user interaction and can be applied for almost any data storage device: NFC or RFID tag, USB-flash drive, etc. The major novelty of the system is real-time encryption key generation algorithm. This approach doesn't require any computation power on the tag, trusted third parties or secure link between tag and information system. So far, the mentioned features transforms to significant advantages of the proposed solution, while compared to existing analogues: OAuth, Opacity and LMAP. At the same time, the integrity of key sequences is not guarantied, that brings motivation for future research in the field.

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.

Overtaking on rural roads may cause severe accidents when oncoming traffic is detected by a driver too late, or its speed is underestimated. Recently proposed cooperative overtaking assistance systems are based on real-time video transmission, where a video stream captured with a camera installed at the windshield of a vehicle is compressed, broadcast through the wireless channel, and displayed to the drivers of vehicles driving behind. In such a system, it is of ultimate importance to deliver video information about the opposite lane with low end-to-end latency and good visual quality. In this paper, we propose reallocating the wireless channel resources in favor of the part of the captured video frame containing the image of the oncoming vehicle. To achieve this goal, we apply automotive radar for oncoming vehicle detection, and we use the image of this vehicle as a region-of-interest (ROI) for the video rate control. We present the theoretical framework, which describes the basics of such an approach and can serve as a useful guideline for the future practical implementation of the overtaking assistance systems. The benefits of our proposal are demonstrated in relation to the practical scenario of H.264/Advance Video Coding (AVC), IEEE 802.11p/Wireless Access for Vehicular Environments (WAVE) intervehicle communication standards, and currently used automotive radars.

The positive effect of traffic localization in P2P systems is widely praised as it allows to decrease the amount of traffic crossing inter-ISP links. At the same time its negative effect is often overlooked. In this paper we concentrate on the biased choice of peers as a way to localize traffic in P2P BitTorrent-like file sharing systems. We demonstrate, evaluate and discuss the negative effect of traffic localization expressing itself in the increase of the average file downloading time as the level of localization increases.

Overtaking on rural roads often becomes dangerous when oncoming traffic is detected by the driver too late or its speed is underestimated. Up-to-date proposals for cooperative overtaking assistance systems rely either on the beaconing of status messages or on real-time video transmission. The beaconing-based system provides limited information to the driver about the upcoming traffic in the form of warnings. Introduction of the video-based system can extend the amount of driver information, thus, increasing the reliability of the assistance. However, when two platoons of vehicles are meeting each other, the quality of the video information can undergo significant degradation since a common multiple access communication channel is used. In this paper we demonstrate that the performance of a video-based overtaking assistant can be significantly improved if codec channel adaptation is undertaken by exploiting the information from the beacons about any forthcoming increase in the load of the multiple access channel used. The benefits of our approach are demonstrated in relation to the practical scenario of H.264/AVC video coding and IEEE 802.11p/WAVE inter-vehicle communication standards.

In this paper we consider call admission control in cellular environment for data sessions and voice calls of high priority. A cell is divided into two zones with different average signal to noise ratio, and each zone uses its own modulation and coding. A dynamic resource allocation scheme is further considered, which allows communication systems to utilize their resources more efficiently. All the idle channels, which are left after the voice calls, are allocated to elastic data sessions to increase their transmission rate. When a voice call arrives, required channels can be released to serve it if only elastic data calls have a minimum required bandwidth to continue their sessions. To evaluate the role of elastic traffic, analytical models with elastic and fixed data sessions are formulated and solved. For both cases we derive blocking probabilities as well as other performance metrics of interest. According to numerical results, elastic data sessions achieve higher channel utilization and their mean transmission time is ten times less than that of fixed data sessions. As a result, the mean number of elastic data sessions in the system is twice less than that of fixed data sessions.

In this paper performance modeling for a mobile stations operating according to CSMA/CA with binary exponential back-off (BEB) is discussed. Unlike the previous studies we concentrate on delay and loss performance metrics experienced by individual stations. To obtain delay distribution of an arbitrary packet we use Geo(i)/G(i)/1/K queuing system representing the number of stations having a frame for transmission. In this system both arrival and service process depend on the number of active station. Delay distribution is obtained as a weighted sum of delay distributions in each state of the system. Individual performance metrics are obtained by formulating and solving a separate Geo/G/1/K queuing system.

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.

Broadcasting of data and control packets is expected to be crucial in vehicular environments. Both safety-related and non-safety applications rely on broadcasting for the exchange of data or status and advertisement messages. According to the specifications of the IEEE 802.11p/WAVE (Wireless Access in Vehicular Environments) standard, vehicles tune into a common frequency during the control channel (CCH) interval, where most of the broadcasting traffic is expected to be delivered. The rest of the time, vehicles switch to one of the available service channels (SCHs) for non-safety related data exchange. Although the WAVE channel switching could heavily affect the network performance, it has not been widely investigated in the literature. In this paper, an analytical model is designed for characterizing the losses of broadcast packets by explicitly accounting for the WAVE channel switching. Loss probabilities have been derived as a function of contention window size, number of vehicles and channel errors.

The demo presents MAEMO-based scalable platform for easy deployment of the Social Location Based Services. This platform allows implementation of the new user-driven social services and incorporation of the user-generated content with the publicly available geo maps.

The next generation of wireless phones promises to change consumers services habits - video streaming, video conferencing, DVB-H etc. will be used practically anytime and anywhere in the world. However, it has been observed that the CPU load and the memory usage is increasing significantly with the increasing data rate and at some point the need for higher processing power and memory is quite evident to support higher data rate. This paper investigates the costs for implementation of higher data rate in mobile station in terms of processing power and memory usage.

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.

Active safety in vehicular environment can be achieved by means of installation of the wireless equipment on the boards of cars. Design of highly efficient protocols for car-to-car communications is a challenging task. If an emergency situation happens on the road, the information about it should be disseminated to the stakeholders with low delay and high reliability. In this paper, we derive a theoretical lower bound for the mean message transmission delay in multi-hop vehicular ad-hoc networks.

Recent advances in wireless communications technology and car industry made it possible to consider wireless Ad-hoc (or mixed Ad-hoc/infrastructure) networks, providing connectivity among vehicles on the road. Such network, often referred to as VANET (Vehicular Ad-hoc Network) is seen to be one of the most valuable concept for improving efficiency and safety of the future transportations, as well as boosting operators' revenue. Thus, several ongoing research projects supported by industry, governments and academia, establishing standards for VANET networks. This paper covers current research challenges and future applications for such networks, discusses possible deployment scenarios and provides some insights to the state-of-the-art of the current standardization of interfaces and protocols on a global scale.

We present an admission control (AC) algorithm for the specific case of pre-provisioned IEEE 802.16 d links for VoIP aggregates. The algorithm approaches AC from a new perspective as admission criterion is speech quality, the sole true quality metric for voice services. As we found the E-Model can be used to reliably estimate speech quality and the resulting R-Score as admission criterion. Moreover, we show that speech quality can be evaluated on aggregate level without compromising individual call's speech quality. The algorithm is simple, fast, and precise and its behaviour is consistent over a range of different deployments.

In recent years wireless local area networks constantly grew in popularity, mainly due to widely deployed IEEE 802.11 standards family. The number of applications that might benefit from those standards increases constantly. We witness this "all wireless" trend, where wireless connectivity is not just about Internet access any more. Numerous WLAN applications have emerged mobile VoIP telephony, positioning, vehicular ad hoc networks (VANET) to name just a few. In most cases certain performance parameters have to be kept to ensure applications' usability. Moreover, the more mobile an environment we deal with, the higher the constrains on link parameters are. The aim of our work was to test performance of 802.11b/g ad-hoc networks in terms of delay analysis in a link establishing process. Our experiments were held under different wireless link conditions to provide reliable results, using typical out-of-the-box IEEE 802.11b and 802.1g cards. To achieve that, we employed a precise scheduling software with several shell scripts, working under Linux OS.

This paper presents an analytical model of steady state throughput of the Slow-but-Steady variant of TCP NewReno as a function of loss event rate, average number of segments lost per loss event, average round trip time, and retransmission timeout value. The presented model is based on the approach proposed by Padhyeetal.for TCP Reno throughput modeling and extends it by capturing the effect of fast recovery algorithm of the Slow-but-Steady variant of TCP NewReno on throughput and taking into consideration slow start phase after timeout expiration. Validation by ns-2 simulations shows good agreement and accuracy of the proposed model.

A large diversity of services based on packet switching in the third generation (3G) wireless system leads to dramatic changes in the characteristics and parameters of the data traffic. It is expected that the traffic in the 3G systems undergo both quantitative and qualitative changes. In particular, there is a considerable increase of a rate of transactions and an amount of information transferred during the transactions. Besides, it is necessary to take into account the notion of self-similarity that take place in the 3G systems due to the high variability of burstiness of the multiservice traffic. For these reasons, the prediction problem of data traffic characteristics becomes more complex when planning the 3G wireless systems. In this paper the estimation method for the main parameters of the data traffic in the 3G mobile networks taking into account the above-mentioned 3G features is considered. It is based on a probabilistic model of events initiated by calls of 3G services. It is proposed to make both a decomposition of 3G services into some subsets in accordance with an average amount of transferred data during transactions and a distribution of 3G prospective users into some subgroups in accordance with the Pareto law.

Based on the differentiated services (DiffServ, [S. Blake, et al., December 1998]) model we develop implementation-ready transmission service for video-on-demand (VoD) traffic. In order to satisfy the demands of real-time nature of VoD traffic we propose to use assured forwarding (AF, [B. Davie, et al., March 2002]) per-hop behavior (PHB) group. We provide specific traffic profile parameters which adequately fit to VoD traffic requirements. Using these parameters and AF PHB class we show how to construct a well-defined transmission service that is suitable for VoD traffic delivery. Our service is analytically tractable. It means that using the quality of service (QoS) parameters which should be provided to VoD traffic we can evaluate the required capacity not only within the DiffServ ingress node, but within the interior nodes too. Our transmission service is designed in such way that it is fully characterized by the parameters of DiffServ ingress node, bounds the losses and delay along the path of behavior aggregate and allows us to predict the QoS degradation which can be experienced by both behavior aggregate and single microflows.

This paper presents results from performance measurements carried out in a differentiated services WAN connecting three major cities in southern Finland. The target of the measurements was to collect precise information about the level of service experienced by a user transmitting files with FTP in a network that implements service differentiation by using drop precedence levels and separate classes for real time and non-real time traffic. Special attention was paid to the behaviour of competing TCP flows produced by users with different service level specifications. Background traffic from ISPs network was injected into our test network in order to create traffic profiles which are typical in a core network. The results show clearly that the SIMA model used for the implementation of the DS mechanisms in the network nodes can provide service differentiation in a useful and fair manner in traffic conditions varying from light load to severe overload.