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.

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

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

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

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

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

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

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.

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.

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

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

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

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

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

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

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

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

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

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.