Despite the prevalence of wireless connectivity in urban areas around the globe, there remain numerous and diverse situations where connectivity is insufficient or unavailable. To address this, we introducemobile wireless infrastructure on demand, a system of unmanned aerial vehicles (UAVs) that can be rapidly deployed to establish an ad hoc wireless network. This network has the capability of reconfiguring itself dynamically to satisfy and maintain the required quality of communication. The system optimizes the positions of the UAVs and the routing of data flows throughout the network to achieve this Quality of Service (QoS). By these means, task agents using the network simply request a desired QoS, and the system adapts accordingly while allowing them to move freely. We have validated this system both in simulation and in real-world experiments. The results demonstrate that our system effectively offers mobile wireless infrastructure on demand, extending the operational range of task agents and supporting complex mobility patterns, all while ensuring connectivity and being resilient to agent failures.

In this work, we introduce Mobile Wireless Infrastructure on Demand: a framework for providing wireless connectivity to multi-robot teams via autonomously reconfiguring ad-hoc networks. In many cases, previous multi-agent systems either assumed the availability of existing communication infrastructure or were required to create a network in addition to completing their objective. Instead our system explicitly assumes the responsibility of creating and sustaining a wireless network capable of satisfying end-to-end communication requirements of a team of agents, called the task team, performing an arbitrary objective. To accomplish this goal, we propose a joint optimization framework that alternates between finding optimal network routes to support data flows between the task agents and improving the performance of the network by repositioning a collection of mobile relay nodes referred to as the network team. We demonstrate our approach with simulations and experiments wherein wireless connectivity is provided to patrolling task agents.

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.

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

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.

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.

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.

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.

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.

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.