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Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts.

An assessment of radio network coverage, usually in the form of a measurement campaign, is essential for multi-base-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, 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, Kriging method offers attractive performance in terms of the absolute error for NB-IoT deployments. By contrast, for Sigfox and LoRaWAN infrastructures, less complex techniques, such as Natural-neighbor, Linear interpolation, or Inverse-Distance Weighting, can achieve comparable (and occasionally even better) accuracy levels.

Despite the immense progress in recent years, efficient solutions for monitoring remote areas are still missing today. This is especially notable in the context of versatile maritime and offshore use cases, owing to a broader span of operating regions and a lack of radio network infrastructures. In this article, we address the noted challenge by delivering a conceptual solution based on the convergence of three emerging technologies: unmanned aerial vehicles (UAVs), battery-less sensors, and wireless powered communication networks (WPCNs). Our contribution offers a systematic description of the ecosystem related to the proposed solution by identifying its key actors and design dimensions together with the relevant resources and performance metrics. A system-level modeling-based evaluation of an illustrative scenario delivers deeper insights into the considered operation and the associated trade-offs. Further, unresolved challenges and perspective directions are underpinned for a subsequent study.

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

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

Future smart vehicles will incorporate high-data-rate communications and high-resolution radar sensing capabilities operating in the millimeter- wave and higher frequencies. These two systems are preparing to share and reuse many common functionalities, such as steerable millimeter- wave antenna arrays. Motivated by this growing overlap, which is advanced further by space and cost constraints, the vehicular community is pursuing a vision of unified vehicular communications and radar sensing that represents a major paradigm shift for next-generation connected and self-driving cars. This article outlines a path to materialize this decisive transformation. We begin by reviewing the latest developments in hybrid vehicular communications and radar systems, and then propose a concept of unified channel access over millimeter-wave and higher frequencies. Our supporting system-level performance characterization relies upon real-life measurements and extensive ray-based modeling to confirm the significant improvements brought by our proposal to mitigating the interference and deafness effects. Since our results aim to open the door to unified vehicular communications and radar sensing, we conclude by outlining the potential research directions in this rapidly developing field.

While the IoT has made significant progress supporting individual machine-type applications, it is only recently that the importance of people as an integral component of the overall IoT infrastructure has started to be fully recognized. Several powerful concepts have emerged to facilitate this vision, whether involving the human context whenever required or directly impacting user behavior and decisions. As these become the stepping stones to develop the IoT into a novel people-centric utility, this article outlines a path to realize this decisive transformation. We begin by reviewing the latest progress in human-aware wireless networking, then classify the attractive human-machine applications and summarize the enabling IoT radio technologies. We continue with a unique system-level performance characterization of a representative urban IoT scenario and quantify the benefits of keeping people in the loop on various levels. Our comprehensive numerical results confirm the significant gains that have been made available with tighter user involvement, and also corroborate the development of efficient incentivization mechanisms, thereby opening the door to future commoditization of the global people-centric IoT utility.

The future of industrial applications is shaped by intelligent moving IoT devices, such as flying drones, advanced factory robots, and connected vehicles, which may operate (semi-)autonomously. In these challenging scenarios, dynamic radio connectivity at high frequencies, augmented with timely positioning-related information, becomes instrumental to improve communication performance and facilitate efficient computation offloading. Our work reviews the main research challenges and reveals open implementation gaps in IIoT applications that rely on location awareness and multi-connectivity in super high and extremely high frequency bands. It further conducts a rigorous numerical investigation to confirm the potential of precise device localization in the emerging IIoT systems. We focus on positioning- aided benefits made available to multi-connectivity IIoT device operation at 28 GHz, which notably improve data transfer rates, communication latency, and the extent of control overhead.