Nokia (Ireland)

Time division multiple access (TDMA) is the medium access control strategy of choice for multihop networks with deterministic delay guarantee requirements. As such, many Internet of Things applications use protocols based on time division multiple access. Optimal slot assignment in such networks is NP-hard when there are strict deadline requirements and is generally done using heuristics that give suboptimal transmission schedules in linear time. However, existing heuristics make a scheduling decision at each time slot based on the same criterion without considering its effect on subsequent network states or scheduling actions. Here, we first identify a set of node features that capture the information necessary for network state representation to aid building schedules using Reinforcement Learning (RL). We then propose three different centralized approaches to RL-based TDMA scheduling that vary in training and network representation methods. Using RL allows applying diverse criteria at different time slots while considering the effect of a scheduling action on meeting the scheduling objective for the entire TDMA frame, resulting in better schedules. We compare the three proposed schemes in terms of how well they meet the scheduling objectives and their applicability to networks with memory and time constraints. One of the schemes proposed is RLSchedule, which is particularly suited to constrained networks. Simulation results for a variety of network scenarios show that RLSchedule reduces the percentage of packets missing deadlines by up to 60% compared to the best available baseline heuristic.

In this chapter, the authors adopt the view of a mobile network operator that, by rolling out its massive multiple input–multiple output (MIMO) based 5G network, aims to offer communication services to both ground users (GUEs) and unmanned aerial vehicles (UAVs) simultaneously, reusing its cellular spectrum and infrastructure. After evaluating the downlink performance of networks with UAV users, they aim to assess whether cellular networks can provide high‐speed uplink aerial links for video streaming purposes. An equally important concern is related to the influence that UAV transmissions have on the uplink performance of terrestrial users. The authors evaluate the network performance and capture the propagation environment between ground base stations and both GUEs and UAVs through the latest 3GPP 3D channel model. They also introduce the network topology and channel model. A common feature of the single‐user and massive MIMO paradigms is an equal time splitting between downlink and uplink data transmissions.

In this article, we evaluate the performance of the parameterized spatial reuse (PSR) framework of IEEE 802.11ax, mainly focusing on its impact on transmission latency. Based on detailed standard-compliant system-level simulations, we provide a realistic analysis of the effects of PSR considering different scenario densities, traffic loads, and access points (APs) antenna capabilities to quantify its performance gains under various scenarios. Our results show that, in medium-density scenarios, PSR can offer up to a $3.8 \times$ reduction in the 5% worst-case latencies for delay-sensitive stations with respect to an 802.11ax system without PSR. Moreover, our study demonstrates that, for low-latency communications, providing the network with PSR capabilities may be an appealing alternative to the deployment of more costly multi-antenna APs.

Path gain in a cluttered industrial environment at 28 GHz is found to be well modeled by diffusion with a single absorption parameter, with 5.2 dB RMS error. Shadow fading variance and azimuthal spread are found to increase with range, also consistent with theory.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. While we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. The paper introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts, and then moves on to experimentally-validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

What will it take for drones, and the whole associated ecosystem, to take off? Arguably, infallible C&C channels for safe and autonomous flying, and high-throughput links for multi-purpose live video streaming. And indeed, meeting these aspirations may entail full cellular support, provided through 5G-and-beyond hardware and software upgrades by both mobile operators and manufacturers of these UAVs. In this article, we vouch for massive MIMO as the key building block to realize 5G-connected UAVs. Through the sheer evidence of 3GPP-compliant simulations, we demonstrate how massive MIMO can be enhanced by complementary network-based and UAV-based solutions, resulting in consistent UAV C&C support, large UAV uplink data rates, and harmonious coexistence with legacy ground users.

In this paper, we focus on the coexistence between a MIMO radar and cellular base stations. We study the interfering channel estimation, where the radar is operated in the “search and track” mode, and the BS receives the interference from the radar. Unlike the conventional methods where the radar and the cellular systems fully cooperate with each other, in this paper, we consider that they are uncoordinated and the BS needs to acquire the interfering channel state information (ICSI) by exploiting the radar probing waveforms. For completeness, both the line-of-sight (LoS) and Non-LoS (NLoS) channels are considered in the coexistence scenario. By further assuming that the BS has limited a priori knowledge about the radar waveforms, we propose several hypothesis testing methods to identify the working mode of the radar, and then obtain the ICSI through a variety of channel estimation schemes. Based on the statistical theory, we analyze the theoretical performance of both the hypothesis testing and the channel estimation methods. Finally, the simulation results verify the effectiveness of our theoretical analysis and demonstrate that the BS can effectively estimate the interfering channel even with the limited information from the radar.

Interactive cloud-based applications such as virtual reality telepresence require flawless data delivery well within interactivity deadlines. These can range from sub-ms intervals for machines-type communications in industrial settings (e.g., multi-view obstacle detection for mobile vehicles) to 100 ms for basic human-human interactivity (e.g., virtual office, remote cockpit). Yet in wireless networks serving mobile users, the support of any quality-of-service (QoS) constraints is challenging due to their inherent instability and best-effort nature. In this context, we propose a novel multi-connectivity framework that allows federating multiple wireless links such as 4G LTE, 5G New Radio, Wi-Fi and WiGig in a simple and backward-compatible manner, and use them for multi-path data delivery with controlled end-to-end throughput, latency and reliability. The proposal is based on software-defined transport-layer proxies that can be deployed instantaneously and upgraded on-demand with no need for any handset or network modifications. As a proof of concept, we evaluate several innovative protocols for user-centric bandwidth and latency control in real-life LTE and Wi-Fi networks.

Considerable attention has been drawn to the lead halide perovskites (LHPs) because of their outstanding optoelectronic characteristics. LHP nanosheets (NSs) grown from single crystalline lead halide possess advantages in device applications as they provide the possibility for control over morphology, composition, and crystallinity. Here, free-standing lead bromide (PbBr2) single-crystalline NSs with sizes up to one centimeter are synthesized from solution. These NSs can be converted to LHP while maintaining the NS morphology. We demonstrate that these perovskite NSs can be processed directly for fabrication of photodetector and laser arrays on a large scale. This strategy will allow high-yield synthesis of large-size perovskite NSs for functional devices in an integrated photonics platform.