Ultrareliable low-latency communication (URLLC) constitutes a key service class of the fifth generation (5G) and beyond cellular networks. Notably, designing and supporting URLLC pose a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multilayer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This article contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to the following: 1) reliability theory; 2) short packet communications; 3) inequalities, distribution bounds, and tail approximations; 4) rare-events simulation; 5) queuing theory and information freshness; and 6) large-scale tools, such as stochastic geometry, clustering, compressed sensing, and mean-field (MF) games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout this article, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.

The Internet of Things (IoT) brings connectivity to a massive number of devices that demand energy-efficient solutions to deal with limited battery capacities, uplink-dominant traffic, and channel impairments. In this work, we explore the use of unmanned aerial vehicles (UAVs) equipped with configurable antennas as a flexible solution for serving low-power IoT networks. We formulate an optimization problem to set the position and antenna beamwidth of the UAV, and the transmit power of the IoT devices subject to average-signal-to-average-interference-plus-noise ratio ($\bar {\text {S}}\overline {\text {IN}}\text {R}$) Quality-of-Service (QoS) constraints. We minimize the worst case average energy consumption of the latter, thus targeting the fairest allocation of the energy resources. The problem is nonconvex and highly nonlinear; therefore, we reformulate it as a series of three geometric programs that can be solved iteratively. Results reveal the benefits of planning the network compared to a random deployment in terms of reducing the worst case average energy consumption. Furthermore, we show that the target$\bar {\text {S}}\overline {\text {IN}}\text {R}$is limited by the number of IoT devices, and highlight the dominant impact of the UAV hovering height when serving wider areas. Our proposed algorithm outperforms other optimization benchmarks in terms of minimizing the average energy consumption at the most energy-demanding IoT device, and convergence time.

Wireless energy transfer (WET) is emerging as an enabling green technology for Internet-of-Things (IoT) networks. WET allows the IoT devices to wirelessly recharge their batteries with energy from external sources such as dedicated radio-frequency transmitters called power beacons (PBs). In this article, we investigate the optimal deployment of PBs that guarantees a network-wide energy outage constraint. Optimal positions for the PBs are determined by maximizing the average incident power for the worst location in the service area since no information about the sensor deployment is provided. Such network planning guarantees the fairest harvesting performance for all the IoT devices. Numerical simulations evidence that our proposed optimization framework improves the energy supply reliability compared to benchmark schemes. Additionally, we show that although both, the number of deployed PBs and the number of antennas per PB, introduce performance improvements, the former has a dominant role. Finally, our proposal allows to extend the coverage area while keeping the total power budget fixed, which additionally reduces the level of electromagnetic radiation in the vicinity of PBs.