This article introduces a sophisticated and adaptable framework combining extreme value theory with radio maps to spatially model extreme channel conditions accurately. Utilizing existing signal-to-noise ratio (SNR) measurements and leveraging Gaussian processes, our approach predicts the tail of the SNR distribution, which entails estimating the parameters of a generalized Pareto distribution, at unobserved locations. This innovative method offers a versatile solution adaptable to various resource allocation challenges in ultrareliable communications. We evaluate the performance of this method in a rate maximization problem with defined outage constraints and compare it with a benchmark in the literature. Notably, the proposed approach meets the outage demands in a larger percentage of the coverage area and reaches higher transmission rates. Finally, we analyze the impact of the localization error on the system performance, highlighting the need for accurate positioning algorithms to enable efficient resource allocation.

Channel state information (CSI) is crucial for achieving ultra-reliable low-latency communication (URLLC) in wireless networks. The main associated problems are the CSI acquisition time, which impacts the latency requirements of time-critical applications, and the estimation accuracy, which degrades the signal-to-interference-plus-noise ratio, thus, reducing communication reliability. In this work, we formulate and solve a minimum-power precoding design problem simultaneously serving multiple URLLC users in the downlink with imperfect CSI. Specifically, we develop an algorithm that exploits state-of-the-art precoding schemes such as maximal ratio transmission and zero-forcing, and adjust the power of the precoders to compensate for the channel estimation error uncertainty based on the extreme value theory framework. Finally, we evaluate the performance of our method and show its superiority with respect to a worst-case robust precoding benchmark.

Two of the main problems to achieve ultra-reliable low-latency communications (URLLC) are related to instantaneous channel state information (I-CSI) acquisition and the coexistence with other service modes such as enhanced mobile broadband (eMBB). The former comes from the non-negligible time required for accurate I-CSI acquisition, while the latter, from the heterogeneous and conflicting requirements of different nodes sharing the same network resources. In this paper, we leverage the I-CSI of multiple eMBB links and the channel measurement’s history of a URLLC user for multi-antenna beamforming design. Specifically, we propose a precoding design that minimizes the transmit power of a base station (BS) providing eMBB and URLLC services with signal-to-interference-plus-noise ratio (SINR) and outage constraints, respectively, by modifying existing I-CSI-based precoding schemes to account for URLLC channel history information. Moreover, we illustrate and validate the proposed method by adopting zero-forcing (ZF) and the transmit power minimization (TPM) precoding with SINR constraints. We show that the ZF implementation outperforms TPM in adverse channel conditions as in Rayleigh fading, while the situation is rapidly reversed as the channel experiences some line-of-sight (LOS). Finally, we determine the confidence levels at which the target outage probabilities are reached. For instance, we show that outage probabilities below 10-3are achievable with more than 99% confidence for both precoding schemes under favorable LOS conditions with 16 transmit antennas and 500 samples of URLLC channel history.

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.