In this paper, we present an approach for resource scheduling in wireless networks based on the Network Slicing (NS) paradigm using Double Deep Q-Network (DDQN) Reinforcement Learning (RL) algorithm. More specifically, we propose a joint power and Scheduling Block (SB) allocation algorithm for networks with NS. The reinforcement learning algorithm applied to the resource allocation problem is formulated using state transitions regarding the system dynamics. We also present an algorithm, namely Network Slicing based on Reinforcement Learning (NSRL) that combines the proposed reinforcement learning based resource allocation with an approach based on reservation and sharing of resources among the slices where each RL agent acts in one slice. Simulations are carried out considering User Equipments (UEs) within a small cell coverage area - (Small Cells) with different Modulation and Coding Schemes (MCS) standardized by the 3rd Generation Partnership Project (3GPP) based on a simplified NS scenario with fifth generation wireless network (5G) characteristics. In the simulations, two slices are considered for the UEs: one considering Ultra-reliable and Low Latency Communications (URLLC) and other related to enhanced Mobile Broadband (eMBB) services. Simulation results show that the NSRL algorithm efficiently allocates power and SBs, outperforming other algorithms in the literature.

In this article, we propose a novel formulation that jointly considers the Virtualized Network Function (VNF) placement at the Radio Access Network (RAN) nodes and the resource allocation for the Transport Network (TN) of sliced and disaggregated RANs. Unlike most works in the literature that address these network aspects separately, we propose a joint approach. Our proposal ensures an end-to-end delay bound for the Ultra-Reliable and Low-Latency Communications (URLLC) use case in an Industry 4.0 scenario, while simultaneously considering the number of admitted User Equipments (UEs), the transmission rate allocation per slice, the functional split of RAN nodes, and the routing paths in the TN. We use Network Calculus (NC) theory to calculate delay along the TN connecting disaggregated RANs deploying network functions at the Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU) nodes. The maximum end-to-end delay is modeled as a constraint in the optimization-based formulation that maximizes the number of admissible flows meeting strict Quality of Service (QoS) requirements, while also taking into account the deployment and operational costs of disaggregated RANs. In this approach, we propose a strategy based on real data from one of the world’s leading Mobile Network Operators (MNOs) to derive coherent estimations to the weights of the proposed objective function. The optimization model leverages a Flexible Functional Split (FFS) approach to provide a new degree of freedom to the resource allocation strategy. Simulation results reveal that, due to its non-linear nature, there is no trivial solution to the proposed optimization problem. Simulation results also show that our proposal guarantees a maximum delay for URLLC use cases in Industry 4.0 while satisfying bandwidth requirements for enhanced Mobile Broadband (eMBB) services.

In this work, we model a 5G downlink channel using millimeter-wave (mmWave) and massive Multiple-Input Multiple-Output (mMIMO) technologies, considering the following localization parameters: Time of Arrival (TOA), Two-Dimensional Angle of Departure (2D-AoD), and Two-Dimensional Angle of Arrival (2D-AoA), both encompassing azimuth and elevation. Our research focuses on the precise estimation of these parameters within a three-dimensional (3D) environment, which is crucial in Industry 4.0 applications such as smart warehousing. In such scenarios, determining the device localization is paramount, as products must be handled with high precision. To achieve these precise estimations, we employ an adaptive approach built upon the Distributed Compressed Sensing—Subspace Orthogonal Matching Pursuit (DCS-SOMP) algorithm. We obtain better estimations using an adaptive approach that dynamically adapts the sensing matrix during each iteration, effectively constraining the search space. The results demonstrate that our approach outperforms the traditional method in terms of accuracy, speed to convergence, and memory use.

Deployment of low‐cost Wi‐Fi sensing applications may impose strict constraints on available computational power, thereby making the reduction of time required to process channel state information (CSI) measurements a matter of interest. Most works on passive sensing for classification tasks achieve this via dimensionality reduction of CSI data prior to the training of machine learning models, which in itself still imposes some computational burden. Subcarrier selection, which is a faster approach and is widely used in another application domain (i.e., vital signal monitoring), is seldom considered; in the few works where it is used, only a variance‐based unsupervised strategy is applied. In this letter, a supervised linear‐complexity subcarrier selection strategy is proposed for enhanced sensing classification accuracy. The approach is validated through practical experiments whose results show that the classification performance can approach or even surpass that obtained via dimensionality reduction, with substantial time savings.