Bringing intelligent decision-making to industrial facilities involves coordinating a complex ecosystem of applications with different performance requirements that often depend on each other. A promising approach to managing this complexity is leveraging loosely coupled software architectures, such as microservices, with distributed infrastructures, such as the Edge-to-Cloud Continuum. The order in which microservices are provisioned in the Edge-to-Cloud Continuum can significantly impact outcomes when distributing tasks to agents. Poor provisioning orderings can lead to non-critical microservices overburdening servers with lower latency while leaving latencysensitive microservices with no option but to run on more distant servers. To address this challenge, we introduce a novel provisioning ordering mechanism within a Multi-Armed Bandit (MAB) algorithm, helping to conquer the problem. Simulations based on real-world specifications indicate a $\mathbf{3 8 \%}$ reduction in latency violations and up to 6% decrease in power consumption compared to an unordered approach.

Cell-free networks outperform cellular networks in many aspects, yet their efficiency is affected by imperfect channel state information (CSI). In order to address this issue, this work presents a robust resource allocation framework designed for the downlink of user-centric cell-free massive multi-input multi-output (CF-mMIMO) networks. This framework employs a sequential resource allocation strategy with a robust user scheduling algorithm designed to maximize the sum-rate of the network and two robust power allocation algorithms aimed at minimizing the mean square error, which are developed to mitigate the effects of imperfect CSI. An analysis of the proposed robust resource allocation problems is developed along with a study of their computational cost. Simulation results demonstrate the effectiveness of the proposed robust resource allocation algorithms, showing a performance improvement of up to 30% compared to existing techniques.

A testbed with open-source tools and commercial equipment is designed to investigate the timing of ML-based xApp operations in DDoS attack detection during the Random Access procedure. Results show high-precision and high-recall ML classifiers operating faster than the Medium Access Control (MAC) layer's Contention Resolution timer. For classifiers with over 97% precision and recall, XGBoost has the fastest execution time and the narrowest estimated run time probability density function.

We propose an algorithm based on Information Theory to detect changes in Ultra-Wideband (UWB) Very-High Frequency (VHF) Synthetic Aperture Radar (SAR) images with high performance and low complexity. Our algorithm models the clutter-plus-noise using six different distributions and computes a scalar statistic for each pixel based on a multi-temporal stack of images. With this statistic, it is then possible to apply hypothesis testing and classification methods to infer the occurrence of a change. In this context, we derive expressions necessary for the entropy-based statistics, including the entropy variance for the Weibull and Rice distributions. We also evaluate the computational time complexity of the algorithm for each distribution studied. Furthermore, a masking strategy is used to reduce false alarms significantly. We show that the mask mapping assumptions are mild in scenarios with stacks of images, allowing its use in many scenarios. Our algorithm achieves a false alarm rate (FAR) of 0.08 and a probability of detection (PD) of 100%, outperforming existing methods on the CARABAS II data set.

Lattice codes are elegant and powerful structures that not only achieve the capacity of the additive white Gaussian noise (AWGN) channel but are also a key ingredient to many multiterminal schemes that exploit linearity properties. However, constructing lattice codes that can realize these benefits with low complexity is still a challenging problem. In this paper, efficient encoding and decoding algorithms are proposed for multilevel binary low-density parity-check (LDPC) lattices constructed via Construction D' whose complexity is linear in the total number of coded bits. Moreover, a generalization of Construction D' is proposed, which relaxes some of the nesting constraints on the parity-check matrices of the component codes, leading to a simpler and improved design. Based on this construction, low-complexity multilevel LDPC lattices are designed whose performance under multistage decoding is comparable to that of polar lattices and close to that of low-density lattice codes on the power-unconstrained AWGN channel.

We propose efficient encoding and decoding algorithms for Construction D` multilevel LDPC lattices whose complexity is linear in the total number of coded bits. Moreover, we propose a generalization of Construction D ` that relaxes some of the nesting constraints on the component codes, leading to a simpler and improved design. Based on this construction, low-complexity multilevel LDPC lattices are designed whose performance under multistage lattice decoding is comparable to that of polar lattices on the power-unconstrained AWGN channel.