Paulo Ricardo Branco da Silva

Mobile network operators (MNOs) worldwide have made sustainability a key priority, driven by the urgent need to reduce energy consumption in their networks. The radio access network (RAN) represents a large share of network energy usage. This significant energy footprint has prompted an increased focus on developing and implementing energy-efficient solutions across the telecommunications industry, particularly in the RAN segment, where the potential for improvement is the greatest. To address this gap, we present a multifold contribution. We overview Open RAN (O-RAN) Alliance specifications and architecture aspects related to energy efficiency (EE), detailing the message exchange among components, deployments, and use cases. Then, we review the state of the art of artificial intelligence (AI)/machine learning (ML)-based applications in energy saving (ES) mechanisms, identifying limitations and strengths. Next, we propose and evaluate a novel use case approach based on the fine-tuning of transmission power. Finally, we highlight open research opportunities in optimizing EE with AI/ML-based solutions.

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.

In this letter, we propose an iterative soft interference cancellation scheme for intra-cluster (ICL) and out-of-cluster (OCL) interference mitigation in user-centric clustered cell-free massive multiple-antenna networks. We propose a minimum mean-square error receive filter with a novel modified parallel interference cancellation scheme to mitigate ICL and OCL interference. Unlike prior work, we model the OCL interference and devise a least squares estimator to perform OCL interference estimation. An iterative detection and decoding scheme that adopts low-density parity check codes and incorporates the OCL interference estimate is developed. Simulations assess the proposed scheme against existing techniques in terms of bit error rate performance.

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.

In this paper, we propose iterative interference cancellation schemes with access points selection (APs-Sel) for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Closed-form expressions for centralized and decentralized linear minimum mean square error (LMMSE) receive filters with APs-Sel are derived assuming imperfect channel state information (CSI). Based on the derived expressions, insights are drawn and general expressions are devised for several cases, namely: firstly, MMSE-SIC filter for the non-scalable CF-mMIMO that uses all APs. Secondly, an MMSE-SIC filter assuming perfect channel state information. Thirdly, in this case we assume no interference cancellation and the linear MMSE filter is obtained. Additionally, we formulate a new Gaussian approximation of the likelihood function by deriving new closed-form expressions for the second order statistics (mean and variance) of the detected signal parameters in presence of channel estimation errors, APs-Sel matrix and multi-user interference (MUI). Since the MMSE-SIC filter experiences error propagation due to erroneous decisions from the previous stages, we develop a list-based detector based on LMMSE receive filters for CF-mMIMO systems that exploits interference cancellation and the constellation points to mitigate the error propagation that occurs in conventional MMSE-SIC receivers. An iterative detection and decoding (IDD) scheme that employs low-density parity-check codes is then developed. Moreover, log-likelihood ratio (LLR) refinement strategies based on censoring and a linear combination of local LLRs are proposed to improve the network performance. We assess the proposed centralized and decentralized IDD schemes against existing approaches in terms of bit error rate performance, complexity, and signaling under perfect CSI and imperfect CSI and verify the superiority of the distributed IDD architecture with LLR refinements.

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.