In optical transport networks, failure localization is usually triggered as a response to alarms and significant anomalous behaviors. However, the recent evolution of network control and management leveraging software-defined networking (SDN) and streaming-based telemetry opens up new possibilities for automated methods that can localize even subtle anomalies, the so-called soft failures. This paper reports the experimental demonstration of a machine-learning-based soft-failure localization framework in a small-scale laboratory setup. The SDN telemetry setup includes ONOS-controlled transponders using NETCONF and an optical line system (OLS) providing telemetry via an OLS domain controller. A shallow artificial neural network (ANN) accomplishes ML-based failure localization with principal component analysis to reduce non-essential information. The ANN is trained by combining field data and synthetic data generated in a digital network twin. The field data trains the ANN to tolerate statistical variations in the network telemetry without failures, while the synthetic data generates artificial single-failure scenarios. We show that the soft-failure localization scheme successfully pinpoints the faulty element in all single failures generated in transponders, fibers, and amplifiers. We also demonstrate the system’s ability to deal with double-failure scenarios.

Soft-failure localization frameworks typically use if-else rules to localize failures based on the received telemetry data. However, in certain cases, particularly in disaggregated networks, some devices may not implement telemetry, or their telemetry may not be readily available. Alternatively, machine-learning-based (ML-based) frameworks can automatically learn complex relationships between telemetry and the fault location, incorporating information from the telemetry data collected network-wide. This paper evaluates an ML-based soft-failure localization framework in scenarios of partial telemetry. The framework is based on an artificial neural network (ANN) trained by optical signal and noise power models that simulate the network telemetry upon all possible failure scenarios. The ANN can be trained in less than 2 min, allowing it to be retrained according to the available partial telemetry data. The ML-based framework exhibits excellent performance in scenarios of partial telemetry, practically interpolating the missing data. We show that in the rare cases of incorrect failure localization, the actual failure is in the localized device’s vicinity. We also show that ANN training is accelerated by principal component analysis and can be carried out using cloud-based services. Finally, the evaluated ML-based framework is emulated in a software-defined-networking-based setup using the gNMI protocol for streaming telemetry.

Targeting soft failure localization, we present a machine learning approach using SDN streaming telemetry of network-wide parameters. The artificial neural network only requires a few training scenarios to achieve adequate interpolation performance. The framework implementation is validated using gNMI telemetry in an emulated NSFNet topology.

In recent years, beamforming has been vital to increasing the spectral and energy efficiency of the current and next-generation wireless communication systems, such as 5G, the Internet of Things (IoT), and beyond. In such a context, this letter extends the previously proposed single user, multiple-input single-output (SU-MISO) phase-transmittance radial basis function (PT-RBF) beamforming to self-organizing wireless network receivers with multiple users, multiple-input multiple-output (MU-MIMO) beamforming. In the proposed novel approach, the PT-RBF is designed to support multiple outputs and multiple layers, an innovation compared to previous shallow single output PT-RBF. On account of this deep neural network architecture, the proposed deep PT-RBF can handle multiple users with different modulation formats and distinct orders, presenting better results when compared either with other complex-valued neural networks or with the normalized least mean square algorithm.

Multi-input multi-output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance—usually, the best performance—but its computational complexity is a limiting factor in practical implementation. In the present work, a novel MIMO scheme using a practically feasible decoding algorithm based on the phase transmittance radial basis function (PTRBF) neural network is proposed. For some practical scenarios, the proposed scheme achieves improved receiver performance with lower computational complexity relative to the maximum likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under 5G wireless Rayleigh channels so that a fair performance comparison with other reference techniques can be established.

This letter proposes the use of quasi-orthogonal space-time block codes (QOSTBC) to enhance link quality and reliability in massive multiple-input multiple-output (m-MIMO) systems subject to independent fading in dynamic channels. It has been shown, however, that the computational complexity of classical decoding algorithms, such as maximum likelihood, can hinder the adoption of QOSTBC codes in systems with many antennas and high-order modulation schemes. Complex-valued neural networks (CVNNs) offer a promising alternative for joint decoding and channel estimation with competitive computational complexity. This work presents an extension of our previously proposed CVNN with supervised training, which incorporates two semi-supervised learning techniques: hard inference learning (HIL) and Gaussian inference learning (GIL). By leveraging non-pilot-aided data, HIL and GIL enable the CVNNs to self-learn from useful information, increasing their tracking ability and robustness in dynamic channels.