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.

The use of beamforming for efficient transmission has already been successfully implemented in practical systems and is absolutely necessary to even further increase spectral and energy efficiencies in some configurations of the next-generation wireless systems and for low earth orbit satellites. A remarkable capacity increase is then achieved and spectral congestion is minimized. In this context, this article proposes a novel complex multiple-input multiple-output radial basis function neural network (CMM-RBF) for transmitter beamforming, based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed CMM-RBF is compared with the least mean square (LMS) algorithm for beamforming with six dipoles arranged in a uniform and circular array and with 16 dipoles in a 2D-grid array. Simulation results show that the proposed solution presents lower steady-state mean squared error, faster convergence rate and enhanced half-power beamwidth (HPBW) when compared with the LMS algorithm in a nonlinear scenario.

Artificial neural networks (ANNs) have become a popular tool in digital signal processing (DSP). Among the widespread ANN architectures, complex-valued neural networks (CVNNs) have been extensively studied in image processing and telecommunications. Unlike their real-valued counterparts, CVNNs can handle signals directly in the complex domain. Due to this capability, CVNNs usually exhibit higher accuracy and improved convergence compared to real-valued neural networks (RVNNs). Despite their improved performance in several applications, CVNNs still lag behind RVNNs in terms of learning techniques and heuristics. In this context, we propose adaptive learning rate approaches for CVNNs, extending the well-known adaptive gradient (AdaGrad), root-mean-square propagation (RMSProp), AdaMax, AMSGrad, softplus AMSGrad (SAMSGrad), Nesterov-accelerated adaptive moment estimation (Nadam), and DiffGrad to the complex domain. Computational complexities of the proposed optimizers are analyzed for CVNN architectures. Results are compared in terms of mean-squared-error convergence.

In optical networks, assessing the quality of transmission (QoT) of future demands is critical for optimized network operation. Given the recent advances in network telemetry and Big Data processing, there has been a growing trend to leverage the telemetry of existing connections to enhance the QoT estimation for future demands. Currently, QoT estimation techniques are categorized into three classes: physics-informed analytical models, machine learning (ML) models, and hybrid models, combining the first two classes. Hybrid models have the potential to achieve accurate QoT estimation with reasonable explainability. In this work, we propose a hybrid QoT estimation technique based on gradient transfer between wavelengths using a simplified network digital twin. The proposed approach relies on the GN model to create a starting point NDT (greenfield), which is then updated with signal-to-noise ratio (SNR) telemetry from deployed transponders (brownfield) using gradient descent techniques. By assuming that neighboring channels have similar performance, the method enables learning from adjacent lightpaths. We use well-established ML optimizers to enhance the convergence rate and reduce steady-state estimation error. Compared with classical analytical models, simulation results indicate improved accuracy with reasonable explainability in network scenarios with imperfect amplifier parameters.

Accurate and low-complexity phase estimation is crucial for optimal data recovery in coherent optical receivers, especially for applications in emerging scenarios such as low-margin optical networks, green networks, probabilistic-shaping modulation, the use of high-order modulation formats in hollow-core fibers, long-haul links operating at low signal-to-noise ratios, and free-space optical links in low-earth orbit satellites, among others. These new developments demand highly efficient and reliable data transmission methods, even under stringent conditions of minimal operational excess margin. This paper introduces an efficient pilot-assisted phase estimation strategy for coherent optical receivers, integrating robust interpolation techniques for quasi-optimal operation. Our approach significantly enhances phase estimation accuracy, addressing the unique challenges posed by these new scenarios. Through comprehensive simulations, we illustrate our method’s superiority over conventional methods, showcasing marked improvements in computational complexity and bit error rate. The results highlight the critical role of sophisticated interpolation in bolstering pilot-assisted phase estimation, offering a promising technique for optimizing performance in next-generation coherent optical receivers.