In this paper we present a new approach of Spectrum Sensing using a Compressive Sensing technique named Finite Rate of Innovation in a Cognitive Radio Network with centralized Spectrum Management based Spectrum Broker in the next generation wireless communications networks. Through this document it will shown under simulations that the use of compressive sensing techniques improves the performance of the control channel in cognitive radio due the traffic control protocol requires smaller packet sizes. The performance of the cognitive network in function of the control packet size, was determinate by analysis of collisions when different secondary users trying to access spectrum resources and they make the request to the Spectrum Broker. We observed that there are fewer collisions between control packets and collision probability is smaller if compressive technique is used, thus improving the performance in a fair resource allocation for cognitive radio networks.

In the process of spectrum sensing applied to wireless communications, it is possible to build interference maps based on acquired power spectral values. This allows the characterization of spectral occupation, which is crucial to take management spectrum decisions. However, the amount of information both in the space and frequency domains that needs to be processed generates an enormous amount of data with high transmission delays and high memory requirements. Meanwhile, compressive sensing is a technique that allows the reconstruction of sparse or compressible signals using fewer samples than those required by the Nyquist criterion. This paper presents a new model that uses compressed multispectral sampling for spectrum sensing. The aim is to reduce the number of data required for the storage and the subsequent construction of power spectral maps with geo-referenced information in different frequency bands. This model is based on architectures that use compressive sensing to analyze multispectral images. The operation of a centralized manager is presented in order to select the power data of different sensors by binary patterns. These sensors are located in different geographical positions. The centralized manager reconstructs a data cube with the transmitted power and frequency of operation of all the sensors based on the samples taken and applying multispectral sensing techniques. The results show that this multispectral data cube can be built with 50% of the samples generated by the devices, and the spectrum cartography information can be stored using only 6.25% of the original data.

In this paper we present a comparison of the peak to average power ratio (PAPR) between ortogonal frequency division multiplexing (OFDM) based on fast Fourier transform (FFT) and Fast-OFDM (FOFDM) based on discrete cosine transform (DCT). It is shown that although the probability of PAPR is slightly higher for systems using the discrete cosine transform, the difference between the conventional OFDM and fast OFDM schemes are not significant as well has been presented in previous papers in the academic community, it is important to remark that the techniques used for PAPR reduction could be used in both schemes without significant performance differences.

In this paper, we show numerical simulations for analyzing spatially multiplexed systems based on orthogonal frequency division multiplexing and multiple-input multiple-output Alamouti code in order to compare the performance of the transmission in a two-core fiber with low coupling, and in few-mode fiber with high differential group delay. These new fibers are the most recent alternatives used for improving the throughput of long-haul and high-speed optical fiber networks. We compare the bit-error-rate of two orthogonal frequency division multiplexing signals, after 100 km of optical fiber, considering only its linear effects. The first one uses an 8 phase-shift keying signal operating at 130 Gbps, and the second one uses a 16 quadrature amplitude modulated signal operating at 170 Gbps. Our results show a better bit error rate performance using a few-mode fiber, compared with a two-core fiber. Additionally, Alamouti code shows a better effectiveness for compensation of linear impairments in a two-core fiber in comparison with a few-mode fiber.

We present ESNformer, a hybrid architecture that couples an Echo State Network (ESN) reservoir with a Transformer encoder for classification of structured, multi-indicator assessment data: a fixed-order vector of complementary indicators per assessment instance rather than a repeated-measures time series. The reservoir acts as a fixed nonlinear feature map over the indicator vector, while self-attention, made position-aware over the fixed column order, learns how each indicator’s evidence contributes to the final decision, so the two components, together, capture local, indicator-level detail and global, cross-indicator interactions within a single, end-to-end trainable model. Interpretability is treated as a first-class design requirement rather than an afterthought: the architecture is paired with an explainability layer combining SHAP feature attribution (reported both globally and per class), the model’s own attention weights, a deletion/insertion faithfulness test that quantitatively verifies which inputs the model actually relies on, and counterfactual maps that translate a prediction into an actionable, inspectable recommendation. We evaluate the architecture on a concrete case study, classifying Braille-literacy instructional recommendations from 15 pedagogical indicators grouped into three categories (Mangold’s, ABKL, and Progresar), using a benchmark of 900 real assessment instances (630 used, together with a class-conditional augmentation procedure, to build a 2100-instance training set) with validation and test partitions (135 instances each) kept exclusively real. On this benchmark, the tuned model reached 85.33% accuracy, 85.90% macro-precision, 85.33% macro-recall, an F1 score of 85.25%, and an AUC of 0.95 on the real test set. SHAP attribution, attention weights, and the faithfulness test converge on the same two dominant indicators (response time and error count): removing them alone collapses accuracy to chance, while retaining only them recovers most of the model’s accuracy. We report this transparently alongside a comparison against ESN-only, Transformer-only, and tabular baselines (logistic regression, decision tree, random forest, XGBoost, and an MLP) on the same data and discuss what the hybrid architecture and its explainability pipeline add beyond what the two dominant indicators already explain and how the approach generalizes to other tabular and mixed-granularity assessment settings that require both predictive accuracy and a verifiable account of what drove each decision.

Energy savings in Wireless Sensor Networks for fish farms is necessary and beneficial. We present a novel energy savings method that minimizes the interpolation errors of sensors' measurements. Sensors follow a working cycle in which they are active when measuring data, and inactive or suspended when data is interpolated from the above measurements and consumed energy is reduced. To our knowledge, we are the first to implement interpolation for energy savings. We improved that model to describe the non-linear property of the consumed energy in the batteries, adding a new variable that explains their real behavior. Several experiments with a prototype of a Wireless Sensor Network with pH, water temperature, and ambient temperature sensors are implemented to validate our method. We made a series of measurements to determine the actual energy savings at each sensor and compared these savings with those predicted by each theory model. The results show that the model is more accurate, presenting less than 5% prediction errors which does not affect fish growth. Furthermore, our paper introduces an energy-saving method for extending WSN lifetime by modeling the non-linear power consumption of sensors' batteries. We propose a new mathematical optimization formulation using an efficient interpolation mechanism that operates in real-time. A real-scale WSN prototype installed over water validates and refines our method. Finally, we showed that the number of interpolated values is of a broader range for aquatic sensors than for outdoor sensors such as ambient temperature. That is, energy savings for fish farming is acceptable.