Traffic demand in wireless communication systems has emerged as a key issue over recent decades. An ever-increasing trend is projected for the next few years, with explosive data traffic expected to materialize and mobile users imposing new quality of service requirements. This growing traffic demand, combined with increasingly complex heterogeneous network (HetNet) scenarios, has presented ever more challenges for mobile network operators in terms of service, coverage, load balancing, and quality of service. Considering the traditional association mechanism based on maximum power received, HetNets tend to remain unbalanced, making it challenging to satisfy mobile users' traffic requirements. In this paper, instead of trying to maximize the achievable downlink rate per user, we couple the cell range expansion (CRE) technique with a particle swarm optimization (PSO) algorithm to maximize the number of users whose downlink requirements are met. The proposed scheme considers both the loads of base stations and the signal-to-interference-plus-noise ratio (SINR) of user equipment to model an objective function that seeks to compute specific CRE bias values per small base station. The proposed scheme is also compared with some classical PSO implementations. Numerical results validate the performance of the proposed schemes, which effectively fulfill users' data traffic requirements by reducing network imbalance.INDEX TERMS Cell range expansion, heterogeneous mobile networks, load balancing, particle swarm optimization, user association.

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Electrocardiogram (ECG) measures the electrical activity of the heart, which can be used in the diagnosis of different heart diseases. In the scientific literature there are many studies that have been applied machine learning for recognizing ECG patterns, where most of them attempt to classify heart beats. This paper presents a novel methodology for automatically classifying seventeen cardiac rhythms by means of digital signal processing and machine learning. The steps before the classification include the mapping of ECG signal to the frequency domain through power spectrum density, class balance with Adaptive Synthetic Sampling algorithm, and statistical normalization. The classifiers employed were Support Vector Machine, Multilayer Perceptron Neural Network, k-Nearest Neighbors, and Random Forest. The results showed accuracy, sensitivity, specificity, and Fleiss' kappa of up to 98.86%, 99.93%, 98.85%, and 89.68%, respectively, which are relatively better than the performance observed in the state-of-the-art works. In addition, this study highlighted that when the class balance procedure is applied, the classification step becomes less complex and can increase in terms of performance.

Gathering channel data to test telecommunication systems is an essential step to guarantee the quality of the product. However, it can be a slow process and demand a considerable amount of effort and investment since it is costly to make field measurements of mmWaves. Having a ready dataset at disposal make things way faster and cheaper, allowing a developer to focus on more specific tasks. This paper presents an entire multimodal dataset with different kinds of information like channel communication, urban traffic and obstacles position, got from two realistic computer simulations made in two different city models: Beijing and Rosslyn. It also includes detailed information on how each data is stored.

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