60 research outputs found
Modeling power control in WCDMA for multimedia support
No full textModern Third Generation Wireless Networks demand more and more resources in order to satisfy customers� needs. And these resources can only be provided by a good Power Control. However, power control needs an algorithm in order to work at the margin of the Quality of Service (QoS) requirements. This work is related to this; we propose a power control algorithm modeled under probabilistic criteria. This means, applying a Markovian model to a MAC Protocol (power control algorithm), in order to optimize the power assigment to each user in the system. This protocol is highly interrelated to the power control functionality in order to extract the maximum capacity and flexibility out of the WCDMA scheme. Once it is done, the algorithm is submitted to different scenarios to demonstrate its capabilities and limitations, also thus analyze its behavior under different conditions and viewpoints. At the end, conclusions are listed, together with future work and proposals to strengthen this work
A multiple beamforming network for unequally spaced linear array based on CORPS
Get PDF"This paper proposes an alternative and innovative way to design a simpler beamforming network (BFN) based on balancing alternated power combiners and dividers, to feed a nonuniformly spaced linear array with Gaussian amplitude and coherent (inphase) signals. Thus, a two-beam design configuration of the feeding network for a nonuniform array with beam steering capability is proposed and analyzed. The nonuniform aperture and the complex inputs of the feeding network are optimized by means of a differential evolution algorithm. In addition, a comparative analysis between a uniform and nonuniform linear array with the proposed feeding network is performed. Simulation results show the advantages and effectiveness of the proposed feeding network exploiting the nonuniformity of the antenna elements, in terms of side lobe level and directivity. Furthermore, research results show an inherent reduction in hardware complexity of the network.
A simple low-complexity precoding technique for MIMO systems
No full textMultiple-input multiple-output (MIMO) systems are still attracting the attention of many researchers and designers for their promising improvements in performance and bandwidth efficiency. This paper presents a low-complexity precoding technique which aims to maximise the MIMO diversity at the receiver. The proposed technique achieves maximum diversity by matching the transmitted data symbols with a suitable precoding matrix from a list of predetermined precoding matrices at the transmitter. This paper shows that significant performance improvements can be obtained with this technique and without any bandwidth degradation. © 2007 IEEE
A fast least-squares solution-seeker algorithm for vector-perturbation
No full textFinding the least-squares solution to a system of linear equations where the unknown vector is comprised of integers, but the matrix coefficient and given vector are comprised of real or complex numbers is a problem equivalent to finding the closest lattice-point to a given point and is well known that the search is hard. However, in communications applications the given vector is not arbitrary but rather is an unknown lattice-point that has been perturbed by an additive offset vector whose statistical properties are known, making it relatively easier to decode. In this paper we will discuss the vector- perturbation technique proposed for solving this problem and analyse a possible solution for overcome the complexity issues. © 2008 IEEE
Prediction of metabolic syndrome in mexicans using machine learning
Get PDFMetabolic syndrome (MetS) is a compelling public health issue in Mexico, with high prevalence rates of overweight, obesity, arterial hypertension, diabetes, high triglycerides, low high-density lipoprotein cholesterol, and high total cholesterol. Despite this, predictive models tailored for under-researched professional groups with sedentary habits are scarce. This study introduces a novel predictive model for MetS using data from the National Center for Health Statistics and a unique dataset of higher education staff. By employing and comparing machine learning algorithms such as decision trees, random forest, artificial neural networks, and adaptive boosting, the research provides new insights into gender and race-specific aspects of MetS. The data was labeled using standards from the International Diabetes Federation and the National Cholesterol Education Program Adult Treatment Panel III to create classification models, which were tested on the higher education staff dataset. Model predictions were assessed using F1-score, accuracy and area under the curve - receiver operating characteristic (AUC-ROC), with random forest, decision tree, and adaptive boosting performing best. The key predictive features identified for MetS prediction include triglycerides, glucose, high-density lipoprotein cholesterol, waist-to-height ratio, and body mass index.
Lattice-reduction for power optimisation using the fast least-squares solution-seeker algorithm
No full textThe power constraint factor in precoding methods plays an important role in the reduction of SER at the receiver. The value of this scaling factor lies in the individual power assigned to the transmit symbols prior the transmission. Such assignment will depend entirely on the matrix condition of the channel, in this case the eigenvalue's power of the channel inverse. Thus, the minimisation of this power constraint will rely on closing the power gap among the transmit symbols, and one solution is to use an auxiliary vector for fixing the matrix condition. This is equivalent to solving the integer leastsquares problem. For achieving this specific goal there are two effective choices: the lattice-reduction, whose objective is to reduce the basis of any given matrix, and the sphere techniques which enumerate all the lattice points inside a sphere centered at the query point. Both choices aim for finding or fitting an approximated least-squares solution occasioning astonishing results in performance when minimising the scaling factor prior to transmit. © 2009 IEEE
Configuration of an IoT microhydraulic power generation system for education
Get PDFInternet of things (IoT) involves the communication of all kinds of things embedded with sensors, electronics, software and people connected to the internet. Knowledge of IoT in the classroom provides an experience for engineering students to explore different career options. Under this scope, an IoT platform on the Arduino UNO and Raspberry Pi 3 development boards was built for academic purposes. The IoT platform was configured to monitor a microhydraulic power generation system used for the study of small-scale hydraulic power production, using a hydraulic head provided by a system of hydraulic pumps in series and/or parallel connection. The platform was designed considering a monitoring station for the acquisition of analog, digital, SPI and PWM data; a control station that receives data from the monitoring station and sends data to the cloud. The communication between modules was established using a publication/subscription system. The platform allows to registrate, visualize and process data directly in the cloud. Meaning that the IoT systems connected to this platform can be monitored from a cell phone, tablet or PC with internet access, promoting immediate access to the emerging information generated in the operating system
Complexity-Improved Sphere Decoder for MIMO Systems Using PSK Modulations
No full textAbstractIt has been shown in recent years that the iterative decoding techniques, improve performance (e.g., bit error rate) of various digital communication systems. Techniques of Multiple-Input Multiple-Output (MIMO) are a key technology to promote and achieve high-speed wireless communications. They demand only a low complexity system for detection since a high CPU processing involves more energy consumption and therefore less flexibility in mobility terms. The sphere decoding (SD) technique has been proposed as an effcient algorithm to solve this problem. SD is known to be an algorithm of polynomial complexity that has become a powerful tool to achieve a high performance close to that given by the maximum likelihood (ML) (which is considered ideal), but with less complexity. This paper proposes a modification to the SD technique in order to reduce its complexity named: improved Less Complexity SD (iLSD)
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