Analysis of energy detection of unknown signals under Beckmann fading channels

(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.The Beckmann fading is a general multipath fading model which includes Rice, Hoyt and Rayleigh fading as particular cases. However, the generality of the Beckmann fading also implies a significant increased mathematical complexity. Thus, relatively few analytical results have been reported for this otherwise useful fading model. The performance of energy detection for multi-branch receivers operating under Beckmann fading is here studied, and the inherent analytical complexity is here circumvented by the derivation of a closed-form expression for the generalized moment generating function (MGF) of the received signal-to-noise ratio (SNR), which is a new and useful result, as it is key for evaluating the receiver operating characteristics. The impact of fading severity on the probability of missed detection is shown to be less critical as the SNR decreases. Monte Carlo simulations have been carried out in order to validate the obtained theoretical expressions.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Proyecto MINECO-FEDER TEC2013-42711-R y TEC2013-44442-P. Junta de Andalucía P11-TIC-7109

CAMELOT - computational-analytical multi-fidelity low-thrust optimisation toolbox

CAMELOT (Computational-Analytical Multi-fidelity Low-thrust Optimisation Toolbox) is a toolbox for the fast preliminary design and optimisation of low-thrust trajectories. It solves highly complex combinatorial problems to plan multi-target missions characterised by long spirals including different perturbations. In order to do so, CAMELOT implements a novel multi-fidelity approach combining analytical surrogate modelling and accurate computational estimations of the mission cost. Decisions are then made by using two pptimisation engines included in the toolbox, a single objective global optimiser and a combinatorial optimisation algorithm. CAMELOT has been applied to a variety of applications: from the design of interplanetary trajectories to the optimal deorbiting of space debris, from the deployment of constellations to on-orbit servicing. In this paper the main elements of CAMELOT are described and two space mission design problems solved using the toolbox are described

Impact of Fading Severity and Receive Antenna Correlation on TAS/MRC under Nakagami Fading

Transmit antenna selection (TAS) is well-known to allow a reduced signal processing complexity while maintaining the diversity order of a MIMO system. Assuming Nakagami fading, we show in this work that antenna correlation in a maximal ratio combining (MRC) receiver, as well as severe fading, have a beneficial impact on ergodic capacity if TAS is used at the transmit end. This is in sharp contrast to MRC reception when a single transmit antenna is considered. We also derive novel closed-form expressions for the average symbol error rate (ASER) of TAS/MRC for different M-ary modulations, generalizing previous works by considering receive antenna correlation where the eigenvalues of the receive correlation matrix have anarbitrary multiplicity. Monte Carlo simulations are performed to validate the analysis. Our results show that, contrary to the behavior of ergodic capacity, antenna correlation and severe fading always have a detrimental impact on ASER for the average SNR values of interest, as in those cases the ASER is dominated by the diversity gain.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech y proyecto Ministerio de Economía y Competividad/FEDER TEC2013-42711-

Automatic planning and scheduling of active removal of non-operational satellites in low earth orbit

In this paper two novel strategies to automatically design an optimized mission to de-orbit up to 10 non-cooperative objects per year are proposed, targeting the region within 800 and 1400 km altitude in LEO. The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches.The first strategy is analogous to the Traveling Salesman Problem: The servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that performs the re-entry. The second strategy is analogous to the Vehicle Routing Problem: The servicing spacecraft rendezvous with an object, spiral it down to a lower altitude orbit, and spiral up to the next target. In order to maximize the number of de-orbited non-operative objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimization algorithm. The incremental planning and scheduling algorithm uses a model based on optimal low-Thrust transfer between objects. The optimization of the transfers is realized using a direct method and an analytical propagator based on a first-order solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the J2 gravitational effect and the atmospheric drag

Automatic trajectory planning for low-thrust active removal mission in Low-Earth Orbit

In this paper two strategies are proposed to de-orbit up to 10 non-cooperative objects per year from the region within 800 and 1400 km altitude in Low Earth Orbit (LEO). The underlying idea is to use a single servicing spacecraft to de-orbit several objects applying two different approaches. The first strategy is analogous to the Traveling Salesman Problem: the servicing spacecraft rendezvous with multiple objects in order to physically attach a de-orbiting kit that reduces the perigee of the orbit. The second strategy is analogous to the Vehicle Routing Problem: the servicing spacecraft rendezvous and docks with an object, spirals it down to a lower altitude orbit, undocks, and then spirals up to the next target. In order to maximise the number of de-orbited objects with minimum propellant consumption, an optimal sequence of targets is identified using a bio-inspired incremental automatic planning and scheduling discrete optimisation algorithm. The optimisation of the resulting sequence is realised using a direct transcription method based on an asymptotic analytical solution of the perturbed Keplerian motion. The analytical model takes into account the perturbations deriving from the $J_2$ gravitational effect and the atmospheric drag

Generalized MGF of Beckmann Fading with Applications to Wireless Communications Performance Analysis

The Beckmann distribution is a general multipath fading model for the received radio signal in the presence of a large number of scatterers, which can thence be modeled as a complex Gaussian random variable where both the in-phase and quadrature components have arbitrary mean and variance. However, the complicated nature of this distribution has prevented its widespread use and relatively few analytical results have been reported for this otherwise useful fading model. In this paper, we derive a closed-form expression for the generalized moment-generating function (MGF) of the signal-to-noise ratio (SNR) of Beckmann fading, which permits to circumvent the inherent analytical complexity of this model. This is a new and useful result, as it is key for evaluating several important performance metrics of different wireless communication systems and also permits to readily compute the moments of the output SNR. Thus, we obtain simple exact expressions for the energy detection performance in Beckmann fading channels, both in terms of the receiver operating characteristic (ROC) curve and of the area under ROC curve. We also analyze the outage probability in interference limited systems affected by Beckmann fading, as well as the outage probability of secrecy capacity in wiretap Beckmann fading channels. Monte Carlo simulations have been performed to validate the derived expressions.Universidad de Málaga. Campus de Excelencia Internacional. Andalucía Tech

Beyond Random Split for Assessing Statistical Model Performance

Even though a train/test split of the dataset randomly performed is a common practice, could not always be the best approach for estimating performance generalization under some scenarios. The fact is that the usual machine learning methodology can sometimes overestimate the generalization error when a dataset is not representative or when rare and elusive examples are a fundamental aspect of the detection problem. In the present work, we analyze strategies based on the predictors' variability to split in training and testing sets. Such strategies aim at guaranteeing the inclusion of rare or unusual examples with a minimal loss of the population's representativeness and provide a more accurate estimation about the generalization error when the dataset is not representative. Two baseline classifiers based on decision trees were used for testing the four splitting strategies considered. Both classifiers were applied on CTU19 a low-representative dataset for a network security detection problem. Preliminary results showed the importance of applying the three alternative strategies to the Monte Carlo splitting strategy in order to get a more accurate error estimation on different but feasible scenarios

Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm

In this paper, a combinatorial optimisation algorithm inspired by the Physarum Polycephalum mould is presented and applied to the optimal trajectory planning of a multiple asteroid tour mission. The Automatic Incremental Decision Making And Planning (AIDMAP) algorithm is capable of solving complex discrete decision making problems with the use of the growth and exploration of the decision network. The stochastic AIDMAP algorithm has been tested on two discrete astrodynamic decision making problems of increased complexity and compared in terms of accuracy and computational cost to its deterministic counterpart. The results obtained for a mission to the Atira asteroids and to the Main Asteroid Belt show that this non-deterministic algorithm is a good alternative to the use of traditional deterministic combinatorial solvers, as the computational cost scales better with the complexity of the problem

Optimised low-thrust mission to the Atira asteroids

Atira asteroids are recently-discovered celestial bodies characterised by orbits lying completely inside the heliocentric orbit of the Earth. The study of these objects is difficult due to the limitations of ground-based observations: objects can only be detected when the Sun is not in the field of view of the telescope. However, many asteroids are expected to exist in the inner region of the Solar System, many of which could pose a significant threat to our planet. In this paper, a small, low-cost, mission to visit the known Atira asteroids and to discover new Near Earth Asteroids (NEA) is proposed. The mission is realised using electric propulsion. The trajectory is optimised to maximise the number of visited asteroids of the Atira group using the minimum propellant consumption. During the tour of the Atira asteroids an opportunistic NEA discovery campaign is proposed to increase our knowledge of the asteroid population. The mission ends with a transfer to an orbit with perihelion equal to Venus's orbit radius. This orbit represents a vantage point to monitor and detect asteroids in the inner part of the Solar System and provide early warning in the case of a potential impact

Gut Microbiota Has a Crucial Role in the Development of Hypertension and Vascular Dysfunction in Toll-like Receptor 7-Driven Lupus Autoimmunity

Our group has investigated the involvement of gut microbiota in hypertension in a murine model of systemic lupus erythematosus induced by Toll-like receptor (TLR)-7 activation. Female BALB/c mice were randomly assigned to four experimental groups: an untreated control (CTR), a group treated with the TLR7 agonist imiquimod (IMQ), IMQ-treated with vancomycin, and IMQtreated with a cocktail of broad-spectrum antibiotics. We carried out faecal microbiota transplant (FMT) from donor CTR or IMQ mice to recipient IMQ or CTR animals, respectively. Vancomycin inhibited the increase in blood pressure; improved kidney injury, endothelial function, and oxidative stress; and reduced T helper (Th)17 infiltration in aortas from IMQ-treated mice. The rise in blood pressure and vascular complications present in IMQ mice were also observed in the CTR mice recipients of IMQ microbiota. Reduced relative populations of Sutterella and Anaerovibrio were associated with high blood pressure in our animals, which were increased after stool transplantation of healthy microbiota to IMQ mice. The reduced endothelium-dependent vasodilator responses to acetylcholine induced by IMQ microbiota were normalized after interleukin-17 neutralization. In conclusion, gut microbiota plays a role in the TLR7-driven increase in Th17 cell, endothelial dysfunction, vascular inflammation, and hypertension. The vascular changes induced by IMQ microbiota were initiated by Th17 infiltrating the vasculature.Comisión Interministerial de Ciencia y Tecnología, Ministerio de Economía y competitividad (MINECO) (SAF2017-84894-R, PID2020-116347RBI00)Junta de Andalucía (CTS 164, P20_00193) with funds from the European Union, and by the Ministerio de Economia y Competitividad, Instituto de Salud Carlos III (CIBER-CV)Instituto de Salud Carlos III (Sara Borrell Program)MINECOEuropean Union (Fondo Europeo de Desarrollo Regional, FEDER