MIMO OTA testing based on transmit signal processing

Usually, multiple-input-multiple-output (MIMO) testbeds are combined with channel emulators for testing devices and algorithms under controlled channel conditions. In this work, we propose a simple methodology that allows over-the-air (OTA) MIMO testing using a MIMO testbed solely, avoiding the use of channel emulators. The MIMO channel is emulated by linearly combining the signals at the testbed transmitter. The method is fully flexible, so it is able to emulate any equivalent baseband narrowband MIMO channel by adequately selecting the weights of the linear combination. We derive closed-form expressions for the computation of such weights. To prove its feasibility, the method has been implemented and tested over a commercial MIMO testbed.This work was supported by the Spanish Government, Ministerio de Ciencia e Innovacain (MICINN), under projects COSIMA (TEC2010-19545-C04-03) and COMONSENS (CSD2008-00010, CONSOLIDER-INGENIO 2010)

Adaptive clustering algorithm for cooperative spectrum sensing in mobile environments

In this work we propose a new adaptive algorithm for cooperative spectrum sensing in dynamic environments where the channels are time varying. We assume a centralized spectrum sensing procedure based on the soft fusion of the signal energy levels measured at the sensors. The detection problem is posed as a composite hypothesis testing problem. The unknown parameters are estimated by means of an adaptive clustering algorithm that operates over the most recent energy estimates reported by the sensors to the fusion center. The algorithm does not require all sensors to report their energy estimates, which makes it suited to be used with any sensor selection strategy (active sensing). Simulation results show the feasibility and efficiency of the method in realistic slow-fading environments.This work has been funded by SODERCAN and Programa Operativo FEDER under grant CAIMAN - 12.JU01.64661, and by the Ministerio de Economía, Industria y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grants TEC2017-86921-C2-1-R (CAIMAN), TEC2013-47141-C4-R (RACHEL) and TEC2016-75067- C4-4-R (CARMEN)

Node activity monitoring in heterogeneous networks using energy sensors

In Heterogeneous Networks, small cells are usually deployed without operator supervision. Their proper operation highly depends on their self-adaptation capability, especially in dense HetNets where various small cells coexist in the same macrocell. This capability requires the small-cell base stations to continuously sense the radio environment, so they can dynamically adapt their operational setting (e.g. transmission power, carrier/channel selection, etc.) to the environmental conditions. In this work we propose a new method for a small base station to monitor the activity of the rest of nodes in the macrocell. We consider a centralized sensing procedure based on the fusion of the energy levels measured by the users of the small cell at their locations. In particular, we present an efficient algorithm that enables the small base station to monitor the activity of the rest of nodes. In addition, the algorithm also provides the gain of the channels between the nodes and the users of the small cell.This work has been funded by the Ministerio de Economía, Industria y Competitividad (MINECO) of Spain under grant TEC2017-86921-C2-1-R (CAIMAN) and under the KERMES Network (TEC2016-81900-REDT/AEI)

Adaptive EM-based algorithm for cooperative spectrum sensing in mobile environments

In this work we propose a new adaptive algorithm for cooperative spectrum sensing in dynamic environments where the channels are time varying. We assume a cooperative sensing procedure based on the soft fusion of the signal energy levels measured at the sensors. The detection problem is posed as a composite hypothesis testing problem. Then, we consider the Generalized Likelihood Ratio Test approach where the maximum likelihood estimate of the unknown parameters (which are the signal-to-noise ratio under the different hypotheses) are obtained from the most recent energy levels at the sensors by means of the Expectation-Maximization algorithm. We derive simple closed-form expressions for both, the E and the M steps. The algorithm can operate even when only a subset of sensors report their energy estimates, which makes it suited to be used with any sensor selection strategy (active sensing). Simulation results show the feasibility and efficiency of the method in realistic slow-fading environments.This work has been funded by SODERCAN and Programa Operativo FEDER under grant CAIMAN - 12.JU01.64661, and by the Ministerio de Economía, Industria y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grants TEC2017-86921-C2-1-R (CAIMAN), TEC2013-47141-C4-R (RACHEL) and TEC2016-75067- C4-4-R (CARMEN)

Code combination for blind channel estimation in general MIMO-STBC systems

The problem of blind channel estimation under space-time block coded (STBC) transmissions is addressed. Firstly, a blind channel estimation criterion that generalizes previous works is proposed. The technique is solely based on second-order statistics (SOS) and if the channel is identifiable, the estimate is obtained as the main eigenvector of a generalized eigenvalue problem (GEV). Secondly, a new transmission technique is proposed to solve the indeterminacies associated to the blind channel estimation problem. The technique is based on the combination of different STBCs, and it can be reduced to a nonredundant precoding consisting in the rotation or permutation of the transmit antennas. Unlike other previous approaches, the proposed technique does not imply a penalty in the transmission rate or capacity of the STBC system, while it is able to avoid the ambiguities in many practical cases, which is illustrated by means of some simulation examples

Performance analysis of SNR-based scheduling policies in asymmetric broadcast ergodic fading channel

We analyze the performance of SNR-based scheduling algorithms in broadcast ergodic fading channels where multiuser selection diversity is exploited. At each channel state, the user with the highest weighted signal-to-noise ratio is selected to be transmitted. The use of weights associated to the users allows us to control the degree of fairness among users and to arrange them according to a prescribed quality of service. These weights parametrize the scheduling algorithms so each set of weights corresponds to a specific scheduling algorithm. Assuming Rayleigh fading broadcast channel, we derive a closed-form expression for the achievable user’s rates as a function of the scheduling algorithm, the channel fading statistics of each user, and the transmit power. With the help of this expression, we solve some interesting inverse problems. For example, for a given arbitrary channel statistics we obtain the optimum scheduling algorithm to achieve a prescribed set of users’ rates with minimum transmit power

Multi-instance multi-label learning in the presence of novel class instances

Multi-instance multi-label learning (MIML) is a framework for learning in the presence of label ambiguity. In MIML, experts provide labels for groups of instances (bags), instead of directly providing a label for every instance. When labeling efforts are focused on a set of target classes, instances outside this set will not be appropriately modeled. For example, ornithologists label bird audio recordings with a list of species present. Other additional sound instances, e.g., a rain drop or a moving vehicle sound, are not labeled. The challenge is due to the fact that for a given bag, the presence or absence of novel instances is latent. In this paper, this problem is addressed using a discriminative probabilistic model that accounts for novel instances. We propose an exact and efficient implementation of the maximum likelihood approach to determine the model parameters and consequently learn an instance-level classifier for all classes including the novel class. Experiments on both synthetic and real datasets illustrate the effectiveness of the proposed approach

La gestión de la demanda de electricidad

El desarrollo económico español se acompaña de una fuerte demanda de la energía. Sin embargo, hasta cierto punto, es posible desvincular el desarrollo económico con el consumo de energía. El informe se centra en la gestión de la electricidad, el conjunto de acciones que intentan influir sobre el uso de los consumidores para ahorrar energía. Las medidas se pueden agrupar en tres categorías. En primer lugar, acciones que facilitan la respuesta de la demanda a los precios de la electricidad e incorporan progresivamente el coste de las externalidades. En segundo lugar, acciones de promoción del ahorro y la eficiencia energética en el consumo eléctrico. Y, por último, acciones transversales de apoyo a las dos categorías anteriores. Las medidas son de naturaleza muy variadas como la aplicación de incentivos económicos destinados a la puesta en marcha de programas de formación y concienciación o la implantación de procedimientos que hagan posible la participación activa de la demanda

Online detection and SNR estimation in cooperative spectrum sensing

ABSTRACT: Cooperative spectrum sensing has proved to be an effective method to improve the detection performance in cognitive radio systems. This work focuses on centralized cooperative schemes based on the soft fusion of the energy measurements at the cognitive radios (CRs). In these systems, the likelihood ratio test (LRT) is the optimal detection rule, but the sufficient statistic depends on the local signal-to-noise ratio (SNR) at the CRs, which are unknown in most practical cases. Therefore, the detection problem becomes a composite hypothesis test. The generalized LRT is the most popular approach in those cases. Unfortunately, in mobile environments, its performance is well below the LRT because the local energies are measured under varying SNRs. In this work, we present a new algorithm that jointly estimates the instantaneous SNRs and detects the presence of primary signals. Due to its adaptive nature, the algorithm is well suited for mobile scenarios where the local SNRs are time-varying. Simulation results show that its detection performance is close to the LRT in realistic conditions.This work was supported in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with European Commission [European Regional Development Fund (ERDF)], under Grant TEC2017-86921-C2-1-R and Grant TEC2017-86921-C2-2-R (CAIMAN) and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICO-CM)

Estima ciega de canal en sistemas MIMO-OSTBC basada en Correlation Matching

In this paper, a new technique for blind channel estimation of multiple-input multiple-output (MIMO) channels under orthogonal space-time block coded (OSTBC) transmissions is proposed. The technique is based on the Correlation Matching criterion and exploits the special structure of OSTBC codes to obtain closed-form channel estimates. The proposed method can be reformulated as a principal component analysis (PCA) problem, which permits a straightforward derivation of computationally efficient batch and adaptive algorithms. Although derived in a different way, the technique generalizes previously proposed methods, establishing a parameter selection criterion which resembles the matched filter. Finally, the performance of the proposed algorithms is evaluated by means of some simulation examples