Results
- Showing results for:
- Reset all filters
Search results
-
Journal articleCofré R, Maldonado C, Rosas De Andraca F, 2018,
Large deviations properties of maximum entropy Markov chains from spike trains
, Entropy, Vol: 20, ISSN: 1099-4300We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability, and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.
-
Journal articleCofré R, Maldonado C, Rosas F, 2018,
Large Deviations Properties of Maximum Entropy Markov Chains from Spike Trains
, Entropy, Vol: 20, Pages: 573-573<jats:p>We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability, and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.</jats:p>
-
Journal articleGoto H, Viegas E, Jensen HJ, et al., 2018,
Smoluchowski equation for networks: merger induced intermittent giant node formation and degree gap
, Journal of Statistical Physics, Vol: 172, Pages: 1086-1100, ISSN: 1572-9613The dynamical phase diagram of a network undergoing annihilation, creation, and coagulation of nodes is found to exhibit two regimes controlled by the combined effect of preferential attachment for initiator and target nodes during coagulation and for link assignment to new nodes. The first regime exhibits smooth dynamics and power law degree distributions. In the second regime, giant degree nodes and gaps in the degree distribution are formed intermittently. Data for the Japanese firm network in 1994 and 2014 suggests that this network is moving towards the intermittent switching region.
-
Conference paperAzari MM, Rosas De Andraca FE, Pollin S, 2018,
Reshaping cellular networks for the sky: major factors and feasibility
, 2018 IEEE International Conference on Communications (ICC), Publisher: IEEE, ISSN: 1938-1883This paper studies the feasibility of supporting drone operations using existent cellular infrastructure. We propose an analytical framework that includes the effects of base station (BS) height and antenna radiation pattern, drone antenna directivity and various propagation environments. With this framework, we derive an exact expression for the coverage probability of ground and drone users through a practical cell association strategy. Our results show that a carefully designed network can control the radiated interference that is received by the drones, and therefore guarantees a satisfactory quality of service. Moreover, as the network density grows the increasing level of interference can be partially managed by lowering the drone flying altitude. However, even at optimal conditions the drone coverage performance converges to zero considerably fast, suggesting that ultra-dense networks might be poor candidates for serving aerial users.
-
Journal articleGarcia Millan R, Pruessner G, Pickering L, et al., 2018,
Correlations and hyperuniformity in the avalanche size of the Oslo Model
, Europhysics Letters: a letters journal exploring the frontiers of physics, Vol: 122, ISSN: 1286-4854Certain random processes display anticorrelations resulting in local Poisson-like disorder and global order, where correlations suppress fluctuations. Such processes are called hyperuniform. Using a map to an interface picture we show via analytic calculations that a sequence of avalanche sizes of the Oslo model is hyperuniform in the temporal domain with the minimal exponent $\lambda=0$ . We identify the conserved quantity in the interface picture that gives rise to the hyperuniformity in the avalanche size. We further discuss the fluctuations of the avalanche size in two variants of the Oslo model. We support our findings with numerical results.
-
Conference paperAzari MM, Rosas F, Chiumento A, et al., 2018,
Uplink performance analysis of a drone cell in a random field of ground interferers
, 2018 IEEE Wireless Communications and Networking Conference (WCNC), Publisher: Institute of Electrical and Electronics Engineers, ISSN: 1558-2612Aerial base stations are a promising technology to increase the capabilities of existing communication networks. However, existing analytical frameworks do not sufficiently characterize the impact of ground interferers on aerial base stations. In order to address this issue, we model the effect of interference coming from coexisting ground networks on the aerial link, which could be the uplink of an aerial cell served by a drone base station. By considering a Poisson field of ground interferers, we characterize aggregate interference experienced by the drone. This result includes the effect of drone antenna pattern, the height-dependent shadowing, and various types of environment. We show that benefits a drone obtains from a better line-of-sight (LoS) at high altitudes is counteracted by a high vulnerability to the interference coming from ground. However, by deriving link coverage probability and transmission rate we show that a drone base station is still a promising technology if the overall system is properly dimensioned according to given density and transmission power of interferers. Particularly, our results illustrate how benefits of such network is maximized by defining the optimal drone altitude and signal-to-interference (SIR) requirement.
-
Conference paperRosas F, Chen K-C, Gunduz D, 2018,
Social diversity for reducing the impact of information cascades on social learning
Collective behavior in online social media and networks is known to be capable of generating non-intuitive dynamics associated with crowd wisdom and herd behaviour. Even though these topics have been well-studied in social science, the explosive growth of Internet computing and e-commerce makes urgent to understand their effects within the digital society. In this work we explore how the stochasticity introduced by social diversity can help agents involved in a inference process to improve their collective performance. Our results show how social diversity can reduce the undesirable effects of information cascades, in which rational agents choose to ignore personal knowledge in order to follow a predominant social behaviour. Situations where social diversity is never desirable are also distinguished, and consequences of these findings for engineering and social scenarios are discussed.
-
Conference paperRosas De Andraca F, Manolakis K, Oberli C, et al., 2018,
Impact of the interference correlation on the decoding error statistics
, 51th Asilomar Conference on Signals, Systems, and Computers, Publisher: IEEE, ISSN: 2576-2303Scenarios where interference is correlated with useful signals are often met in communications systems. Interestingly, even though uncorrelated interference mitigation has been extensively studied by the literature, there is still lack of insight into how correlation can affects information transfer processes. In this paper we formally address this issue and show how a positive correlation between the interference and useful signal power introduces a non-intuitive increase in the diversity gain, improving the error statistics with respect to the uncorrelated case. In contrast, the diversity gain decreases under negative correlation, degrading the system performance. These findings are confirmed by simulations for scenarios of practical relevance.
-
Conference paperRosas De Andraca F, Azari MM, Murillo Y, et al., 2018,
Coverage maximization for a poisson field of drone cells
, 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2017), Publisher: IEEE, ISSN: 2166-9589The use of drone base stations to provide wireless connectivity for ground terminals is becoming a promising part of future technologies. The design of such aerial networks is however different compared to cellular 2D networks, as antennas from the drones are looking down, and the channel model becomes height-dependent. In this paper, we study the effect of antenna patterns and height-dependent shadowing. We consider a random network topology to capture the effect of dynamic changes of the flying base stations. First we characterize the aggregate interference imposed by the co-channel neighboring drones. Then we derive the link coverage probability between a ground user and its associated drone base station. The result is used to obtain the optimum system parameters in terms of drones antenna beamwidth, density and altitude. We also derive the average LoS probability of the associated drone and show that it is a good approximation and simplification of the coverage probability in low altitudes up to 500 m according to the required signal-to-interference-plus-noise ratio (SINR).
-
Conference paperAzari MM, Rosas F, Chiumento A, et al., 2018,
Coexistence of terrestrial and aerial users in cellular networks
, 2017 IEEE Globecom Workshops (GC Wkshps), Publisher: Institute of Electrical and Electronics EngineersEnabling the integration of aerial mobile users into existing cellular networks would make possible a number of promising applications. However, current cellular networks have not been designed to serve aerial users, and hence an exploration of design parameters is required in order to allow network providers to modify their current infrastructure. As a first step in this direction, this paper provides an in-depth analysis of the coverage probability of the downlink of a cellular network that serves both aerial and ground users. We present an exact mathematical characterization of the coverage probability, which includes the effect of base stations (BSs) height, antenna pattern and drone altitude for various types of urban environments. Interestingly, our results show that the favorable propagation conditions that aerial users enjoy due to their altitude is also their strongest limiting factor, as it leaves them vulnerable to interference. This negative effect can be substantially reduced by optimizing the flying altitude, the base station height and antenna down-tilt angle. Moreover, lowering the base station height and increasing down-tilt angle are in general beneficial for both terrestrial and aerial users, pointing out a possible path to enable their coexistence.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.