The Impact of Antenna Height Difference on the Performance of Downlink Cellular Networks

Capable of significantly reducing cell size and enhancing spatial reuse, network densification is shown to be one of the most dominant approaches to expand network capacity. Due to the scarcity of available spectrum resources, nevertheless, the over-deployment of network infrastructures, e.g., cellular base stations (BSs), would strengthen the inter-cell interference as well, thus in turn deteriorating the system performance. On this account, we investigate the performance of downlink cellular networks in terms of user coverage probability (CP) and network spatial throughput (ST), aiming to shed light on the limitation of network densification. Notably, it is shown that both CP and ST would be degraded and even diminish to be zero when BS density is sufficiently large, provided that practical antenna height difference (AHD) between BSs and users is involved to characterize pathloss. Moreover, the results also reveal that the increase of network ST is at the expense of the degradation of CP. Therefore, to balance the tradeoff between user and network performance, we further study the critical density, under which ST could be maximized under the CP constraint. Through a special case study, it follows that the critical density is inversely proportional to the square of AHD. The results in this work could provide helpful guideline towards the application of network densification in the next-generation wireless networks.Comment: conference submission - Mar. 201

Test experience on an ultrareliable computer communication network

The dispersed sensor processing mesh (DSPM) is an experimental, ultra-reliable, fault-tolerant computer communications network that exhibits an organic-like ability to regenerate itself after suffering damage. The regeneration is accomplished by two routines - grow and repair. This paper discusses the DSPM concept for achieving fault tolerance and provides a brief description of the mechanization of both the experiment and the six-node experimental network. The main topic of this paper is the system performance of the growth algorithm contained in the grow routine. The characteristics imbued to DSPM by the growth algorithm are also discussed. Data from an experimental DSPM network and software simulation of larger DSPM-type networks are used to examine the inherent limitation on growth time by the growth algorithm and the relationship of growth time to network size and topology

Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting

A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions. We use these protocols for a systematic study of the performance of the IAS MEG inverse solver, comparing it with three standard inversion methods, wMNE, dSPM, and sLORETA. To avoid the bias of anecdotal tests towards a particular algorithm, the proposed protocols are Monte Carlo sampling based, generating an ensemble of activity patches in each brain region identified in a given atlas. The performance in correctly identifying the active areas is measured by how much, on average, the reconstructed activity is concentrated in the brain region of the simulated active patch. The analysis is based on Bayes factors, interpreting the estimated current activity as data for testing the hypothesis that the active brain region is correctly identified, versus the hypothesis of any erroneous attribution. The methodology allows the presence of a single or several simultaneous activity regions, without assuming that the number of active regions is known. The testing protocols suggest that the IAS solver performs well with both with cortical and subcortical activity estimation

Test-retest reliability of the magnetic mismatch negativity response to sound duration and omission deviants

Mismatch negativity (MMN) is a neurophysiological measure of auditory novelty detection that could serve as a translational biomarker of psychiatric disorders, such as schizophrenia. However, the replicability of its magnetoencephalographic (MEG) counterpart (MMNm) has been insufficiently addressed. In the current study, test-retest reliability of the MMNm response to both duration and omission deviants was evaluated over two MEG sessions in 16 healthy adults. MMNm amplitudes and latencies were obtained at both sensor- and source-level using a cortically-constrained minimum-norm approach. Intraclass correlations (ICC) were derived to assess stability of MEG responses over time. In addition, signal-to-noise ratios (SNR) and within-subject statistics were obtained in order to determine MMNm detectability in individual participants. ICC revealed robust values at both sensor- and source-level for both duration and omission MMNm amplitudes (ICC = 0.81-0.90), in particular in the right hemisphere, while moderate to strong values were obtained for duration MMNm and omission MMNm peak latencies (ICC = 0.74-0.88). Duration MMNm was robustly identified in individual participants with high SNR, whereas omission MMNm responses were only observed in half of the participants. Our data indicate that MMNm to unexpected duration changes and omitted sounds are highly reproducible, providing support for the use of MEG-parameters in basic and clinical research

Sequential Monte Carlo samplers for semilinear inverse problems and application to magnetoencephalography

We discuss the use of a recent class of sequential Monte Carlo methods for solving inverse problems characterized by a semi-linear structure, i.e. where the data depend linearly on a subset of variables and nonlinearly on the remaining ones. In this type of problems, under proper Gaussian assumptions one can marginalize the linear variables. This means that the Monte Carlo procedure needs only to be applied to the nonlinear variables, while the linear ones can be treated analytically; as a result, the Monte Carlo variance and/or the computational cost decrease. We use this approach to solve the inverse problem of magnetoencephalography, with a multi-dipole model for the sources. Here, data depend nonlinearly on the number of sources and their locations, and depend linearly on their current vectors. The semi-analytic approach enables us to estimate the number of dipoles and their location from a whole time-series, rather than a single time point, while keeping a low computational cost.Comment: 26 pages, 6 figure

Influence of stator/rotor pole combination on electromagnetic performance in all/alternate poles wound partitioned stator doubly salient permanent magnet machines.

In this paper, the influence of stator/rotor pole combinations on electromagnetic performance in partitioned stator (PS) doubly salient (DS) permanent magnet (PM) (DSPM) (PS-DSPM) machines is investigated, in terms of open-circuit flux-linkage, back-EMF, cogging torque, on-load torque characteristics. Analytical deduction shows that by modifying the all poles wound winding to alternate poles wound winding in the 12/11- and 12/13 stator/rotor pole PS-DSPM machines, the fundamental distribution factor and hence the fundamental winding factor can be enhanced, resulting higher torque density. Consequently, among the 12-stator-pole all and alternate poles wound PS-DSPM machines, the 10- and 11-rotor-pole machines exhibit the highest torque density, respectively. However, the 12/10- and 12/14-pole alternate poles wound PS-DSPM machines suffer from higher phase back-EMF even harmonics, resulting larger torque ripple. The 12/10- and 12/11-pole all and alternate poles wound prototypes are built and tested to verify the FE analysis