On the DMT of TDD-SIMO Systems with Channel-Dependent Reverse Channel Training

This paper investigates the Diversity-Multiplexing gain Trade-off (DMT) of a training based reciprocal Single Input Multiple Output (SIMO) system, with (i) perfect Channel State Information (CSI) at the Receiver (CSIR) and noisy CSI at the Transmitter (CSIT), and (ii) noisy CSIR and noisy CSIT. In both the cases, the CSIT is acquired through Reverse Channel Training (RCT), i.e., by sending a training sequence from the receiver to the transmitter. A channel-dependent fixed-power training scheme is proposed for acquiring CSIT, along with a forward-link data transmit power control scheme. With perfect CSIR, the proposed scheme is shown to achieve a diversity order that is quadratically increasing with the number of receive antennas. This is in contrast with conventional orthogonal RCT schemes, where the diversity order is known to saturate as the number of receive antennas is increased, for a given channel coherence time. Moreover, the proposed scheme can achieve a larger DMT compared to the orthogonal training scheme. With noisy CSIR and noisy CSIT, a three-way training scheme is proposed and its DMT performance is analyzed. It is shown that nearly the same diversity order is achievable as in the perfect CSIR case. The time-overhead in the training schemes is explicitly accounted for in this work, and the results show that the proposed channel-dependent RCT and data power control schemes offer a significant improvement in terms of the DMT, compared to channel-agnostic orthogonal RCT schemes. The outage performance of the proposed scheme is illustrated through Monte-Carlo simulations.Comment: Accepted for publication in IEEE Transactions on Communication

Caching with Unknown Popularity Profiles in Small Cell Networks

A heterogenous network is considered where the base stations (BSs), small base stations (SBSs) and users are distributed according to independent Poisson point processes (PPPs). We let the SBS nodes to posses high storage capacity and are assumed to form a distributed caching network. Popular data files are stored in the local cache of SBS, so that users can download the desired files from one of the SBS in the vicinity subject to availability. The offloading-loss is captured via a cost function that depends on a random caching strategy proposed in this paper. The cost function depends on the popularity profile, which is, in general, unknown. In this work, the popularity profile is estimated at the BS using the available instantaneous demands from the users in a time interval $[0,\tau]$. This is then used to find an estimate of the cost function from which the optimal random caching strategy is devised. The main results of this work are the following: First it is shown that the waiting time $\tau$ to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite, provided the user density is greater than a predefined threshold. In this case, $\tau$ is shown to scale as $N^2$, where $N$ is the support of the popularity profile. Secondly, a transfer learning-based approach is proposed to obtain an estimate of the popularity profile used to compute the empirical cost function. A condition is derived under which the proposed transfer learning-based approach performs better than the random caching strategy.Comment: 6 pages, Proceedings of IEEE Global Communications Conference, 201

A Learning-Based Approach to Caching in Heterogenous Small Cell Networks

A heterogenous network with base stations (BSs), small base stations (SBSs) and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as $N^2$, where $N$ is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of $N$ and scales linearly with the dimension of the distribution parameter.Comment: 12 pages, 5 figures, published in IEEE Transactions on Communications, 2016. arXiv admin note: text overlap with arXiv:1504.0363

Online Learning Models for Content Popularity Prediction In Wireless Edge Caching

Caching popular contents in advance is an important technique to achieve the low latency requirement and to reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process (PPP), optimal content placement caching probabilities are derived for known popularity profile, which is unknown in practice. In this paper, online prediction (OP) and online learning (OL) methods are presented based on popularity prediction model (PPM) and Grassmannian prediction model (GPM), to predict the content profile for future time slots for time-varying popularities. In OP, the problem of finding the coefficients is modeled as a constrained non-negative least squares (NNLS) problem which is solved with a modified NNLS algorithm. In addition, these two models are compared with log-request prediction model (RPM), information prediction model (IPM) and average success probability (ASP) based model. Next, in OL methods for the time-varying case, the cumulative mean squared error (MSE) is minimized and the MSE regret is analyzed for each of the models. Moreover, for quasi-time varying case where the popularity changes block-wise, KWIK (know what it knows) learning method is modified for these models to improve the prediction MSE and ASP performance. Simulation results show that for OP, PPM and GPM provides the best ASP among these models, concluding that minimum mean squared error based models do not necessarily result in optimal ASP. OL based models yield approximately similar ASP and MSE, while for quasi-time varying case, KWIK methods provide better performance, which has been verified with MovieLens dataset.Comment: 9 figure, 29 page

ENRICHMENT OF IN VIVO EFFICACY OF CATECHIN RICH EXTRACT WITH THE APPLICATION OF NANOTECHNOLOGY

Objective: The primary goal of this study was to convert a natural catechin-rich extract into nanoparticles by using a biodegradable and non-toxic polymer Eudragit L 100 to address the various biopharmaceutical problems of catechin.Methods: Nanoparticles were prepared by emulsion solvent evaporation technique using Eudragit L 100 in increasing concentration. Optimization of processing conditions like a selection of organic solvents, diluent and surfactant concentrations, drug and polymer ratio and method of drying to increase the biological efficiency were duly attempted. Parameters such as dynamic light scattering, zeta potential, SEM and energy-dispersive X-ray spectroscopy were assessed for the evaluation of nanoparticles.Results: The entrapment efficiency was found to be between 35-45 % with methanol compared to other organic solvents. The zeta potential values of all the formulations were in the range of±30 mV to±60 mV) which confirms moderate to good stability. A rapid or ‘burst' effect of the drug release in pH 6.8 buffer showing 92 % in the first 30 min which gradually decreased to 52 % by the end of 180 min but in the pH 7.4, the release was found to be moderate. SEM and DLS indicated particles were of spherical shape lying in a nanometer range of 100 to 200 nm with a proportional influence of polymer on the particles size.Conclusion: Nanoformulations were found to be more stable and confirmed the presence of major elements such as carbon and oxygen. The findings collectively indicate that it may be worthwhile to apply nanotechnology for the design of an advanced oral dosage form for an enhanced bioavailability and biological efficacy

Investigating the contribution of geometrical parameters and immersion time on fracture toughness of jute fabric composites using statistical techniques

The aim of this work is to evaluate the failure analysis of jute fabric reinforced composite using statistical technique for marine applications. A fracture mechanics approach is proposed to determine the failure load and fracture toughness of the composite. Jute fiber reinforced epoxy composites were fabricated by hand lay-up technique. The polymer composite is intended for marine applications as an alternative material, mainly in high moisture environments. Thus an investigation was conducted to evaluate the fracture mechanic parameter by immersing composite specimens in sea water. The statistical method of Taguchi was used for experimental design. Edge Notched Tension (ENT) and Single Edge Notched Bend (SENB) test were conducted according to the ASTM E1922 and ASTM D5045 respectively. Main effect graphs are obtained to study the effect of a/w (Crack length to width ratio), thickness and immersion time of composite on fracture mechanics parameters. The approach of calculating the percentage of contribution of control factors was established using by analysis of variance (ANOVA). The influence of fracture parameters of control factors can be analysed by using 3-D response surface graph (RSM). The Validation of experimental results for fracture parameters agreed well with the predicted values