Showing papers by "Sheetal Kalyani published in 2013"
09 Jun 2013
TL;DR: The coverage probability in a fully- and partially-loaded cellular network is analysed and is obtained using properties of lattice sums which were first used in physics to analyse potentials in crystal structures.
Abstract: In cellular networks, all base stations (BSs) do not continuously transmit i.e., the BSs transmit only when their queues are non-empty. This implies that the system resources are only partially loaded, and the dynamics of such a network differs significantly from that of a fully-loaded system. The coverage probability in a fully- and partially-loaded cellular network is analysed. We consider a regular spatial arrangement of base stations, and obtain the coverage probability in the presence of interference. More specifically, expressions for coverage probability are obtained for square and hexagonal lattices with full and partial loading. The coverage probability is obtained using properties of lattice sums which were first used in physics to analyse potentials in crystal structures.
4 citations
TL;DR: Simulation results indicate that the performance of the proposed estimator is comparable to that of the optimal minimum mean square error estimator, even though it does not have any knowledge of the channel statistics.
Abstract: In orthogonal frequency division multiple access-based systems where channel frequency response (CFR) estimation has to be carried out using only the user-specific (localised) pilots within a small time frequency block, the accuracy of the estimates suffer because of the limited number of pilots and imperfect knowledge of the channel statistics. A biased estimator is proposed for the estimation of CFR over the time frequency block. Hypothesis tests are designed to ascertain the time and frequency selectivity of the CFR within the region of interest, and the outcome of these tests are used to determine a vector shrinkage target for the biased estimator. Simulation results indicate that the performance of the proposed estimator is comparable to that of the optimal minimum mean square error estimator, even though it does not have any knowledge of the channel statistics.
1 citations
TL;DR: An automatic student admission recommendation system that selects a set of institutions by considering a setof student prerequisites and semantically match them with their parameters is suggested.
Abstract: Education is a complex systematic engineering, which is the guarantee of training high-quality talent, helping society make full use of educational outcomes and promote the healthy development of education. To step-in to that high quality education students have a chaos in evaluating the best among the several institutions and select the one. In this paper, we suggest an automatic student admission recommendation system that selects a set of institutions by considering a set of student prerequisites and semantically match them with their parameters. Fuzzy clustering technique is applied on categorized data for suggesting better suited colleges for a particular student based on his/her course option. Since the same student can opt for more than one college for a particular course, depending upon multiple parameters, fuzzy clustering acts as the best suited method for seminary recommendation. The relative fuzzy score called “degree of membership” calculated for each college indicates the membership of a particular student to different institutions. Subjective evaluation of the algorithm is tested on synthetic dataset and the experiments produce promising results. Keywords: Synthetic dataset, Fuzzy sets, Clustering, Text Categorization, Smart selection