Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Measurement of Ds+ production and nuclear modification factor in Pb-Pb collisions at sNN=2.76 TeV

Abstract: The production of prompt D$_s^+$ mesons was measured for the first time in collisions of heavy nuclei with the ALICE detector at the LHC. The analysis was performed on a data sample of Pb-Pb collisions at a centre-of-mass energy per nucleon pair, $\sqrt{s_{\rm NN}}$, of 2.76 TeV in two different centrality classes, namely 0-10% and 20-50%. D$_s^+$ mesons and their antiparticles were reconstructed at mid-rapidity from their hadronic decay channel D$_s^+\rightarrow\phi\pi^+$, with $\phi\rightarrow$K$^-$K$^+$, in the transverse momentum intervals $4< p_{\rm T}<12$ GeV/$c$ and $6< p_{\rm T}<12$ GeV/$c$ for the 0-10% and 20-50% centrality classes, respectively. The nuclear modification factor $R_{\rm AA}$ was computed by comparing the $p_{\rm T}$-differential production yields in Pb-Pb collisions to those in proton-proton (pp) collisions at the same energy. This pp reference was obtained using the cross section measured at $\sqrt{s}= 7$ TeV and scaled to $\sqrt{s}= 2.76$ TeV. The $R_{\rm AA}$ of D$_s^+$ mesons was compared to that of non-strange D mesons in the 10% most central Pb-Pb collisions. At high $p_{\rm T}$ ($8< p_{\rm T}<12$ GeV/$c$) a suppression of the D$_s^+$-meson yield by a factor of about three, compatible within uncertainties with that of non-strange D mesons, is observed. At lower $p_{\rm T}$ ($4< p_{\rm T}<8$ GeV/$c$) the values of the D$_s^+$-meson $R_{\rm AA}$ are larger than those of non-strange D mesons, although compatible within uncertainties. The production ratios D$_s^+$/D$^0$ and D$_s^+$\D$^+$ were also measured in Pb-Pb collisions and compared to their values in proton-proton collisions.

A low-complexity hybrid algorithm based on particle swarm and ant colony optimization for large-MIMO detection

Abstract: A low-complexity hybrid algorithm for large-MIMO detection is proposed.Hybridization of ant colony and particle swarm optimization algorithms.Superior performance over existing ant colony optimization algorithms.The hybrid algorithm achieves near optimal bit error rate performance. With rapid increase in demand for higher data rates, multiple-input multiple-output (MIMO) wireless communication systems are getting increased research attention because of their high capacity achieving capability. However, the practical implementation of MIMO systems rely on the computational complexity incurred in detection of the transmitted information symbols. The minimum bit error rate performance (BER) can be achieved by using maximum likelihood (ML) search based detection, but it is computationally impractical when number of transmit antennas increases. In this paper, we present a low-complexity hybrid algorithm (HA) to solve the symbol vector detection problem in large-MIMO systems. The proposed algorithm is inspired from the two well known bio-inspired optimization algorithms namely, particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm. In the proposed algorithm, we devise a new probabilistic search approach which combines the distance based search of ants in ACO algorithm and the velocity based search of particles in PSO algorithm. The motivation behind using the hybrid of ACO and PSO is to avoid premature convergence to a local solution and to improve the convergence rate. Simulation results show that the proposed algorithm outperforms the popular minimum mean squared error (MMSE) algorithm and the existing ACO algorithms in terms of BER performance while achieve a near ML performance which makes the algorithm suitable for reliable detection in large-MIMO systems. Furthermore, a faster convergence to achieve a target BER is observed which results in reduction in computational efforts.