Qualification: 
Ph.D, M.Tech, B-Tech
t_arpita@blr.amrita.edu

Dr. Arpita Thakre currently serves as Assistant Professor(SG) at the department of Electronics and Communication Engineering, Amrita School of Engineering, Bengaluru campus.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

K. Latha and Arpita Thakre, “Performance of Dual mode OFDM-IM using Reduced Complexity Receiving Technique”, in IEEE ICACCI Sept. , 2018.

2018

Conference Paper

L. Girish and Arpita Thakre, “Filtered OFDM with Index Modulation”, in IEEE ICACCI Sept. , 2018.

2018

Conference Paper

S. Ganesh, Sundar, S., and Arpita Thakre, “Performance Improvement in Rayleigh Faded Channel using Deep Learning”, in IEEE ICACCI Sept., 2018.

2017

Conference Paper

Arpita Thakre, “Reduced Complexity Detection of Under-determined Single Carrier Spatial Modulation Systems with Multiple Active Antennas”, in IEEE ANTS, 2017.

2010

Conference Paper

Arpita Thakre and Giridhar, K., “Improvement in Channel Estimation in LTE Downlink”, in WWRF, 2010.

2009

Conference Paper

J. Paulo Carv da Costa, Arpita Thakre, Roemer, F., and Haardt, M., “Comparison of model order selection techniques for high-resolution parameter estimation algorithms”, in Proc. 54th International Scientific Colloquium (IWK), (Ilmenau, Germany), Sept. 2009, 2009.

2009

Conference Paper

Arpita Thakre and Giridhar., K., “2-D Normalized Frequency Estimation Using 4-way Tensor Processing”, in 20th IEEE Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2009.

2009

Conference Paper

Arpita Thakre, Haardt, M., and Giridhar, K., “Single Snapshot R-D Unitary Tensor-ESPRIT Using an Augmentation of the Tensor Order”, in 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2009.[Abstract]


Single snapshot R-D unitary tensor-ESPRIT using an augmentation of the tensor order (SS-U-TE-ATO) is a subspace-based parameter estimation technique for R-dimensional (R-D) undamped harmonics using a single snapshot of data. In SS-U-TE-ATO the measurement data is packed into a measurement tensor to which spatial smoothing is applied. We propose the construction of R higher-order tensors from the spatially smoothed tensor by exploiting the inherent structure of the spatially smoothed tensor. In the next step of SS-U-TE-ATO, R enhanced real-valued signal subspace estimates, one for each dimension, are obtained from the R complex-valued higher-order tensors. We show that SS-U-TE-ATO performs significantly better than unitary tensor-ESPRIT (U-TE) directly applied to the spatially smoothed tensor. Moreover, for each dimension, SS-U-TE-ATO is almost insensitive to changes in the number of sensors per subarray provided that the number of subarrays is greater than the number of sources. Thereby we avoid the problem of selecting the optimum subarray size for a given source configuration. More »»

2008

Conference Paper

V. Ganapathy and Arpita Thakre, “Power Allocation Schemes for Cognitive Radios”, in IEEE Communication System Software and Middleware (COMSWARE), 2008.

2008

Conference Paper

Arpita Thakre and Giridhar., K., “Enhanced Channel Estimation and Tracking for Single Carrier Uplink Transmission Scheme”, in National Conference on Communications (NCC), 2008.

2008

Conference Paper

Arpita Thakre, “Symbol-duration Extended Interleaved FDMA as Uplink Multiple Access Technique for IEEE 802.16m”, in IEEE 802.16m standards #53 meeting, 2008.

Publication Type: Journal Article

Year of Publication Publication Type Title

2010

Journal Article

Arpita Thakre, Haardt, M., Roemer, F., and Giridhar, K., “Tensor-Based Spatial Smoothing (TB-SS) Using Multiple Snapshots”, IEEE Transactions on Signal Processing, vol. 58, pp. 2715-2728, 2010.[Abstract]


Tensor-based spatial smoothing (TB-SS) is a preprocessing technique for subspace-based parameter estimation of damped and undamped harmonics. In TB-SS, multichannel data is packed into a measurement tensor. We propose a tensor-based signal subspace estimation scheme that exploits the multidimensional invariance property exhibited by the highly structured measurement tensor. In the presence of noise, a tensor-based subspace estimate obtained via TB-SS is a better estimate of the desired signal subspace than the subspace estimate obtained by, for example, the singular value decomposition of a spatially smoothed matrix or a multilinear algebra approach reported in the literature. Thus, TB-SS in conjunction with subspace-based parameter estimation schemes performs significantly better than subspace-based parameter estimation algorithms applied to the existing matrix-based subspace estimate. Another advantage of TB-SS over the conventional SS is that TB-SS is insensitive to changes in the number of samples per subarray provided that the number of subarrays is greater than the number of harmonics. In this paper, we present, as an example, TB-SS in conjunction with ESPRIT-type algorithms for the parameter estimation of one-dimensional (1-D) damped and undamped harmonics. A closed form expression of the stochastic Crame??r-Rao bound (CRB) for the 1-D damped harmonic retrieval problem is also derived. More »»

2009

Journal Article

Arpita Thakre, Haardt, M., and Giridhar, K., “Single Snapshot Spatial Smoothing With Improved Effective Array Aperture”, IEEE Signal Processing Letters, vol. 16, pp. 505-508, 2009.[Abstract]


Spatial smoothing is a widely used preprocessing scheme for direction-of-arrival (DOA) estimation of more than one source from a single snapshot, although the effective array aperture gets reduced by this process. In this paper we propose a preprocessing scheme applicable for DOA estimation algorithms that exploit the shift invariance property of the array steering matrix and call it spatial smoothing with improved aperture (SSIA). SSIA, when applied to a noise corrupted data vector, improves the effective array aperture significantly as opposed to conventional spatial smoothing. Simulations confirm the significant performance gain provided by SSIA in conjunction with Unitary ESPRIT. More »»

Research Students 

Name of the Student Year of Registration Area of Research
Sriram Ganesh (B.Tech.) Completed  Deep Learning for Rayleigh Faded Channel
Lavanya (M. Tech.) Completed Filtered-OFDM
Rahul (B. Tech.) Completed UW-OFDM
S. Sumathi (Ph. D.) Since July 2017 Non-Orthogonal Multiple Access
S. Latha (Ph. D.) Since July 2017 Energy Efficient Wireless Communication Transceiver Design
Mohaneesh (B. Tech.) Completed Generalized Spatial Modulation
K. Jeevapriya Since February 2017 Speech Recognition in Indian Languages