Qualification:
Ph.D, M.E, BE
s_krithiga@cb.amrita.edu

Dr. Kirthiga S. currently serves as Assistant Professor at the department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore Campus. She joined Amrita Vishwa Vidyapaeetham in 2003. She pursued her B. E. Electronics and Communication in 1996 from Coimbatore Institute of Technology. Later, she worked in the capacity of R&D Engineer in Embedded Systems division of Tata Elxsi, Bangalore for 4 years from1996 to 2000. Dr. Kirthiga then pursued her M. E. in Communication Systems in 2003 from PSG College of Technology.

She successfully defended her Ph. D. in the field of Millimeter Wave Communication and obtained Ph.D in 2015 from Amrita Vishwa Vidyapeetham. Her research interests include Signal processing techniques for Cognitive Radio, MIMO, Cooperative Communications, Millimeter Wave MIMO systems. Presently Dr. Kirthiga is working on the multidimensional channel modelling, estimation and prediction of channel parameters for polarized MIMO for land mobile satellite applications. She is a member of Institution of Electronics and Telecommunication Engineers.

## Research Expertise

#### PG project

• Multidimensional channel modelling, estimation and prediction of channel parameters for polarized MIMO for land mobile satellite applications

#### UG project

• Superimposed pilot based channel estimation for MIMO systems

Proposed research opportunities for prospective researchers

• Realization of spectral efficient coded modulation techniques in Cooperative Communication
• Spectrum Sensing in Cognitive Radio
• Power efficient algorithms for Green Communications
• MIMO for Land Mobile Satellite Applicaitons
• Millimeter Wave MIMO systems for 5G

## Teaching

• Wireless Communication
• Mutlicarrier and MIMO systems
• Millimeter Wave Personal Communication Systems
• Digital Communication
• Digital Signal Processing

## Publications

### Publication Type: Conference Proceedings

Year of Conference Title

2016

S. .S, .R, J., and Dr. Kirthiga S., “Superimposed pilot based channel estimation for MIMO systems”, Joint International Conference on Artificial Intelligence and Evolutionary Computations in Engineering Systems. SRM University, Kattankulathor, Chennai, 2016.[Abstract]

Estimation of the channel has been one of the major concern in any communication system. Although various channel estimation techniques are available for MIMO (Multiple Input Multiple Output) systems, the problem of wastage of bandwidth due to the transmission of training sequences prior to the transmission of actual data exists. Use of superimposed pilot sequence that accomplishes transmission of both the pilot and data simultaneously has been proposed as an effective alternative. The work carried out includes both simulation and real time implementation of channel models and channel estimation techniques. Appropriate channel models chosen include shadowing model and markov model to address large scale fading effects, and rician model for small scale fading effects. Employing channel estimation techniques for the above listed channel models is the novel idea aimed for satellite applications. Also different algorithms for channel estimation like Least Square (LS), Minimum Mean Square Error (MMSE) and Linear Minimum Mean Square Error (LMMSE) have been compared. Furthermore, merits and demerits of superimposed and conventional pilots are analysed by making performance comparisons. Hardware implementation is done using USRP (Universal Software Radio Peripheral). More »»

2009

A. .W, Dr. Kirthiga S., and Dr. Jayakumar M., “Linear Precoding based blind channel estimation in OFDM systems for fading environments”, National Conference in Recent trends in VLSI, Information Processing and Communications. 2009.

2009

, ,, Dr. Kirthiga S., and Dr. Jayakumar M., “Pilot symbol assisted channel estimation and modulation scheme estimation in adaptive modulation system for OFDM systems”, National Conference in Recent Trends in Communication and Signal Processing. 2009.

2009

A. .W, Dr. Kirthiga S., and Dr. Jayakumar M., “Blind channel estimation of OFDM systems in fading environments using Singular Value Decomposition”, National Conference in Recent Trends in Communication and Signal Processing. 2009.

### Publication Type: Journal Article

Year of Conference Title

2015

L. Kumar Goel, Sasi K Kottayil, Dr. Kirthiga S., and Dr. Jayakumar M., “SMART GRID TECHNOLOGIES Performance Studies and Review of Millimeter Wave MIMO Beamforming at 60 GHz”, Procedia Technology, vol. 21, pp. 658 - 666, 2015.[Abstract]

Millimeter wave systems offer high date rate due to huge bandwidth but suffers from poor link budget. This is due to the blockage of the millimeter wave signal by the obstacles of size comparable to that of the wavelength of the signal. Various analysis in improving the signal strength is reported. One of them suggests use of directional antenna which guarantees signal delivery if line of sight communication between the transmitter and receiver exists. The other alternative scheme is Multi Input Multi Output (MIMO) beamforming that uses the channel statistics to steer the beam thereby improving the multiplexing gain and beamforming gain. In this paper, extensive research work carried out by us in MMW MIMO at 60GHz covering aspects related to MIMO fixed-beam, adaptive beam and multibeam beamformers addressing line-of-sight and non-line-of-sight channel for MMW 60GHz system is reviewed. More »»

2014

Dr. Kirthiga S. and Dr. Jayakumar M., “Performance of Dualbeam MIMO for Millimeter Wave Indoor Communication Systems”, Wireless Personal Communications, vol. 77, no. 1, pp. 289–307, 2014.[Abstract]

Millimeter wave (MMW) communication provides high data rates for the personal area networks with the availability of 57–64 GHz unlicensed spectrum, in indoor environment. Multipath fading being pre-dominant in indoor, multi input multi output (MIMO) technology is considered to be the ideal choice compared with the existing systems. As spatial diversity in both transmit and receive enhances the diversity gain, the performance of the system is further enhanced by introducing transmit beamforming based antenna beam diversity. In classical {\$}{\$}2{\backslash}times 2{\$}{\$} 2 × 2 MIMO, a diversity gain of 4 is achieved, whereas in this work, space time block code matrix of code rate 1/2 and dualbeam {\$}{\$}2{\backslash}times 2{\$}{\$} 2 × 2 MIMO with diversity gain 8 is considered. Dualbeam is generated by antenna array with four elements per array with out of phase feed configuration. The weight vector of the beamforming network is out of phase as to reduce the interference between the beams. The dualbeam transmitter is designed with unknown channel state information. Training symbols are transmitted to train and track the channel statistics at the receiver. The proposed work is carried out for MMW indoor system. The indoor channel is modeled using Triple Saleh–Valenzuela (TSV) model that takes into account both time of arrival and the angle of arrival information of the rays. Channel estimation is done for classical MIMO and the above proposed model in both Rayleigh and TSV channel. The orthogonal beams facilitate linear processing in the receiver. Hence maximum ratio combiner with maximum likelihood decoder is used in the receiver to decode the transmitted data. Classical MIMO and dualbeam MIMO are evaluated with respect to bit error rate and channel models. An improved diversity order is achieved with dualbeam MIMO compared to classical MIMO, with a power gain of 1.6 dB. The dualbeam MIMO using TSV is found to perform better compared to dualbeam MIMO using Rayleigh in the low Energy per bit to Noise level $$(\hbox {E}_{\mathrm{b}}/\hbox {N}_{0})$$ with a power gain of 2 dB. More »»

2012

Dr. Kirthiga S. and Dr. Jayakumar M., “Performance and Capacity analysis of MIMO system at 5 GHz and 60GHz in Indoor Environment.”, WSEAS Transactions on Communications, vol. 11, 2012.

2012

Dr. Kirthiga S. and Dr. Jayakumar M., “Performance and capacity analysis of MIMO system at 5 GHz and 60GHz in indoor environment”, WSEAS Transactions on Communications, vol. 11, pp. 415-426, 2012.[Abstract]

More »»

2010

B. Balakrishnan, Geethu, T. K., Govindankutty, N., Pradeep, P., Karnani, V., Dr. Kirthiga S., and Dr. Jayakumar M., “Discrete state space channel modeling and channel estimation using Kalman filter for OFDMA systems”, Proceedings of the 12th international conference on Networking, VLSI and signal processing, pp. 283–287, 2010.[Abstract]

In this paper, a communication system using Orthogonal Frequency Division Multiple Access (OFDMA) is implemented. An iterative Kalman filtering algorithm for estimation of the time-variant Rayleigh fast fading channel is proposed. The Rayleigh channel is approximated to be a Jakes process which is modelled using an autoregressive model. An autoregressive (AR) channel model is used to provide the state space estimates necessary for Kalman filter based channel estimation. More »»

### Publication Type: Conference Paper

Year of Conference Title

2015

A. Muralidharan, Venkateswaran, P., Ajay, S. G., D. Prakash, A., Arora, M., and Dr. Kirthiga S., “An adaptive threshold method for energy based spectrum sensing in Cognitive Radio Networks”, in 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kumaracoil, 2015.[Abstract]

Today, communication systems face major shortages in availability of free spectrum. Cognitive radio has evolved as the most feasible technology which can empower us to effectively utilize the available spectrum. A vital component of a Cognitive Radio Network is spectrum sensing, i.e. its ability to detect the presence of a primary user. This paper employs an adaptive threshold detection algorithm based on an image binarization technique. A novel approach is proposed to fix the standard deviation threshold whereby the system dynamically trains itself based on previously iterated decision statistics. Such data is computed on the basis of calculated probability of detection, probability of false alarm, standard deviation coefficient and ratio of occupied bandwidth to the total bandwidth. Simulation results show the vastly improved performance compared to the conventional energy detector. At low Sound to Noise Ratio the performance improvement index shows an increase of 30 percent in comparison to the conventional energy detector. More »»

2015

A. Muralidharan, Venkateswaran, P., Ajay, S. G., D. Prakash, A., Arora, M., and Dr. Kirthiga S., “An adaptive threshold method for energy based spectrum sensing in cognitive radio networks: A practical implementation using a blind spectrum sensing method”, in 2015 International Conference on Control Instrumentation Communication and Computational Technologies, ICCICCT 2015, 2015, pp. 8-11.[Abstract]

Today, communication systems face major shortages in availability of free spectrum. Cognitive radio has evolved as the most feasible technology which can empower us to effectively utilize the available spectrum. A vital component of a Cognitive Radio Network is spectrum sensing, Le. its ability to detect the presence of a primary user. This paper employs an adaptive threshold detection algorithm based on an image binarization technique. A novel approach is proposed to fix the standard deviation threshold whereby the system dynamically trains itself based on previously iterated decision statistics. Such data is computed on the basis of calculated probability of detection, probability of false alarm, standard deviation coefficient and ratio of occupied bandwidth to the total bandwidth. Simulation results show the vastly improved performance compared to the conventional energy detector. At low Sound to Noise Ratio the performance improvement index shows an increase of 30 per cent in comparison to the conventional energy detector. More »»

2014

Dr. Kirthiga S., Govindankutty, A., Krishnan, S., and Nair, S. P., “Transmit beamforming using singular value decomposition”, in Electronics and Communication Systems (ICECS), 2014 International Conference on, 2014.[Abstract]

In this paper, a beamforming scheme has been proposed for a multiple input multiple output channel which forms a closed loop system. This wireless channel incorporates binary phase shift keying modulation technique. The fact that the channel state information is known to the transmitter and receiver has been utilized in generating the channel matrix. Singular value decomposition has been performed over the channel matrix to obtain the eigen values and eigen vectors. Transmit beamforming makes use of precoded symbols and transmits them over independent Rayleigh fading channels. The precoded symbols are obtained by multiplying the input with the unitary matrix. At the receiver, symbols are combined using maximal ratio combiner and reshaped to extract the original signal by multiplying the received signal with conjugate transpose of unitary matrix. Comparison has been done on bit error rate without beamforming and that with beamforming. Simulation results prove that bit error rate performance with beamforming for a MIMO system outperforms a MISO system. More »»

2011

D. S. Manojna, Dr. Kirthiga S., and Jayakumar, M., “Study of 2x2 Spatial Multiplexed System in 60 GHz Indoor Environment”, in Process Automation, Control and Computing (PACC), 2011 International Conference on, Coimbatore, 2011.[Abstract]

The Millimeter Waves are allocated with 60GHz frequency range as an unlicensed band worldwide. Millimeter waves become very useful for short-range communications because of its high data rate, its large-available bandwidth of 7GHz and the Oxygen absorption present at that band. The 60GHz indoor channel contains much of multipath components and needs the use of statistical parameters in modeling the channel. At 60 GHz, the free space loss is higher and thus requires the use of antennas with more pattern directivity along with having small antenna dimensions. Considering these factors, in this paper, Triple Saleh Valenzuela (TSV) model is chosen as a suitable model for millimeter waves. The performance of Spatial Multiplexed system for TSV channel model is simulated by assuming a simple indoor Line of Sight (LOS) environment model. Assuming perfect channel estimation, the Bit Error Rate (BER) performance of the 2x2 system is investigated for Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Likelihood (ML) receivers. More »»

2010

Dr. Kirthiga S. and Dr. Jayakumar M., “AutoRegressive channel modeling and estimation using Kalman filter for downlink LTE systems”, in Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India, 2010.[Abstract]

In this paper, the channel for the downlink of Long-Term Evolution (LTE) that uses Orthogonal Frequency Division Multiple Access (OFDMA) is modelled and estimated. The Rayleigh channel is approximated to be a Jakes process which is modelled using an autoregressive (AR) model. An iterative Kalman filtering algorithm for estimation of the time-variant Rayleigh fast fading channel is proposed. An AR channel model is used to provide the state space estimates necessary for Kalman filter based channel estimation. The Kalman algorithm, using state space concepts, computes the channel matrix which can then be used to estimate the baseband signal transmitted. Since this algorithm uses both pilot sequences and the underlying channel model to estimate the channel, they are more bandwidth efficient compared to only data-based algorithms. The channel quality index obtained in this estimation technique can be used in the dynamic allocation of subcarriers to multi-user. The performance is compared with blind channel estimation technique, subspace based Singular Value Decomposition (SVD). From the simulation results, it is verified that significant signal-to-noise ratio (SNR) gain and bit-error rate (BER) is achieved using Kalman filter compared to SVD. More »»
Faculty Research Interest: