- Wireless Networks
- Machine Learning and Signal Processing
- Software Defined Networking
- Communication and Computing
- PhD in Electrical Engineering, University of California – San Diego
- MS in Telecommunications, Indian Institute of Science – Bangalore, India
- BS in Electronics and Communication Engineering, National Institute of Technology – Warangal, India
Ramesh Annavajjala is an Affiliate Research Associate Professor at Northeastern University’s College of Computer and Information Science. He received his Bachelor’s Degree in Electronics and Communication Engineering from the National Institute of Technology (NIT, Warangal, India, May 1998), his Master’s Degree in Telecommunications from the Indian Institute of Science (IISc, Bangalore, India, January 2001), and his PhD in Electrical Engineering from the University of California at San Diego (UCSD, La Jolla, CA, June 2006.)
Dr. Annavajjala has served as a Distinguished Member of Technical Staff at the Altiostar Networks Inc., a Tewksbury, Massachusetts-based startup company, developing cloud-RAN optimized 4G LTE base-station products. Prior to that, he was a Principal Member of Research Staff at the Mitsubishi Electric Research Labs (MERL), in Cambridge, MA. He has also held industry positions at ArrayComm LLC (San Jose, CA, USA), Synopsys Inc., (Bangalore, India) and CMC R&D Center (Hyderabad, India).
Dr. Annavajjala received the Fred W. Ellersick MILCOM award for the “Best Paper in the Unclassified Technical Program” for his paper titled “Design and Performance Evaluation of High-Capacity Mobile Troposcatter Links Under Mobility, Frequency Selectivity, and Antenna Pointing Errors” which was presented at the 2018 IEEE MILCOM conference. He was a recipient of the Purkayastha/TimeLine Ventures graduate fellowship (2002–2003), a co-recipient of the best paper award from the IEEE WPMC 2009 conference, and was a guest editor for the special issue Wireless Cooperative Networks of the EURASIP Journal on Advanced Signal Processing. He was nominated for the MIT TR-35. He has published more than 60 papers in international journals and conferences, made numerous contributions to commercial wireless standards, and is a co-inventor of 10 U.S. patents (granted).
What is your research focus?
Connectivity and security challenges in massive internet-of-things (IoT) devices; Distributed data fusion using cooperative sensing; Autonomy and accountability of cyber-physical systems; Interference management in heterogeneous wireless networks; Coordinated transmission technologies for cellular infrastructure systems; Data-driven and analytics-based self-optimizing wireless networks; Ultra-reliable and low-latency networking for mission-critical systems, and Massive multi-antenna systems leveraging the millimeter-wave unlicensed frequency bands
What is your educational background?
I was trained in the field of Electrical and Computer Engineering with a focus on Communications Theory and Systems.
R. Chopra, C. R. Murthy and R. Annavajjala, "Physical Layer Security in Wireless Sensor Networks Using Distributed Co-Phasing," in IEEE Transactions on Information Forensics and Security.
Design and Performance Evaluation of High-Capacity Mobile Troposcatter Links Under Mobility, Frequency Selectivity, and Antenna Pointing Errors
R. Annavajjala and J. Zagami, "Design and Performance Evaluation of High-Capacity Mobile Troposcatter Links Under Mobility, Frequency Selectivity, and Antenna Pointing Errors," MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM), Los Angeles, CA, 2018, pp. 1-9.
Integrated acquisition and tracking scheme for channel estimation in millimeter wave wireless networks
L. S. Pillutla and R. Annavajjala, "Integrated acquisition and tracking scheme for channel estimation in millimeter wave wireless networks," in Proc. IEEE 28Th Annual Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Oct. 2017.
In this paper we consider performance of an integrated beam acquisition and tracking scheme for channel estimation in the millimeter-wave (mmWave) wireless networks. By assuming a block based transmission scheme in which pilot symbols are interspersed along with data slots we propose a two stage acquisition scheme namely successive interference cancellation plus iterative regularized least squares (SIC+IRLS) scheme that can be used for acquiring/estimating angle-of-arrival (AoA), angle-of-departure (AoD) and multi-path gains at the pilot slots. To facilitate channel tracking in data slots we also propose a particle filter (PF) based algorithm. Our simulation results demonstrate the superior performance of the integrated (SIC+IRLS)-PF approach as against that of the (SIC+IRLS)-Kalman filter (KF) approach, especially in fast varying conditions.
L. S. Pillutla and R. Annavajjala, "Bayesian CRLB for Joint AoA, AoD and Multipath Gain Estimation in Millimeter Wave Wireless Networks," in Proc. IEEE Global Communications Conference (GLOBECOM), Dec. 2017.
In this paper, we present an analysis of the nonrandom and the Bayesian Cramer-Rao lower bound (CRLB) for the joint estimation of angle-of-arrival (AoA), angle-of-departure (AoD), and the multipath gain in the millimeterwave (mmWave) wireless networks. Our analysis is applicable to multipath channels with Gaussian noise and independent path parameters. Numerical results based on uniform AoA and AoD in [0, π), and Rician fading path gains, reveal that the Bayesian CRLB decreases monotonically with an increase in the Rice factor. Further, the CRLB obtained by using beam forming and combining code books generated by quantizing directly the domain of AoA and AoD was found to be lower than those obtained with other types of beam forming and combining code books.
R. Chopra, C. R. Murthy and R. Annavajjala, “Multi-stream distributed co-phasing,” IEEE Trans. Signal Processing, vol. 65, no. 4, pp. 1042-1057, Feb. 2017.
R. Chopra, R. Annavajjala and C. R. Murthy, “Distributed co-phasing with autonomous constellation selection,” IEEE Trans. Signal Processing, vol. 65, no. 21, pp. 5798-5811, Nov. 2017.
R. Chopra, C. R. Murthy, and R. Annavajjala, “Multi-stream distributed co-phasing: Design and analysis,’’ in Proc. IEEE Signal Processing Advances in Wireless Communications (SPAWC), July 2016.
R. Annavajjala, “Low-complexity distributed algorithms for uplink CoMP in heterogeneous LTE networks,” in the special issue on Cloud Radio Access Networks, Journal on Communication and Networks, Apr. 2016.
R. Annavajjala, C. Yu and J. Zagami, “Communication over non-Gaussian channels—Part I: Information rates and optimum signal detection,” in Proc. IEEE MILCOM 2015, October, 2015.
Communication over non-Gaussian channels – Part II: Channel estimation, mismatched receivers, and error performance with coding
R. Annavajjala, C. Yu and J. Zagami, “Communication over non-Gaussian channels—Part II: Channel estimation, mismatched receivers and error performance with coding,” Proc. IEEE MILCOM 2015, October 2015.
An online learning approach to throughput optimization in wireless networks under dynamic and unknown interference conditions
R. Annavajjala, C. Yu and J. Zagami, “An online learning approach to throughput optimization in wireless networks over dynamic and unknown interference conditions,” in Proc. IEEE Machine Learning in Signal Processing (MLSP) Conference, September 2015.
A. Manesh, C. R. Murthy, and R. Annavajjala, “Physical layer data fusion via distributed co phasing with general signal constellations,” IEEE Trans. Signal Processing, vol. 63, no. 17, pp. 4660-4672, September 2015.
Analysis of Error Probability with Maximum Likelihood Detection over Discrete-Time Memoryless Noncoherent Rayleigh Fading Channels
R. Annavajjala and C. R. Murthy, “Analysis of error probability with maximum likelihood detection over discrete-time memoryless noncoherent Rayleigh fading channels,” in Proc. IEEE Vehicular Technology Conference (VTC-Fall), September 2015.
It is known that the capacity of the discrete-time memoryless noncoherent Rayleigh fading channels (DTM-NRFC) is achieved by a discrete constellation with finite number of mass points and when one of the mass points is located at the origin . In this paper, we present the maximum likelihood detection (MLD) error performance on DTM-NRFC for a discrete constellation with coding and spatial diversity. In the absence of outer coding, the error probability with MLD is derived in a surprisingly simple closed-form. On the other hand, with coding and diversity, our error probability expressions can be evaluated via saddle-point approximation techniques.