Devendra Jalihal

Orthogonal Frequency Division Multiplexing (OFDM) systems are sensitive to timing and frequency estimation errors. A DFT spread modified polyphase training sequence is proposed which has better merit factor and peak to average power ratio. By using the proposed training sequence two estimators are presented for timing and frequency synchronization. The CRLB expressions in closed form for these two estimators are also presented under the assumption of strict decoupling of timing and frequency offsets. Simulation results show that the MSE of the proposed estimators follow their respective CRLB. © 2011 IEEE.

Video caching provides an effective solution to the problem of increasing power consumption due to Internet infrastructure. The present work is the extension of [1] where we provided an approximate solution for optimal caching size. In this paper, we classify videos in N sets based on estimated arrival rate and analyze the power consumption of the video service. We analytically obtain the value of threshold for arrival rate to decide which videos to cache. We derive lower and upper bounds for caching size and show that the optimal caching size lies between these bounds. Further, we obtain a condition for the existence of lower and upper bounds. We perform simulations to analyze the performance of these bounds with optimal caching size. © 2014 IEEE.

We study a problem of sequential frame detection in an asynchronous framework, where a single frame of length N slots is transmitted uniformly in a large interval of known size A slots. In this setup, we seek to characterize the scaling needed of N and the channel (input) parameters for asynchronous optimal frame synchronization. We note that the framework permits a natural trade-off between N and \alpha , where \alpha is the synchronization threshold of the channel (usually parameterized by the channel input). We present a general framework that permits this trade-off and then characterize the scaling needed of both N and \alpha as a function of the asynchronism period A. Finally, we apply our results to the AWGN channel as an illustration.

Smart meter is an advanced energy meter which not only measures the electrical energy consumption, but also provides real time data on power consumed by electrical loads. Any smart meter needs an infrastructure, wired/wireless, for data communication with the central server. To enable this two-way communication link, we propose a narrowband Power Line Communication (PLC) system operating at a rate of few kbps and this can be deployed over existing outdoor/indoor power lines. Our design demonstrates reusing the existing wired electrical network to provide communication between the smart meter and a central server. A low-cost dedicated hardware for measuring the power usage of electrical appliance is designed. Further, a user interface is developed to remotely access the power usage measurements stored at the central hub database with features of power management. With the usage data at hand, smart decisions can be made in saving energy and, reducing carbon foot-prints. Our design uses low cost and off the shelf devices and no additional wiring as it communicates over the existing power line infrastructure.

We consider the problem of detecting the presence of a spatially correlated multichannel signal corrupted by additive Gaussian noise (i.i.d across sensors). No prior knowledge is assumed about the system parameters such as the noise variance, number of sources and correlation among signals. The non-parametric detection statistics were formed based on the statistical covariances obtained through Bartlett decomposition of sample covariance matrix. They are designed such that the detection performance is immune to the uncertainty in the knowledge of noise variance. The analysis presented verifies the invariability of threshold value and identifies a few specific scenarios where the proposed statistics have better performance compared to generalised likelihood ratio test (GLRT) statistics. The sensitivity of the statistic to correlation among streams, number of sources and sample size at low signal to noise ratio are discussed. © 2013 IEEE.

This paper presents computationally efficient algorithms for a Digital Signal Processor (DSP) implementation of transmultiplexors, timing and carrier recovery, and detection of data for on-board satellite receiver. The transmultiplexor which generates a time domain sequence of a Frequency Division Multiplexed (FDM) signal is implemented using DFT filter banks. Carrier and timing recovery and data detection are implemented assuming no training sequence. It is very important to design efficient algorithms to perform these tasks so that computational requirements are kept to a minimum thereby saving power. The paper proposes a novel 4th power based algorithm to determine the carrier offset. It is based on the idea that if there are P samples per symbol, then one of them is closer to the ideal Nyquist sampling instant than others, and, has the least ISI. A recursive method to isolate this minimum-ISI sample is proposed. This sample is used to estimate the carrier phase. Since the method identifies the least ISI sequence, it is said to perform both the carrier and timing offset estimation. After compensating for constant frequency offset, timing recovery is performed using a modified Peak Average Energy Criterion (PAEC) algorithm. A Digital Phase Locked Loop (DPLL) is used to track the finer variation in frequency offset. Results of simulations evaluating performance of these algorithms are presented for a number of frequency and timing offset situations. It is seen that the computational load is maximum at "start-up". We have proposed a particular start-up scheme and calculated its peak computational load in Millions of Instructions per second (MIPs).

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Design of robust test statistics which mitigate the channel and noise uncertainties are the essential requirement of detection applications. Covariance absolute value (CAV) detection is one of the non-parametric detection methods which claims robustness [1]. Achieving the theoretical probability of detection performance depends on the accuracy in calculating the thresholding parameter, which in turn depends on the distribution of the test statistic under the null hypothesis. Since the exact analysis of distribution is cumbersome, approximation techniques are used. We present approximation techniques which achieve performance very close to the one obtained from exact distribution of the test statistic (using Monte-Carlo simulation). Further, an equivalent test statistic compared to CAV is proposed which uses the Bartlett decomposition of the sample covariance matrix and its performance is compared with CAV. The robustness of the proposed test statistic is verified for the noise uncertainty model assumed [2]. © 2012 IEEE.

The exact time-of-arrival (TOA) information of the multi-path signals is crucial for optimal channel estimation in vehicular OFDM systems. Super-resolution algorithms such as ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique) have been applied to retrieve this information from embedded pilots in the received OFDM symbols. These algorithms have high applicability for vehicular wireless environs characterized by high doppler frequencies which leads to faster auto-correlation averaging. In this paper, we propose to improve upon the classical ESPRIT algorithm by incorporating the iterative reduced rank Hankel approximation (RRHA) technique. We show that this allows achieving of a lower MSE (mean square error) compared to the classical ESPRIT algorithm. Further, we portray that the degree of improvement brought forth increases with the increased number of RRHA-iterations. © 2009 IEEE.

Space-time trellis codes (STTCs) have been designed for quasi-static Rayleigh channels. When these codes are directly used for additive white Gaussian noise (AWGN) channels or Ricean channels with a high K factor, their frame error-rate (FER) performance is no longer optimal. We propose open-loop (no feedback) preprocessing methods for improving the error-rate performance of these codes when used in channels with a high K. For K = 0 (Rayleigh channels), these open-loop schemes generally do not alter the FER performance of the STTCs. The preprocessing operation at the transmitter is either to rotate the constellation points in one of the transmit antennas relative to the other by some angle, or suitably divide (unequally) the total power between the two transmit antennas. The angle of rotation and power division is optimized by maximizing the dfree of the STTCs. Simulation results show that for AWGN or Ricean fading channels with a large K, the FER performance can be significantly improved by doing either rotation or power division at the transmitter. While the FER performance is nearly unchanged for quasi-static Rayleigh channels, the improvement in performance is between 1 to 3 dB for Ricean or AWGN channels. © 2005 IEEE.