Search

By making use of the sensors and AI (SensAI) algorithms for a specialized task, Application Specific INstrumentation (ASIN) framework uses less computational overhead and gives a good performance. This work evaluates the feasibility of the ASIN framework dependent Communication based Sensing (CommSense) system using 5th Generation New Radio (5G NR) infrastructure. Since our proposed system is backed up by 5G NR infra, this system is termed as 5G-CommSense. In this paper, we have used NR channel models specified by the 3rd Generation Partnership Project (3GPP) and added white Gaussian noise (AWGN) to vary the signal to noise ratio at the receiver. Finally, from our simulation result, we conclude that the proposed system is practically feasible., Comment: 3 pages, Accepted in conference

Hard-decision decoding does not preserve the diversity order. This results in severe performance degradation in fading channels. In contrast, soft-decision decoding preserves the diversity order at an impractical computational complexity. For a linear block code $\mathscr{C}(n,k)$ of length $n$ and dimension $k$, the complexity of soft-decision decoding is of the order of $2^k$. This paper proposes a novel hard-decision decoder named Flip decoder (FD), which preserves the diversity order. Further, the proposed Flip decoder is `universally' applicable to all linear block codes. For a code $\mathscr{C}(n,k)$, with a minimum distance ${d_{\min}}$, the proposed decoder has a complexity of the order of $2^{({d_{\min}}-1)}$. For low ${d_{\min}}$ codes, this complexity is meager compared to known soft and hard decision decoding algorithms. As it also preserves diversity, it is suitable for IoT, URLLC, WBAN, and other similar applications. Simulation results and comparisons are provided for various known codes. These simulations corroborate and emphasize the practicality of the proposed decoder., Comment: Transacton of 10 pages with 4 figures

A simple closed-form solution to the discrete Lyapunov equation(DLE) is established for certain families of matrices. This solution isexpressed in terms of the eigen decomposition (ED) for which closed-form solutions are known for all 2 × 2, 3 × 3 and certain familiesof matrices. For general matrices, the proposed ED based closed-formsolution can be used as an efficient numerical solution when the EDcan be computed. The result is then extended to give closed-formsolutions for a generalization of the DLE, called the Stein equation.The proposed explicit solution’s complexity is of the same order asiterative solutions and significantly smaller than known closed-formsolutions. These solutions may prove convenient for analysis and syn-thesis problems related to these equations due to their compact form.

A simple closed-form solution to the discrete Lyapunov equation (DLE) is established for certain families of matrices. This solution is expressed in terms of the eigen decomposition (ED) for which closed-form solutions are known for all 2x2; 3x3 and certain families of matrices. For general matrices, the proposed ED based closed-form solution can be used as an efficient numerical solution when the ED can be computed. The result is then extended to give closed-form solutions for a generalization of the DLE, called the Stein equation. The proposed explicit solution's complexity is of the same order as iterative solutions and significantly smaller than known closed-form solutions. These solutions may prove convenient for analysis and synthesis problems related to these equations due to their compact form., Submitted to IEEE LCSS on 4th March

In this article, we present a novel localization and activity classification method for aerial vehicle using mmWave frequency modulated continuous wave (FMCW) Radar. The localization and activity classification for aerial vehicle enables the utilization of mmWave Radars in security surveillance and privacy monitoring applications. In the proposed method, Radar’s antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. The height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival. The aerial vehicle’s activity is classified using machine learning methods on micro-Doppler signatures extracted from Radar measurements taken in an outdoor environment. To evaluate performance, various light weight classification models such as logistic regression, support vector machine (SVM), Light gradient boosting machine (GBM), and a custom lightweight convolutional neural network (CNN) are investigated. Based on the results, the logistic regression, SVM, and Light GBM achieve an accuracy of 93%. Furthermore, the custom lightweight CNN can achieve activity classification accuracy of 95%. The performance of the proposed lightweight CNN is also compared with the pre-trained models (VGG16, VGG19, ResNet50, ResNet101, and InceptionResNet). The proposed lightweight CNN suits best for embedded and/or edge computing devices.

The period after the COVID-19 wave is called the Echo-period. Estimation of crowd size in an outdoor environment is essential in the Echo-period. Making a simple and flexible working system for the same is the need of the hour. This article proposes and evaluates a non-intrusive, passive, and costeffective solution for crowd size estimation in an outdoor environment. We call the proposed system as LTE communication infrastructure based environment sensing or LTE-CommSense. This system does not need any active signal transmission as it uses LTE transmitted signal. So, this is a power-efficient, simple low footprint device. Importantly, the personal identity of the people in the crowd can not be obtained using this method. First, the system uses practical data to determine whether the outdoor environment is empty or not. If not, it tries to estimate the number of people occupying the near range locality. Performance evaluation with practical data confirms the feasibility of this proposed approach.

A piece of user equipment (UE), typically, has access to multiple radio access technologies (RATS). Moreover, apart from the standard primary cellular network, the secondary cellular networks can assist the primary network in downlink UE communications. In this way, the data can reach the UE through multiple entities. This paper exploits the multiple entities’ idea by proposing a cross-layer scheme that combines data to improve the block error rate (BLER) and the throughput. For this, we define a new entity, called the super-MAC, just above the Medium Access Control (MAC) layer. More specifically, we propose data duplication (at the transmitter) and combining (at UE) at the super-MAC, where the super-MAC gets the Radio Link Layer protocol data unit (RLC-PDU) and sends multiple-copies across various interfaces to different MAC-entities. In doing so, the super-MAC attaches a unique sequence number to a group of RLC-PDUs together. At the UE, the data from different MAC entities are combined at super-MAC to clear any block error. The super-MAC operates in between the Cyclic Redundancy Check and Forward Error Correction stages of the HARQ process. The additional complexity introduced by the scheme is negligible in front of the existing operations. Moreover, the average latency improves due to the significant improvement in the Block Error rate (BLER) that the combining scheme offers over the BLER of the conventional standalone system. Also, since the errors significantly reduce, the throughput shows significant improvement. Finally, the proposed scheme is an advancement in HARQ to reduce retransmissions, and hence it is suitable for the next-generation networks like B5G or 6G to adopt the super-MAC.

Water quality assessment has undeniable value for humanity’s well-being. Despite that, most of the existing techniques struggle to measure the quality of running water due to costly infrastructure or large evaluation time. Recently, quality measurement has been demonstrated using spectrum analysis. However, these modern systems need complicated signal processing algorithms and high processor capabilities for decision making, have elaborate optical assemblies and sizable power requirement making them bulky and expensive to operate. Thus, a refined technique with simplified implementation, low cost, and reliable long-time operation is essential to address this challenge. We present, for the first time to our knowledge, a holistic approach towards a simple and rapid method for determination of water quality parameters in flowing water. This is based on the selection of characteristic wavelengths for the parameters considered and usage of narrowband LEDs as the inspection light source. Thereby incorporating the benefits of optical sensing such as electromagnetic immunity, selectivity, sensitivity, etc. The specific wavelengths of 560nm, 860nm, and 635nm have been demonstrated to have a dominant effect due to pH, turbidity, and total dissolved solids (TDS) respectively, from regression analysis. Using only these wavelengths, an evaluation system capable of determining the light absorption after passing through water has been designed and developed. The obtained optical responses are subsequently related to water parameters, specifically pH, TDS, and turbidity. Experiments were performed to evaluate samples and then validate this technique against standard instruments for both flowing and sampled water setups. It is shown from the measurement results that pH, turbidity, and TDS have linear regression coefficients of 0.9773, 0.9617, 0.8271 and 0.9691, 0.9729, 0.76 for flowing and sampled water arrangements, respectively.

Maximum likelihood detection is infeasible in uplink multiuser massive multiple-input and multiple-output (m-MIMO) systems due to the large dimension of the MIMO systems. Accordingly, suboptimal or near-optimal alternatives like linear minimum mean square error (LMMSE) detector and Zero Forcing (ZF) are used. However, the LMMSE and the ZF detectors need matrix inversion, which is computationally costly. We propose two detection schemes for massive MIMO, which compute an approximate inverse based on the Cayley-Hamilton theorem, and have quadratic complexity in the number of users. Simulation results exhibit the similarity of the BER performance of the proposed schemes to that of the ideal ZF or LMMSE.

The proposed work describes a novel way to use the existing communication infrastructure based sensing to monitor the change in the environment (CommSense). The key idea to this technology lies in focusing on already known information from received signal e.g. Pilot carriers in the communication signal frames. Comparing the received signal with the expected properties of the pilot, we can get an estimate of change in the environment based on change in channel properties. Existing Long Term Evolution telecommunication infrastructure is utilized for this environment sensing task. After the channel characteristics are estimated, those can be used to obtain phenomenological knowledge of the environment using Application Specific INstrumentation framework. This system was implemented using software defined radio platform and GNURadio software support.

We demonstrated a vehicle detection and classification method based on Long Term Evolution (LTE) communication infrastructure based environment sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyzes it to estimate the change in the environment. For vehicle detection and subsequent classification, our setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, we take advantage of the processing that happens in a LTE receiver user equipment (UE). We tap into the channel estimation and equalization block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. We use CSI values for with and vehicle and without vehicle case in outdoor open road environment. Being a receiver only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). In this paper, we establish our claim by analyzing field-collected data. Live LTE downlink (DL) signal is captured using modeled LTE UE using software defined radio (SDR). The detection analysis and classification performance shows promising results and ascertains that, LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment., This paper is a postprint of a paper submitted to and accepted for publication in IET Radar, Sonar & Navigation and is subject to IET Radar, Sonar & Navigation Copyright

This letter considers cooperative spectrum sensing (CSS) in a heterogeneous cognitive radio network, where K nonidentical cognitive users (CUs) and a fusion center (FC) collaboratively detect the primary user's signal using the N-out-of-K rule in the presence of erroneous control channels. We formulate a generalized optimal fusion problem (GOFP) to optimize the Nout-of-K rule for the Bayesian test and then solve the GOFP to obtain the optimal value of N. It follows that existing results on the optimal value of N in the N-out-of-K rule are special cases of the proposed solution. We then propose a framework to obtain the smallest number of CUs required in CSS while satisfying a target error bound at the FC. Numerical results are presented using the energy detector as an example.

User equipment (UE), in general, has access to multiple radio access technologies (RATS). Besides, secondary base-stations can also aid the UE’s communication. Thus data can reach the UE from the primary base-station via multiple secondary base-stations. Taking advantage of the benefits of using such multiple paths, we propose in this paper a cross-layer scheme that improves block error rate (BLER) and throughput. We propose data duplication and integration at the Packet Data Convergence Protocol (PDCP) sublayer of layer 2 for downlink operations of 4G LTE / 5G NR. At the PDCP of ENodeB (gnodeB), the PDCP protocol data unit (PDU) is duplicated and sent to the UE via various paths. All these PDUs have the same sequence number (SN). Data from various networks are aptly linked using the PDCP assigned SN at EnodeB to obtain a composite and improved version of the PDU at UE. Although the cross-layer technique involves new procedures when combining data at PDPCP, the average delay for a PDCP-based aggregation scheme is very small compared with the standalone scheme because of the significant improvement in the block error rate (BLER) provided by the scheme over the BLER of the traditional standalone system. Finally, the BLER improvement results in throughput enhancement.

In this article, we present a novel angle and height estimation technique for aerial vehicles using mmWave frequency modulated continuous wave (FMCW) Radar. In the proposed method, Radar’s antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. Height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival.

BackgroundA major challenge in controlling the COVID-19 pandemic is the high false-negative rate of the commonly used standard RT-PCR methods for SARS-CoV-2 detection in clinical samples. Accurate detection is particularly challenging in samples with low viral loads that are below the limit of detection (LoD) of standard one- or two-step RT-PCR methods.MethodsWe implement a three-step approach for SARS-CoV-2 detection and quantification that employs reverse transcription, targeted cDNA preamplification and nano-scale qPCR based on the Fluidigm 192.24 microfluidic chip. We validate the method using both positive controls and nasopharyngeal swab samples.ResultsUsing SARS-CoV-2 synthetic RNA and plasmid controls, we demonstrate that the addition of a preamplification step enhances the LoD of the Fluidigm method by 1,000-fold, enabling detection below 1 copy/μl. We applied this method to analyze 182 clinical NP swab samples previously diagnosed using a standard RT-qPCR protocol (91 positive, 91 negative) and demonstrate reproducible detection of SARS-CoV-2 over five orders of magnitude (< 1 to 106viral copies/μl). Crucially, we detect SARS-CoV-2 with relatively low viral load estimates (ConclusionThe three-step nano-scale RT-qPCR method can robustly detect SARS-CoV-2 in samples with relatively low viral loads (< 1 viral copy/μl) and has the potential to reduce the false negative rate of standard RT-PCR-based diagnostic tests for SARS-CoV-2 and other viral infections.SummaryWe test, implement and report the results of a microfluidic RT-qPCR assay system involving sequential RT, preamplification and nano-scale qPCR that can robustly detect SARS-CoV-2 in clinical samples with viral loads less than 1 copy/ul.

Block codes are typically not used for fading channels as soft decision decoding is computationally intensive and hard decision decoding results in performance loss. In this paper we propose a diversity preserving hard decision decoding scheme for parity check codes (PCC) over Rayleigh fading channels. The proposed flip decoding scheme has linear complexity in the block length. Theoretical analysis and simulation results verify the correctness of the proposed detection scheme., Comment: Submitted to IEEE WCL on 19th Oct 2020

The rapid increase in number of vehicles in recent times has adversely affected the travel time, traffic blocks, and accidents. Random search for a parking space contributes around 30% of city traffic which costs a significant amount of time and energy. Hence, smart parking solutions that detect and allocate vacant parking spaces in real-time are essential to minimize this traffic congestion. In this paper, we propose a novel method to detect the occupancy status of an outdoor parking space using Long Term Evolution (LTE)-based Channel State Information (CSI) and Convolutional Neural Network (CNN). This supervised classification method can provide real-time status of the occupancy. In this study, we analyze the performance of the proposed method by comparing with other CSI-based localization techniques. Through numerical results, we show that the proposed method outperforms the state-of-the-art techniques.

In this paper, we characterize the energy efficiencies of two important spectrum sharing paradigms, namely the interweave based spectrum sharing (ISS) and the underlay based spectrum sharing (USS). We demonstrate that although USS can have higher spectral efficiencies than the ISS, the USS suffers from poor energy efficiency. The primary reason for this is shown to be use of excessive power for small rate gain for the unlicensed or the secondary user (SU) in the USS. On the other hand, if the spectrum sensing errors are kept in control in the ISS, the SU achieves most of its communications through interference free transmissions and consequently utilizes less power to transmit more, thereby being energy efficient.

Cyber Physical Systems (CPS) are more vulnerable to cyber and physical attacks than the conventional systems because of the tight integration of the cyber, communication and control aspects of CPS. Deceptive attacks are very severe on such systems because they go undetected when classical detectors like chi-squared detector is used. Once such deceptive attack that we focus in this paper are false data injection attacks, where the attacker inputs carefully designed false attack vectors. Chi-square detector which detects based on the statistics of deviations of the residual i.e. difference of observed measurement and estimated measurement fails to detect these attacks, since the statistics is not changed in these attacks. “Cosine similarity detector” was proposed to detect False Data Injection (FDI) attacks in smart grid scenario. However, we show that there exists attack vectors that remain stealthy to both chi-square as well as the cosine-square detectors. We propose an random coding scheme to modify the measurements at random, unknown to the attacker while known to the estimator. We establish that our coding scheme in conjunction with the cosine similarity detector provides promising detection performance.

In this work, we describe a new method of using long-term evolution (LTE) telecommunication infrastructure to sense environmental changes. We call it LTE communication-based sensing (Com-mSense). It can be used in various environment-sensing tasks, e.g., sea level monitoring, security of large unmanned landscapes, snow avalanche monitoring, and activity detection inside forests. The main concept of this technology is to focus on the known signal embedded in the data frames. It compares the received signal with the expected reference signal to estimate the change in the environment. LTE telecommunication infrastructure is used for this environment-sensing task because of its wide coverage. In addition, LTE uses a two-dimensional (2D) frame structure of orthogonal frequency division multiplexing (OFDM) and multiple input and multiple output (MIMO), which may yield better performance compared to previous communication standards. This procedure of extracting channel characteristics using commensal radar principles does not affect the existing telecommunication system, because it is a receive-only system. After the channel characteristics are estimated, they can be used to obtain phenomenological knowledge of the environment using an application-specific instrumentation (ASIN) framework. The capability analysis of this scheme is presented in this article, first in simulation and then using field-collected data captured with the help of a software-defined radio (SDR) platform.

The design of the optimal nonrandomized hard decision fusion rule under the Neyman–Pearson criterion is known to be exponential in complexity. In this letter, we formulate a more generalized version of this problem called the “generalized decision fusion problem (GDFP)” and relate it to the classical $\text{0--1}$ knapsack problem. Consequently, we show that the GDFP has a worst-case polynomial time solution. Numerical results are presented to verify the effectiveness of the proposed solution.

The main purpose of this paper is to review recent developments and research issues of non-traditional machining processes (NTM) in bio-medical engineering. The miniaturization ability has enabled NTM processes to be applied in many areas of biology, medicine, and biomedical engineering. As these processes have been proved much more reliable, economic and accurate in machining materials used in the above fields as these processes have set new standards of how easily a tough and resilient material can be machined with such precision and in turn lead to saving lives of millions of human beings who come under such surgical circumstances. This paper highlights the machining processes involved in the making of tools and equipment used in various Bio-medical fields like dentistry, optometry, implant tools and much more.

This work adopts the artificial neural network (ANN) for predicting the efficiency of a double-pipe heat exchanger which employs T-W tape inserts with different wing-width ratios (w/W) of 0.31, 0.47, and 0.63. The effects of friction factor (f), Nusselt number (Nu), and thermal performance (η) are predicted using the established multi-layer ANN. Different scenarios are examined using two parameters as inputs to the ANN: Reynolds number (Re) and w/W. The results prove that the developed ANN model is able to accurately predict the experimental data. The obtained mean square error has a value of less than 0.7 as compared to the experimental values. Furthermore, the proposed ANN-based approach is also effective to predict the thermal parameters, with the least variance and high precision. In addition, a multiple linear regression is employed to check the efficiency of the proposed model from which it is demonstrated that the suggested neural network method provides useful guidance for accurately predicting the thermal parameters with the least variance. The configuration of 2–10-1 is found to be the best for the current model, with a mean absolute error of 0.546896.

In this letter, we obtain a generalized expression for the optimal number of cognitive users ${M}$ for the ${K}$ -out-of- ${M}$ rule that minimizes the Bayes risk at the fusion center over noisy control channels. We show that many existing and new results for optimal ${M}$ are special cases of the proposed solution. Numerical results are presented using energy detector. However, the expressions for optimal ${M}$ obtained in this letter are applicable to any detector used in cooperative spectrum sensing.

Cognitive radio (CR) systems are potentially capable of mitigating the spectrum shortage of contemporary wireless systems. In this paper, we provide a brief overview of CR systems and the important research milestones of their evolution, along with their standardization activities, as a result of their research. This is followed by the detailed analysis of the interweave policy-based CR network (CRN) and by a detailed comparison with the family of underlay-based CRNs. In the interweave-based CRN, sensing of the primary user’s (PU) spectrum by the secondary user’s (SU) has remained a challenge, because the sensing errors prevent us from fulfilling the significant throughput gains that the concept of CR promises. Since missed detection and false alarm errors in real-time spectrum sensing cannot be avoided, based on a new approach, we quantify the achievable rates of the interweave CR by explicitly incorporating the effect of sensing errors. The link between the PU transmitter and the SU transmitter is assumed to be fast fading. Explicitly, the achievable rate degradation imposed by the sensing errors is analyzed for two spectrum sensing techniques, namely, for energy detection and for magnitude squared coherence-based detection. It is demonstrated that when the channel is sparsely occupied by the PU, the reusing techniques that are capable of simultaneously providing low missed detection and false alarm probabilities cause only a minor degradation to the achievable rates. Furthermore, based on the achievable rates derived for underlay CRNs, we compare the interweave CR and the underlay CR paradigms from the perspective of their resilience against spectrum sensing errors. Interestingly, in many practical regimes, the interweave CR paradigm outperforms the underlay CR paradigm in the presence of sensing errors , especially when the SNR at the SU is below 10 dB and when the SNR at the PU is in the range of 10–40 dB. Furthermore, we also provide rules of thumb that identify regimes, where the interweave CR outperforms the underlay CR.

In this paper, we propose a channel reservation scheme for a cognitive radio network where a fraction of channels are strictly reserved for the Primary user (PU) transmissions only. The PU’s and the SU’s data packets arrive according to a Poisson process. We develop a probabilistic model for the PU occupancy and the probabilities of transmission with and without interference. Using this occupancy model we characterize the PU’s and the SU’s average achievable data rate when the PU faces spectrum sensing errors. We then show that using a reservation based scheme significantly improves the PU data rate at a marginal loss to the SUs data rate. We also derive the optimal number of channel to be reserved that maximizes sum rate.

This paper considers the cooperative spectrum sensing (CSS) in a cognitive radio (CR) network, where K secondary users (SUs) are used to determine the primary user's (PU's) signal using the m-out-of-K rule. In CR networks, the SUs must opportunistically utilizes to the PU's channel without affecting the quality of service of the PU. Therefore, we aim to optimize the m value and the K value of the m-out-of-K rule to achieve the maximum channel throughput of the CR network (throughput of the PU plus throughput of the secondary network) under sufficient protection to the PU. The results are illustrated using the energy detector.

In many communications and signal processing optimization problems, the water-filling problem plays an important role in resource optimization. In this paper we show that the water-filling problem is equivalent to the classical Knapsack Problem, which is known to be NP-hard. Consequently, using the linear complexity solutions for water-filling problems and the equivalence of Knapsack Problem with water-filling problem, we present new linear time approximate solutions for Knapsack problem.

The Maximum Likelihood Detector (MLD) offers an optimal bit-error-ratio for an un-coded multiple-input-multiple-output (MIMO) communication system. The computational complexity, however, of MLD increases significantly for detection of higher order modulated signal streams with multiple transmit antennas. To reduce this complexity while simultaneously also achieving near optimal performance, we propose a novel technique that modifies the sub-optimal Zero Forcing Detector (ZFD) by using the minimum eigen vector of the matrix $\mathbf{HH}^{H}$ , where H is the channel matrix and $\mathbf{H}^{H}$ is its Hermitian. The performance in terms of BER and computational complexity of the proposed technique is compared to that of the MLD. Simuations show that the BER performance of the proposed technique is near optimal whereas the computational complexity is signiflcantly reduced.

This article reviews the computation of discrete Fourier transform which is termed as fast Fourier transform (FFT). It starts with discussion on a few terms used in this transform with one of its classifications followed by the presentation of the computer algorithm. Finally, its implementation on Matlab with few applications is covered.

In this paper, we present a novel, fast algorithm for allocating the transmit power to the secondary users (SUs) to maximize the total SU throughput in a cognitive radio network (CRN) while maintaining interference to the primary user (PU) within an acceptable range. The problem is formulated so as to apply Karush-Kuhn-Tucker (KKT) conditions to derive a fast, low complexity algorithm which requires less number of floating point operations. The performance of the proposed algorithm is verified by simulations.

In this letter, we present a novel method of solving the Generalized Waterfilling problem (GWFP) using equivalence with a waterfilling problem (WFP) which has a closed form solution. Consequentially, a class of WFPs with only one-step called ‘1-step’ WFP is used to derive a low complexity algorithm which has the smallest worst case complexity reported. The proposed algorithm is verified by simulations.

Cognitive radio is the enabling technology for license-exempt access to the TV White Spaces (TVWS). There is ever increasing demand of users in the broadcasting and communication services. Large portions of unused spectrum in the UHF/VHF bands exist in India which can be used on geographical basis. This paper describes a study on path loss variation in UHF/VHF bands in India. The aim of this study is to develop and optimize a path loss model based on Linear minimum mean square error estimation (LMMSE) for India. We propose the LMMSE based Optimized Perez-Vega Zamanillo propagation path loss model. The measured path loss values, collected across India, are compared with proposed Optimized Perez-Vega Zamanillo path loss model and other existing path loss models. It is found that Optimized Perez-Vega Zamanillo propagation path loss model has the least root mean square Error (RMSE) of 13.98 dB. Other existing path loss models have root mean square Error(RMSE) value greater than 24 dB. Therefore, Optimized Perez-Vega Zamanillo propagation path loss model is best suited for predicting coverage area, interference analysis in India for TVWS

In this paper a full-wavelength dipole antenna is designed and analyzed for unlicensed ultra-wide band available at 60GHz frequency. The geometry of this antenna consists of four semicircular shape arms. Each arm is quarter-wavelength long. The Radiated fields, directivity, power density, and input impedance of the proposed antenna are formulated and these theoretical results are validated by comparing with the simulated results obtained from HFSS. The geometry of the proposed antenna gives advantage over planar antennas in that it can be embroidered on fabric.

A simulation of ridge waveguide with graded junctions is presented. Transmission losses are plotted for non-graded, linearly graded and parabolically graded junctions at 530 THz for a 1micron waveguide length. Simulations show little difference in performance of non-graded and linearly graded waveguides. Parabolically graded perform worst among all.

This paper describes a novel way to use the existing communication infrastructure of LTE (Long Term Evolution) to monitor the change in the environment along with its feasibility analysis. This system is named LTE-CommSense by the authors and can be used in many environment sensing objectives namely monitoring of crop growth over a period of time, disaster monitoring, sea state monitoring, snow avalanche monitoring, security of large unmanned landscapes etc. This technology focuses on already known information from received signal e.g. reference symbols in the LTE communication signal frames. In the equalizer block of the receiver, the equalizer tap coefficients gets modified while training the equalizer using reference symbols. These modifications depend on the channel through which the received signal has passed through. Therefore the adaptive equalizer tap coefficients contain information related to the channel condition. We propose to use these coefficients to get an estimate of change in the channel properties. LTE spectrum and infrastructure will be utilized to get channel characteristics without affecting the existing communication system. After the channel information is received, phenomenological knowledge about the environment will be obtained using ASIN (Application Specific INstrumentation) phylosophy. We present a feasibility analysis of the novel scheme in this paper. We used LTE specific channel models from ITU (International Telecommunication Union) with AWGN noise as the dataset and nearest neighbour based classifier to validate this scheme.

A novel approach for determining the E Field values in a complex medium has been proposed, which can serve as an alternative to FDTD method.

VHF/UHF bands generally have favourable propagation characteristics in terms of penetration and coverage and so is being considered for cognitive radio (CR) communications. Delay spread is an important parameter that needs to be considered in the design of a communication system. Using Root mean square Delay Spread (RDS) models, we can design a cognitive radio in such a way that it is adaptive to the RDS of the fading channel, thus minimizing errors in transmission of signals. Therefore, we propose a novel RDS model which incorporates the effects of propagation distance, transmitter height and transmitter carrier frequency. Finally, measured delay spread was compared with the proposed model and known delay spread models favourably.

State estimation is essential in monitoring any Cyber-Physical System. When there is a wireless communication channel present between sensor and remote estimator, the imperfections associated with wireless channel like packet loss and quantization error due to limited capacity of the channel will have major impact on estimation. Hence, studying the effect of these imperfections is very important for critical cyber-physical system applications. In this paper, we model and study the effect of quantization and the joint effect of both packet loss and quantization on the estimation and give error covariance bounds.

In this paper, a novel approach for solving the `Water- filling problem with peak power constraints for individual resources' also known as Cave-Filling Problem (CFP) is proposed. The proposed algorithm calculates a lower bound for the number of resources that are assigned positive powers, K, and then calculates the K powers only once. This lowers the total computational cost of the proposed algorithm significantly. Simulation results validate the proposed algorithm.

Rader abd Brenner's ‘real-factor’ FFT can be applied to Radix-4 FFT to fetch saving in the multiplication counts. However in turn the number of addition count increases which results in increase in total flop count. For this in this paper two levels of saving ideas are proposed. First is a slight modification to Rader and Brenner's ‘real-factor’ FFT for Radix-4, which not only reduces the multiplication but also makes the total flop count equals to standard Radix-4 FFT. Second is to apply the scaling operation to the Twidlle Factors(TF) similar to Tangent FFT like one proposed by Frigo for split radix so that the net computational complexity is of the order of 4Nlog2N computation, where N is the size of FFT. As such the complexity order is same as Standard Split Radix FFT.

In this paper, we present expressions for optimal number of secondary users (SUs) by minimizing the global error rate for a given fusion rule at the fusion center (FC). Expressions for optimal number of SUs are presented for AND, OR and MAJORITY fusion rules. We show that optimal number of SUs depends on effective probability of false alarm (Pf e ) and effective probability of miss detection (P me ) of a SU over erroneous control channel. Using improved energy detector as an example feasibility regions are derived for OR, AND and MAJORITY rules.