The increase in the use of autonomous vehicles has motivated a paradigm shift in the transportation domain as it redefines the boundaries of urban mobility by augmenting safety measures. This research paper explores an innovative approach to enhance 6G millimeter-wave (mmWave) beam prediction for vehicle-to-infrastructure (V2I) communications by using generative adversarial networks (GANs). By generating synthetic data samples effectively and balancing the real-world dataset, we improve the accuracy of beam prediction models significantly. Our proposed method of training random forests on synthetic data (RFGAN) to predict beam indices provides the solution for imbalanced class issues and significantly improves the predictive performance of mmWave beam selection, contributing to more reliable and efficient V2I communications. This work also performs comparative analysis with state-of-the-art models in top-K evaluation metrics, average power loss, and overhead savings related to adapting to the new approach.

The majority of practical studies and analyses in the context of the Internet of Things (IoT) have been carried out assuming that data packet generation follows theoretical models (typically a Poisson process with exponentially distributed packet interarrival times) without previous experimental validation and supporting evidence. In contrast to this approach, this article proposes a novel experimental and mathematical framework to determine statistical models for IoT data traffic. Based on empirical data generated by common smart home devices (e.g., ambient temperature, luminous intensity, atmospheric pressure, and motion sensors) recorded over a full year using an experimental IoT subsystem, this article first shows that real IoT traffic does not follow the Poisson process model conventionally assumed in the literature, but rather depends on the type of application. Consequently, we estimate the empirical statistical distribution of the interarrival between data packets for several smart home applications. The empirical distribution of the packet interarrival times is fitted with some well-established classical statistical distributions using the method of moments as well as maximum-likelihood estimation techniques, and the goodness of fit is quantified using the Kolmogorov-Smirnov (KS) test. Moreover, we also carry out a regression analysis to provide mathematical relations between the distribution parameters and the considered physical input parameters (ambient temperature, luminous intensity, and atmospheric pressure), which is particularly useful in practical scenarios. Furthermore, an exhaustive analysis of the variation of parameters over different time scales and the autocorrelation characteristics of the data packet generation are included as well. In summary, this article provides accurate traffic models suitable for real-life IoT scenarios that can be used for an adequate design and optimization of future communication networks to efficiently support IoT services.

The emergence of innovative technologies for mobile devices has enabled us to crowdsource large scale tasks to these devices. This gives rise to a new paradigm where mobile clouds can be accessed by an end user for data storage and processing over a wireless connection. A crowd-cloud system consists of a central Consumer Crowd Interface and several crowd-clouds. Each crowd-cloud comprises of mobile devices which can perform computations and storage tasks allotted to them. This paper presents a mathematical formulation of the total energy consumption in such a system including energy for offloading, computation and downloading of tasks to and from mobile devices. The optimization problem is solved using Lagrangian dual-decomposition approach.

This work proposes a novel experimental and mathematical framework to determine the statistical models for the Internet of Things (IoT) data traffic. Conventionally, it is assumed that the data packet generation for IoT based applications follows a Poisson process with exponentially distributed packet inter-arrival time. Based on such generalized premise, majority of the network related theoretical and practical analysis of the IoT platforms are carried out. Based on empirical data for a smart home application recorded for over 10 weeks duration using proposed IoT subsystem, in this paper we estimate the empirical statistical distribution of the IoT data traffic generated by temperature, light intensity and motion sensors. The inter-arrival between the data packets generated from different sensing modules of the IoT smart home application subsystem is determined. The Empirical Cumulative Distribution Function (ECDF) of the estimated time duration is fitted with few of the well-established classical statistical distributions using Method of Moments (MoM) and Maximum Likelihood (ML) estimation techniques. The goodness of fit is quantified using Kolmogrov-Smirnov (KS) test. The parameters of the fitted distributions are determined as a function of the physical input parameters. The results reveal source IoT traffic does not follow a Poisson process which is conventionally assumed in the literature, but rather depends on the type of application.

In this paper, we propose a framework to optimize the packet length and modulation level to determine the optimal packet size (OPS) for topology aware cognitive radio sensor networks (CRSNs) using a variable rate modulation scheme. A generalized network topology with specific node density of the Primary Users (PUs) is accounted to estimate the OPS. Based on stochastic geometry and non-linear optimization techniques, a joint multivariate optimization problem is formulated to determine the OPS for the topology dependent CRSNs.

This work proposes a novel mathematical approach to accurately model data traffic for the Internet of Things (IoT). Most of the conventional results on statistical data traffic models for IoT are based on the underlying assumption that the data generation follows standard Poisson or Exponential distribution which lacks experimental validation. However, in some of the use case applications a single statistical distribution is not adequate to provide the best fit for the inter-arrival time of the data packets generation. Based on the real data collected for over 10 weeks using our customized experimental IoT prototype for smart home application, in this paper we have established this very fact, citing barometric air pressure as an example. The statistical distribution of the inter-arrival time between the data packets for a specified barometric pressure fluctuation threshold is initially determined by approximating the best-fit with a set of standard classical distributions. The goodness-of-fit with the empirical data is numerically quantified using Kolmogorov-Smirnov (KS) Test. Furthermore, it is observed that any single standard distribution is unable to provide a good fit which is at least less than 10%. Therefore, a novel weighted distribution scheme is proposed that could provide an acceptable fit. The weighing factor including the location, scaling and weighing parameters of the best fitting distribution are estimated and analyzed. The distribution parameters are finally expressed as a function of the differential pressure value that can be used for different theoretical analysis and network optimization.

Restless multi-armed bandits(RMAB) with partially observable states have been extensively studied for scheduling in opportunistic communication systems. These RMAB models assume that when the decision maker plays a particular arm, it gathers information about system state through feedback signals. These models allow only one state transition in a single decision interval. In this paper, we propose a cumulative feedback model, where multiple state transitions occur in a decision interval. We formulate opportunistic scheduling in communication systems and relay selection problem as partially observable RMAB with cumulative feedback. In this model, state of an arm is not observable whether it is played or not. But a belief about the state is maintained and it is updated at end of each decision interval based on feedback from the played arm. If an arm is not played, then no feedback is available. But belief is updated with natural evolution. In case of large number of channel state transitions in a decision interval for not-played arms, we approximate belief with stationary probability. For this scenario we solve partially observable RMAB using the Whittle index policy. A closed-form expression for Whittle index is obtained for a special case. The efficacy of this policy is illustrated via some numerical examples and it is also compared with other policies.

The idea of D2D relay has received much interest recently as an essential ingredient in next generation networks. Future networks with user relay assistance, will have issues regarding relay employment considering the trade offs between throughput gain and cost incurred. In this work, we formulate the relay employment problem, where a source assesses a candidate relay's “employability” by accounting for the channel evolution with time in an unacknowledged transmission mode. We present a myopic policy which takes an initial belief about channel states as input and outputs a recommended sequence of actions. This sequence specifies whether to use a relay or not at each time slot. The myopic policy has different structures that impacts the gain obtained by the source. We present an analysis of this policy structure and provide sufficiency conditions for each of them. The myopic policy is compared with the one step decision policy which does not account for channel evolution. Numerical results show that relative gains up to 30% are obtained by myopic policy over one step decision policy.

In this paper, we propose a generalized model for underlay based cognitive radio (CR) that is based on the information theoretic interference channel model. Utilizing the achievable rate region for the two user interference channel proposed by Han and Kobayashi, we establish achievable rates for the unlicensed or the Secondary User (SU) that depends upon a given target rate for the licensed or the Primary User. Hence, the transmission of the SU is rate limited due to the PU achievable rate. Unlike the traditional approach for underlay, our results bring out the impact of SU transmission on the set of achievable rates for the PU and also of the effect of PU target rate (which may be called its QoS) on the set of achievable rate for the SU.

In commercial ultrasound systems, the transmit module typically generates the time delayed excitation pulses to steer and focus the acoustic beam. However, the ultrasound transmitter module in these systems has limited access to medical ultrasound researchers. In this paper, we have presented the development of a programmable architecture for 8-channel ultrasound transmitter for medical ultrasound research activities. The proposed architecture consists of 8 transmit channels and Field Programmable Gate Array (FPGA) based configurable delay profile to steer acoustic beam, transmit frequency and pulse pattern length depending on the medical application. Our system operates in pulse-echo mode, with ultrasound transmit frequency up to 20 MHz, excitation voltage up to 100 Vpp, and individual channel control with single high speed Serial Peripheral Interface (SPI). Pre-calculated delay profiles per scanline are generated in Matlab, based on physical parameters of 8 element linear transducer array which are used to steer and focus the ultrasound beam. An experiment is carried with our transmit module to transmit ultrasound into gelatin phantom, acquired echoes and processed for B-mode imaging. The results show that this transmit platform can be used for ultrasound imaging researches and also for medical diagnosis.

This paper investigates the sequential opportunistic channel sensing and access problem of an unlicensed or secondary user (SU) in presence of multiple licensed or primary users (PU). The SU senses one channel at a time for the presence of PU. The sensing is considered to be imperfect, i.e, the presence or absence of PU may not be detected correctly. The problem of selection of best channel amongst the sequentially scanned channels for the transmission of SU is modeled as an Optimal Stopping Problem (OSP) which also takes into account the cost of scan per channel. A simple threshold based policy is presented as a solution to OSP in which SU after sensing the channel to be free of PU activity checks the channel quality by comparing the channel gain with a threshold and if the channel gain is more than the threshold then SU selects this channel for transmission. The threshold is seen to be dependent on probabilities of channel occupancy and detection errors. The variation of threshold with respect to detection errors is analyzed and supported with plots. The plots for the variation are presented which match closely with the analytical results.

This paper addresses distributed sub-carrier bandwidth sizing and power allocation for the Secondary Users (SUs) in a Cognitive Radio (CR) network. The system entails an Orthogonal Frequency Division Multiplexing (OFDM)-based multi-user environment which deploys overlay spectrum sharing by the Primary Users (PUs) and SUs, and Frequency Division Multiple Access (FDMA) for the SUs. The objective of the formulated problem is maximizing each SUs throughput within a node power budget, interference threshold on the PU band, consideration of the interference caused to the neighboring SUs, as well as the coherence bandwidth of each SU. Motivated by the nonconvex structure of the problem and the need to execute the resource allocation in a distributed manner, we propose a sub-optimum algorithm to achieve the objective within the specified constraints. Simulation results highlight the contribution of the proposed distributed resource allocation algorithm and demonstrate its effectiveness.

In this paper, we present a recursive hardware efficient multilevel lifting 2-D Discrete wavelet transform (DWT) based on dual scan architecture (DSA). Proposed pipelined architecture can be used for any image size with a little modification in length of line buffers. In recent years multilevel DWT is utilized in many applications because of good energy compaction in higher level DWT coefficients. The control path is defined in a manner that the interleaved clock cycles are efficiently utilized to get multilevel DWT coefficients. The proposed scanning technique enables the column processor to process with minimum latency. The actual computation time (ACT) required is only N2/2 with additional latency of just three clock cycles for first level, where N is the width of the image. Latency of proposed architecture to compute the j level coefficients is 2=3(4j− 1). Proposed design uses two line buffers with N/2 size along with a multiplexer for interleaving operations to compute multilevel DWT. Proposed scheme for multilevel architecture can be used for both lifting (5,3) and (9,7) filters with modification in only 2-D DSA block.

The paper presents a new paradigm from the perspective of pervasive on-body computing through an innovative polymerized textile, which exhibits sensing and radiation properties. A radical, first of its kind, sensor fabricated from unsaturated polymer resin textile, establishes a dynamic link connecting human thermodynamics to electrical ambiance. A dynamic fabrication process of esterification and η-polymerization is developed, which is articulately arrested using an innovatively formulated retardant, yielding a permanent thermally unstable partially oriented yarn (tuPOY). A prudently established nontrivial interchange phenomenon is founded, presenting an inimitable calibration mechanism of the sensors and charting a novel relationship of exuberated energy to lattice kinetics of tuPOY. This meticulously researched conducting medium of tuPOY, fabricated from aromatic polyamides, also presents an avant-garde architecture for proliferation of electrical and thermal signals concomitantly between the sensors and its transmission circuit. A power generating unit (PGU) delineates the power mining from thermal energy dissipated from the body, presenting a new dimension in operational power dynamics. A textile composite antenna is premeditated exclusively from radiating tuPOY-based patch and substrate, an archetype reporting in published literature. The judiciously designed antenna, with tuPOY coupled as its patch, and substrate operate as shields against the radiations directed towards the body leading to a self-sustained sculpt. The back-end hardware of the test setup conceptualizes an automated physician machine (APM) presenting a standalone architecture. The artificial intelligence core of APM is modeled on weighted multiclass support vector machines (wmSVMs). The capturing of signal variations, devoid of any metallic components, presents a singular facet of research and amalgamates various interdisciplinary fields, while providing a robust architecture with minimum tradeoffs.

The major contribution of this paper is distributed power allocation for a multi-user multi-channel Cognitive Radio (CR) network which deploys overlay spectrum sharing by the Primary Users (PUs) and Secondary Users (SUs), and Frequency Division Multiplexing Access (FDMA) for the SUs. The transmit power of each SU is constrained by an individual node power budget, the interference threshold on the PU band, as well as the interference caused to the neighboring SUs. Motivated by the non-convex structure of the problem and the need to execute the power allocation distributedly, the problem is cast within a game theoretic frame-work. Pay-off functions for the SUs are formulated to suit the system model, and the Nash Equilibrium for the game is analyzed. Simulation results are provided to validate the mathematical concepts and to evaluate the performance of the proposed distributed power control algorithm in comparison with other relevant schemes.

Advances in 3G and 4G technology have offered many possibilities for developing novel applications using sensors embedded in hand held devices like cell phones. Mobility of cell phone based wireless sensor network has a critical issue of gathering sensed information in an energy efficient and delay sensitive manner. In this paper we provide a human mobility based stable clustering algorithm for data aggregation in single hop cell phone based sensor network incorporating mobility of cell phone users. We present a human mobility aware weighted clustering algorithm for data aggregation under Truncated Levy Walk (TLW) mobility model. Our approach is to select stable Cluster Heads (CH) to save the energy expenditure of network back bone formation. We have compared our algorithm with WCA [9] of mobile adhoc network and with MRECA [6] algorithm of mobileadhoc sensor network which we consider to be closely related with our work. WCA algorithm's mobility parameter is not effectively capturing mobility of human walk. Our Human mobility aware Weighted Cluster based Data Aggregation algorithm (Hm-WCDA) effectively captures human walk characteristics and thereby stabilizes the back bone network. We have evaluated performance of our algorithm primarily with stability related parameters such as number of dominant set (DS) updates, number of reaffiliations and number of cluster heads, which directly effects the energy consumption of the algorithm. The simulation results show that our algorithm is more energy-efficient and reduces the energy consumption by 12.5 percent as compared to MRECA and by 7 percent as compared to WCA for cluster radius of 400m.

The prime focus of this work is in describing a symbiotic architecture for a Cognitive Radio network, called Cognitive Relaying with Frequency Incentive. The incumbent Primary User (PU) of the spectrum, with a weak transmission link, seeks cooperation from the cognitive Secondary User (SU) nodes in its vicinity, and in return rewards them with incentive frequency bands for their own communication. We propose two schemes (Band-Set Minimization and Edge-by-Band Minimization) to achieve this objective. Cross-layer optimization problems are formulated to create the frequency incentive while ensuring a guaranteed throughput for the PU, as well as for the SU's to efficiently access this incentive. The proposed schemes are extensively compared, and simulation results are furnished to demonstrate their differences and individual merits from the perspective of both the PU and SUs.

In this paper we present a spatial domain method to fuse multifocus images. Here the fused image pixel is selected from one of the source images based on a novel selection criterion that utilizes the statistical properties of the neighborhood. The eigen value of the unbiased estimate of the covariance matrix of an image block depends on the strength of edges in the block and thus provides a good base for selecting/ rejecting a pixel, giving preference to the pixel with the higher eigen value and thus the sharper neighborhood. To prevent a noise pixel from getting selected as a fused image pixel, a continuity constraint is imposed on the selection criteria. The performance of the method have been extensively tested on several pairs of multifocus images and compared quantitatively with existing methods. Experimental results show that the proposed method improves fusion quality by reducing loss of information by almost 50% and noise by more than 95%. It also show that evaluation based on widely used criteria like entropy, gradient, deviation, may not be enough; as in some cases, these criteria are not consistent with the ground truth. It demonstrates that Petrovic metrics are in correlation with the ground truth as well as visual quality.

The prime focus of this work is in developing a protocol for a symbiotic architecture called Cognitive Relaying with Time Incentive for Multiple Primary Users (CRTI-M). The rationale of CRTI-M is that the Primary Users (PUs) of a Cognitive Radio (CR) network, with weak transmission links, seek cooperation from the cognitive Secondary User (SU) nodes in their vicinity, and in return reward them with an incentive time for their own communication. Each PU has its own distinct bandwidth of operation; however, when relaying through the SU network it can use the bandwidth of the other PUs as well to enhance its throughput. A cross-layer optimization problem is formulated to that effect in a multi-hop multi-channel SU network. The time incentive that should be awarded to the SUs by each PU is analyzed by means of a utility-based decision-making process, and its efficient utilization is proposed. To make the CRTI-M scheme practically realizable, a MAC scheduling protocol is devised within a unified framework of the PUs and SUs. Simulation results are furnished to demonstrate the proof of concept.

This work is directed towards a symbiotic architecture called Cognitive Relaying with Time Incentive (CRTI) for multiple Primary Users (PUs) in a Cognitive Radio network. The rationale of CRTI is that the PU with a weak transmission link seeks cooperation from the cognitive Secondary User (SU) nodes in its vicinity, and in return rewards them with an incentive time for their own communication. Since each PU has its own distinct bandwidth of operation, it is crucial to decide the most favorable use of the spectrum when relaying through the SU network. We propose two schemes-Band-Restricted Relaying and All-Band Relaying to achieve this objective. Cross-layer optimization problems are formulated for the relaying schemes which create the time incentive, as well as for the SUs to efficiently access this incentive. The proposed schemes are compared, and simulation results are furnished to demonstrate their differences and individual merits from the PU and SU's perspective.

Cognitive Radio (CR) improves spectral efficiency by allowing unlicensed (secondary) users to concurrently operate with licensed (primary) users. The side-lobe interference from an OFDM-based secondary user (SU) causes unwanted interference to the primary user (PU) of the spectrum. While interference mitigation to the PU spectral band has received a lot of attention in recent literature, the novelty of our work is in considering sub-bands of the PU, and ensuring that the interference to each of them is below a specified threshold. The approach is realistic and more effective in protecting the PU from the SU interference. We formulate an optimization problem for power allocation for a single SU to maximize its throughput, within a power budget, and an interference constraint on each PU sub-band. After extensively analyzing the solution form, we propose an iterative algorithm to meet the aforementioned objective. A power allocation algorithm is also proposed for a multiple SU scenario, which involves assigning sub-carriers to each user, besides the power and PU interference constraint, to maximize the sum throughput. Simulation results are provided, for both single and multi-user cases, which indicate that the proposed algorithms outperform those which have been previously presented in literature.

Exact expressions are obtained for the bit error rate (BER) for coherent and noncoherent decode and forward (DF) cooperative systems with upto three relays between the source and destination. The piecewise linear (PL) combiner is employed at the receiver. BER analysis is done using a contour integral approach for evaluating the Gil-Pelaez integral involving the characteristic function (CF) of the decision variable. This removes restrictions on relay location, imposed by the direct approach. Simulation results are provided to support the analysis and the relay diversity gain is demonstrated through BER plots.

The statistics of gamma conditionally Gaussian (CG) random variables are derived in closed form. These variables can be loosely defined to be normally distributed with mean and variance proportional to a gamma random variable. In this paper, we provide exact expressions for the bit error rate (BER) for single relay maximum likelihood (ML) decode and forward (DF) cooperative systems in Nakagami-m fading for binary phase shift keying (BPSK). This is done by expressing the ML decision variable in terms of functions of gamma CG random variables. For the piecewise linear (PL) approximation to the ML detector, a closed form expression for the BER is obtained. Simulation results are provided to verify the validity of the derived analytical expressions.

In our work, we consider a querying application of a Multihop Cell phone Sensor Networks (MCpSN). It requires data to be sensed within a time window and further uploaded at the Querying Access Point within a time deadline. Considering a Random Waypoint distribution of cell phone users, we propose a novel, energy-efficient Spatio-Temporal Power Adaptive (STPA) protocol for this application. STPA is compared to purely spatially/temporally adaptive schemes through simulations.

This paper proposes new MAC layer based back-off and retransmission strategies for throughput enhancement, in broadcast transmissions of inter vehicular communication. We present adaptive back off strategy which is based on the concept that there exists an optimum contention window (CW) period in a broadcast CSMA/CA system, given the fixed number of users. We also propose a simple probabilistic approach for retransmission which is based on analyzing the local information. Simulation results shows the improvement in the throughput using adaptive back-off and probabilistic retransmission strategies compared to fixed back-off strategy.

A hybrid error concealment scheme for H.264/AVC that uses the concept of watermarking, Overlapping Boundary Matching Algorithm (OBMA) and Projection Onto Convex Sets (POCS) is proposed in this paper. The advantage of this scheme is that, watermarking is performed during the encoding process itself. The specific features of both intra coded and inter coded frames are exploited for this purpose. The proposed method chooses watermarking or OBMA or POCS or a combination of them selectively for error concealment which minimizes computational complexity. Simulations are performed to show that the proposed method works well with those videos which have abruptly moving blocks.

Cognitive relaying is a recent communication paradigm that increases network coverage and improves spectral efficiency by allowing unlicensed (secondary) users to concurrently operate with licensed (primary) users. The major contribution of this paper is in proposing a bit allocation algorithm to be used in a cognitive relay network. The dynamic spectrum access in an OFDM-based cognitive relay network may lead to unwanted interference to the primary user. Assuming knowledge of the instantaneous channel gains for all links in the network and the primary user band, the proposed algorithm meets the twofold challenge of achieving optimum secondary system throughput while ensuring minimum interference to the primary. In its first pass, the proposed algorithm achieves optimum number of bits per subcarrier. However, in the process of achieving integer granularity for practical constellation sizing, the interference constraint may be violated. A second pass of the algorithm tackles this condition by executing a interference-based greedy bit removal. Simulation results are provided, which are indicative of the fact that the proposed algorithm exhibits enhanced performance for both primary and secondary users in the cognitive radio network.

Energy optimized Path Unaware Layered Routing Protocol (E-PULRP) for dense 3D Underwater Sensor Network (UWSN) is proposed and analysed in this paper. In the proposed E-PULRP, sensor nodes report events to a stationary sink node using on the fly routing. E-PULRP consists of a layering phase and communication phase. In the layering phase, a layering structure is presented wherein nodes occupy different layers in the form of concentric shells, around a sink node. The layer widths and transmission energy of nodes in each layer are chosen taking into consideration the probability of successful packet transmission and minimization of overall energy expenditure in packet transmission. During the communication phase, we propose a method to select intermediate relay nodes on the fly, for delivering packets from the source node to sink node. We develop a mathematical framework to analyse the energy optimization achieved by E-PULRP. We further obtain expressions for throughput, delay and derive performance bounds for node densities and packet forwarding probabilities, for given traffic conditions. A comparison is made between the results obtained based on simulations and analytical expressions. The energy efficiency is also demonstrated in comparison with existing routing protocol for underwater sensor networks.

Maximum-likelihood (ML) detectors for single-input-single-output (SISO) relay systems are well known. In this paper, closed form expressions for the bit error rate (BER) for multiple-input-multiple-output (MIMO) relay systems employing ML based decode and forward (DF) cooperative diversity are obtained. The DF operation at the relay intelligently employs the multiple antennas available at the relay for receive diversity on the source-relay link and space-time coding (STC) on the relay-destination link resulting in an extension of the ML detection rule for SISO systems to MIMO systems as well. For the piecewise linear (PL) approximation to the ML detector, exact expressions for the BER are obtained for single-relay systems with both the source and relay supporting multiple antennas. For a multirelay system, each relay having multiple antennas, approximate expressions for the BER are obtained. This is done by finding the statistics of conditionally Gaussian (CG) random variables, that appear in the decision variable. Through numerical results obtained from the BER expressions, it is shown that using multiple antennas at the source as well as the relay leads to significant improvement in BER performance.

In this paper, we consider moving event localization using data collected from a Multihop Cellular Sensor Network (MCSN). The main contribution of the paper is a novel structure- free Distributed Velocity Dependent (DVD) waiting time based protocol. We compare the performance of the proposed DVD protocol with that of a Centralized approach and the existing Randomized Waiting time (RW) protocol. Simulation results show that DVD exhibits a performance superior to RW with respect to center of event localization error and end-to-end delay. We also present simulation results on the the sensitivity analysis based comparison between DVD, RW and the Centralized scheme.

The bit error rate (BER) performance analysis of maximum-likelihood (ML) based decode and forward (DF) cooperative diversity systems has been a subject of considerable interest. Exact analysis of ML-DF transmission has been considered a challenging problem due to the nonlinear characteristic of the ML detector. In this paper, we provide exact expressions for the BER of ML-DF cooperative systems employing a single relay. We extend these results to the case of multiple relays for the piecewise linear (PL) combiner, that is known to be a close approximation of the ML detector. This is done by using a novel theory of conditionally Gaussian random variables. By expressing the ML decision variable in terms of functions of conditionally Gaussian variables, exact expressions for the BER of the ML-DF system are obtained. Through simulation results, we verify the validity of the derived analytical expressions.

Tight closed form lower bounds for the average bit error rate (BER) are derived for a dual hop cooperative network employing nonregenerative relays for the multiple input multiple output (MIMO) relay channel experiencing Rayleigh fading. The bounds are obtained for three different nonregenerative relaying schemes. The lower bounds for the BER are obtained using the moment generating function (MGF) approach by evaluating the MGF of the end-to-end equivalent signal to noise ratio (SNR) of the system. From the BER expressions obtained, we also show that the diversity order for the MIMO relay cooperative system with each relay having M antennas increases approximately by a factor M from that of a system with single-antenna relays. Simulation results confirm that the analytical expressions for the lower bounds are very tight and can thus be used to get approximate values of the BER.

Closed form expressions for the bit error rate (BER) for a multiple input multiple output (MIMO) relay system employing maximum likelihood (ML) based decode and forward (DF) cooperative diversity are obtained. The DF operation at the relay involves maximal ratio combining (MRC) on the source-relay link and space-time coding (STC) on the relay-destination link. Exact expressions of the BER are obtained for the case of a single relay supporting two antennas. For a system employing a large number of relays, each having two antennas, approximate expressions for the BER are obtained using the piecewise linear (PL) approximation for the ML detector. This is done by finding the statistics of conditionally Gaussian random variables, that appear in the decision variable. The validity of the analytical expressions is then verified through simulations. Through numerical results obtained from the BER expressions for large number of relays, it is then shown that the loss in diversity order due to DF can be compensated by using multiple antennas at the relay.

In this paper we derive and propose constrained blind DS-CDMA UWB multiuser detection algorithm to suppress the multi access interference (MAI) in multipath channels, and studied its bit error probability performance in the presence of narrowband interference. Variance of the receiver output is minimized subject to appropriate constraints. Receiver does not require spreading code knowledge of all users other than that of the user of interest. Simulation results show that bit error probability performance of the proposed algorithm is better than that of adaptive minimum mean square error (MMSE) detector in multipath channels, and much better in the presence of multipath channels with narrow-band interference.

Finding an optimal routing path in adverse underwater environment in an Underwater Sensor Network (UWSN) has always been a challenging task. Maintaining the network life for a longer duration by utilizing nodes' energy is also critical. By taking these issues into consideration we propose an Energy optimized Path Unaware Layered Routing Protocol (E-PULRP) for dense underwater 3D sensor networks. An up-link transmission is considered, where a set of underwater sensor nodes report events to the stationary sink node. E-PURLP consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric spheres, around a sink node. The radii of the concentric spheres as well as the transmission energy of the nodes in each layer are chosen considering probability of successful packet transmissions and minimum overall energy expenditure. In the second phase (communication phase), we propose a method to choose intermediate relay nodes andontheflyrouting algorithm for packet delivery from source node to sink node across the identified relay nodes. The energy analysis of the E-PULRP has been carried out and shown that it is energy efficient. The performance in terms of throughput and delay is also analyzed.

In this paper analytical relationship between transmission range and the network connectivity is obtained as a function of number of nodes for CDMA based multihop cellular network (MCN). We show that for a network of n uniformly distributed nodes in a single cell of unit radius, the transmission range r should be sufficiently larger than radic(2 ln n/n-1) to achieve asymptotic full connectivity. The distribution of the nodes may not be uniform in case of mobility. In such case a mobility model dependent lower bound on transmission range is obtained. We show that for RWP model the lower bound for the nodes which lie completely inside the cell is same as the corresponding uniform node distribution case. However, for nodes on boundary the transmission range lower bound is3radic(ln/n) which is larger than the corresponding uniform node distribution case. Our findings show that more transmission range is required at the boundary to establish better connectivity and hence a variable transmission range control mechanism is necessary. In addition to lower bound, we also propose a method to choose the optimal value of transmission range using a scheduling mechanism and ensure that the optimal value is always greater than the lower bound. Thus obtained transmission range could be used to select the transmission power of the nodes in a meaningful way and hence the nodes' isolation could be avoided and the spectral efficiency can be increased. We demonstrate empirically, that the proposed transmission range control mechanism increases the network connectivity as well as spatial reuse of the resources.

We propose a path unaware layered routing protocol (PULRP) for dense underwater 3D sensor networks. An uplink transmission is considered, where a set of underwater sensor nodes report events to the sink node. PURLP algorithm consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric spheres, around a sink node. The radius of the concentric spheres is chosen based on probability of successful packet forwarding as well as packet delivery latency. In the second phase (communication phase), we propose a method to choose the intermediate relay nodes and an on the fly routing algorithm for packet delivery from source node to sink node across the chosen relay nodes. The proposed algorithm, PULRP finds the routing path on the fly and hence it does not require any fixed routing table, localization or time synchronization processes. Our findings show that the proposed algorithm has a considerably better successful packet delivery rate compared to the under water diffusion (UWD) algorithm proposed in the paper by Lee et al. (2007) and Dijkstra's shortest path algorithm. In addition the delay involved in PULRP is comparable with that of UWD.

Antenna sharing by multiple users using cooperative diversity has been shown to mimic the performance of traditional multiple input, multiple output (MIMO) systems. While this was originally done using a single antenna at each relay, the benefits of cooperative diversity based on multiantenna relays has been a subject of considerable interest. Maximum likelihood (ML) detectors for cooperative diversity through single antenna relays are well known. In this paper, we propose to combine the benefits of cooperative diversity, and transmit and receive diversity offered by MIMO systems. This is done within the ML based decode and forward (DF) cooperative diversity framework, by using multiple antennas at each relay, through ML based processing on the source-relay link and space-time coding on the relay-destination link. Through simulations, we then show that the performance of MIMO relay based systems is superior to those having a single antenna at each relay.

Watermark retrieval in wavelet domain has been proved to be more robust from significant coefficients than high absolute coefficients when a single copy of the watermark is embedded in the original data. The highest absolute coefficients refer to the coefficients with the highest absolute values in any selected band and significant coefficients refer to the coefficients with the highest significance factor with respect to their inter-band dependencies in a wavelet transformed image. The watermark energy can be maximised at each of the selected coefficient by quantising it to the maximum allowable level suggested by a human visual system model. However, as the attacks become very severe, a single copy of the watermark is not sufficient for correct retrieval. Hence, the authors propose a method of optimum retrieval of the watermark from multiple embedded copies under very severe attacks. The authors propose to use the Chair–Varshney decision fusion rule to decide each bit in the watermark instead of the majority rule for optimum watermark retrieval. Simulations are performed to show the superiority of the method with different numbers of watermark copies under various attacks. Extensive simulations are carried out to plot the receiver operating characteristics in order to compare the proposed method with the majority rule.

Abstract We propose path unaware layered routing protocol (PULRP) for 2D underwater sensor networks (UWSNs) with mobile nodes. The steady‐state distribution of mobile nodes in UWSNs is nonuniform in general. Hence, we use a mobility model‐dependent node distribution. The proposed PULRP algorithm consists of two phases. In the first phase (layering phase), a layering structure is presented which is a set of concentric circles, around a sink node. The radii of the concentric circles are chosen based on equal distribution of nodes in every layer. A distributed power control mechanism is also introduced. The power level of nodes in a particular layer is chosen such that communication occurs only with nodes in the next layer. In PULRP, we consider multihop communication from source to sink. Therefore, in the second phase (communication phase), we propose a method to choose the intermediate relay nodes and an on the fly routing algorithm for packet delivery from source node to sink node across the chosen relay nodes. The proposed algorithm, PULRP finds the routing path on the fly and hence, it does not require any fixed routing table, localization, or time synchronization processes. We demonstrate the performance of PULRP using random waypoint (RWP) mobility model in a simulated underwater environment. Our findings show that the proposed algorithm has considerably better throughput (successful packet delivery rate) compared to the underwater diffusion (UWD) algorithm for various node densities as well as node velocities. In addition, the delay performance of PULRP is also better than that of UWD. Copyright © 2008 John Wiley & Sons, Ltd.

In order to reduce the complexity of the mobile receiver, we develop a linear preceding filter based only on the statistical knowledge of the channel. Moreover, the proposed prefilter (precoder) is based on minimizing the probability of error in downlink multiuser transmission. We investigate two approaches for the proposed algorithm. In the first approach we consider a common FIR preceding filter for all users, and jointly minimize the probability of error of all users. In the second approach, we assume separate precoders for each user which are obtained by minimizing the probability of error for the respective user. In order to fully utilize the knowledge available at the transmitter, in both the approaches, the filter weights are computed conditioned on the transmitted bit vector sequence. This also makes the computation of the optimal prefilter coefficients linear in the number of users. We compare the results of the proposed approach, with results based on assuming complete knowledge of the channel. Simulation results clearly show that precoders based only on the statistical knowledge of channel, do provide acceptable BERs. Moreover, individual precoders provide better BER as compared to joint precoders.

We propose a CDMA-OFDM access mechanism for Multihop Cellular Networks (MCN). We construct groups within the MCN, where each group comprises of a source node, a destination node, their intermediate relay nodes and assign a correlated PN sequence to each such group. Within a particular group, a single carrier is assigned to each intermediate hop. The sub carriers assigned to the intermediate hops in a given group are mutually orthogonal. Hence the proposed OFDM is FDMA in nature. The sub carriers and transmit power levels to the relay nodes are assigned in such a way as to maximize the end-to-end throughput. The end-to-end throughput is formulated by assuming a Rayleigh flat fading channel between nodes. We present a method to reuse the PN codes assigned to the groups in a particular cell. We also introduce a novel architecture for MCN. Simulation results show that the proposed access mechanism achieves better end-to-end throughput and bit error rate (BER) performance as compared to standard access mechanisms like CDMA and OFDM-FDMA. Furthermore the proposed access mechanism has considerably higher BER performance in the presence of multiple transmit sources.

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment without using a priori information about the target dynamic. We propose a neural-network-based tracking algorithm, incorporating a interacting multiple model and show that it is possible to track both maneuvering and nonmaneuvering targets simultaneously in the presence of dense clutter. Moreover, it can be used for real-time application. The proposed method overcomes the problem of data association by using the method of expectation maximization and Hopfield network to evaluate assignment weights. All validated observations are used to update the target state. In the proposed approach, a probability density function (pdf) of an observed data, given target state and observation association, is treated as a mixture pdf. This allows to combine the likelihood of an observation due to each model, and the association process is defined to incorporate an interacting multiple model, and consequently, it is possible to track any arbitrary trajectory

We propose an effective technique using a wavelet-based temporal decomposition algorithm to detect single-pixel targets with motion from frame to frame. We next integrate the proposed detection algorithm with an interacting multiple-model method and multiple filter bank approach to provide an effective solution for tracking multiple single-pixel nonmaneuvering and maneuvering targets. Through Monte Carlo simulations, we establish the efficiency and robustness of the proposed approach. Based on exhaustive empirical study, we demonstrate the effectiveness of our proposed approach in tracking multiple single-pixel targets in a sequence of infrared images with clutter and occlusion due to moving clouds in airborne applications.

Demodulators based on the minimum mean squared error (MMSE) criterion are optimal for 'ideal' AWGN channels. However, in the case of wireless multipath channels, wherein ISI is inherent, MMSE based algorithms do not offer the optimal framework for demodulation. Minimum probability of symbol error (MPOSE) based detectors have been shown to perform significantly better than MMSE based approaches in these scenarios under a variety of modulation and multiplexing schemes. High computational complexity, however, prohibits the use of these algorithms in practical communication systems. The major challenge now is to reduce the computational burden of the MPOSE algorithms. This paper, as a step in this direction, discusses two already published MPOSE algorithms and proposes a modification to them whereby we not only achieve significant gains in computational complexity but also improve the BER performance. The system is also found to be robust to error propagation due to wrong estimation of the ISI term.

Detection and tracking of approaching targets in an infrared (IR) image sequence is important for surveillance applications. In this paper an algorithm is proposed which provides a complete solution (track while scan) for detection and tracking for IRST system. The proposal method uses only motion as a cue to detect the target. Detection is followed by tracking. In a real scenario, the movement of a target is arbitrary and no a priori information is available. We propose a tracking method which tracks maneuvering and nonmaneuvering targets simultaneously using a filter bank. The switch-over amongst the filters is based on a single-step decision logic.

We propose an algorithm for detection and tracking of small and fast moving objects, like a ping pong ball or a cricket ball, in sports video sequences. For detection, the proposed method uses only motion as a cue; moreover it does not use any texture information. Our method is able to detect the object with very low contrast and negligible texture content. Along with detection, we also propose a tracking algorithm using the multiple filter bank approach. Thus we provide a complete solution. The tracking algorithm is able to track maneuvering as well as non-maneuvering movements of the object without using any a priori information about the target dynamics

Data association and model selection are important factors for tracking multiple targets in a dense clutter environment. We propose a sequential probabilistic multiple hypotheses tracking (PMHT) based algorithm using interacting multiple modelling (IMM), namely the IMM-PMHT algorithm. Inclusion of IMM enables any arbitrary trajectory to be tracked without any a priori information about the target dynamics. IMM allows us to incorporate different dynamic models for the targets and PMHT helps to avoid the uncertainty about the measurement origin. It operates in an iterative mode using an expectation-maximization (EM) algorithm. The proposed algorithm uses only measurement association as missing data, which simplifies E-step and M-step. It is computationally more efficient, and an important characteristic of our proposed algorithm is that it operates in a single batch model, i.e. sequential, and hence can be used for real time tracking.

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no a priori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion (/spl plusmn/20 pixels) using multiple filter bank in an IR image sequence in the presence of clutter and occlusion due to clouds. The use of multiple filters is not new, but the novel idea here is that it uses single-step decision logic to switch over between filters. Our approach does not use any a priori knowledge about maneuver parameters, nor does it exploit a parameterized nonlinear model for the target trajectories. This is in contrast to: (i) interacting multiple model (IMM) filtering which required the maneuver parameters, and (ii) extended Kalman filter (EKF) or unscented Kalman filter (UKF), both of which require a parameterized model for the trajectories. We compared our approach for target tracking with IMM filtering using EKF and UKF for nonlinear trajectory models. UKF uses the nonlinearity of the target model, where as a first order linearization is used in case of the EKF. RMS for the predicted position error (RMS-PPE) obtained using our proposed methodology is significantly less in case of highly maneuvering target.

Performance of any tracking algorithm depends upon the model selected to capture the target dynamics. In real world applications, no a priori knowledge about the target motion is available. Moreover, it could be a maneuvering target. The proposed method is able to track maneuvering or nonmaneuvering multiple point targets with large motion (/spl mnplus/20 pixels) using multiple filter bank in an IR image sequence in the presence of clutter and occlusion due to clouds. The use of multiple filters is not new, but the novel idea here is that it uses single-step decision logic to switch over between filters. Our approach does not use any a priori knowledge about maneuver parameters, nor does it exploit a parameterized nonlinear model for the target trajectories. This is in contrast to: (i) interacting multiple model (IMM) filtering which required the maneuver parameters, and (ii) extended Kalman filter (EKF) or unscented Kaiman filter (UKF), both of which require a parameterized model for the trajectories. We compared our approach for target tracking with IMM filtering using EKF and UKF for non-linear trajectory models. UKF uses the nonlinearity of the target model, where as a first order linearization is used in case of EKF. RMS for the predicted position error (RMS-PPE) obtained using our proposed methodology is significantly less in case of highly maneuvering target.