Search

The Device-to-Device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. IoT plays a significant role in the FinTech industry, especially in data security and context-aware applications. D2D communication in cellular networks has emerged as a competent technology for the upcoming future cellular networks. To improve spectral efficiency and data secrecy, we propose a two-phase network coding based two-way Decode-and-Forward (DF) relaying policy for D2D communication. Firstly, a suitable relay node is selected considering the end-to-end signal-to-interference plus noise ratio (SINR). Then, a XOR-based DF operation is performed when the signal transmitted by both the D2D users (DUEs) is successfully decoded at the relay node. In order to analyze the performance of the proposed two-way communication system, we derive outage probability expression as a function of various system parameters like SINR, position of both the relays and DUEs, power splitting factor, number of relays, and data rate. Further, we also derive an expression for error probability and average throughput of D2D link to evaluate a fair comparison of the proposed approach with the traditional methods. The analytical results are also validated with the simulation results to justify the efficacy of the proposed method.

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.

The Device-to-device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. Resource management is an important aspect for realization of D2D communication in upcoming IoT networks. In this article, we select underlay in-band D2D communication as it is more beneficial in terms of spectral efficiency even though it comes at the cost of interference with cellular communication. To deal with this difficulty, we propose a novel downlink resource sharing strategy, where multiple D2D users (D2Ds) share the resource of a single cellular user (CU), and each D2D pair reuses multiple channels from different cellular users (CUs). The proposed scheme adopts a channel selection technique, wherein multiple channels of CU can be shared by each D2D user. Furthermore, optimal power for each D2D user is determined through the Lagrangian dual optimization method. The formulated power control maximization scheme nicely balances the total transmission power of D2D and D2D sum-rate. The proposed channel and power allocation problem aims at maximizing the D2D sum-rate by increasing the number of active D2D links while preserving the quality of service (QoS) of CU. Finally, a relationship between energy efficiency (EE) and transmit power of D2D is investigated through an EE maximization problem. The overall system performance is evaluated in terms of the D2D shared ratio, throughput gain of the network, and computational complexity of the proposed optimal strategy. Further, the merits of using the proposed resource sharing scheme over the existing schemes are also verified through numerical results.

In modern days, image segmentation is one of the most important processing step in the field of computer vision and image processing. It helps to identify object, reconstruct shape, classify and estimate volume of an object. In the last few decades, many algorithms have been developed to eradicate the various segmentation problems such as weak edge detection, inhomogeneous image segmentation, accurate object shape identification and classification. Among them, one of the popular active contour models namely level set model is extensively used to eliminate the problem of topological changes during curve evolution. Earlier, the active contour models were unable to deal with sudden topological changes which led to poor segmentation results. Thus, the paper investigates several level set models in various applications of modern imaging. Therefore, it is necessary to understand the formulation of various level set models with their characteristics before applying them to solve the segmentation problem. In this paper, authors have extensively studied the formulation of various level set models and their application in different types of images. Further, the authors have discussed their contributions to level set framework and open research challenges for researchers.

The exponentially growing need for higher data rates and the ubiquitous usage of smartphones has led to unprecedented issues in providing seamless communication while satisfying bandwidth constraints. Enabling Device-to-Device (D2D) communication in the millimeter-wave (mm-Wave) network enhances throughput and bandwidth but at the cost of higher pathloss attenuation. Also, D2D communication introduces interference in the traditional cellular network (CN), leading to degradation in the system performance. Thus, the proposed scheme applies dynamic mode selection criteria for efficient D2D communication in a 5G mm-Wave CN. The radius of coverage of D2D users (D2Ds) is derived for switching of the modes, i.e. from inner to outer. The outer mode switching occurs when the D2Ds suffer from extensive pathloss attenuation or with increase in coverage radius. Furthermore, power optimization is done to obtain optimal D2D transmit power thereby, enhancing the data rate, which is solved using the Lagrangian dual optimization technique. The performance metric, coverage probability is also derived which depicts the capability of the proposed scheme at a higher pathloss attenuation maintaining the Quality-of-Service (QoS) constraint. Simulation results prove the efficacy of the proposed scheme. Finally, the proposed scheme is compared with the state-of-the-art schemes which further proves the superiority of the proposed scheme in terms of better efficiency.

Relay aided device-to-device (D2D) communication has the potential to increase the capacity and coverage of the network and enhance the quality of service (QoS). Here, in this paper we propose a single cell where D2D and cellular users co-exist in an underlaying 5G mm-Wave cellular network. Two modes are proposed for D2D communication which minimizes the interference namely, direct and relay mode. Direct mode operates at 2 GHz carrier frequency while relay mode implements full duplex amplify and forward (FDAF) relay strategy at 28 GHz frequency. Conditions are applied on the derived expression for radius of coverage, R t for switching of modes. The closed form expressions of performance metrices namely, spectral efficiency (SE) and energy efficiency (EE) are derived using stochastic geometry as a tool for both the modes to evaluate the system performance. Expressions for probability distribution function (PDF) and cumulative distribution function (CDF) are also derived and verified using simulation. Further, simulation results validate the efficacy of the proposed scheme. Also, comparison of the proposed method with the existing methods depicts the better performance of the system.

An active contour model to segment the images is proposed by combining local binary fitting (LBF) energy function and modified Laplacian of Gaussian (MLoG) energy function. A MLoG energy function based on a new boundary indicator function or edge stop function (ESF) is introduced to smoothen the homogeneous regions and enhance the edge information of objects. Also, MLoG energy term with LBF energy term is incorporated to drive the initial contour towards the object boundary. Finally, the penalty term is replaced with a new optimized potential function, which can improve the corresponding speed function. By adding the optimized area energy term, contour position is accelerated towards the object boundary. Further, the addition of MLoG based on new ESF, makes the proposed model insensitive to the initial contour. Experiments are performed on various real images, MS-COCO 2014 train data set images and Segmentation Evaluation Database images shared in Weizmann Institute of Science website. The proposed model provides better segmentation results compared to the other state of the art models in terms of segmentation accuracy, F-score and CPU execution time. Further, experimental results also prove the robustness of the proposed model in terms of contour initialization, intensity inhomogeneity and noise.

In this paper, a novel actor-critic reinforcement learning (RL) based on policy gradient is proposed to solve the channel and power allocation issues for a device-to-device (D2D) enabled cellular network when prior traffic information was not provided to the base station (BS). Furthermore, in this paper, we design a system to learn the optimal policy for resource and power allocation between cellular users (CUs) and D2D users, aimed at maximizing the sum rate of the overall system. Since the behavior of wireless channels, and the received reward in each state associated with the system, is stochastic in nature, the dynamic property of the environment allows us to apply an actor-critic RL technique to learn the best policy through continuous interaction with the surroundings. The policy-based approach is better than a value-based scheme, such as Q-learning, because it takes the help of policy space in order to maximize the expected throughput. The actor adopts a parameter-based stochastic policy for continuous actions, while the critic evaluates the policy through its overall performance, and criticizes the actor for the policy it follows. Through numerical simulations, we verify the performance of our proposed work with the existing methods.

Enabling D2D communication in the mm-Wave band has many obstacles which must be mitigated. The primary concern is the introduction of interference from various sources. Thus, we focused our work on the performance of decode and forward (DF) relay assisted D2D communication in the mm-Wave band for increasing the coverage probability and energy efficiency (EE). Three modes are proposed for D2D communication to prevail. Bit-wise binary XOR operation is executed at the relay node which increases the security feature. Radius of coverage expression is derived which indicates the switching of the modes. The diffused incoherent scattering power is also considered as a part of power consumption. Furthermore, a unique relay selection scheme, Dynamic relay selection (DRS) method is proposed to select the optimal relay for information exchange. Comparison of the proposed DF relay scheme with amplify and forward (DF) scheme is also shown. Finally, simulation results prove the efficacy of the proposed work.

In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. In IoMT enabled Computer aided diagnosis (CAD) system, the Health-related information is stored via the internet, and supportive data is provided to the patients. The development of various smart devices is interconnected via the internet, which helps the patient to communicate with a medical expert using IoMT based remote healthcare system for various life threatening diseases, e.g., brain tumors. The brain tumor is one of the most dreadful diseases ever known to human beings. Often, the tumors are predecessors to cancers. The survival rates for these diseases are very low. So, early detection and classification of tumors can save a lot of lives. IoMT enabled CAD system plays a vital role in solving these problems. Deep learning, a new domain in Machine Learning, has attracted a lot of attention in the last few years. The concept of Convolutional Neural Networks (CNNs) has been widely used in this field. In this paper, we have classified brain tumors into three classes, namely glioma, meningioma and pituitary, using transfer learning model. The features of the brain MRI images are extracted using a pre-trained CNN, i.e. GoogLeNet. The features are then classified using classifiers such as softmax, Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The proposed model is trained and tested on CE-MRI Figshare dataset. Further, Harvard medical repository dataset images are also considered for the experimental purpose to classify four types of tumors, and the results are compared with the other state-of-the-art models. Performance measures such as accuracy, precision, recall, specificity, and F1 score are examined to evaluate the performances of the proposed model.

An image segmentation model using histogram-based image fitting (HF) energy is proposed to identify objects with poorly defined boundaries. The proposed energy model considers an improved fitting energy function based on normalized histogram and average intensities of objects inside as well as outside the contour curve. The fitting energy functions are computed before the curve evolution thereby reducing the complexity of intensity inhomogeneity images. Further, a new signed pressure force function is incorporated in the proposed energy model which can increase the efficiency of the curve evolution process at blur edges or at weak edge regions. The comparative analysis of the proposed energy model produces better segmentation results compared to the other state-of-the-art energy models namely the Li et. al. model, local binary fitting (LBF), and Chen-Vese (C-V) models. The proposed model is also robust to intensity inhomogeneity. In addition, the calculation of the Jaccard Index (JI) proves the robustness of the proposed energy model.

Device-to-device (D2D) communication enabled cellular system is capable of enhancing the spectrum utilization and throughput performance of the system. However, D2D users (D2Ds) in close proximity share its radio resources with the cellular users (CUs), thereby adding an additional amount of interferences from D2D transmitters and affecting the performance of cellular networks. To reduce these problems, we propose a novel channel selection and power allocation scheme incorporating quality of service (QOS) parameter for CU and D2D pairs. In order to improve the spectral efficiency, an efficient resource sharing strategy wherein multiple channels of CUs can be reused by each D2D pair is introduced. To cope up with the overloaded traffic at the base station (BS) due to the coexistence of different types of users and to reduce the computational complexity of the network, we divide our problem of resource sharing into two sub-problems. The channel assignment which is a linear programming problem determines the number of channels that can be shared by each D2D pair. Secondly, the power allocation for D2D pairs is a non-convex non-linear programming problem and is difficult to solve. Thus, the problem is transformed into a convex form using the exponential form of transmit power in the Laplace domain. By using Lagrangian method, the optimal power of D2Ds is determined such that it maximizes D2D sum-rate while preserving QOS of CU, thereby not affecting the normal cellular communication. The paper also elaborates on the throughput performances of D2D pairs which is affected by the position of D2Ds and CUs and distance of separation between D2Ds and CUs. Thus, we choose the radius of coverage as a metric for D2D communication. The effectiveness of proposed resource sharing and power allocation scheme is also illustrated through numerical examples.

The effect of pathloss attenuation on D2D communication in mmWave underlaying 5G networks is unprecedented. Pathloss attenuation is catered through a variety of sources into the wireless communication system. It is imperative to model a scheme through which the attenuation can be minimized. This paper focuses on a scheme based on the trade-off between pathloss attenuation and distance between the D2D users for enhancing QoS of the cellular as well as D2D users. This is achieved by mode assignment through reuse and dedicated or cellular mode. The decision is made based on the distance between the D2D users and also the pathloss attenuation attached to it as pathloss plays an equally pivotal role for a mmWave network. Finally, a comparison is also made with other existing schemes to prove its efficacy.

Electroencephalography (EEG) is a popular noninvasive method used to record and analyse the electrical activity of the brain. Despite the poor spatial resolution, this tool provides a very high temporal resolution. With the use of a large number of channels, it is necessary to select the relevant features in EEG analysis. Hence, this research paper aims to identify the features that are capable of differentiating an attention-demanding task- induced brain activity from the resting state condition. Eleven different features including mean, root mean square, band power, skewness, mode, data range, interquartile range (IQR) and three Hjorth parameters are extracted from alpha, beta and gamma frequency bands of EEG. Each feature is tested using the statistical tool called paired t-test. Results demonstrate the importance of feature selection step for the recognition process. Hjorth parameters have shown significant statistical difference (p

A novel interference management paradigm has been proposed for overlay mode of Device-to-Device (D2D) communication in mm-Wave 5G networks. The theory proposes alternate offer bargaining game (AOBG) for the problem eradication which is a non-symmetric Nash bargaining solution. In this game, players considered are D2D users in the cell. The payoffs received by the players are transmit power. Neighboring D2D users compete with each other for higher signal to interference plus noise ratio (SINR). The proposed algorithm proves that equilibrium can be achieved wherein the system throughput can be increased without compromising the SINR. The numerical results show the effectiveness of the algorithm. Furthermore, simulation results of the proposed scheme have been compared with the existing scheme.

Interference poses to be a grave threat to wireless communication systems. Quest for higher data rate and higher energy efficiency has led to a paradigm shift. Since the inception of cellular technology in 1980, researchers are in search of a communication system which can exhibit high data rate, low latency, higher capacity, energy efficiency and spectrum efficiency. This led the researchers to explore 5G, operating in mmWave band (30–300 GHz) which is expected to support uncompressed video streaming, 3D telepresence, holographic communication and other multimedia applications. But all these technological advances suffer from a foe, i.e., cross-channel interference and co-channel interference. Thus, this paper chronologically discusses the various models applied to minimize or eradicate the problem of interference in 5G communication systems. Future research directions have also been discussed.

Edge detection is a fundamental step in image segmentation and one of the most important steps to recognize the features of objects. In this paper, we proposed an edge detection method using modified Moore-Neighbor algorithm. The Modified Moore-neighbor algorithm is used as boundary tracing for detection and feature extraction. Further, range filter is incorporated with modified Moore-Neighbor to detect the edges of objects. The proposed edge detection can extract the true edges as well as features of an object and is robust to noise. We have considered both objective evaluation and subjective evaluation to verify the results. The image analysis parameters are considered for subjective evaluation. Further, a total number of 24 BSD (Berkeley Segmentation Database) images are considered for subjective evaluation using the proposed method and compared with the other existing methods. The results obtained using the proposed modified Moore-Neighbor algorithm for different test images and BSD images are found to be promising. Therefore, proposed edge detection method warrants further investigation.

In this paper, we investigate the bit error rate (BER) performance of Device-to-device (D2D) communication assisted by selective detect and forward (DTF) relay node for a cooperative multiple input multiple output (MIMO) system with multiple antennas. We assume that the relay and destination D2D nodes consist of multiple antennas. We first formulate the closed form BER expressions for a D2D link assisted by a relay node with multiple antennas. Further, the asymptotic tight approximation for BER is analyzed under high signal-to-interference plus noise (SINR) regime. Numerical results are presented to validate the analytical results and also depict the tightness of the approximation. Further, the performance improvement of our proposed relaying scheme is compared with the other existing relaying schemes.

This paper addresses the uplink resource allocation problem for energy harvested device to device (D2D) communication. The problem is formulated to maximize the D2D throughput while considering the quality of service (QOS) for the cellular user (CU). The maximization problem is non-concave mixed integer nonlinear programming (MINLP) in nature. Thus, to reuse the resource block (RB) of CU, a heuristic approach called space matching algorithm (SMA) is deployed based on the minimum distance criteria. We estimate the total harvested energy required for D2D communication and also derive the relationship between energy harvesting efficiency factor and average percentage of active D2D pairs along with variation of total energy harvested with respect to active no of D2D pairs. We also verify our proposed scheme with the existing scheme such as JOPRA and EPA.

A novel level set model is proposed by regularizing local fitting energy to segment the intensity inhomogeneous images. In proposed model, local image fitting energy information is incorporated with penalty energy function. To segment intensity inhomogeneous images, a circular window filter is used to identify homogeneous regions. In addition, local information of homogeneous regions is constructed in form of local fitting function. A new double well potential function is formulated to regularize the contour curve and is used a penalty energy term which is added with local fitting functional in order to formulate multi-scale energy function. The experimental results show the robustness of proposed method compared to other level set models and deep learning based level set model. The comparative study of Jaccard similarity index (JSI) values and segmentation accuracy also validate the preciseness of the proposed model. Further, the proposed model yields better segmentation results compared to the other state-of-the-art models in terms of higher precision and recall values and lesser computational time. In addition, the proposed model is computationally efficient and robust to noise as well as contour initialization.

A new region-based active contour model in level set formulation is proposed to segment medical images with poorly defined boundaries. From literature, it is observed that the traditional methods often fail to detect weak boundaries for images with intensity inhomogeneity. However, the machine learning (ML) algorithms are highly effective for such images but due to the noise most pixels are misclassified. Therefore in this paper, we propose a region-based active contour model using ML. In this paper, we consider an active contour driven by local Gaussian distribution (LGD) fitting energy which is known as LGD model. Further, this active contour LGD model is integrated with fuzzy k-nearest neighbor (k-NN) for added accurate segmentation. Also the energy stop function (ESF) of LGD model is modified to combine with k-NN. The results obtained are compared with the existing state-of-the-art models and the proposed method is clearly a triumph. The experimental results proves that the proposed model provides higher accuracy results for medical image segmentation and is robust compared to the other existing methods.

A new level set method is proposed to segment medical images by introducing intensity inhomogeneity and a double well potential function. First of all, an adaptive weighted median filter (AWMF) is used to define the region with respect to intensity inhomogeneity. Then, the analysis is performed by incorporating an energy function with intensity inhomogeneity-free images. Thereafter a new double well potential function is introduced that reduces the computational time. Finally, segmentation is performed on medical images by minimizing the overall energy function. The experimental results show the robustness of the proposed method in comparison to the existing methods in terms of computational time and Jaccard Index (JI).

This study presents a novel analytical approach for evaluation of bit error rate (BER) performance of an impulse radio-ultra-wideband (IR-UWB) energy detector-pulse position modulation (ED-PPM) system, using dual-hop cooperative amplify and forward (AF) relaying strategy with optimum diversity combining, over IEEE 802.15.4a UWB channel. An approximate expression for BER has been derived based on energy detection principle. The analytical BER expression thus derived is validated with computer simulations, which confirm the accuracy in approximation used in investigation of BER. Furthermore, it is observed that the BER performance of IR-UWB ED-PPM system, improves substantially using dual-hop cooperative AF scheme, compared with non-cooperative scheme. Despite its poor BER performance, ED systems are preferred over AC systems, because of simplicity and low complexity.

In this paper, a new model of image segmentation is proposed by considering L0 regularizer term. A new energy function is formulated by utilizing Local Gaussian Distribution model based on binary level set function followed by introducing a L0 gradient regularizer as regularizing term. Instead of zero level set, the binary level set function is applied to differentiate between foreground and background regions. The regularization function is used to calculate the interfaces between foreground sub-regions and regularization term L0 which helps to evolve the curve. The proposed energy function is solved using minimization algorithm to achieve promising results in terms of segmentation accuracy. The different sets of experiments are performed on real and medical images. The proposed model provides higher segmentation accuracy results in less computational time compared to the other state-of-the-art models. Further, the results are found to be superior as compared to other existing models in terms of robustness to noise and intensity inhomogeneity.

In pathology, the count of WBC is done manually which yields imperfect results and hematology devices may solve the issues of erroneous results but the cost is very high. The motivation of this work is to provide an automated computer aided system (CAS) which analyses the microscopic blood images. In analysis, the image segmentation is a vital step and if any error occurs in this step inaccurate results may be obtained during cell detection. Initially, the microscopic blood image is converted to a binary image. Next, the segmentation process is employed to detect the blood cells from blood-smeared images. The thresholding estimation method is used to identify the proper blood cells from the gradient image. The gradient based region growing method is applied to detect the boundary of cells so as to prevent any kind of contact between the cells. The watershed transformation is applied over the thresholding based gradient image. The thresholding estimation method is very much efficient to detect the connected cells in gradient image. Finally, Circular Hough (CH) transformation is used to identify and count the different blood cells i.e., WBCs, RBCs, platelets etc from the microscopic blood image. The accuracy of this method is found 91% using a database of 50 blood samples obtained from microscope. Statistical analysis of image quality analysis i.e., SSIM (Structural Similarity Index Measure) of outcome images using the proposed method is also found to be higher than the other existing methods.

In this paper, we investigate the performance of device-to-device (D2D) communication assisted by two-way detect and forward (DF) relaying. Here, we derive an accurate approximate expression for sum-rate of two-way relaying D2D link under varying interference from cellular users and base station (BS). The analytical results reveal that D2D communication using relaying strategy provides a better and reliable communication in terms of maximum achievable sum-rate as compared to cellular communication mode. The two-way relaying technique improves bit error probability (BEP) by mitigating interference in the first phase of two-way transmission. On the basis of instantaneous signal to noise ratio (SNR), the combiner at the destination node follows an adaptive weight update process to improve the BEP performance in the second phase. We also analyze heuristic mode selection and resource allocation strategies in cellular network in association with multi-node two-way relay scheme, which not only boosts the energy efficiency of the system but also extends the coverage of cellular network.

The recent developments in radio technologies, paves its way to impulse radio (IR) ultra-wideband (UWB) communication, which is used for low power, short range and high bandwidth communication, thereby exploiting a large portion of radio spectrum. In this paper, a brief review of the work done by various researchers on coherent and non-coherent IR-UWB receivers has been analysed, based on their bit error rate (BER) performances, as well as pros and cons of using these receivers. An in depth study on the receivers concludes that, non-coherent IR-UWB receiver is preferred over its counterpart coherent IR-UWB receiver even though it comes at the expense of poor BER performance. The simulation results prove that, though the performances are same, the low complexity of energy detector (ED) receivers gives an edge over the autocorrelation receivers. Further, ED receiver suffers from noise, which paves way to using weighted ED (WED) receiver. The superiority of WED receivers over all the other non-coherent UWB receivers is further confirmed by the simulation performed in AWGN and IEEE 802.15.4a UWB channels. It can also be concluded from the review that, some special receivers such as generalized likelihood ratio test, multi-symbol differential detector and decision feedback differential transmitted reference, when clubbed with UWB systems, lead to further improvement in BER performance.

Ultra-wideband (UWB) is a booming technology in the field of wireless communication. This paper presents a brief idea related to the various coherent and non-coherent IR-UWB detectors. Due to the limitation in transmit power spectral density of UWB system, the major challenges faced by UWB system includes, achieving Quality of Service, system performance and coverage area. So, the combination of UWB system with cooperative communication will not only improve the system performance, but also help in expanding coverage area of signals. A brief review of the work done by various researchers in the field of cooperative impulse radio (IR) UWB communication is also presented in this paper. The working principle and performance analysis of the various coherent and non-coherent IR-UWB detectors using cooperative relay strategies are also discussed at large in this paper. The various fixed cooperative relay strategies used for cooperative UWB communication is Amplify and Forward, Decode and Forward and Detect and Forward. From the simulation results it can be inferred that, even though IR-UWB DTR receiver gives a much better BER performance than IR-UWB ED receiver using both cooperative and non-cooperative strategies, yet ED receiver is preferred because of its less complexity and low power consumption. Future prospects in the field of cooperative IR-UWB communication have also been discussed in this paper.

The paper evaluates and compares the bit error (BER) performance of the various non-coherent IR-UWB receivers in IEEE 802.15.4a UWB environment and AWGN channel. The various non-coherent IR-UWB schemes discussed are TR, RTR, ATR, RATR, DTR and ED receivers. The simulation results clearly show that under the same conditions, ED receiver outperforms TR receiver and is similar to DTR and RATR in performance, despite its low power consumption, in AWGN and IEEE 802.15.4a UWB CM1 and CM2 environment for number of frames, Nf = 1. As Nf increases to 2, DTR and RATR receiver reveals its superiority over the other non-coherent IR-UWB schemes. Inspite of this performance degradation, low complexity ED receivers are preferred over AC receivers.

Non-Coherent Autocorrelation (AR) UWB receivers are preferred over coherent UWB receivers because it requires no channel state information (CSI) and hence is less complex in nature. The paper investigates the performance of Transmitted Reference (TR) and Differential Transmitted Reference (DTR) scheme using AR UWB receivers. Theoretical analysis of BER performance of non-coherent IR-UWB receivers is also discussed at large and compared with simulation results. Analytical and simulation results reveal that non-coherent IR-UWB DTR receiver outperforms TR receiver by a margin of 3-4 dB in AWGN and IEEE 802.15.4a UWB environment.

Non-Coherent Ultrawideband (UWB) Autocorrelation (AR) Receivers require no channel state information, hence are preferred over coherent UWB receivers. The paper discusses the effectiveness of major UWB schemes using AR receivers: the Transmitted Reference (TR) scheme, Averaged Transmitted Reference (ATR) and Differential Transmitted Reference (DTR) scheme. Performance comparison using Matlab Simulation reveals that ATR and DTR receiver outperforms the conventional TR receiver by 3-4 dB both in AWGN and IEEE 802.15.4a environment.

Summary A novel analytical representation of bit error rate (BER) performance of an impulse-radio ultra-wideband energy detector on–off keying system using cooperative dual-hop amplify and forward relay technology, with various diversity combining schemes over IEEE 802.15.4a environment is presented in this paper. In particular, the approximate expressions based on energy detection principle are derived for various diversity combining cases, namely linear optimal combining, linear combining, and selective combining. Simulation results depict an improvement in BER performance, with increase in number of relay paths (L) and decrease in number of frames per symbol (Nf). Furthermore, the BER performance of the impulse-radio ultra-wideband energy detector on–off keying system improves substantially using dual-hop cooperative amplify and forward scheme, compared with that of non-cooperative or single link scenario. Among the diversity combining schemes, linear optimal diversity combining performs better when compared with linear diversity combining and selective combining. The analytical BER expressions are validated with the simulation results, which confirm the accuracy and precision in approximation used in the investigation of BER. Copyright © 2015 John Wiley & Sons, Ltd.