Terahertz (THz) communications, utilizing ultra-wide bandwidths of hundreds of GHz, are essential for meeting the surging data demands of sixth-generation (6G) networks. However, the integration of wideband THz signals with ultra-massive multiple-input multiple-output (UM-MIMO) arrays introduces dual wideband effects (delay squint and beam squint) and spatial non-stationarity (SNS) alongside challenges such as line-of-sight (LoS) blockage, which degrade channel estimation accuracy and system performance. To address these challenges, this paper proposes agridless iterative atomic norm minimization(GI-ANM)-based channel estimator (CE), formulating a convex optimization framework that operates in the continuous parameter space, enabling joint estimation of delays, frequency-dependent gains, and angular variations, effectively handles dual wideband effects and SNS. By eliminating grid mismatch errors inherent in conventional compressed sensing methods, such as orthogonal matching pursuit (OMP), generalized simultaneous OMP, and sparse Bayesian learning, the proposed CE achieves superior accuracy and robustness under 100% LoS blockage, SNS, and dual wideband effects, even in low signal-to-noise ratio environments. Simulation results demonstrate significant improvements in normalized mean square error (NMSE) and bit error rate (BER) performance for THz UM-MIMO systems compared to state-of-the-art methods, including in joint subcarrier correlation and the near-field scenario. The proposed GI-ANM-based CE offers a scalable, high-performance solution for advancing reliable and efficient ultra-wideband THz UM-MIMO systems in 6G networks.

Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is an stringent requirement for several wireless systems like sonar, radar, communications, and dual-function radar communication (DFRC). Due to the use of high carrier frequency and bandwidth, most of these systems are designed with multiple antennae and subcarriers. Although the resolution is high in the large array regime, the DoA-ToA estimation accuracy of the practical on-grid estimation methods still suffers from estimation inaccuracy due to the spectral leakage effect. In this article, we propose DoA-ToA estimation methods for multi-antenna multi-carrier systems with an orthogonal frequency division multiplexing (OFDM) signal. In the first method, we apply discrete Fourier transform (DFT) based coarse signature estimation and propose a low complexity multistage fine-tuning for extreme enhancement in the estimation accuracy. The second method is based on compressed sensing, where we achieve the super-resolution by taking a 2D-overcomplete angle-delay dictionary than the actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP method, we apply the low complexity 2D-OMP method on the matrix data model that makes the use of CS methods practical in the context of large array regimes. Through numerical simulations, we show that our proposed methods achieve the similar performance as that of the subspace-based 2D-MUSIC method with a significant reduction in computational complexity.

This paper presents a novel Wideband BeamDomain Channel Model (BDCM) tailored for cellular communication systems using massive multiple-input multiple-output (MIMO) with Uniform Circular Arrays (UCAs) in accelerating vehicular scenarios. The proposed model transforms the Geometry-Based Stochastic Model (GBSM) into the BDCM framework using beamforming matrices to enable efficient beam sampling. The derived channel model incorporates Doppler shifts, delays, and acceleration-induced effects to capture dynamic vehicular environments accurately. The paper further derives steering vectors, beam sampling vectors, and Orbital Angular Momentum (OAM) wave vectors specific to UCA configurations, employing far-field assumptions and Bessel function approximations. Simulation results validate the model's performance, highlighting its potential for next-generation communication systems involving dynamic and high-mobility scenarios.

With the growth of the 5G network, a wide range of heterogeneous systems have become an integral part of it. The perspective of heterogeneity has made the 5G new radios (NRs) waveform-hungry to meet the system capacity and reliability in highly mobile environments. Conventional NR systems use orthogonal frequency division multiplexing (OFDM), which suffers from the Doppler effect in high-mobility environments. This paper presents the novel waveforms for the NR system using orbital angular momentum (OAM) modes to make the MIMO system even more spectral efficient. The waveforms are orthogonal time-frequency space mode (OTFSM) and orthogonal time-sequency multiplexing mode (OTSMM). In OTFSM, the symbols are in the delay-Doppler-mode domain, and in OTSMM, they are in the delay-sequency-mode domain. Further, we significantly improve the system’s performance in terms of bit error rate at higher modulation orders by incorporating theN-dimensional (N-D) mapper into the OTFSM and OTSMM modulation methods. A low-complex detector is also designed for these waveforms, and its performance is compared with that of the orthogonal frequency mode division multiplexing waveforms. The simulation results depict the superiority of our novel waveforms in terms of enhanced system capacity and reliability in high-mobility environments.

Intelligent Reflecting Surface (IRS) is emerging as an exciting new technology enabling communication beyond $\mathbf{5 G}$. IRS can maneuver the direction of radio signal propagation as per requirement, introducing intelligent beamforming in the channel. Jointly acquiring the channel state information (CSI) and optimal phase matrix of the IRS has been one of the challenges in IRS-based communication. Acquisition of the phase matrix of the IRS for the estimated CSI leads to a complicated optimization problem with a large signaling overhead. State-of-the-art optimization techniques to resolve this problem are found to be highly complex. This work proposes a deep learning-based approach like deep neural networks (DNNs) to find the phase matrix concerning IRS to optimize the spectral efficiency (SE) of the IRS-enabled millimeter-wave (mmWave) system. However, the CSI of the system is obtained by a sparse Bayesian learning (SBL) scheme. The DNN predicts the optimal phase matrix for the estimated CSI. An intelligent grouping of the IRS cells is also proposed to reduce the training overhead. It is further shown that an effective grouping of cells will reduce the computational complexity compared to non-grouping without significant SE loss.

Autonomous unmanned aerial vehicle (UAV) operations heavily depend on the global positioning system (GPS) for localization and mission planning. However, there are environments, like dense urban areas, areas below dense canopy cover, and cloudy weather conditions, where GPS positioning is restricted or unavailable. Besides, the extensive dependence on GPS technology creates vulnerabilities to GPS spoofing, where signals are manipulated to interfere with navigation systems illicitly. This paper proposes a method for estimating desired object geo-location as well as UAV geo-position for uninterrupted autonomous navigation in the satellite-out-of-coverage area. Identifying the unknown object followed by obtaining its Cartesian coordinate in the aerial image through multiple frame rotations, the projection of the geo-location from the Cartesian coordinates to the satellite map, and estimating the self geoposition for navigation are the key contributions of this work. The performance of the proposed model is assessed through field experiments. The result shows the obtained geo-location of the object is very close to the actual geo-location with minimal error.

In 5G and beyond (5GB) wireless communication networks, the full-duplex two-way relay (FDTWR) system emerges as a promising technology with the potential to significantly enhance spectral efficiency (SE). The integration of FDTWR with millimeter wave (mmWave) technology further amplifies this potential, supporting exceptionally high data rates. However, the utilization of the mmWave spectrum poses challenges, particularly the need for meticulous beam alignment, as the antenna gain plays a crucial role in determining the ergodic capacity of a millimeter-wave communication link. Adding to the complexity, the inherent self-interference within FDTWR systems introduces a notable impact on the achievable data rate. In the context of this paper, we delve into a comprehensive analysis of the spectral efficiency of beamformed Non-Line-of-Sight (NLoS) links within mmWave fading channels, specifically focusing on the FDTWR system. We also consider the intra-cluster angular spread, acknowledging the angular distribution of rays forming the NLoS mmWave multi-path cluster required for optimizing FDTWR systems with complex channel characteristics.

Recent advancements in wireless technologies beyond 5G have brought a significant surge in user traffic demands and heightened expectations for reliability. To meet the requirements, the base stations must transition to densified configurations. Conventional terrestrial networks are primarily designed for populated areas, leaving underserved regions such as overcrowded areas, disaster zones, and sparsely populated areas with inadequate coverage. In response to these challenges, aerial networks have emerged as a crucial adaptable deployment strategy. Unfortunately, due to the 3D deployment of aerial networks, coverage and rate analysis carried out for cellular networks are found to be inadequate and intractable. This paper presents a 3D model of aerial networks conceptualizing a hemisphere with frequency reuse, where aerial base stations are distributed as independent Poisson point processes with thinning to facilitate tractability. The proposed model uses frequency reuse to expand coverage while maintaining the trade-off with average capacity for the target user demographic. This paper presents a closed-form analysis for coverage and average capacity in dense aerial networks for downlink.

The Internet of Things (IoT) is a promising application for 5G networks because it provides connected vehicle networks with an optimal blend of cost, latency, and speed. The IoT devices involved with vehicles are called vehicular IoT (V-IoT), requiring a dependable wireless communication system for safe and efficient operation. Compared to conventional waveforms like orthogonal frequency division multiplexing, the orthogonal time frequency space (OTFS) is superior due to its delay-Doppler domain modulation in high-mobility circumstances of V-IoTs. Integrating the$N$-dimensional ($N$-D) mapper with the improved minimum Euclidean distance (MED) into the conventional OTFS modulation technique for V-IoT can considerably enhance the bit error rate (BER) at higher modulation orders. So, we present an ND-OTFS-based V-IoT system with the proposed system's detector complexity analysis and the minimum Euclidean distance of the$N$-D signal constellations. The simulation findings demonstrate that for higher modulation orders in various time-variant channels, the proposed ND-OTFS-based V-IoT system outperforms the conventional OTFS-based V-IoT system regarding BER performance.

Terahertz (THz) band communications, renowned for their ultra-wide bandwidth of several hundred gigahertz (GHz), are pivotal for meeting the growing demands of wireless data traffic in the upcoming sixth-generation (6G) wireless communication era. However, the utilization of extensive bandwidth and large antennas in THz communications introduce challenges, including delay and beam squint effects, collectively termed dual wideband effects. In this work, we explore an iterative atomic norm minimization (ANM)-based gridless frequency-selective channel estimation tailored for THz ultra-massive multiple-input multiple-output orthogonal frequency division multiplexing (UM-MIMO-OFDM) systems under dual wideband effects. Unlike prevailing research focused on on-grid compressed sensing (CS)-based channel estimation, this approach surpasses on-grid CS-based methods such as sparse Bayesian learning (SBL), orthog-onal matching pursuit (OMP), generalized simultaneous orthog-onal matching pursuit (GSOMP), and classical least squares (LS) channel estimators. Its superiority lies in overcoming the grid mismatch problem, a prevalent issue in on-grid CS-based methods, as demonstrated through evaluation using normalized mean square error (NMSE) as a key metric.

The design and development of sub-Terahertz (sub-THz) cellular systems entail the need for new channel models that can precisely predict channel characteristics beyond 100GHz in outdoor and dynamic environments. This work proposes a novel multiple-input and multiple-output (MIMO) channel model for cellular communication, developed within the framework of a class of spatio-temporal stochastic processes called ambit-process. The modeling methodology effectively captures the typicalities of sub-THz propagation like molecular absorption and scattering of the evolving multipaths while accounting for the propagation delay of electromagnetic waves across large array apertures deployed at the transmitter and the receiver. This allows for an accurate characterization of the spatial-wideband effect along with other relevant spatio-temporal attributes of the channel. Numerical simulations indicate a good level of agreement between the spectral efficiency and spatio-temporal correlation of the proposed model against a state-of-the-art stochastic Terahertz (THz) channel model and measurements reported in the literature.

The design and development of Terahertz (THz) and sub-Terahertz (sub-THz) communication systems entail the need for new channel models that can precisely predict channel attributes at such frequencies (≥100 GHz) in outdoor and dynamic environments. This work proposes a novel hybrid-stochastic ultra-wideband channel model for sub-THz bands, developed within the framework of a class of spatio-temporal stochastic processes called the ambit-process. The proposed model is capable of supporting bandwidths of upto 1 GHz. The spatio-temporal evolution of the ambit framework allows for a spatially consistent, reasonably accurate and tractable characterization of the fading statistics and multipath propagation of the cellular channels. We leverage a recently proposed convolution-based low-complexity algorithm with necessary modifications to study key features of the microcellular sub-THz channel like associated diffused reflection and scattering, molecular absorption, spatio-temporal correlations, and consistency between the time-evolving delay and Doppler of the multipaths. Simulation results on path loss, shadowing, delay spread, and channel correlations indicate that the ambit model accurately captures the typicalities of an urban microcellular sub-THz channel and agrees well with the measurement results reported in the literature.

The exponential growth of communication devices such as Internet of Things (IoT) devices, Augmented Reality/Virtual Reality (AR/VR) devices, sensors/actuators, mobile devices, and many others have led to a substantial increase in the quantity of traffic generated. This heavy traffic may cause significant delays for delay-sensitive applications and burden the fronthaul in Cloud-RAN architecture. Supporting all types of communications with the C-RAN architecture will be expensive and inefficient for telecom network operators (TNOs) and devices. Consequently, a fog computing-based RAN has evolved to reduce the heavy burden on fronthaul and ensure timely delivery of requested content for delay-sensitive applications. Fog computing resolves this problem by bringing computational resources and networking closer to users or devices, which is particularly useful for delay-sensitive applications. The cloud has extensive computing and resource capabilities, but its latency is more significant than fog computing-based RAN. In this paper, we examine a market in which TNOs lease third-party deployed fog access points (F-APs) in order to optimize the utilization of fog computing-based RAN architecture and F-APs resources. By employing a fog computing paradigm and efficiently using available fog node resources, the fronthaul infrastructure can relieve some of its burdens. Reducing service latency for end users is one of the most promising advantages of the fog paradigm. A user agrees to utilize fog resources in this endeavor and vice versa. Users and fog nodes do not provide usable utility and cost functions, so we implement a market controller to regulate the market. To maximize the social welfare of the network for participating users and fog nodes, we devise a two-sided auction method to assign computational resources and appropriate compensation.

We extend a scatter/target detection framework for the Uniform Linear Array (ULA) with non-negligible spatial delay across its aperture length. We present a simple yet effective Discrete Fourier Transform (DFT) based approach for detecting sparsely distributed targets. We investigate and develop the system model for observing controlled leakage at the low subcarrier-antenna index and exploit this fact to avoid path overlapping. In order to detect the number of targets correctly, we develop a peak finding-based (unlike clustering-based) target detection algorithm by identifying the peaks present in the noisy DFT spectrum. Furthermore, we demonstrate the utility of percentile-based threshold for sparse wireless scenarios. We show the criterion of selecting among various percentile thresholds under different noise levels through evaluations of appropriate numerical metrics in simulations.

A 5G network’s use-case in the Internet of Things (IoT) is a breakthrough, offering networks the ability to handle billions of connected devices with the proper blend of speed, latency, and cost. The IoT networks implemented in high-speed scenarios like intra and intervehicular communications in autonomous driving vehicles, high-speed vehicles, and trains will experience a Doppler effect. Orthogonal frequency-division multiplexing, the popular transmission technology for existing new-radio IoT (NR-IoT), is limited in providing reliable connections in high-speed vehicular scenarios. The performance of such system degrades with higher-order antenna configuration due to the lack of channel state information in highly mobile environments. The recently proposed orthogonal time-frequency space (OTFS) modulation is a strong contender that can handle high mobility but requires efficient transceiver design to be deployed in vehicular NR-IoT (V-IoT) systems. To conserve the resources and minimize the air time of the devices, we have proposed an embedded pilot design in the Delay–Doppler domain for the V-IoT systems. In the designed frame structure, the pilot’s position is optimized as per the vehicle speed to maximize the spectral efficiency of the system. The increase in spectral efficiency is at the cost of interference in the channel search region of the received Delay–Doppler domain OTFS signal. So a new joint estimator and the low-complex detector are proposed to handle the interference. The proposed efficient transceiver design with the spectral efficient pilot patterns allows us to conserve resources and remove complex encoder–decoders like the low-density parity check in NR-IoT.

Large gatherings may cause the existing radio frequency (RF) network to reach its user capacity limit in the deployment area. So, to provide reliable communication to the users, we can deploy visible light communication (VLC) enabled unmanned aerial vehicles (UAVs) as an auxiliary network to the existing RF infrastructure. Our paper proposes a strategy for the efficient deployment of these VLC-enabled UAVs. It ensures the guaranteed quality of service without inter-UAV interference and does not violate the UAV’s user capacity limit. We compare the performance of the proposed algorithm for VLC-enabled UAV network with random, genetic, and K-means deployment algorithms in terms of outage and number of VLC-enabled UAVs for a fixed number of user equipments. Further, we performed an exhaustive analysis concerning the variation of irradiance and illumination with the different UAV parameters like altitude and coverage radius.

Nowadays, the extensive use of the UAV-enabled network in different applications demands intelligent deployment planning to exploit several benefits of UAV. This paper proposes an energy-efficient 3-D deployment strategy for a hotspot scenario to offer a guaranteed Quality of Service (QoS) with no interUAV interference and UAV capacity limit violation. The 3-D placement of the UAVs is addressed by the horizontal, followed by vertical placement optimization. A square circle packing technique is presented for the horizontal placement of UAVs over the hotspot area. Under vertical placement planning, the altitude of the UAV is optimized to save battery energy. The performance of the square packing technique is assessed for a real-time hotspot scenario modeled by Matérn cluster point process and the performance is compared with K-means algorithm, Genetic algorithm and the best known circle packing techniques.

Spatia1 delays introduced by multi-antenna systems can not be ignored for high signalling BandWidth (BW). For designing an efficient transceiver algorithm and estimating channel parameters for wideband systems, it is quite essential to incorporate the spatial wideband effect to conventional Multiple Input Multiple Output (MIMO) systems. In addition to this, channel identification and signal detectability is also very low for propagation at high frequencies like MillimeterWave (mmWave) and TeraHertz (THz) in presence of high transceiver noise. In this paper, we show the effect of significant spatial delay for spatio-temporal wideband systems. Further, we propose an algorithm to estimate spatio-temporal signature using Deep Learning (DL) framework. We show the utility of Denoising Convolutional Neural Network (DnCNN) for channel response recovery corresponding to very low amplitude channel gain in presence of a very high receiver noise. We recover the spatiotemporal diversity branches for Signal-to-Noise Ratio (SNR) as low as -10dB. Further, we develop a Local Gravitation based Clustering (LGC) framework to identify the number of physical paths for a user and their respective spreads in delay-angle domain. We show, system simulations for Orthogonal Frequency Division Multiplexing (OFDM) waveform over a Quadrature Phase Shift Keying (QPSK) modulation scheme under Rayleigh fading channel model.

Visible light communication is a promising technology for communication in an intra-vehicle scenario which can support high throughput without causing any electromagnetic interference. In contrast to RF link, the VLC link is not isotropic, i.e., it highly depends on the line of sight links (LOS). Therefore, it is important to understand the impact of receiver orientation and user mobility on the performance of VLC system. In the first part of the work, the effect of receiver orientation on the LOS channel is explored. The receiver orientation is modeled as a truncated Gaussian distribution. Based on the statistics of receiver orientation, channel and received signal to noise ratio (SNR) statistics are obtained for both the single input single output (SISO) and multiple input single output (MISO) systems. For the SISO system, closed-form expression for the bit error rate (BER) and outage probability are obtained. For the MISO VLC system, the BER and outage probability are evaluated numerically. The impact of Field of View (FOV) which corresponds to the aperture on the receiver from the transmitter on the performance metrics such as outage probability and BER are analyzed. To capture the impact of mobility on the performance of the system, random way-point mobility model is considered. Closed-form expression for the PDF of the LOS channel gain and BER is derived under the condition of angular variations are more dominant than the overall radial-gain spread for a coherence-period. The derived results help to explore the joint impact of random receiver orientation and mobility on the performance.

Orthogonal Time Frequency Space (OTFS) modulation is a 2-D modulation technique where the time-varying multipath channel is equivalent to a time-invariant delay-Doppler channel. The information symbols that are coherently combined along the multiple delay-Doppler diversity branches experience the same channel gain. New Radio (NR) based Internet of Things (IoT) in 5G standard can support high-speed automated vehicular network that can be termed as vehicular IoT (V-IoT). At high speeds, the orthogonal frequency division multiplexing-based transmission suffers from high intercarrier (or symbol) interference, making it unsuitable. In this paper, we have proposed a less complex NR-based IoT (NR-IoT) system with OTFS that performs better than NR-IoT with low-density parity-check (LDPC). We thereby eliminate the computational complexity of LDPC decoding in our scheme. We have evaluated our proposed technique of uncoded NR-IoT with OTFS over the extended vehicular channel model-A and ultra-reliable low latency communication channel. We have achieved a 6 dB SNR gain with our proposed NR-IoT system with OTFS.

Natural disasters are time-varying in nature. They adversely affect backbone datacenter (DC) networks, thereby resulting in huge loss of data within a short span of time. Maximizing last-minute data backup in an endangered DC network hit by a progressive disaster is, therefore, of utmost importance to ensure data protection and service continuation. Thus, in this article, we propose a novel Mixed-Integer Linear Program (MILP) and a heuristic (D-RADDAR) with reduced time-complexity to address risk-aware emergency backup in endangered DC networks hit by dynamic disasters. The mathematical framework for maximizing the overall last-minute backup percentage is developed. A quantitative analysis of the risk associated with such last-minute backups is carried out. The proposed algorithms are evaluated in terms of the DC backup success percentage and compared to existing dynamic backup algorithms in the literature. Extensive simulations indicate that among all existing heuristics, the D-RADDAR achieves maximum data evacuation while maintaining minimal risk and having a polynomial time-complexity.

Network failure caused by disasters results in communication disruption and huge data loss in backbone Data Center (DC) networks. To minimize such losses, in this paper, we consider a risk-aware dynamic backup model that significantly reduces data-loss in a time-varying network hit by a progressive disaster. We propose a mixed-integer linear program (MILP) for risk-aware backup maximization over a dynamic network hit by a progressive disaster. Further, we perform extensive numerical simulations to show that our proposed MILP performs significantly well compared to existing algorithms which only address emergency backup in static networks hit by stationary disasters.

Exploiting Unmanned Aerial Vehicle ( UAV ) to meet the user demand at an event area for a short duration seems to be a perfect solution. However, the optimal deployment of UAVs in the desired region is a challenging task. This paper proposed an efficient placement algorithm that satisfies the UAV capacity constraint, interference constraint, and maintain the quality of service demand. Moreover, a cost function is designed to make the placement decision easy. The performance analysis of the proposed algorithm is evaluated for real-time user distribution. The network performance with the proposed deployment strategy is compared with another strategy, namely Hexagonal circle packing.

Millimeter wave (mm-Wave) frequency band combined with multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) is a key enabler for fifth generation (5G) technology. However, the requirement of high power emission at the mm-Wave transmitter to combat the significant path loss, the enormous bandwidth, and the high frequency design limitations of the integrated circuits involved in mm-Wave systems, result in severe nonlinear distortion in the signals attributed by the RF power amplifier (PA) and other RF circuits. This nonlinear distortion causes non-orthogonality in OFDM subcarriers which in collusion with frequency selective channel may pose great challenges to signal detection. In this paper, we propose an efficient technique for channel estimation and data detection in the presence of nonlinear distortion for mm-Wave MIMO-OFDM systems. The nonlinear impairment causes the posterior distribution of data symbols as non-Gaussian and analytically intractable. To tackle it, an iterative algorithm based on particle filter (PF) for data detection is designed. The detected symbols are then used to estimate and update the channel gains using a sequential maximum likelihood (SQML) estimation. Simulation results validate the proposed algorithm.

Perturbation in discrete dynamic systems (e.g., a manoeuvring aircraft, an autonomous vehicle having frequent lane change or turning, etc.) makes the linear tracking filter(e.g., Kalman Filter (KF)) sub-optimal. The existing robust filters such as Ridge-based KF (Ridge-KF), Tikhonov-based KF (TRKF), and minimax algorithm perform enhanced estimation under low and moderate perturbations only. The investigation of severe perturbation for multi-degree of freedom (m-DOF) motion of manoeuvres is carried out in this research article. The severity of perturbation due to m-DOF motion makes the observation matrix ill-posed for the tracking filters. To enhance the estimation performance (in terms of root means square error (RMSE)), we propose a novel algorithm called TLSKF. It offers much-improved performance than KF, Ridge-KF, and TRKF under high perturbation for estimating state parameters (position, velocity, etc.). Further, TLSKF approaches to KF at extremely low perturbation. The improvement in the RMSE enhances the tracking resolution of the radar. The comparison with existing literature is also strived to justify the novelty of the proposed algorithm.

Network failure caused by disasters results in communication disruption and huge data loss in backbone Datacenter (DC) networks. To minimize such losses, in this paper, we consider an early- warning triggered risk-aware mutual backup model, which significantly reduces the last-minute backup duration. We specifically consider the joint optimization of probabilistic backup path and site selection and the amount of data to be backed up. We propose a mixed integer linear program (ILP) for backup time minimization using dual DCs at backup sites. Further, we perform extensive numerical simulations to show that, in case of disasters, dual DC backup with Risk-aware probabilistic path selection gives significantly shorter backup windows as compared to existing algorithms.

In this paper, we investigate the performance of two different MIMO modulation schemes, namely, generalised spatial modulation (GSM) and spatial multiplexing (SMP) of an indoor visible light communication system (VLC) for both LOS and NLOS (Diffused reflections) scenario by considering vertical and angular detectors. In GSM, we consider two different activation patterns to show the impact of antenna selection on BER performance. In addition, we show that angular detectors outperform vertical detectors in terms of BER. Furthermore, we study the effect of field of view (FOV) on the performance of angular detectors. In order to analyse the impact of activation pattern at high SNR, we use normalised minimum distance metric for different receiver structure in an indoor environment.

We have proposed a machine-learning based classification framework for cognitive radar for target state classification. Based on the estimated frequency of the received signal at the radar receiver, we have classified three rotational movements (yaw, pitch, and roll) of a maneuvering aircraft motion. Direct classification of the data sets for the different rotational movement was found non-separable. It is difficult to find a classier to construct linear boundary for the classification of this data sets. We intended to design an algorithm for this problem. The proposed algorithm is applied on separable, half separable and non-separable data sets. The success rate of the classifier was verified in terms of cross-validation, mean square error, type I and type II error. The algorithm has shown a success rate of approximately 87.28% and 99.15% for not-separable and separable data sets respectively. It also shows that the increment in the accuracy by 6.86% as compared with the conventional approach [12].

Cloud Radio Access Network (C-RAN) has been proposed as a new paradigm shift in the Radio Access Network (RAN) technology as a part of the fifth generation Long-Term Evolution Advanced (LTE-A) networks to support better spectral and energy efficiency along with the high availability. In this paper, we discuss implementation details of a C-RAN Fronthaul with the help of USRP-based transceivers and LabView platform. To the best of our knowledge, this is the first implementation of a C-RAN architecture that functionally splits the radio resource head (RRH) and the baseband processing unit (BBU) at the physical layer (Split 8) and transfers the completely unprocessed raw signal elements from the RRH to the BBU pool at the cloud for signal processing. We explore TCP and UDP as alternate protocols for fronthaul data transfer to the cloud. In order to evaluate the performance of a C-RAN fronthaul and the interplay of different performance parameters for fronthaul data transfer, we observe various metrics like the receiver goodput and the latency and compare the performance between a C-RAN setup and a generic distributed RAN setup.We observe that TCP works better for Ethernet fronthauling compared to UDP, as it provides reliable data delivery. The analysis discussed in this paper gives insights about the implementation and performance of a C- RAN environment which is essential for designing efficient fronthauling and functional splits of a C-RAN architecture.

Spectrum handoff has a negative impact on the performance of cognitive users (CUs) in terms ofhandoff delay in cognitive radio (CR) networks. In this study, a pre‐emptiveresume priority (PRP) M/G/1/K queuing network model is proposed with a finitenumber of allowable interruptions for proactive decision spectrum handoff schemein order to minimise the cumulative handoff delay (CHD) and total service time(TST) for CUs. The CHD and TST for different proactive decision handoff schemes:non‐switching spectrum handoff, switching spectrum handoff, and random spectrumhandoff are modeled under the proposed PRP M/G/1/K queuing network model. Comprehensive results of CHD and TST are obtained to compare the performances ofthe proactive decision handoff schemes under the proposed PRP M/G/1/K queuingnetwork model. This study also presents an analytical framework to examine theeffect of primary users activity and buffer size on spectrum handoff delayperformance with a finite number of allowable interruptions in a CR network. Thereafter, the optimal buffer size ( K ) is estimated for theproposed PRP M/G/1/K queuing network model, which gives performance similar tothe infinite buffer size PRP M/G/1 queuing model with negligible (<1%)error.

Full-duplex two-way relay (FD-TWR) system has potential to increase the spectral efficiency in the future 5G wireless system. Full-duplex transceiver suffers from inevitable self-interference (SI) which can be alleviated by active self-interference cancellation (SIC) method. However, the mitigation capability of SIC mechanism is limited specifically due to inherent non-linearities of transmitter and receiver front end. As a consequence, residual self-interference (RSI) will degrade the system's signal-to-noise ratio (SNR) and throughput. Non-linearity in RF power amplifier in collusion with time-variant channel results is a great challenge in efficient signal detection and successful SI suppression. In contrast to classical schemes, which consider non-linear distortion at the transmitter, we present a semi-blind data detection and non-linear channel estimation in the presence of RSI at the receiver. Attributed to non-linearity, the target posterior probability density function is mathematically intractable. In this paper, a sequential importance sampling based particle filtering is used for joint data detection and estimation. Intractable distribution is approximated by using weighted random measures. A Taylor's series expansion is used to locally linearize the non-analytic form of distribution. Numerical results validate the joint detection and channel estimation scheme. The robustness of the scheme is verified in presence of RSI under high mobility.

Distributed multiple-input multiple-output (DMIMO) system with orthogonal frequency division multiplexing (OFDM) modulation is an emerging paradigm for high data rate and cell coverage extension. In order to make the paradigm shift from conventional network to intelligent DMIMO-OFDM systems, one must address the fundamental bottleneck in synchronization and channel estimation for coherent reception. The involvement of multiple cooperative nodes increases the number of corresponding channels and synchronization impairments to be estimated in comparison with collocated multiple input multiple output (MIMO) systems. As a consequence, the traditional pilot-based estimator requires a large pilot overhead to effectively estimate multiple impairments. This paper presents semi-blind space alternating generalized expectation maximization (SB-SAGE) algorithm for jointly estimating the multiple carrier frequency offsets (MCFOs) and frequency-selective channel gains in DMIMO-OFDM systems. SB-SAGE estimator uses soft information of partially received data symbols along with pilot symbols to obtain improved MCFOs and channel estimates with significantly reduced length of pilot overhead. It also increases spectral efficiency of the systems. The proposed estimator converges in almost two iterations and achieves significant improvement over pilot-based methods.

Millimeter wave (mm-Wave) is an emerging paradigm towards 5G technology that can support high data rate. The foreseen potential of mm-Wave is limited by huge path loss incurred due to the high frequency operation which can be alleviated by high emission power at the transmitter. This in concurrence with the enormous bandwidth of mm-Wave and high frequency design limitations of the integrated circuits enforce the power amplifier (PA) into non-linear region. Further, the non-linear distortion in collusion with frequency selective channel and carrier frequency offset (CFO) degrade the signal detection performance. To solve this problem, we propose a semi-blind joint estimation of CFO and frequency selective channel gains followed by data detection in the presence of PA non-linearity. The presence of non-linearity results in the posterior probability distribution of complex data symbol to be non-Gaussian and hence, analytically intractable. Therefore, sequential importance resampling based particle filter (PF) is suggested for approximating the intractable posterior distribution of interest by the weighted random probability samples (particles) to detect the data symbols. The detected symbols are then used to jointly update the channel gains and CFO using a novel sequential maximum likelihood (ML) estimation. Extensive simulation results validate the proposed algorithm. This novel scheme enhances the non-linear signal detection performance in presence of CFO and frequency selective channel at the receiver.

Massive multiple-input multiple-output (MIMO) is a breakthrough technology equipped with a large number of antennas and radio frequency (RF) chains, which increases complexity and circuit power consumption at the RF front-ends. Power utilisation of analog-to-digital converters is a major concern in RF chains, so one-bit massive MIMO systems are seen as one of the potential solutions to this problem. Further, channel state information (CSI) accuracy at the base station is one of the crucial needs for realising the benefits of one-bit massive MIMO. The existing pilot based estimators demand additional pilots for enhancing the CSI accuracy which in turn reduces the spectral efficiency of the system. To address this limitation, we propose an iterative semi-blind based channel estimator for one-bit massive MIMO systems in a pilot contaminated scenario. The proposed algorithm consists of two stages namely, initialisation and iteration. The initial channel estimate is obtained from pilot based initialisation stage, which is further refined in iteration stage with the help of both pilot and a few data symbols. The proposed semi-blind algorithm improves estimation accuracy with minimum number of pilot symbols. Through simulations, we show that the proposed scheme achieves a considerable improvement in mean square error and bit error rate against the existing pilot based estimators at the cost of a nominal increase in computational complexity. Moreover, the proposed algorithm attains convergence in two iterations for all the considered scenarios. The proposed estimator is spectral and power-efficient in comparison to the pilot based algorithms. To the best of our knowledge, it is the first attempt of channel estimation in pilot contaminated one-bit massive MIMO systems.

Antenna beamforming is a key enabler for the deployment of reliable millimetre wave communication systems. Indoor millimetre wave network standards have adopted a multi‐stage codebook‐based beam training protocol with an objective to reduce the number of preamble transmissions required to identify the optimum transmit–receive beam pair. Multi‐stage beam training schemes entail moderate search complexity; however certain implementation level challenges have an adverse impact on the search success efficiency. Moreover, it is desirable to reduce the complexity further in a dynamic environment with limited mobility where beam training needs to be repeated often. In this tudy, he authors develop a low‐complexity algorithm for millimetre wave beam training within a heuristic numerical optimisation framework. Based on the classical Rosenbrock direct search method, he authors propose a threshold acceptance feature augmented with a divide‐and‐conquer strategy and direction of arrival aided initialisation, specifically to provide resilience to link blockage and enhance the search success performance, respectively. The method facilitates ease of implementation, and is applicable to generic phased array architectures irrespective of array geometry. he authors further evaluate the system bit error rate to benchmark the performance of the proposed scheme vis‐a‐vis optimal beamforming schemes.

Directional antennas in millimeter wave (mmWave) communication networks enable spatial reuse by reducing interference during concurrent transmission. However, higher spatial multiplexing gain is realized at the cost of increased power consumption in antenna arrays. In this paper, we develop a multi-slot scheduling scheme based on mixed integer linear programming (MILP) for energy efficient scheduling in mmWave concurrent transmission. Energy efficiency (EE) is defined as the ratio of data rate achieved to the sum of circuit power consumption and transmit power. EE is achieved by the joint optimization of data rate and power consumption; while the optimum data rate request is calculated using a reinforced learning strategy, power consumption is minimized through antenna beamwidth control. We also prove that the energy efficient scheduling problem is NP-complete, and propose three novel low complexity energy efficient scheduling algorithms for mmWave networks - energy efficient directional medium access control (EEDMAC)-Naive, EEDMAC-Greedy, and EEDMAC-Double Greedy (DGreedy). The proposed multi-slot scheduling scheme is seen to outperform the existing protocol in terms of EE, and delay fairness. We further analyze the degradation in system performance due to antenna scanning range. The simulation results provide fresh insights into resource allocation and scheduling for mmWave directional communications.

Two-way relay network based on full-duplex technique has the potential to enhance the spectral efficiency significantly, and increase capacity in future 5G mobile communication systems. However, the self-interference of full-duplex communication severely limits the performance of a two-way relay network. The cancellation of self-interference for multi-relay full-duplex two-way relay systems in highly mobile environment is very challenging due to time-frequency doubly selective channel on the one hand and multiple carrier frequency offsets on the other hand. In this paper, we propose a novel semi-blind estimator to jointly estimate multiple carrier frequency offsets and doubly selective self-interference channels in highly mobile two-way relay systems with orthogonal frequency-division multiplexing modulation in the presence of residual self-interference. We use discrete prolate spheroidal basis expansion model to capture rapid time variations of the channel. The proposed iterative space-alternating generalized expectation maximization-based semi-blind algorithm uses received data symbols along with received pilot symbols to obtain improved frequency offsets and channel estimate with significantly less number of pilot overhead. The proposed estimator converges in almost two iterations and achieves significant improvement over the pilot-based method. The Cramer-Rao lower bounds of the semi-blind joint estimation are also derived.

Two-way relay network (TWRN) with orthogonal frequency division multiplexing (OFDM) is an emerging paradigm, for high spectral efficiency, increased capacity in 5G wireless communications. The TWRN allows bidirectional information exchange between two users with the assistance of intermediate relay nodes by efficiently using the available spectral resources. By removing back- propagating self-interference each source node obtains the information of other source node. The accuracy of the self-interference cancellation heavily depends on the precision of the joint estimation of impairments i.e. tracking the individual oscillators and perfect channel state information (CSI). In this paper, we propose two expectation maximization (EM) based algorithms, expectation conditional maximization (ECM) and space alternating generalized expectation maximization (SAGE) which iteratively estimate multiple carrier frequency offsets (MCFOs) and frequency-selective channel coefficients in amplify-and-forward (AF) multi-relay cooperative TWR communication systems over a quasi-static channel. The Cramer-Rao lower bounds (CRLBs) for the joint estimation of multiple impairments are derived. The mean square error (MSE) performances of the proposed estimators achieve corresponding CRLBs. The performance is also evaluated by means of system bit error rate (BER). SAGE based algorithm greatly reduces the complexity compared to the ECM based algorithm.

Millimeter wave (mmWave) communications utilizing directional antennas are capable to support multi-Gbps data rate spanning indoor short range to outdoor cellular scenarios. However, unique channel characteristics of mmWave channels including high pathloss, penetration loss and sparse multi-path characteristics requires new channel models for mmWave network design. In this paper, a novel geometry based model is introduced for outdoor directional non-line-of-sight (NLOS) mmWave channels. By approximating signal propagation with first order reflections from buildings, a more tractable analytical expression for average NLOS received signal power is obtained. Further, the accuracy of the model is tested using exhaustive simulations for different input parameters such as building density, antenna beamwidth etc.

Due to the large swath of available bandwidth, millimeter wave (mmWave) spectrum is potentially suitable to meet enhanced network capacity requirements of future generation communication systems. However, the high Analog to Digital Converter (ADC) sampling rate required to process wide band signals in the mmWave regime results in significant power consumption in the receiver hardware. In this work, an Orthogonal Frequency Division Multiplexing (OFDM) based subsampling architecture is proposed to achieve power efficient receiver design. Subbanding in association with orthogonal spreading codes using subsampling technique reduces the high sampling rate requirement of the receiver. Power consumption in the receiver is further reduced by utilizing low resolution ADC with minimal degradation in Bit Error Rate (BER) performance. In addition to this, OFDM transmission with reduced number of Fast Fourier transform (FFT) points alleviates the multi-path effects significantly, and improves the spectral efficiency. The combined effect of ADC bit resolution and subsampling on the system error performance and power consumption is studied.

Due to the large available spectrum, millimeter wave (mmWave) technology is evolving as a promising candidate for next generation cellular communication systems. Recently, much attention has been focussed on the SINR coverage and rate analysis of mmWave networks. However, the existing coverage and rate performance analysis of mmWave networks available in literature consider single slope path loss model. In this paper, we analyze the downlink SINR coverage performance of mmWave networks with the critical distance dependent dual slope path loss model at the mmWave operating frequency of 28 GHz. Furthermore, we consider two different LOS probability functions (Blockage models) to incorporate blockages into path loss model, different base station (BS) densities and different transmit antenna beam pattern to analyze the performance of mmWave network. Our analysis shows that the downlink SINR coverage scales up under the dual slope path loss model as compared to the simplistic path loss model at low base station density (zeta). The spectral efficiency increases as BS density increases. Moreover, we find the higher limit on BS density that improves spectral efficiency. Also, our empirical results show the impact of different transmit antenna pattern and LOS probability functions on the SINR coverage and rate performance of mmWave network.

In this paper, we have proposed an iterative space alternating generalized expectation maximization (SAGE) based semi-blind channel estimation technique for massive multiple-input multiple-output (MIMO) system in multi-cell pilot contamination prone scenario. The benefits of massive MIMO depend largely on the accuracy of channel state information (CSI) available at the base station (Transmitter). The proposed method improves the accuracy of conventional pilot based minimum mean squared error (MMSE) estimator by iteratively updating the estimate with the help of SAGE algorithm. The received data signals are incorporated along with the received pilot signals to obtain an improved channel estimate without the additional pilot symbols, which in turn improves the spectral efficiency of the system. Most of the existing estimators reported in the literature assume full knowledge of large scale fading coefficients of the interfering cells which is practically infeasible and incurs heavy overheads. The proposed estimator overcomes this problem with the help of an estimate obtained from the received samples. The proposed algorithm converges in almost one iteration, and achieves significant improvement over the existing pilot based estimator reported in the literature.

Cooperative communication or distributed multi- input multi-output (DMIMO) system is an emerging paradigm for increasing spectral efficiency, coverage extension, small cell deployment. In such a system, the signal received at the destination is characterized by multiple carrier frequency offset (MCFO) and channel gains as relays are distributed over space. On the other hand, as orthogonal frequency division multiplexing (OFDM) is very sensitive to frequency offset. DMIMO-OFDM system faces a fundamental problem of synchronization. In this paper, we propose two iterative algorithms based on expectation conditional maximization (ECM) and space alternating generalized expectation maximization (SAGE) to jointly estimate MCFOs and channel gains for a quasi-static channel in estimate-and-forward (EF) multi-relay cooperative communication system. The performances of the estimators are evaluated by means of mean square error (MSE) and system bit error rate (BER). The results indicate that in the high SNR regime, the BER performance of the system with SAGE algorithm results in 1 dB SNR improvement as compared to that with ECM.

Cooperative communication or distributed multi-input multi-output (DMIMO) system is a key enabler of coverage extension and enhancement of link reliability. The fundamental difficulty in deployment of DMIMO is synchronization, tracking independent oscillators and propagation delays. On the other hand, as orthogonal frequency division multiplexing (OFDM) is very sensitive to multiple timing offset (MTO) and multiple carrier frequency offset (MCFO). DMIMO-OFDM system faces a fundamental problem of synchronization. In this paper, we propose two iterative algorithms based on expectation conditional maximization (ECM) and space alternating generalized expectation maximization (SAGE) to jointly estimate MCFO and MTO in estimate-and-forward (EF) multi-relay cooperative communication system over a quasi-static channel. The performance of the estimators are evaluated by means of mean square error (MSE) and system bit error rate (BER). The results indicate that in the high SNR domain, the BER performance of the system with SAGE algorithm results in 1 dB SNR improvement as compared to that with ECM in presence of inter carrier interferences (ICI).

Millimetre wave bands are envisaged to be utilised for next generation ultra‐high data rate communications due to the availability of large bandwidths. However, high sampling rate requirement at these frequencies leads to significant power consumption in the devices. In this study, a subband‐based subsampled radio architecture is proposed for indoor millimetre wave communications with the objective of improving power efficiency along with scalable data rates and multi‐user support under varying Quality of Service (QoS) requirements. An optimum bandwidth partitioning scheme is presented to support the proposed architecture. Subbanding in association with orthogonal spreading codes enables the reduction of sampling rate requirement at the receiver, resulting in significant power savings for medium data rate communication systems. Furthermore, the performance evaluation of the scheme is performed by deriving closed‐form analytical expressions for probability of bit error under line‐of‐sight (LOS) and non‐LOS conditions considering the effect of antenna half‐power beamwidth. The analytical expressions and simulation results for different requirements including maximum data rate, variable number of users, and variable QoS are evaluated and compared.

Millimeter wave communications is a promising technology for future multi-Gbps wireless networks. However, the wide bandwidth and high sampling rate requirement involved leads to significant power consumption in the communication systems. In this paper we propose a subband based subsampled system architecture for indoor millimeter wave communications with a primary objective of improving energy efficiency in the transceiver with scalable data rates and multi-user support for varying Quality of Service (QoS) requirements. An optimum bandwidth partitioning scheme in support of proposed system architecture is also presented. Subbanding in association with orthogonal spreading codes enables the reduction of sampling rate requirement at the receiver, resulting in significant power savings for medium data rate communication systems. The BER simulation results for variable data rate, number of users, and QoS support are presented for the proposed scheme.

Clock synchronization is a crucial issue in the operation of wireless sensor networks. Although the existing synchronization algorithms under linear clock model assumptions perform well for short periods, they will become problematic for applications with long-term requirements. In this paper, we consider a more realistic and flexible relationship model for two clocks and exploit a Taylor expansion to approximate the relationship. Based on this model and a two-way time message exchange procedure, an estimation algorithm is proposed to recover the relationship and then achieve the synchronization. Finally, simulation results demonstrate that the proposed algorithm improves the accuracy of synchronization as compared to existing algorithms in many scenarios, and is also robust to different distributions of random delays.

In sub-band ultra-wideband (SUWB) systems, the use of spreading codes in conjunction with sub-banding enables energy efficient reduced sampling rate receiver designs. In this work, the orthogonal frequency division multiplexing (OFDM) technique is proposed for SUWB systems as a means to mitigate the multipath fading effects of the channel. The OFDM demodulation performed at the sub-sampled rate with reduced number of discrete Fourier transform (DFT) points provides scope for low power receiver implementations. Moreover, OFDM improves the flexibility as bandwidth resources can be allocated with improved granularity at integral multiples of the OFDM sub-channel bandwidth. The requisite correlation properties of the spreading codes is relaxed in the proposed OFDM-SUWB system and more number of spreading codes can be used when compared to the existing SUWB system. Also, a simple channel estimation method exploiting the low complexity advantage of the inherent spreading code based receiver is proposed. Simulation results in terms of the bit error rate (BER) performance are presented over the IEEE 802.15.4a channel models and also comparisons with the multi-band OFDM (MB-OFDM) system are made demonstrating the usefulness of the proposed scheme.

In this paper we present the closed form expression of bit-error-rate (BER) of a convolution coded MB-OFDM Ultra-Wideband (UWB) system. The analysis considers the log-normal fading statistics of UWB channels and captures the estimation error variances of timing and carrier frequency offset (CFO) estimation by our already published synchronizers ATS [1] and MBAFS [2] respectively. The derivation invokes moment generating function (MGF) for log-normal fading statistics and the Gauss-Hermite quadrature integration to deliver average BER expression for rate Rc coded QPSK modulated MB-OFDM system with ATS and MBAFS synchronizers and least square (LS) channel estimator. The analytical result is validated with simulation in the high delay spread UWB channel model CM3. This analysis helps in thorough understanding on the performance of an OFDM based communication system in an Ultra-Wideband environment.

A cross-correlation function (CCF) based timing synchronization algorithm of low complexity, applicable for single-band transmission is presented. Novelty of the synchronizer lies in utilization of specific preamble structure of the Multi-band OFDM frame format. This threshold based algorithm also takes care of the fact that stronger multipath components frequently appear on the later clusters. Performance of the algorithm is measured in terms of mean-squared-error and synchronization probability. Performance is compared with another single-band based timing algorithm by Yak et al. [2]. The probability of correct detection is analyzed mathematically to verify the experimental results.

A sub-band based ultra wideband (SUWB) system is proposed. The technique provides scope for using the ultra wideband bandwidth efficiently by exploiting the available link margin for short range communications. The bandwidth of 500 MHz or more is divided into a fixed number of sub-bands. The data transmission scheme over multiple sub-bands can be designed to achieve higher data rate, higher reliability or support multi-user access. The SUWB system facilitates low power implementations by reducing the sampling rate requirements and also by the use of an orthogonal spreading code based interference rejection and multi-path cancellation receiver. The SUWB system avoids the need for individual down-conversion and filtering of the sub-bands. The requisite properties of the spreading codes are also provided. The simulation results in terms of the BER performance of the method for the IEEE 802.15.4a channel models are presented. The desirable performance is obtained for low and medium delay spread channels even without employing any equalization method.

An Ultra-Wideband (UWB) channel exhibits frequency dependent delay characteristics in different bands of transmission. In [6] we studied and quantified the differences of delay parameters over frequency bands 3.1-4.6 GHz in several UWB channel models (CM). Based on this observation, we proposed an adaptive timing synchronization scheme (ATS) which estimates and maintains the timing delays of each band separately. In this paper, we modify our ATS algorithm [6] by optimal selection of the threshold for timing estimation to reduce the mean-squared-error (MSE) and increase the synchronization probability of the estimator. The performance of UWB Multi- band OFDM (MB-OFDM) system with modified ATS algorithm is studied through computer simulations. We show that modified ATS gives signal-to-noise ratio (SNR) improvement of 1.7 dB at Bit Error Rate (BER) of 2times10-2, 2.2 dB at BER of 7times10-3, and 1.1 dB at BER of 5times10-3for CM4, CM3, and CM2 respectively for uncoded MB-OFDM system over a non-adaptive synchronization scheme [5]. Also, algorithmic computational complexity analysis of modified ATS is provided for its usability evaluation.

This work explores theoretically the unique aspects of different signal processing stages in a Multi-Band (MB) Orthogonal Frequency Division Multiplexing (OFDM) based system in realistic Ultra-Wideband (UWB) channel. In the process of analysis, it emphasizes the significant aspects of signal processing steps in UWB receiver design comparing a narrow-band system. The analysis preliminary considers perfect timing and frequency synchronization which is extended later with a frequency offset error and its correction. Performance of the offset estimator in MB-OFDM system via computer simulation is provided in order to support the mathematical analysis.

A frequency offset estimation scheme is presented for coarse frequency offset estimation of Ultra-Wideband (UWB) Multi-Band Orthogonal Frequency Division Multiplexing (MB- OFDM) systems. Cramer Rao Lower Bound (CRLB) for variance of the estimated frequency offset is derived and compared with the simulated results of the proposed scheme. Performance of the MB-OFDM system in terms of the Bit Error Rate (BER) with suggested frequency offset estimator is also reported. The algorithm is tested by computer simulation over multiple UWB channel models (CM), CM1: 0-4m line-of-sight, CM2: 0-4m non-line-of-sight (NLOS), CM3: 4-10m NLOS, and CM4: 25 nsec. delay spread as suggested by IEEE 802.15 Channel Modeling sub-committee. With higher number of iteration the algorithm shows improvement of the system performance by 8.8 dB in both CM1 and CM2; 7.6 dB for CM3 and 8.3 dB for CM4 at BER=10-4.