Xavier Fernando - Profile on Academia.edu (original) (raw)

Papers by Xavier Fernando

Sensors, Sep 10, 2023

The aim of this systematic review was to identify the correlations between spectrum sensing, clus... more The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

Future Internet

In combination with the expected traffic avalanche foreseen for the next decade, solutions suppor... more In combination with the expected traffic avalanche foreseen for the next decade, solutions supporting energy-efficient, scalable and flexible network operations are essential. Considering the myriad of user case requirements, THz and mmW bands will play key roles in 6G networks. While mmW is known for short-rate LOS connections, THz transmission is subjected to even severe propagation losses, resulting in very short-range connections. In this context, we evaluate a dynamic multi-band user association algorithm to optimize connectivity in coexisting RF/mmW/THz networks. The algorithm periodically calculates association scores for each user–base station pair based on real-time channel conditions across bands, accounting for factors like signal strength, link blockage risk and noise. It then reassociates users in batches to balance loads while considering user priorities and network conditions. We simulate the algorithm’s performance within a realistic propagation model, where high pat...

Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to creat... more Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to create high-resolution images from low-resolution ones. It has important applications in fields 2 such as medical imaging and security surveillance. While traditional methods such as interpolation 3 and reconstruction-based models have been used in the past, deep learning techniques have recently 4 gained attention due to their superior performance and computational efficiency. This article proposes 5 an Autoencoder based Deep Learning Model for SSIR, in particular, a light model that uses fewer 6 parameters without compromising performance. The down-sampling part of the Autoencoder 7 mainly uses 3 by 3 convolution and has no subsampling layers. The up-sampling part uses transpose 8 convolution and residual connections from the down sampling part. The model is trained using a 9 subset of the VILRC ImageNet database. The model is evaluated using quantitative metrics PSNR, 10 SSIM as well as qu...

This article uses a case study of urban Canada to explore the contentious issue of spectrum scarc... more This article uses a case study of urban Canada to explore the contentious issue of spectrum scarcity. Drawing upon infrastructure studies, this article argues for more critical approaches to this essential element of contemporary communications. The first part of the article explores positions of various actors in the antagonistic debate regarding spectrum scarcity in the lead up to the Canadian 700 MHz spectrum auction, held in 2014. The second part of the article provides unique empirical data for spectrum traffic on licensed frequencies in a busy urban location. The article reaches an unanticipated conclusion that demonstrates shortcomings in current allocation methods.

Electronics

Smart multiple-input multiple-output (MIMO) antennas with advanced signal processing algorithms a... more Smart multiple-input multiple-output (MIMO) antennas with advanced signal processing algorithms are necessary in future wireless networks, such as 6G and beyond, for accurate space division multiplexing and beamforming. Such a MIMO antenna will yield better network coverage and tracking. This paper presents a smart MIMO antenna configuration with a highly innovative beamforming technique using several nonlinear configurations of dipole arrays. Phase delay factors are optimized at the transmitter to form a single beam and then to steer the beam towards a particular direction. A number of phase shifters are added in order to obtain maximum directional gain. This configuration also significantly increases the power gain of the MIMO antenna at a low cost and with operational simplicity. The paper also demonstrates how the beam width and beamsteering can be effectively controlled. Wolfram Mathematica software was used to generate the three-dimensional radiated beam patterns of the transm...

Electronics

Smart Antennas are important to provide mobility support for many enhanced 5G and future wireless... more Smart Antennas are important to provide mobility support for many enhanced 5G and future wireless applications and services, such as energy harvesting, virtual reality, Voice over 5G (Vo5G), connected vehicles, Machine-to-Machine Communication (M2M), and Internet of Things (IoT). Smart antenna technology enables us to reduce interference and multipath problems and increase the quality in communication signals. This paper presents a number of nonlinear configurations of dipole arrays for forming a single beam in any desired direction. We propose three, four, six, and eight-element array structures to perform this single beam-steering functionality. The proposed array configurations with multiple axes of symmetry (in the azimuthal plane) decrease the computational repetitions in optimizing respective weight factors for beam-steering. The optimized weight factors are obtained through the Least Mean Square (LMS) method. MATLABTM is used to calculate optimized weight factors as well as t...

2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017

There has been a lot of work on emergency reporting in smart transportation system, but we find v... more There has been a lot of work on emergency reporting in smart transportation system, but we find very less information about road sides assets reporting and management. Currently available mechanisms do not efficiently handle asset management and any emergency reporting. Asset management is based on either reactive maintenance reported by people or preventative scheduled maintenance. In this article, we present a reporting architecture for emergency situations and, management through Foglets and visible light communication (VLC). Foglet is the processing agent in Fog computing, a computing platform that provide services with improved QoS and reduced latency. VLC use the visible portion of the spectrum and has proved itself to be promising technology in terms of capability, capacity and safety as compared to conventional RF communication.

IEEE Access, 2020

Widespread proliferation of wireless coverage has enabled culmination of number of advanced locat... more Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

IET Wireless Sensor Systems, 2021

Cognitive wireless sensor networks (CWSN) are severely energy constrained and radio frequency (RF... more Cognitive wireless sensor networks (CWSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve the network lifetime. In many CWSN applications, node mobility imposes challenges owing to changing network topology. Therefore, the design of a new medium access control (MAC) protocol that can handle node mobility as well as energy harvesting is required. A cluster-based multihop MAC protocol (RMAC-M) is proposed that incorporates RF energy harvesting in a mobility-aware CWSN. Our protocol selects cluster heads using an algorithm based on an R-factor parameter consisting of residual node energy, residual node data and node speed, with appropriate weights. It then transmits data packages using a multitier super cluster head routing mechanism without the need for neighbour discovery. The multitier clustering and RFWEH mechanisms boost the energy performance of the network, increasing its lifetime. On the other hand, time slots allocated for RFWEH increase delay, thereby affecting system latency. Owing to its unique nature, the proposed algorithm has no comparable protocols in the literature. For the sake of completeness, RMAC-M is compared with well-known MAC protocols such as LEACH-M and KoNMAC that do not have energy harvesting or mobility features. Simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, promising a self-sustainable network in terms of energy. Furthermore, despite the allocation of time slots for energy harvesting, critical network parameters such as throughput, packet loss and average delay remain within target levels.

2019 53rd Asilomar Conference on Signals, Systems, and Computers, Nov 1, 2019

Image similarity measurement is a common issue in a broad range of applications in image processi... more Image similarity measurement is a common issue in a broad range of applications in image processing, recognition, classification and retrieval. Conventional image similarity measures are often limited to specific applications and cannot be applied in general scenarios. The theory of Kolmogorov complexity provides a universal framework for a generic similarity metric based on information distance between objects. Normalized Information Distance (NID) has been shown to be a valid and universal distance metric applicable in measurement of similarity of any two objects, and has been successfully applied to a wide range of applications in the past. The difficulty of NID lies in the non-computable nature of the Kolmogorov complexity, and thus approximation has to be applied in practice. Here we propose a perceptually-inspired Normalized Conditional Compression Distance (NCCD) measure by using the Divisive Normalization Transform (DNT) as a means to model the non-linear behavior of the Human Visual System (HVS) in reducing statistical dependencies of visual signals for efficient representation, and show that this perceptual extension of NID can be used in a wide range of image processing applications, including texture classification and face recognition.

EURASIP Journal on Wireless Communications and Networking, 2018

Optical wireless communications (OWC) has emerged as a strong candidate for wireless communicatio... more Optical wireless communications (OWC) has emerged as a strong candidate for wireless communications, due to the capacity limitation in the radio frequency (RF) spectrum. Especially visible light communication (VLC) has great potential for short-range outdoor vehicular communications, as vehicle LED lights also transmit data. However, outdoor VLC channels vary fast and, experience multipath scattering and reflection resulting in time domain dispersion. Outdoor VLC links are also subjected to high levels of ambient noise, especially from the sun. Orthogonal frequency-division multiplexing (OFDM), which has proven robustness to multi path fading and noise effects in RF links can also be deployed in VLC links. In this paper, optical OFDM (O-OFDM) along with adaptive modulation scheme is investigated in VLC for vehicle to vehicle (V2V) communications. A (2 × 2) multiple input multiple output (MIMO) channel, with multiple polarimetric bidirectional reflections and realistic sunlight interference is considered. Two schemes of O-OFDM; direct current biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are investigated. Simulation results of the proposed model show increase in data rates up to 50 Mbps along with reduced bit error rate (BER) under both line of sight (LOS) and non-LOS and high noise conditions.

IEEE Transactions on Intelligent Transportation Systems, 2017

Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent... more Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent transportation systems, and advanced driver assistance systems. Most conventional multi-target tracking algorithms are not ideal for multi-vehicle tracking, since they assume that the targets move independently of one another. However, due to traffic volume and limited lane resources, vehicles have to interact with their neighbors, resulting in highly dependent motions. To address this limitation, this paper proposes a novel multi-vehicle tracking algorithm for the single-lane case that considers motion dependence across vehicles by integrating the car-following model (CFM) into the tracking process with on-road constraints. A new CFM-based motion model that describes the dependent motion of vehicles in the single-lane case is proposed, and the notion of car-following clusters is defined. In order to exploit all available information in sensor measurements, the proposed algorithm updates the state estimates of car-following clusters by utilizing a stacked-update strategy. Furthermore, the variable structure interacting multiple model estimator is modified and integrated into the proposed algorithm to handle maneuvers that may violate the CFM. Simulation results demonstrate the superiority of the proposed multi-vehicle tracking algorithm over other state-of-the-art multi-vehicle tracking algorithms.

Journal of Information Policy, 2017

This article uses a case study of urban Canada to explore the contentious issue of spectrum scarc... more This article uses a case study of urban Canada to explore the contentious issue of spectrum scarcity. Drawing upon infrastructure studies, this article argues for more critical approaches to this essential element of contemporary communications. The first part of the article explores positions of various actors in the antagonistic debate regarding spectrum scarcity in the lead up to the Canadian 700 MHz spectrum auction, held in 2014. The second part of the article provides unique empirical data for spectrum traffic on licensed frequencies in a busy urban location. The article reaches an unanticipated conclusion that demonstrates shortcomings in current allocation methods.

Resource Allocation in OFDM-Based Cognitive Radio Systems

Springer briefs in electrical and computer engineering, May 22, 2018

Resource allocation problem in OFDM based CRNs has been widely studied under different settings i... more Resource allocation problem in OFDM based CRNs has been widely studied under different settings in the open literature. A power allocation grouping scheme based on the interference channel gain, pulse shape and frequency distance is presented in Hosseini and Falahati (Power allocation grouping scheme considering constraints in two separate stages for OFDM-based cognitive radio system. In: Proceedings of the IEEE international conference on electrical information and communication technology (EICT), pp 1–6, Feb 2014) in order to improve capacity while the interference power for PUs stays at constant level. At the first stage, power is assigned to some groups based on the grouping scheme and at the second stage, the remaining power is allocated to others with water-filling algorithm.

IEEE Transactions on Vehicular Technology, Nov 1, 2017

Power Allocation Using Geometric Water Filling for OFDM-Based Cognitive Radio Networks

Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Ort... more Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Orthogonal frequency division multiplexing (OFDM) is a potential technology providing many advanced functionalities in terms of power and rate control for cognitive radio networks (CRNs). Power allocation for CRNs is a crucial task for better interference management. In this paper, a subcarrier assignment scheme and a novel power allocation algorithm using geometric water filling is presented for OFDM based CRNs. This algorithm is optimized such a way to maximize the sum rate of secondary users by allocating power more efficiently, while constraining the 1) total transmit power, 2) individual subchannel transmit power as well as 3) individual subcarrier peak power of secondary users, for a given interference level to the primary users. Numerical results show that this algorithm provides better utilization of power resources thus maximizes the sum rate than the existing algorithms.

Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-Based Deep Reinforcement Learning Approach

IEEE Transactions on Vehicular Technology

Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance vehicular netw... more Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power vehicles to offload computation-intensive and delay-sensitive applications, making it a promising solution. However, optimal resource allocation between edge servers is a complex issue due to vehicle mobility and dynamic data traffic. To address this issue, we propose a Lyapunov-based Multi-Agent Deep Deterministic Policy Gradient (L-MADDPG) method that jointly optimizes computing task distribution and radio resource allocation to minimize energy consumption and delay requirements. We evaluate the trade-offs between the performance of the optimization algorithm, queuing model, and energy consumption. We first examine delay, queue and energy models for task execution at the vehicle or RSU, followed by the L-MADDPG algorithm for jointly optimizing task offloading and resource allocation problems to reduce energy consumption without compromising performance. Our simulation results show that our algorithm can reduce energy consumption while maintaining system performance compared to existing algorithms.

IEEE Transactions on Vehicular Technology, 2022

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (... more Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles' mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department.

Mobility Aware Channel Allocation for 5G Vehicular Networks using Multi-Agent Reinforcement Learning

ICC 2021 - IEEE International Conference on Communications, 2021

Reinforcement learning is a machine learning technique that focuses on exploring an uncharted ter... more Reinforcement learning is a machine learning technique that focuses on exploring an uncharted territory exploiting of current knowledge. This paper proposes a Mobility Aware Channel Allocation (MACA) algorithm for 5G Vehicular Networks using a combination of Multi-Agent Reinforcement Learning (MARL) and Semi-Markov Decision Process (SMDP). In this work, we use multiple autonomous agents operating in a common environment to address the sequential decision-making problem to optimize the long-term rewards. In MACA, first we predict the mobility of vehicles using Teammate-Learning model as it allows the vehicles to cooperate and collaborate with each other without prior coordination. Secondly, during SMDP resource allocation phase, MARL inputs are applied to the Action Selection model for each vehicle based on their priorities. This is done at Road-Side Units (RSUs). Through numerical results and evaluations, we verify that the proposed method demonstrates efficient channel allocation and high packet delivery ratio as compared in the scenario of vehicles with multiple (high, medium, and low) priorities to existing conventional SMDP and Greedy algorithms.

SpringerBriefs in Electrical and Computer Engineering, 2019

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Sensors, Sep 10, 2023

The aim of this systematic review was to identify the correlations between spectrum sensing, clus... more The aim of this systematic review was to identify the correlations between spectrum sensing, clustering algorithms, and energy-harvesting technology for cognitive-radio-based internet of things (IoT) networks in terms of deep-learning-based, nonorthogonal, multiple-access techniques. The search results and screening procedures were configured with the use of a web-based Shiny app in the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow design. AMSTAR, DistillerSR, Eppi-Reviewer, PICO Portal, Rayyan, and ROBIS were the review software systems harnessed for screening and quality assessment, while bibliometric mapping (dimensions) and layout algorithms (VOSviewer) configured data visualization and analysis. Cognitive radio is pivotal in the utilization of an adequate radio spectrum source, with spectrum sensing optimizing cognitive radio network operations, opportunistic spectrum access and sensing able to boost the efficiency of cognitive radio networks, and cooperative spectrum sharing together with simultaneous wireless information and power transfer able increase spectrum and energy efficiency in 6G wireless communication networks and across IoT devices for efficient data exchange.

Future Internet

In combination with the expected traffic avalanche foreseen for the next decade, solutions suppor... more In combination with the expected traffic avalanche foreseen for the next decade, solutions supporting energy-efficient, scalable and flexible network operations are essential. Considering the myriad of user case requirements, THz and mmW bands will play key roles in 6G networks. While mmW is known for short-rate LOS connections, THz transmission is subjected to even severe propagation losses, resulting in very short-range connections. In this context, we evaluate a dynamic multi-band user association algorithm to optimize connectivity in coexisting RF/mmW/THz networks. The algorithm periodically calculates association scores for each user–base station pair based on real-time channel conditions across bands, accounting for factors like signal strength, link blockage risk and noise. It then reassociates users in batches to balance loads while considering user priorities and network conditions. We simulate the algorithm’s performance within a realistic propagation model, where high pat...

Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to creat... more Single Image Super Resolution (SSIR) is a problem in computer vision where the goal is 1 to create high-resolution images from low-resolution ones. It has important applications in fields 2 such as medical imaging and security surveillance. While traditional methods such as interpolation 3 and reconstruction-based models have been used in the past, deep learning techniques have recently 4 gained attention due to their superior performance and computational efficiency. This article proposes 5 an Autoencoder based Deep Learning Model for SSIR, in particular, a light model that uses fewer 6 parameters without compromising performance. The down-sampling part of the Autoencoder 7 mainly uses 3 by 3 convolution and has no subsampling layers. The up-sampling part uses transpose 8 convolution and residual connections from the down sampling part. The model is trained using a 9 subset of the VILRC ImageNet database. The model is evaluated using quantitative metrics PSNR, 10 SSIM as well as qu...

This article uses a case study of urban Canada to explore the contentious issue of spectrum scarc... more This article uses a case study of urban Canada to explore the contentious issue of spectrum scarcity. Drawing upon infrastructure studies, this article argues for more critical approaches to this essential element of contemporary communications. The first part of the article explores positions of various actors in the antagonistic debate regarding spectrum scarcity in the lead up to the Canadian 700 MHz spectrum auction, held in 2014. The second part of the article provides unique empirical data for spectrum traffic on licensed frequencies in a busy urban location. The article reaches an unanticipated conclusion that demonstrates shortcomings in current allocation methods.

Electronics

Smart multiple-input multiple-output (MIMO) antennas with advanced signal processing algorithms a... more Smart multiple-input multiple-output (MIMO) antennas with advanced signal processing algorithms are necessary in future wireless networks, such as 6G and beyond, for accurate space division multiplexing and beamforming. Such a MIMO antenna will yield better network coverage and tracking. This paper presents a smart MIMO antenna configuration with a highly innovative beamforming technique using several nonlinear configurations of dipole arrays. Phase delay factors are optimized at the transmitter to form a single beam and then to steer the beam towards a particular direction. A number of phase shifters are added in order to obtain maximum directional gain. This configuration also significantly increases the power gain of the MIMO antenna at a low cost and with operational simplicity. The paper also demonstrates how the beam width and beamsteering can be effectively controlled. Wolfram Mathematica software was used to generate the three-dimensional radiated beam patterns of the transm...

Electronics

Smart Antennas are important to provide mobility support for many enhanced 5G and future wireless... more Smart Antennas are important to provide mobility support for many enhanced 5G and future wireless applications and services, such as energy harvesting, virtual reality, Voice over 5G (Vo5G), connected vehicles, Machine-to-Machine Communication (M2M), and Internet of Things (IoT). Smart antenna technology enables us to reduce interference and multipath problems and increase the quality in communication signals. This paper presents a number of nonlinear configurations of dipole arrays for forming a single beam in any desired direction. We propose three, four, six, and eight-element array structures to perform this single beam-steering functionality. The proposed array configurations with multiple axes of symmetry (in the azimuthal plane) decrease the computational repetitions in optimizing respective weight factors for beam-steering. The optimized weight factors are obtained through the Least Mean Square (LMS) method. MATLABTM is used to calculate optimized weight factors as well as t...

2017 IEEE 85th Vehicular Technology Conference (VTC Spring), 2017

There has been a lot of work on emergency reporting in smart transportation system, but we find v... more There has been a lot of work on emergency reporting in smart transportation system, but we find very less information about road sides assets reporting and management. Currently available mechanisms do not efficiently handle asset management and any emergency reporting. Asset management is based on either reactive maintenance reported by people or preventative scheduled maintenance. In this article, we present a reporting architecture for emergency situations and, management through Foglets and visible light communication (VLC). Foglet is the processing agent in Fog computing, a computing platform that provide services with improved QoS and reduced latency. VLC use the visible portion of the spectrum and has proved itself to be promising technology in terms of capability, capacity and safety as compared to conventional RF communication.

IEEE Access, 2020

Widespread proliferation of wireless coverage has enabled culmination of number of advanced locat... more Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-ofsight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning. INDEX TERMS Indoor positioning system (IPS), location-based services (LBS), machine learning (ML), non-line-of-sight (NLOS), wireless positioning, indoor tracking.

IET Wireless Sensor Systems, 2021

Cognitive wireless sensor networks (CWSN) are severely energy constrained and radio frequency (RF... more Cognitive wireless sensor networks (CWSN) are severely energy constrained and radio frequency (RF) wireless energy harvesting (RFWEH) has been shown to improve the network lifetime. In many CWSN applications, node mobility imposes challenges owing to changing network topology. Therefore, the design of a new medium access control (MAC) protocol that can handle node mobility as well as energy harvesting is required. A cluster-based multihop MAC protocol (RMAC-M) is proposed that incorporates RF energy harvesting in a mobility-aware CWSN. Our protocol selects cluster heads using an algorithm based on an R-factor parameter consisting of residual node energy, residual node data and node speed, with appropriate weights. It then transmits data packages using a multitier super cluster head routing mechanism without the need for neighbour discovery. The multitier clustering and RFWEH mechanisms boost the energy performance of the network, increasing its lifetime. On the other hand, time slots allocated for RFWEH increase delay, thereby affecting system latency. Owing to its unique nature, the proposed algorithm has no comparable protocols in the literature. For the sake of completeness, RMAC-M is compared with well-known MAC protocols such as LEACH-M and KoNMAC that do not have energy harvesting or mobility features. Simulation results show that the proposed protocol increases the lifetime of the CWSN nodes substantially, promising a self-sustainable network in terms of energy. Furthermore, despite the allocation of time slots for energy harvesting, critical network parameters such as throughput, packet loss and average delay remain within target levels.

2019 53rd Asilomar Conference on Signals, Systems, and Computers, Nov 1, 2019

Image similarity measurement is a common issue in a broad range of applications in image processi... more Image similarity measurement is a common issue in a broad range of applications in image processing, recognition, classification and retrieval. Conventional image similarity measures are often limited to specific applications and cannot be applied in general scenarios. The theory of Kolmogorov complexity provides a universal framework for a generic similarity metric based on information distance between objects. Normalized Information Distance (NID) has been shown to be a valid and universal distance metric applicable in measurement of similarity of any two objects, and has been successfully applied to a wide range of applications in the past. The difficulty of NID lies in the non-computable nature of the Kolmogorov complexity, and thus approximation has to be applied in practice. Here we propose a perceptually-inspired Normalized Conditional Compression Distance (NCCD) measure by using the Divisive Normalization Transform (DNT) as a means to model the non-linear behavior of the Human Visual System (HVS) in reducing statistical dependencies of visual signals for efficient representation, and show that this perceptual extension of NID can be used in a wide range of image processing applications, including texture classification and face recognition.

EURASIP Journal on Wireless Communications and Networking, 2018

Optical wireless communications (OWC) has emerged as a strong candidate for wireless communicatio... more Optical wireless communications (OWC) has emerged as a strong candidate for wireless communications, due to the capacity limitation in the radio frequency (RF) spectrum. Especially visible light communication (VLC) has great potential for short-range outdoor vehicular communications, as vehicle LED lights also transmit data. However, outdoor VLC channels vary fast and, experience multipath scattering and reflection resulting in time domain dispersion. Outdoor VLC links are also subjected to high levels of ambient noise, especially from the sun. Orthogonal frequency-division multiplexing (OFDM), which has proven robustness to multi path fading and noise effects in RF links can also be deployed in VLC links. In this paper, optical OFDM (O-OFDM) along with adaptive modulation scheme is investigated in VLC for vehicle to vehicle (V2V) communications. A (2 × 2) multiple input multiple output (MIMO) channel, with multiple polarimetric bidirectional reflections and realistic sunlight interference is considered. Two schemes of O-OFDM; direct current biased optical OFDM (DCO-OFDM) and asymmetrically clipped optical OFDM (ACO-OFDM) are investigated. Simulation results of the proposed model show increase in data rates up to 50 Mbps along with reduced bit error rate (BER) under both line of sight (LOS) and non-LOS and high noise conditions.

IEEE Transactions on Intelligent Transportation Systems, 2017

Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent... more Multi-vehicle tracking is crucial in many applications, such as traffic surveillance, intelligent transportation systems, and advanced driver assistance systems. Most conventional multi-target tracking algorithms are not ideal for multi-vehicle tracking, since they assume that the targets move independently of one another. However, due to traffic volume and limited lane resources, vehicles have to interact with their neighbors, resulting in highly dependent motions. To address this limitation, this paper proposes a novel multi-vehicle tracking algorithm for the single-lane case that considers motion dependence across vehicles by integrating the car-following model (CFM) into the tracking process with on-road constraints. A new CFM-based motion model that describes the dependent motion of vehicles in the single-lane case is proposed, and the notion of car-following clusters is defined. In order to exploit all available information in sensor measurements, the proposed algorithm updates the state estimates of car-following clusters by utilizing a stacked-update strategy. Furthermore, the variable structure interacting multiple model estimator is modified and integrated into the proposed algorithm to handle maneuvers that may violate the CFM. Simulation results demonstrate the superiority of the proposed multi-vehicle tracking algorithm over other state-of-the-art multi-vehicle tracking algorithms.

Journal of Information Policy, 2017

This article uses a case study of urban Canada to explore the contentious issue of spectrum scarc... more This article uses a case study of urban Canada to explore the contentious issue of spectrum scarcity. Drawing upon infrastructure studies, this article argues for more critical approaches to this essential element of contemporary communications. The first part of the article explores positions of various actors in the antagonistic debate regarding spectrum scarcity in the lead up to the Canadian 700 MHz spectrum auction, held in 2014. The second part of the article provides unique empirical data for spectrum traffic on licensed frequencies in a busy urban location. The article reaches an unanticipated conclusion that demonstrates shortcomings in current allocation methods.

Resource Allocation in OFDM-Based Cognitive Radio Systems

Springer briefs in electrical and computer engineering, May 22, 2018

Resource allocation problem in OFDM based CRNs has been widely studied under different settings i... more Resource allocation problem in OFDM based CRNs has been widely studied under different settings in the open literature. A power allocation grouping scheme based on the interference channel gain, pulse shape and frequency distance is presented in Hosseini and Falahati (Power allocation grouping scheme considering constraints in two separate stages for OFDM-based cognitive radio system. In: Proceedings of the IEEE international conference on electrical information and communication technology (EICT), pp 1–6, Feb 2014) in order to improve capacity while the interference power for PUs stays at constant level. At the first stage, power is assigned to some groups based on the grouping scheme and at the second stage, the remaining power is allocated to others with water-filling algorithm.

IEEE Transactions on Vehicular Technology, Nov 1, 2017

Power Allocation Using Geometric Water Filling for OFDM-Based Cognitive Radio Networks

Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Ort... more Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Orthogonal frequency division multiplexing (OFDM) is a potential technology providing many advanced functionalities in terms of power and rate control for cognitive radio networks (CRNs). Power allocation for CRNs is a crucial task for better interference management. In this paper, a subcarrier assignment scheme and a novel power allocation algorithm using geometric water filling is presented for OFDM based CRNs. This algorithm is optimized such a way to maximize the sum rate of secondary users by allocating power more efficiently, while constraining the 1) total transmit power, 2) individual subchannel transmit power as well as 3) individual subcarrier peak power of secondary users, for a given interference level to the primary users. Numerical results show that this algorithm provides better utilization of power resources thus maximizes the sum rate than the existing algorithms.

Task Offloading and Resource Allocation in Vehicular Networks: A Lyapunov-Based Deep Reinforcement Learning Approach

IEEE Transactions on Vehicular Technology

Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance vehicular netw... more Vehicular Edge Computing (VEC) has gained popularity due to its ability to enhance vehicular networks. VEC servers located at Roadside Units (RSUs) allow low-power vehicles to offload computation-intensive and delay-sensitive applications, making it a promising solution. However, optimal resource allocation between edge servers is a complex issue due to vehicle mobility and dynamic data traffic. To address this issue, we propose a Lyapunov-based Multi-Agent Deep Deterministic Policy Gradient (L-MADDPG) method that jointly optimizes computing task distribution and radio resource allocation to minimize energy consumption and delay requirements. We evaluate the trade-offs between the performance of the optimization algorithm, queuing model, and energy consumption. We first examine delay, queue and energy models for task execution at the vehicle or RSU, followed by the L-MADDPG algorithm for jointly optimizing task offloading and resource allocation problems to reduce energy consumption without compromising performance. Our simulation results show that our algorithm can reduce energy consumption while maintaining system performance compared to existing algorithms.

IEEE Transactions on Vehicular Technology, 2022

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (... more Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles' mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department.

Mobility Aware Channel Allocation for 5G Vehicular Networks using Multi-Agent Reinforcement Learning

ICC 2021 - IEEE International Conference on Communications, 2021

Reinforcement learning is a machine learning technique that focuses on exploring an uncharted ter... more Reinforcement learning is a machine learning technique that focuses on exploring an uncharted territory exploiting of current knowledge. This paper proposes a Mobility Aware Channel Allocation (MACA) algorithm for 5G Vehicular Networks using a combination of Multi-Agent Reinforcement Learning (MARL) and Semi-Markov Decision Process (SMDP). In this work, we use multiple autonomous agents operating in a common environment to address the sequential decision-making problem to optimize the long-term rewards. In MACA, first we predict the mobility of vehicles using Teammate-Learning model as it allows the vehicles to cooperate and collaborate with each other without prior coordination. Secondly, during SMDP resource allocation phase, MARL inputs are applied to the Action Selection model for each vehicle based on their priorities. This is done at Road-Side Units (RSUs). Through numerical results and evaluations, we verify that the proposed method demonstrates efficient channel allocation and high packet delivery ratio as compared in the scenario of vehicles with multiple (high, medium, and low) priorities to existing conventional SMDP and Greedy algorithms.

SpringerBriefs in Electrical and Computer Engineering, 2019

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.