Moazzam Tiwana - Academia.edu (original) (raw)

Papers by Moazzam Tiwana

Research paper thumbnail of Optimisation automatique des paramètres RRM des réseaux LTE en utilisant l'apprentissage statistique

Le secteur des télécommunications mobiles a connu une croissance très rapide dans un passé récent... more Le secteur des télécommunications mobiles a connu une croissance très rapide dans un passé récent avec pour résultat d'importantes évolutions technologiques et architecturales des réseaux sans fil. L'expansion et l'hétérogénéité de ces réseaux ont engendré des coûts de fonctionnement de plus en plus importants. Les dysfonctionnements typiques de ces réseaux ont souvent pour origines des pannes d'équipements ainsi que de mauvaises planifications et/ou configurations. Dans ce contexte, le dépannage automatisé des réseaux sans fil peut s'avérer d'une importance particulière visant à réduire les coûts opérationnels et à fournir une bonne qualité de service aux utilisateurs. Le dépannage automatisé des pannes survenant sur les réseaux sans fil peuvent ainsi conduire à une réduction du temps d'interruption de service pour les clients, permettant ainsi d'éviter l'orientation de ces derniers vers les opérateurs concurrents. Le RAN (Radio Access Network) d...

Research paper thumbnail of Neural Networks for Energy-Efficient Self Optimization of eNodeB Antenna Tilt in 5G Mobile Network Environments

Research paper thumbnail of Recognition of finger movements using EEG signals for control of upper limb prosthesis using logistic regression

Biomedical Research-tokyo, 2017

Brain computer interface decodes signals that the human brain generates and uses them to control ... more Brain computer interface decodes signals that the human brain generates and uses them to control external devices. The signals that are acquired are classified into movements on the basis of feature vector after being extracted from raw signals. This paper presents a novel method of classification of four finger movements (thumb movement, index finger movement, middle and index finger combined movement and fist movement) of the right hand on the basis of EEG (Electroencephalogram) data of the movements. The data-set was obtained from a right-handed neurologically intact volunteer using a noninvasive BCI (Brain-Computer Interface) system. The signals were obtained using a 14 channel electrode headset. The EEG signals that are obtained are first filtered to retain alpha and beta band (8-30 Hz) as they contain the maximum information of movement. Power Spectral Density (PSD) is used for analysis of the filtered EEG data. Classification of the features is done using various classifiers....

Research paper thumbnail of Self-organizing inter-cell interference coordination in 4G and beyond networks using genetic algorithms

Automatika, 2017

The design objective of the 4G and beyond networks is not only to provide high data rate services... more The design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.

Research paper thumbnail of Distributed self optimization techniques for heterogeneous network environments using active antenna tilt systems

Telecommunication Systems, 2018

Active antenna systems in 4G and upcoming 5G networks offer the ability to electronically steer a... more Active antenna systems in 4G and upcoming 5G networks offer the ability to electronically steer an antenna beam in any desired direction. This unique feature makes them a suitable candidate for realizing self organizing network (SON) architectures in 5G for optimizing of key performance indicators like throughput, file transfer time etc. In this paper, we aim to analyse the effect of increasing number of input variables and complexity of learning techniques on the performance of the network. We compare performance of simple stochastic cellular learning automata (SCLA) technique with only one input to comparatively complex Q-learning technique with two or more inputs. We use FTP flow based 5G network simulator for our work. The SON architecture model proposed, is distributed with optional inter cell communication. Simulation results reveal that increasing complexity of learning process does not necessarily benefit the system performance. The simple SCLA technique shows more robust performance compared to Q-learning case. However, within the same technique increasing the number of input variables does benefit the system, indicating that a complex technique can ultimately prove beneficial in complicated scenarios provided it is able to quickly process and adapt to the environment.

Research paper thumbnail of GA Based Estimation of Sparse MIMO Channels with Superimposed Training

Elektronika ir Elektrotechnika, 2018

Multiple-input multiple-output (MIMO) techniques are foreseen to play a vital role in future 5G c... more Multiple-input multiple-output (MIMO) techniques are foreseen to play a vital role in future 5G cellular networks. This paper presents a novel approach that employs genetic algorithm (GA) to estimate the sparse uplink MIMO channels using superimposed training sequence (SiT). At each transmitter (user) a training sequence is mathematically added with the data bits; thus, avoiding the overhead of dedicated frequency/time slots used for the training. On the receiver side (base station), signals received at all the receive antennas are jointly processed by employing the proposed method to obtain channels' estimate. Then, a linear minimum mean square error (LMMSE) equalizer estimates the data sequences sent by transmitter. A computer simulation based performance analysis of the proposed method is presented, where performance evaluation is done using metrics of normalized channel mean square error (NCMSE), as well as, bit error rate (BER). A comparative analysis of the proposed method with notable SiT least squares (SiT-LS) and SiT-LMMSE methods in the literature is conducted, which clearly demonstrates that the proposed method outperforms both the existing techniques.

Research paper thumbnail of Energy and area spectral efficiency trade-off for MC-CDMA with carrier frequency offset

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2017

Intercell interference is a major factor that limits the capacity of cellular wireless communicat... more Intercell interference is a major factor that limits the capacity of cellular wireless communication systems. This paper proposes an accurate statistical model that caters to interference and noise to determine the ergodic capacity. A new expression for ergodic capacity is derived that enables us to calculate area spectral efficiency (ASE) and energy efficiency (EE). This expression has been used to calculate and compare ASE and EE for low and high traffic scenarios with various signal-to-noise ratios and intercell distances.

Research paper thumbnail of A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets

Radioengineering, 2016

Long Term Evolution Advanced (LTE-A) Heterogeneous Networks (HetNet) are an important aspect of 5... more Long Term Evolution Advanced (LTE-A) Heterogeneous Networks (HetNet) are an important aspect of 5th generation mobile communication systems. They consists of high power macrocells along with low power cells i.e. picocells and femtocells to fill up macrocell coverage gaps. HetNet permit deployment of femtocells by users for added flexibility, but then interference issues between neighbouring cells have to be addressed as all femtocells use the same frequency channels for transmission. To mitigate this problem, LTE-A standard offers two new features, one is carrier aggregation in which Component Carriers (CC) form the basic aggregate units shared among cells and the other is enhanced Inter-Cell Interference Coordination (eICIC) through X2 interface. The physical implementation of these features is left open to research. This paper investigates two distinct techniques for orthogonal CC selection through Stochastic Cellular Learning Automata (SCLA) to improve the QoS performance of a femtocell. The first technique uses SCLA with user feedback, and the second technique uses SCLA with a central publishing server where all cells upload their past used CC vectors. SCLA methods are better suited for Self Organizing Network (SON) as they do not require synchronized cell coordination, have low complexity and have good optimization characteristics. The simulation results show that the techniques enhance the cell edge performance considerably.

Research paper thumbnail of Bio-Inspired PVDF-Based, Mouse Whisker Mimicking, Tactile Sensor

Applied Sciences, 2016

The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker m... more The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker mimicking, tactile sensor is presented. Unlike previous designs reported in the literature, this sensor mimics the mouse whisker not only mechanically, but it also makes macro movements just like a real mouse whisker in a natural environment. We have developed a mathematical model and performed finite element analysis using COMSOL, in order to optimise the whisker to have the same natural frequency as that of a biological whisker. Similarly, we have developed a control system that enables the whisker mimicking sensor to vibrate at variable frequencies and conducted practical experiments to validate the response of the sensor. The natural frequency of the whisker can be designed anywhere between 35 and 110 Hz, the same as a biological whisker, by choosing different materials and physical dimensions. The control system of this sensor enables the whisker to vibrate between 5 and 236 Hz.

Research paper thumbnail of GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

Elektronika ir Elektrotechnika, 2016

This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimatio... more This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A nonrandom and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature-the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10-1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size.

Research paper thumbnail of Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

Automatika ‒ Journal for Control, Measurement, Electronics, Computing and Communications, 2014

Original scientific paper With the evolution of broadband mobile networks towards LTE and beyond,... more Original scientific paper With the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as cooperative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput.

Research paper thumbnail of Airborne internet access through submarine optical fiber cables

IEEE Transactions on Aerospace and Electronic Systems, 2015

Internet access for passengers travelling in aircrafts is thought to be one of the unresolved maj... more Internet access for passengers travelling in aircrafts is thought to be one of the unresolved major challenges for ubiquitous Internet provision. Vast oceanic remote regions along the busy air routes of the world require low-cost, reliable, and high-speed Internet for the aircraft. Satellite links can provide Internet coverage in such remote areas; however, their services are still costly with low bandwidth and longer delays. Fortunately, the submarine optical cables deployed across the oceans pass along the same busy air routes. These cables can be utilized as high-speed Internet backbone for wireless Internet access to the aircraft. Dedicated ships stationed along these submarine optical fiber cables can be exploited to provide Internet, security, and navigation services to aircrafts and ships. A novel architecture for such a ground/sea-to-air access network is proposed. A complete solution, design, and analysis of the proposed technique are thoroughly discussed. In contrast to the traditional land mobile radio cellular systems, the high speed of the aircraft results in reduced available handover time margins. To address the challenges related to the high-speed mobility of aircraft, an analysis for the impact of various parameters on the performance of handovers is presented. Using the proposed analytical model, a mathematical relation for the handover margin with the velocity of aircraft, direction of the aircraft's motion, and propagation environment is derived on the basis of path-loss propagation model.

Research paper thumbnail of Auto-tuning in LTE networks using joint RRM optimization

2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2015

Balancing of load reduces call blocking and 3GPP considers it as an important part of Self-Organi... more Balancing of load reduces call blocking and 3GPP considers it as an important part of Self-Organizing Networks because of its efficiency in increasing network capacity. Moreover, Inter-Cell Interference Coordination (ICIC) is also a key radio resource management parameter to enhance system performance of next generation networks. In this work, joint optimization of above mentioned RRM parameters has been proposed for the improvement in system KPIs (Key Performance Indicators). Improvement has been compared with the results achieved by optimization of single RRM parameter and comparison shows that joint optimization technique improves system performance significantly.

Research paper thumbnail of Self-Organizing Networks: A Packet Scheduling Approach for Coverage/Capacity Optimization in 4G Networks Using Reinforcement Learning

Elektronika ir Elektrotechnika, 2014

The next generation mobile networks LTE and LTE-A are all-IP based networks. In such IP based net... more The next generation mobile networks LTE and LTE-A are all-IP based networks. In such IP based networks, the issue of Quality of Service (QoS) is becoming more and more critical with the increase in network size and heterogeneity. In this paper, a Reinforcement Learning (RL) based framework for QoS enhancement is proposed. The framework achieves the coverage/capacity optimization by adjusting the scheduling strategy. The proposed selfoptimization algorithm uses coverage/capacity compromise in Packet Scheduling (PS) to maximize the capacity of an eNB subject to the condition that minimum coverage constraint is not violated. Each eNB has an associated agent that dynamically changes the scheduling parameter value of an eNB. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal scheduling parameter. The learning framework is designed to operate in an environment with varying traffic, user positions, and propagation conditions. A comprehensive analysis on the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage as well as capacity in terms of throughput.

Research paper thumbnail of A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility

Journal of Network and Systems Management, 2013

With the evolution of broadband mobile networks towards LTE and beyond, the support for the inter... more With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach.

Research paper thumbnail of Closed loop blood glucose control in diabetics

Biomedical Research-tokyo, 2017

Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Especially, type... more Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Especially, type-1 diabetics have required strict glycemic control. In this paper, close loop control system is designed to normalize the high blood glucose level for diabetes patients. Glucose-insulin dynamics in blood plasma are represented by Bergman minimal mathematical model which is used as base model. The dynamical variations between different and same individual poses a major challenge in designing of controller for biological systems. The contribution of this research work lies in designing a backstepping based nonlinear controller which perfectly deals with nonlinearities present in the system. In order to visualize the robust behavior, meal and exercise are added as a disturbance factor and controller effectively track the set point value of 70 mg/dL from an initial state of hyperglycemia. The control criteria imposed on the proposed controller are hyperglycemia and hypoglycemia.

Research paper thumbnail of Data Ridden Pilots Based Estimation of Sparse Multipath Massive MIMO Channels Using Orthogonal Matching Pursuit

Elektronika ir Elektrotechnika

Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology f... more Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results...

Research paper thumbnail of Statistical Learning for Automated RRM: Application to eUTRAN Mobility

2009 Ieee International Conference on Communications, Jun 14, 2009

Self organizing network (SON) functionalities are currently developed to improve network performa... more Self organizing network (SON) functionalities are currently developed to improve network performance and management tasks. SON functionalities require efficient utilization of data extracted from the network. In this context, the paper has two objectives. First it is shown that one can use simple statistical learning techniques such as regression to extract a model from data. The model comprises closed form

Research paper thumbnail of Enhancemant of the Statistical Learning Automated Healing (SLAH) technique using packet scheduling

Emerging Technologies (ICET), 2012 International …, 2012

ABSTRACT Automated healing aims to reduce cost of network operations by automated fault diagnosis... more ABSTRACT Automated healing aims to reduce cost of network operations by automated fault diagnosis and rectification. This paper investigates the use of Packet Scheduling (PS) in automated healing. PS has been integrated into a previously proposed scheme of Statistical Learning Automated Healing (SLAH) for LTE. SLAH locally optimizes the Radio Resource Management (RRM) parameters of the faulty eNodeBs (eNBs). SLAH uses Logistic Regression (LoR) to extract the closed form relationship between the RRM parameters and the network measurements which are in the form of Key Performance Indicators (KPIs). This paper uses PS in SLAH to achieve the required minimum coverage constraint on an eNB by coverage/capacity compromise. Similarly, if this minimum coverage requirement of an eNB is already satisfied, additional capacity gain for an eNB can be achieved. This enhanced SLAH methdology has been used to rectify faults due to excessive interference suffered by an eNB from its first tier neighbours. Simulation results of the case study done prove that this technique converges in few iterations.

Research paper thumbnail of Enhancing RRM optimization using a priori knowledge for automated troubleshooting

… in Mobile, Ad Hoc and Wireless …, 2010

The paper presents a methodology that combines statistical learning with constraint optimization ... more The paper presents a methodology that combines statistical learning with constraint optimization by locally optimizing Radio Resource Management (RRM) or system parameters of poorly performing cells in an iterative manner. The statistical learning technique used is Logistic Regression (LR) which is applied on the data in the form of RRM-KPI (Key Performance Indicator) pairs. LR extracts closed form (functional) relations, known as the model, between KPIs and RRM parameters. This model is then processed by an optimization engine which proposes a new RRM parameter value. The RRM parameter value is reinserted in the network/simulator to generate corresponding KPI vector constituting generated RRM-KPI pair. First, only the a priori RRM-KPI pairs which are based upon the a priori model information are used for the model extraction. Then, as the optimization iterations progress, the generated pairs are given more importance in model extraction and the model is iteratively refined. The use of the a priori knowledge has the advantage of avoiding wrong initial models due to noisy data, allows much faster convergence and makes it more suitable for the off-line implementation. The proposed method is applied to troubleshoot an Inter-Cell Interference Coordination (ICIC) process in a LTE network which is based on soft-frequency reuse scheme.

Research paper thumbnail of Optimisation automatique des paramètres RRM des réseaux LTE en utilisant l'apprentissage statistique

Le secteur des télécommunications mobiles a connu une croissance très rapide dans un passé récent... more Le secteur des télécommunications mobiles a connu une croissance très rapide dans un passé récent avec pour résultat d'importantes évolutions technologiques et architecturales des réseaux sans fil. L'expansion et l'hétérogénéité de ces réseaux ont engendré des coûts de fonctionnement de plus en plus importants. Les dysfonctionnements typiques de ces réseaux ont souvent pour origines des pannes d'équipements ainsi que de mauvaises planifications et/ou configurations. Dans ce contexte, le dépannage automatisé des réseaux sans fil peut s'avérer d'une importance particulière visant à réduire les coûts opérationnels et à fournir une bonne qualité de service aux utilisateurs. Le dépannage automatisé des pannes survenant sur les réseaux sans fil peuvent ainsi conduire à une réduction du temps d'interruption de service pour les clients, permettant ainsi d'éviter l'orientation de ces derniers vers les opérateurs concurrents. Le RAN (Radio Access Network) d...

Research paper thumbnail of Neural Networks for Energy-Efficient Self Optimization of eNodeB Antenna Tilt in 5G Mobile Network Environments

Research paper thumbnail of Recognition of finger movements using EEG signals for control of upper limb prosthesis using logistic regression

Biomedical Research-tokyo, 2017

Brain computer interface decodes signals that the human brain generates and uses them to control ... more Brain computer interface decodes signals that the human brain generates and uses them to control external devices. The signals that are acquired are classified into movements on the basis of feature vector after being extracted from raw signals. This paper presents a novel method of classification of four finger movements (thumb movement, index finger movement, middle and index finger combined movement and fist movement) of the right hand on the basis of EEG (Electroencephalogram) data of the movements. The data-set was obtained from a right-handed neurologically intact volunteer using a noninvasive BCI (Brain-Computer Interface) system. The signals were obtained using a 14 channel electrode headset. The EEG signals that are obtained are first filtered to retain alpha and beta band (8-30 Hz) as they contain the maximum information of movement. Power Spectral Density (PSD) is used for analysis of the filtered EEG data. Classification of the features is done using various classifiers....

Research paper thumbnail of Self-organizing inter-cell interference coordination in 4G and beyond networks using genetic algorithms

Automatika, 2017

The design objective of the 4G and beyond networks is not only to provide high data rate services... more The design objective of the 4G and beyond networks is not only to provide high data rate services but also ensure a good subscriber experience in terms of quality of service. However, the main challenge to this objective is the growing size and heterogeneity of these networks. This paper proposes a genetic-algorithm-based approach for the self-optimization of interference mitigation parameters for downlink inter-cell interference coordination parameter in Long Term Evolution (LTE) networks. The proposed algorithm is generic in nature and operates in an environment with the variations in traffic, user positions and propagation conditions. A comprehensive analysis of the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage in terms of call accept rate as well as capacity in terms of throughput.

Research paper thumbnail of Distributed self optimization techniques for heterogeneous network environments using active antenna tilt systems

Telecommunication Systems, 2018

Active antenna systems in 4G and upcoming 5G networks offer the ability to electronically steer a... more Active antenna systems in 4G and upcoming 5G networks offer the ability to electronically steer an antenna beam in any desired direction. This unique feature makes them a suitable candidate for realizing self organizing network (SON) architectures in 5G for optimizing of key performance indicators like throughput, file transfer time etc. In this paper, we aim to analyse the effect of increasing number of input variables and complexity of learning techniques on the performance of the network. We compare performance of simple stochastic cellular learning automata (SCLA) technique with only one input to comparatively complex Q-learning technique with two or more inputs. We use FTP flow based 5G network simulator for our work. The SON architecture model proposed, is distributed with optional inter cell communication. Simulation results reveal that increasing complexity of learning process does not necessarily benefit the system performance. The simple SCLA technique shows more robust performance compared to Q-learning case. However, within the same technique increasing the number of input variables does benefit the system, indicating that a complex technique can ultimately prove beneficial in complicated scenarios provided it is able to quickly process and adapt to the environment.

Research paper thumbnail of GA Based Estimation of Sparse MIMO Channels with Superimposed Training

Elektronika ir Elektrotechnika, 2018

Multiple-input multiple-output (MIMO) techniques are foreseen to play a vital role in future 5G c... more Multiple-input multiple-output (MIMO) techniques are foreseen to play a vital role in future 5G cellular networks. This paper presents a novel approach that employs genetic algorithm (GA) to estimate the sparse uplink MIMO channels using superimposed training sequence (SiT). At each transmitter (user) a training sequence is mathematically added with the data bits; thus, avoiding the overhead of dedicated frequency/time slots used for the training. On the receiver side (base station), signals received at all the receive antennas are jointly processed by employing the proposed method to obtain channels' estimate. Then, a linear minimum mean square error (LMMSE) equalizer estimates the data sequences sent by transmitter. A computer simulation based performance analysis of the proposed method is presented, where performance evaluation is done using metrics of normalized channel mean square error (NCMSE), as well as, bit error rate (BER). A comparative analysis of the proposed method with notable SiT least squares (SiT-LS) and SiT-LMMSE methods in the literature is conducted, which clearly demonstrates that the proposed method outperforms both the existing techniques.

Research paper thumbnail of Energy and area spectral efficiency trade-off for MC-CDMA with carrier frequency offset

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES, 2017

Intercell interference is a major factor that limits the capacity of cellular wireless communicat... more Intercell interference is a major factor that limits the capacity of cellular wireless communication systems. This paper proposes an accurate statistical model that caters to interference and noise to determine the ergodic capacity. A new expression for ergodic capacity is derived that enables us to calculate area spectral efficiency (ASE) and energy efficiency (EE). This expression has been used to calculate and compare ASE and EE for low and high traffic scenarios with various signal-to-noise ratios and intercell distances.

Research paper thumbnail of A Novel Stochastic Learning Automata Based SON Interference Mitigation Framework for 5G HetNets

Radioengineering, 2016

Long Term Evolution Advanced (LTE-A) Heterogeneous Networks (HetNet) are an important aspect of 5... more Long Term Evolution Advanced (LTE-A) Heterogeneous Networks (HetNet) are an important aspect of 5th generation mobile communication systems. They consists of high power macrocells along with low power cells i.e. picocells and femtocells to fill up macrocell coverage gaps. HetNet permit deployment of femtocells by users for added flexibility, but then interference issues between neighbouring cells have to be addressed as all femtocells use the same frequency channels for transmission. To mitigate this problem, LTE-A standard offers two new features, one is carrier aggregation in which Component Carriers (CC) form the basic aggregate units shared among cells and the other is enhanced Inter-Cell Interference Coordination (eICIC) through X2 interface. The physical implementation of these features is left open to research. This paper investigates two distinct techniques for orthogonal CC selection through Stochastic Cellular Learning Automata (SCLA) to improve the QoS performance of a femtocell. The first technique uses SCLA with user feedback, and the second technique uses SCLA with a central publishing server where all cells upload their past used CC vectors. SCLA methods are better suited for Self Organizing Network (SON) as they do not require synchronized cell coordination, have low complexity and have good optimization characteristics. The simulation results show that the techniques enhance the cell edge performance considerably.

Research paper thumbnail of Bio-Inspired PVDF-Based, Mouse Whisker Mimicking, Tactile Sensor

Applied Sciences, 2016

The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker m... more The design and fabrication of a Polyvinylidene fluoride (PVDF) based, mouse (or rodent) whisker mimicking, tactile sensor is presented. Unlike previous designs reported in the literature, this sensor mimics the mouse whisker not only mechanically, but it also makes macro movements just like a real mouse whisker in a natural environment. We have developed a mathematical model and performed finite element analysis using COMSOL, in order to optimise the whisker to have the same natural frequency as that of a biological whisker. Similarly, we have developed a control system that enables the whisker mimicking sensor to vibrate at variable frequencies and conducted practical experiments to validate the response of the sensor. The natural frequency of the whisker can be designed anywhere between 35 and 110 Hz, the same as a biological whisker, by choosing different materials and physical dimensions. The control system of this sensor enables the whisker to vibrate between 5 and 236 Hz.

Research paper thumbnail of GA Based Sensing of Sparse Multipath Channels with Superimposed Training Sequence

Elektronika ir Elektrotechnika, 2016

This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimatio... more This paper proposes an improved Genetic Algorithms (GA) based sparse multipath channels estimation technique with Superimposed Training (ST) sequences. A nonrandom and periodic training sequence is proposed to be added arithmetically on the information sequence for energy efficient channel estimation within the future generation of wireless receivers. This eliminates the need of separate overhead time/frequency slots for training sequence. The results of the proposed technique are compared with the techniques in the existing literature-the notable first order statistics based channel estimation technique with ST. The normalized channel mean-square error (NCMSE) and bit-error-rate (BER) are chosen as performance measures for the simulation based analysis. It is established that the proposed technique performs better in terms of the accuracy of estimated channel; subsequently the quality of service (QoS), while retrieving information sequence at the receiver. With respect to its comparable counterpart, the proposed GA based scheme delivers an improvement of about 1dB in NCMSE at 12 dB SNR and a gain of about 2 dB in SNR at 10-1 BER, for the population size set at twice the length of channel. It is also demonstrated that, this achievement in performance improvement can further be enhanced at the cost of computational power by increasing the population size.

Research paper thumbnail of Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

Automatika ‒ Journal for Control, Measurement, Electronics, Computing and Communications, 2014

Original scientific paper With the evolution of broadband mobile networks towards LTE and beyond,... more Original scientific paper With the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as cooperative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput.

Research paper thumbnail of Airborne internet access through submarine optical fiber cables

IEEE Transactions on Aerospace and Electronic Systems, 2015

Internet access for passengers travelling in aircrafts is thought to be one of the unresolved maj... more Internet access for passengers travelling in aircrafts is thought to be one of the unresolved major challenges for ubiquitous Internet provision. Vast oceanic remote regions along the busy air routes of the world require low-cost, reliable, and high-speed Internet for the aircraft. Satellite links can provide Internet coverage in such remote areas; however, their services are still costly with low bandwidth and longer delays. Fortunately, the submarine optical cables deployed across the oceans pass along the same busy air routes. These cables can be utilized as high-speed Internet backbone for wireless Internet access to the aircraft. Dedicated ships stationed along these submarine optical fiber cables can be exploited to provide Internet, security, and navigation services to aircrafts and ships. A novel architecture for such a ground/sea-to-air access network is proposed. A complete solution, design, and analysis of the proposed technique are thoroughly discussed. In contrast to the traditional land mobile radio cellular systems, the high speed of the aircraft results in reduced available handover time margins. To address the challenges related to the high-speed mobility of aircraft, an analysis for the impact of various parameters on the performance of handovers is presented. Using the proposed analytical model, a mathematical relation for the handover margin with the velocity of aircraft, direction of the aircraft's motion, and propagation environment is derived on the basis of path-loss propagation model.

Research paper thumbnail of Auto-tuning in LTE networks using joint RRM optimization

2015 12th International Bhurban Conference on Applied Sciences and Technology (IBCAST), 2015

Balancing of load reduces call blocking and 3GPP considers it as an important part of Self-Organi... more Balancing of load reduces call blocking and 3GPP considers it as an important part of Self-Organizing Networks because of its efficiency in increasing network capacity. Moreover, Inter-Cell Interference Coordination (ICIC) is also a key radio resource management parameter to enhance system performance of next generation networks. In this work, joint optimization of above mentioned RRM parameters has been proposed for the improvement in system KPIs (Key Performance Indicators). Improvement has been compared with the results achieved by optimization of single RRM parameter and comparison shows that joint optimization technique improves system performance significantly.

Research paper thumbnail of Self-Organizing Networks: A Packet Scheduling Approach for Coverage/Capacity Optimization in 4G Networks Using Reinforcement Learning

Elektronika ir Elektrotechnika, 2014

The next generation mobile networks LTE and LTE-A are all-IP based networks. In such IP based net... more The next generation mobile networks LTE and LTE-A are all-IP based networks. In such IP based networks, the issue of Quality of Service (QoS) is becoming more and more critical with the increase in network size and heterogeneity. In this paper, a Reinforcement Learning (RL) based framework for QoS enhancement is proposed. The framework achieves the coverage/capacity optimization by adjusting the scheduling strategy. The proposed selfoptimization algorithm uses coverage/capacity compromise in Packet Scheduling (PS) to maximize the capacity of an eNB subject to the condition that minimum coverage constraint is not violated. Each eNB has an associated agent that dynamically changes the scheduling parameter value of an eNB. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal scheduling parameter. The learning framework is designed to operate in an environment with varying traffic, user positions, and propagation conditions. A comprehensive analysis on the obtained simulation results is presented, which shows that the proposed approach can significantly improve the network coverage as well as capacity in terms of throughput.

Research paper thumbnail of A Novel Framework of Automated RRM for LTE SON Using Data Mining: Application to LTE Mobility

Journal of Network and Systems Management, 2013

With the evolution of broadband mobile networks towards LTE and beyond, the support for the inter... more With the evolution of broadband mobile networks towards LTE and beyond, the support for the internet and internet based services is growing. However, the size and operational costs of mobile networks are also growing. Self Organizing Networks (SON) are introduced as a part of the specifications of the LTE standard with the purpose of reducing the Operation and Maintenance costs of the mobile networks. This paper introduces a novel framework for automated Radio Resource Management (RRM) in LTE SON. This framework deals with the self-optimization and self-healing features of SON. The data mining technique of linear regression has been used to derive the functional relationship, known as model, between Key Performance Indicators and RRM parameters. The proposed framework uses this model in two ways: first, for network monitoring, which is the first step of the self-healing procedure and secondly, to devise a handover auto-tuning algorithm as part of the self-optimization procedure. The detailed results obtained for the finished case studies, demonstrate the effectiveness and usefulness of this approach.

Research paper thumbnail of Closed loop blood glucose control in diabetics

Biomedical Research-tokyo, 2017

Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Especially, type... more Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Especially, type-1 diabetics have required strict glycemic control. In this paper, close loop control system is designed to normalize the high blood glucose level for diabetes patients. Glucose-insulin dynamics in blood plasma are represented by Bergman minimal mathematical model which is used as base model. The dynamical variations between different and same individual poses a major challenge in designing of controller for biological systems. The contribution of this research work lies in designing a backstepping based nonlinear controller which perfectly deals with nonlinearities present in the system. In order to visualize the robust behavior, meal and exercise are added as a disturbance factor and controller effectively track the set point value of 70 mg/dL from an initial state of hyperglycemia. The control criteria imposed on the proposed controller are hyperglycemia and hypoglycemia.

Research paper thumbnail of Data Ridden Pilots Based Estimation of Sparse Multipath Massive MIMO Channels Using Orthogonal Matching Pursuit

Elektronika ir Elektrotechnika

Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology f... more Massive Multiple-Input Multiple-Output (MIMO) is envisioned to be a strong candidate technology for the upcoming 5th generation (5G) of wireless communication networks. This research work presents a novel Compressed Sensing (CS) and Superimposed Training (SiT) based technique for estimating the sparse uplink channels in massive MIMO systems. The proposed technique involves arithmetic addition of a periodic, but low powered training sequence with each user’s information sequence. Consequently, separately dedicated resources for the pilot symbols are not needed. Moreover, to attain the estimates of the Channel State Information (CSI) in the uplink, the sparsity exhibited by the MIMO channels is exploited by incorporating CS based Orthogonal Matching Pursuit (OMP) algorithm. For decoding the transmitted information symbols of each user, a Linear Minimum Mean Square Error (LMMSE) based equalizer is incorporated at the receiving Base Station (BS). Based on the obtained simulation results...

Research paper thumbnail of Statistical Learning for Automated RRM: Application to eUTRAN Mobility

2009 Ieee International Conference on Communications, Jun 14, 2009

Self organizing network (SON) functionalities are currently developed to improve network performa... more Self organizing network (SON) functionalities are currently developed to improve network performance and management tasks. SON functionalities require efficient utilization of data extracted from the network. In this context, the paper has two objectives. First it is shown that one can use simple statistical learning techniques such as regression to extract a model from data. The model comprises closed form

Research paper thumbnail of Enhancemant of the Statistical Learning Automated Healing (SLAH) technique using packet scheduling

Emerging Technologies (ICET), 2012 International …, 2012

ABSTRACT Automated healing aims to reduce cost of network operations by automated fault diagnosis... more ABSTRACT Automated healing aims to reduce cost of network operations by automated fault diagnosis and rectification. This paper investigates the use of Packet Scheduling (PS) in automated healing. PS has been integrated into a previously proposed scheme of Statistical Learning Automated Healing (SLAH) for LTE. SLAH locally optimizes the Radio Resource Management (RRM) parameters of the faulty eNodeBs (eNBs). SLAH uses Logistic Regression (LoR) to extract the closed form relationship between the RRM parameters and the network measurements which are in the form of Key Performance Indicators (KPIs). This paper uses PS in SLAH to achieve the required minimum coverage constraint on an eNB by coverage/capacity compromise. Similarly, if this minimum coverage requirement of an eNB is already satisfied, additional capacity gain for an eNB can be achieved. This enhanced SLAH methdology has been used to rectify faults due to excessive interference suffered by an eNB from its first tier neighbours. Simulation results of the case study done prove that this technique converges in few iterations.

Research paper thumbnail of Enhancing RRM optimization using a priori knowledge for automated troubleshooting

… in Mobile, Ad Hoc and Wireless …, 2010

The paper presents a methodology that combines statistical learning with constraint optimization ... more The paper presents a methodology that combines statistical learning with constraint optimization by locally optimizing Radio Resource Management (RRM) or system parameters of poorly performing cells in an iterative manner. The statistical learning technique used is Logistic Regression (LR) which is applied on the data in the form of RRM-KPI (Key Performance Indicator) pairs. LR extracts closed form (functional) relations, known as the model, between KPIs and RRM parameters. This model is then processed by an optimization engine which proposes a new RRM parameter value. The RRM parameter value is reinserted in the network/simulator to generate corresponding KPI vector constituting generated RRM-KPI pair. First, only the a priori RRM-KPI pairs which are based upon the a priori model information are used for the model extraction. Then, as the optimization iterations progress, the generated pairs are given more importance in model extraction and the model is iteratively refined. The use of the a priori knowledge has the advantage of avoiding wrong initial models due to noisy data, allows much faster convergence and makes it more suitable for the off-line implementation. The proposed method is applied to troubleshoot an Inter-Cell Interference Coordination (ICIC) process in a LTE network which is based on soft-frequency reuse scheme.