Underwater Wireless Video Transmission using Acoustic OFDM

The current project aims to design and implement an acoustic OFDM system for underwater video transmissions. The thesis work combines a theoretical part, whose objective is to choose the appropriate techniques to deal with the characteristics of the targeted channel, and a practical part regarding the system deployment and experimental test

Effect of Doppler shift on adaptive OFDM modulation for cognitive radio application

In this thesis, the effect of Doppler shift on adaptive orthogonal frequency-division multiplex (OFDM) modulation in a cognitive radio (CR) application is investigated. We present Monte Carlo simulations of OFDM modulation in which a group or groups of subcarriers are modulated using quadrature phase-shift keying (QPSK) modulation and 16-ary quadrature amplitude modulations (16QAM). We show that turning off some subcarriers does not affect the performance as long as the effective Eb/No remains the same. We also present Monte Carlo simulations where the power ratio of two sets of subcarriers is changed while maintaining the same total power in order to investigate the effect on performance. Finally, we consider a two-user CR scenario and investigate the performance effect on a primary user by a secondary user in terms of various Doppler shift offsets where both the primary user and secondary user use OFDM modulations.http://archive.org/details/effectofdopplers1094548119Lieutenant, Republic of Korea NavyApproved for public release; distribution is unlimited

Resource allocation in networks via coalitional games

The main goal of this dissertation is to manage resource allocation in network engineering problems and to introduce efficient cooperative algorithms to obtain high performance, ensuring fairness and stability. Specifically, this dissertation introduces new approaches for resource allocation in Orthogonal Frequency Division Multiple Access (OFDMA) wireless networks and in smart power grids by casting the problems to the coalitional game framework and by providing a constructive iterative algorithm based on dynamic learning theory.  Software Engineering (Software)Algorithms and the Foundations of Software technolog

An Optimization Theoretical Framework for Resource Allocation over Wireless Networks

With the advancement of wireless technologies, wireless networking has become ubiquitous owing to the great demand of pervasive mobile applications. Some fundamental challenges exist for the next generation wireless network design such as time varying nature of wireless channels, co-channel interferences, provisioning of heterogeneous type of services, etc. So how to overcome these difficulties and improve the system performance have become an important research topic. Dynamic resource allocation is a general strategy to control the interferences and enhance the performance of wireless networks. The basic idea behind dynamic resource allocation is to utilize the channel more efficiently by sharing the spectrum and reducing interference through optimizing parameters such as the transmitting power, symbol transmission rate, modulation scheme, coding scheme, bandwidth, etc. Moreover, the network performance can be further improved by introducing diversity, such as multiuser, time, frequency, and space diversity. In addition, cross layer approach for resource allocation can provide advantages such as low overhead, more efficiency, and direct end-to-end QoS provision. The designers for next generation wireless networks face the common problem of how to optimize the system objective under the user Quality of Service (QoS) constraint. There is a need of unified but general optimization framework for resource allocation to allow taking into account a diverse set of objective functions with various QoS requirements, while considering all kinds of diversity and cross layer approach. We propose an optimization theoretical framework for resource allocation and apply these ideas to different network situations such as: 1.Centralized resource allocation with fairness constraint 2.Distributed resource allocation using game theory 3.OFDMA resource allocation 4.Cross layer approach On the whole, we develop a universal view of the whole wireless networks from multiple dimensions: time, frequency, space, user, and layers. We develop some schemes to fully utilize the resources. The success of the proposed research will significantly improve the way how to design and analyze resource allocation over wireless networks. In addition, the cross-layer optimization nature of the problem provides an innovative insight into vertical integration of wireless networks

Power allocation in carrier aggregation MIMO systems with different power constraints

The target set by the International Telecommunication Union (ITU) for the next generation of mobile communications, IMT-Advanced, is to achieve up to 1 Gb/s peak data rates. The 3rd Generation Partnership Project (3GPP) introduced Carrier Aggregation (CA) technology in its latest Long Term Evolution Advanced (LTE-Advanced) standards in order to meet the performance goals of the next generation, the fourth generation, 4G. The introduction of CA in LTE-Advanced system poses a challenge to the power control function of a CA-MIMO radio link. The problem appears when multiple Carrier Components (CCs), within a single or multiple frequency bands, are allocated to a user. The two challenges studied in this thesis are the different channel characteristics in the different CCs and the multiple power constraints imposed on the mobile equipment: per-CC, per-antenna and per-total transmit power available. This thesis studies the bit error rate (BER) performance of a CA-MIMO radio link with the Modified Hybrid Gradient Optimal Power Allocation (MHGOPA) algorithm. In order to examine the validity of the MHGOPA algorithm, the results are compared to a baseline uniform power allocation approach. The results of the simulations are obtained for different environments: Indoor Hotspot, Urban Microcell, Suburban Microcell and Urban Macrocell. The results show that the MHGOPA algorithm generally outperforms the baseline uniform power allocation when the channel conditions are good with typical SNR values above 8-10 dB, depending on the environment. The results also show a marginal improvement on the BER in some scenarios when relaxing the constraints on the antennas. The simulations also show that giving primary carrier components (PCC) a privilege in power results in a large degradation in overall performance

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem