Electrical and Electronic Engineering - Theses
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ItemOptimal and Game-theoretic Resource Allocations for Multiuser Wireless Energy-Harvesting and Communications Systems( 2019)The fifth generation (5G) of wireless cellular networks will see a paradigm shift towards extreme base station densification with massive amounts of data transmissions, massive number of mobile users, and massive number of antenna systems. To support this, network resources such as power and bandwidth will need to be efficiently allocated to multiple nodes with consideration for energy-efficiency, fairness, security, and scalability. The advancements in wireless power transfer have enabled high power conversion efficiency over practical transmission ranges. This can result in overcoming the energy constraints of wireless nodes such as mobile users, sensors, and IoT, and cutting the wires to recharging stations. As such, this results in a new dimension to resource allocation for traditional information-based communication systems due to extra consideration of energy harvested. The focus of this thesis is to design new resource allocation frameworks based on optimization-techniques and game-theory for future wireless energy-harvesting and communications systems. We consider the two main wireless power transfer (WPT) and communications technologies namely 1) Simultaneous wireless information and power transfer (SWIPT) which transfer power and information from the same access point, and 2) wireless-powered communications (WPC) with separate energy access points (EAPs) for power signals and data access points (DAPs) for information signals. For multi-user SWIPT systems with fairness constraints, we developed a max-min energy harvesting solution while satisfying the sum power budget and minimum user rate. By using the max-min energy harvesting solution we solved the dual problem which is the max-min rate satisfying minimum user energy-harvesting levels and sum power constraints. All these problems are NP-hard in nature, thus, we decompose the problem into distinct stages and developed efficient algorithms to tackle them. Furthermore, we provided insights on the total transmit power, channel bandwidth and minimum required rate considerations for the practical implementation and feasibility of energy harvesting in SWIPT systems. Security is a key concern in SWIPT systems due to the broadcast transmission of energy and information signals. Towards this end, we have developed a new optimization algorithm for secure SWIPT in OFDMA networks with multiple legitimate users communicating in the presence of an eavesdropper. The objective of our optimization framework is to maximize the total harvested-power satisfying a minimum secrecy capacity constraint for each legitimate user. We also optimized the power splitting ratio between the information and power transmission for legitimate users. To obtain deeper insights, we investigated novel game-theoretic formulations to facilitate individual user utility maximization when the users are rational players. We designed a Stackelberg game with users as multiple leaders deciding power splitting ratios as their strategy, and the base station as the follower deciding transmit power. For WPC, we proposed a new model where the EAP also acts as a relay for information signals between the users and the DAP. Specifically, we design an energy trading game between multiple relay-energy access points (REAP) and DAPs for buying energy for users. The same REAPs can be used for relaying information to the DAP based on the channel states. We exploited the cooperative communication advantages for information transfer in energy harvesting. Numerical examples for showing the effectiveness of all the proposed algorithms are given.
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ItemPerformance analysis of Hidden Markov Model based tracking algorithms( 1997)This thesis investigates the performance of Hidden Markov Model (HMM) based tracking algorithms. The algorithms considered have applications in frequency line tracking and target position tracking. The performance of these algorithms are investigated by a combination of theoretical and simulation based approaches. The theoretical based approach focuses on deriving upper bounds on probabilities of error paths in the output of the tracker. Upper bounds on specific error paths, conditioned on typical true paths are derived for a HMM based frequency line tracker that uses continuous valued observation vectors. These bounds are derived by enumerating possible estimated state sequences, and using necessary conditions on the Viterbi scores of these sequences. The derived upper bounds are found to compare well with simulation results. Next, upper bounds on average error event probabilities (averaged over all possible true paths) are derived for the same HMM based frequency tracker. Here, 'error event' refers to a brief divergence of the estimated track from the true path. Numerical computation of the derived upper bounds are shown to compare well with simulation results. Using these bounds a theorem is established which states that optimum tracking, corresponding to minimum error probability, is achieved when model transition probabilities are matched to 'true' transition probabilities of the underlying signal. Other interesting features of this algorithm are analysed, including robustness of the algorithm to variations in model transition probabilities, and characterisation of the benefits of using HMM based tracking as opposed to a simple approach based on isolated Maximum Liklihood estimators. The theoretical analysis is extended to two other HMM based frequency line trackers that use discrete valued observation vectors. A comparative study of the three HMM based frequency line trackers is carried out to arrive at conditions for the superiority of one algorithm over another. The simulation based approach to analysing performance consists of a combination of Monte-Carlo (MC) and Importance Sampling (IS) simulations. MC simulations are carried out at moderate SNR where required computation time for estimating performance measures is feasible. At high SNR, the error probabilities are small and the required computation time becomes infeasible. To overcome this, importance sampling schemes are designed which reduce the computation time by orders of magnitude. Importance sampling is a modified Monte-Carlo method which is useful in the simulation of rare probabilities. The basic principle is to use a different simulation density to increase the relative frequency of "important" events and then weight the observed data in order to obtain an unbiased estimate of the parameter of interest. In this thesis, a systematic procedure based on minimizing an upper bound to the IS estimator variance is used in the simulation density design. High efficiency gains, of order 1013 are demonstrated with the proposed scheme.