Recently, distributed detection has been intensively studied. The prevailing model for distributed detection (DD) is a system involving both distributed local sensors and a fusion center. In a DD system, multiple sensors work collaboratively to distinguish between two or more hypotheses, e.g., the presence or absence of a target. In this thesis, the classical DD problem is reexamined in the context of wireless sensor network applications. For minimize the error probability at the fusion center, we consider the conventional method that designs the optimal binary local sensor decision rule in a channel-aware system, i.e., it integrates the transmission channel characteristics for find the optimal binary local sensor decision threshold to minimize the error probability at the fusion center. And there have different optimal local sensor decision thresholds for different channel state information. Because of optimal multi-bit (soft) local sensor decision is more practical than optimal binary local sensor decision. Allowing for multi-bit local sensor output, we also consider another conventional method that designs the optimal multi-bit (soft) local sensor decision rule in a channel-aware system. However, to design the optimal local sensor decision rule, both of two conventional methods are easily trapped into local optimal thresholds, which are depended on the pre-selected initialization values. To overcome this difficulty, we consider several modified Simulated Annealing (SA) algorithms. Based on these modified SA algorithms and two conventional methods, we propose two novel SA algorithms for implementing the optimal local sensor decision rule. Computer simulation results show that the employments of two novel SA algorithms can avoid trapping into local optimal thresholds in both optimal binary local sensor decision problem and optimal multi-bit local sensor decision problem. And two novel SA algorithms offer superior performance with lower search points compared to conventional SA algorithm.

While the channel capacity of an isolated noise-limited wireless link is well-understood, the same is not true for the interference-limited wireless links that coexist in the same area and occupy the same frequency band(s). The performance of these wireless systems is coupled to each other due to the mutual interference. One such wireless scenario is modeled as a network of simultaneously communicating node pairs and is generally referred to as an interference channel (IC). The problem of characterizing the capacity of an IC is one of the most interesting and long-standing open problems in information theory. A popular way of characterizing the capacity of an IC is to maximize the achievable sum-rate by treating interference as Gaussian noise, which is considered optimal in low-interference scenarios. While the sum-rate of the single-band SISO IC is relatively well understood, it is not so when the users have multiple-bands and multiple-antennas for transmission. Therefore, the study of the optimal sum-rate of the multi-band MIMO IC is the main goal of this thesis. The sum-rate maximization problem for these ICs is formulated and is shown to be quite similar to the one already known for single-band MIMO ICs. This problem is reduced to the problem of finding the optimal fraction of power to be transmitted over each spatial channel in each frequency band. The underlying optimization problem, being non-linear and non-convex, is difficult to solve analytically or by employing local optimization techniques. Therefore, we develop a global optimization algorithm by extending the Reformulation and Linearization Technique (RLT) based Branch and Bound (BB) strategy to find the provably optimal solution to this problem. We further show that the spatial and spectral channels are surprisingly similar in a multi-band multi-antenna IC from a sum-rate maximization perspective. This result is especially interesting because of the dissimilarity in the way the spatial and frequency channels affect the perceived interference. As a part of this study, we also develop some rules-of-thumb regarding the optimal power allocation strategies in multi-band MIMO ICs in various interference regimes. Due to the recent popularity of Interference Alignment (IA) as a means of approaching capacity in an IC (in high-interference regime), we also compare the sum-rates achievable by our technique to the ones achievable by IA. The results indicate that the proposed power control technique performs better than IA in the low and intermediate interference regimes. Interestingly, the performance of the power control technique improves further relative to IA with an increase in the number of orthogonal spatial or frequency channels. / Master of Science

O principal objetivo dos veículos híbridos é diminuir o consumo de combustível em relação a veículos convencionais. Para isso, existe a necessidade de realizar a integração dos diferentes sistemas do trem-de-força e coordenar o seu funcionamento através de estratégias de controle. Tais estratégias são desenvolvidas e simuladas em conjunto com um modelo computacional da planta do veículo antes de serem aplicadas em uma unidade de controle eletrônica. O presente estudo tem como objetivo analisar o gerenciamento de energia em um veículo híbrido elétrico não-plugin do tipo série-paralelo visando à diminuição de consumo de combustível. O método de otimização global é utilizado para encontrar as variáveis de controle que resultam no mínimo consumo de combustível em um determinado ciclo de condução. Na primeira etapa, um modelo computacional da planta do veículo e da estratégia de controle não-ótima são criados. Os resultados obtidos da simulação são então comparados com dados experimentais do veículo operando em dinamômetro de chassis. A seguir, o método de otimização global é aplicado ao modelo computacional utilizando programação dinâmica e tendo como objetivo a minimização do consumo de combustível total ao final do ciclo. Os resultados mostram considerável redução do consumo de combustível utilizando otimização global e tendo como variável de controle não só a razão de distribuição de torque mas também os pontos de operação do motor de combustão. Os modelos computacionais criados nesse trabalho são disponibilizados e podem ser usados para o estudo de diferentes estratégias de controle para veículos híbridos. / The main goal of hybrid electric vehicles is to decrease engine emission and fuel consumption levels. In order to realize this, one must perform the powertrain system integration and coordinate its operation through supervisory control strategies. These control strategies are developed in a simulation environment containing the plant model of the powertrain before they can be implemented in a real-time control unit. The goal of this work is to analyze the energy management strategy which minimizes the fuel consumption in a series-parallel non-plugin hybrid electric vehicle. Global optimization is used for finding the control variables that result in the minimum fuel consumption for a specific driving cycle. In a first stage, a computational model of vehicle plant and non-optimal control strategy are created. The results from the simulation are compared against experimental data from chassis dynamometer tests. Next, a global optimization strategy is applied using dynamic programming in order to minimize total fuel consumption at the end of the driving cycle. The results from the optimization show a considerable fuel consumption reduction having as control variables not only the torque-split strategy but also the engine operating points. As contribution from this work, the computational models are made available and can be used for analyzing different control strategies for hybrid vehicles.

O principal objetivo dos veículos híbridos é diminuir o consumo de combustível em relação a veículos convencionais. Para isso, existe a necessidade de realizar a integração dos diferentes sistemas do trem-de-força e coordenar o seu funcionamento através de estratégias de controle. Tais estratégias são desenvolvidas e simuladas em conjunto com um modelo computacional da planta do veículo antes de serem aplicadas em uma unidade de controle eletrônica. O presente estudo tem como objetivo analisar o gerenciamento de energia em um veículo híbrido elétrico não-plugin do tipo série-paralelo visando à diminuição de consumo de combustível. O método de otimização global é utilizado para encontrar as variáveis de controle que resultam no mínimo consumo de combustível em um determinado ciclo de condução. Na primeira etapa, um modelo computacional da planta do veículo e da estratégia de controle não-ótima são criados. Os resultados obtidos da simulação são então comparados com dados experimentais do veículo operando em dinamômetro de chassis. A seguir, o método de otimização global é aplicado ao modelo computacional utilizando programação dinâmica e tendo como objetivo a minimização do consumo de combustível total ao final do ciclo. Os resultados mostram considerável redução do consumo de combustível utilizando otimização global e tendo como variável de controle não só a razão de distribuição de torque mas também os pontos de operação do motor de combustão. Os modelos computacionais criados nesse trabalho são disponibilizados e podem ser usados para o estudo de diferentes estratégias de controle para veículos híbridos. / The main goal of hybrid electric vehicles is to decrease engine emission and fuel consumption levels. In order to realize this, one must perform the powertrain system integration and coordinate its operation through supervisory control strategies. These control strategies are developed in a simulation environment containing the plant model of the powertrain before they can be implemented in a real-time control unit. The goal of this work is to analyze the energy management strategy which minimizes the fuel consumption in a series-parallel non-plugin hybrid electric vehicle. Global optimization is used for finding the control variables that result in the minimum fuel consumption for a specific driving cycle. In a first stage, a computational model of vehicle plant and non-optimal control strategy are created. The results from the simulation are compared against experimental data from chassis dynamometer tests. Next, a global optimization strategy is applied using dynamic programming in order to minimize total fuel consumption at the end of the driving cycle. The results from the optimization show a considerable fuel consumption reduction having as control variables not only the torque-split strategy but also the engine operating points. As contribution from this work, the computational models are made available and can be used for analyzing different control strategies for hybrid vehicles.