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• W2242789155 abstract "The rapid advance in wireless communications and the growth in demand of wireless Internet services have resulted in a tremendous increase in complexity and heterogeneity of wireless networked systems. The management, operation and optimization of such systems is an increasing difficult task. In this thesis we address some of that complexity and cross-layer interactions by employing statistical signal processing techniques to analyze, model and optimize wireless networked systems for several representative examples across the protocol stack. For the physical and transport layers, in Chapter 2 we consider the performance of TCP over multi-antenna (MIMO) wireless systems, in particular the BLAST and the space-time block coding (STBC) systems. We analyze the impact of automated-repeat-request (ARQ), packet combining and antenna correlation on the overall system performance. Our results provide some meaningful insights on cross-layer issues, and in particular we show that increasing spectral efficiency does not necessary result in an increase of the TCP throughput. In order to further characterize the TCP/MIMO systems, in Chapter 3 we develop an analytical framework for the analysis of the performance of TCP/UDP over MIMO systems. The model incorporates several design parameters such as fading, space-time transmission schemes, modulation, channel coding and ARQ. We apply the framework to several applications such as the optimization of transmission rate and the analysis of buffer occupancy among others. For the MAC layer, in Chapter 4 we develop an optimized IEEE 802.11 DCF protocol that dynamically adjusts the backoff parameters according to the predictive distribution of the number of competing terminals in the network. The estimation is based on sequential Monte Carlo methods and outperforms existing state-of-the-art approaches. We further design a Nash equilibrium strategy that effectively prevents rogue terminals from changing those backoff parameters for their own benefit (e.g., to gain unfair access to the network), making the algorithm safely applicable in a complete distributed fashion. To complement that Nash equilibrium strategy, in Chapter 5 we propose a robust nonparametric sequential detector based on the Kolmogorov-Smirnov (K-S) statistics that detects such misbehaving terminals and has a performance comparable to the optimum detectors for any range of misbehavior strategies. Finally, in Chapter 6 we present a robust K-S sequential detector for MAC layer denial-of-service (e.g., jamming) attacks, based on how 'explainable' collisions are given the transmission pattern of the observed terminals. We show that the detector is very sensitive to abnormal changes in the network, having a very short detection latency and high detection accuracy." @default.
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• W2242789155 date "2007-01-01" @default.
• W2242789155 modified "2023-09-26" @default.
• W2242789155 title "Statistical signal processing for wireless networks: modeling, analysis and optimization" @default.
• W2242789155 hasPublicationYear "2007" @default.
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