應用於混合式無線感測網路之偵測與估計演算法

在本篇論文中，我們基於正交式與非正交式無線感測網路，提出了混合式無線感測網路，此種無線感測網路混合了傳統的兩種多重存取通道且由系統均方誤差(或系統錯誤偵測機率)及系統中斷機率兩個面相展示此種通道可以提供無線感測網路更多樣化的效能選擇。基於混合式無線感測網路，以下為主要的論文貢獻。在第二章中，使用脈衝振幅調變，考慮在所有感測器可使用的總功率必須小於某個系統設定值的情況下，本篇論文提出感測器之最佳以及次佳的功率分配技術，且在次佳的功率分配技術下，系統均方誤差的數學表示式也被推導出來。此外，針對不同的系統參數，本篇論文也根據此均方誤差的數學表示式分析系統之漸進行為。本篇論文並證明，此次佳功率分配技術之系統效能在和某種系統參數趨近於無限大下，系統均方誤差會趨近於最佳化功率分配的系統均方誤差。本篇論文也證明出混合式無線感測網路之系統中斷機率具有之多樣性階數和所使用之正交通道數目成正比。藉由綜合比較系統均方誤差和系統中斷機率，由數值結果可以看到所提出之混合式無線感測網路可提供更多樣化的效能選擇。在第三章中，使用相位調變及差分相位調變，本篇論文提出了在感測器皆為完美感測情況下的最大似然估計法則，並證明其在某種情況下的系統性能會趨近非完美感測情況下最大似然估計法則所能提供的系統性能。本章並也針對在假設感測器皆為完美感測情況下最大似然估計法則的修正型克拉美羅下界並推出此法則之機率密度函數並由數值比較可以看出相對應之分析結果的正確性。在第四章中，使用M-ary 相位鍵移調變及M-ary 差分相位鍵移調變，其相對應之最大後置機率判定法則及考慮感測器皆為完美感測情況下簡化之判定法則皆被提出，並且證明出來簡化之判定法則在某種系統參數趨近於無限大下，所能提供之系統性能會趨近於最大後置機率判定法則。在M-ary 相位鍵移調變之下，本篇論文也證明出混合式無線感測網路之系統中斷機率具有之多樣性階數和所使用之正交通道數目成正比。藉由綜合比較系統錯誤偵測機率和系統中斷機率，由數值結果可以看到所提出之混合式無線感測網路可提供更多樣化的效能選擇。

關鍵字

分散式估計；分散式偵測；無線感測網路；多重存取通道；均方誤差；中斷機率；脈衝振幅調變；相位調變；差分調變； M-ary 相位鍵移；偵測錯誤機率

並列摘要

In this thesis, a hybrid multiple access channel (MAC) between sensor to fusion center (FC) links in wireless sensor networks (WSNs) is proposed, and the corresponding distributed estimation and detection problems are investigated. The main contributions of this thesis are described in the following. In Chapter 2, the distributed estimation of a scalar signal by a power-constrained WSN is investigated where the amplify-and-forward sensor signals are relayed to a FC over a hybrid MAC. The proposed hybrid MAC is a composite of conventional orthogonal and coherent MACs, and shown to offer more performance choices than both conventional channels in terms of mean-squared error (MSE) performance and system outage probability (SOP). Both random and deterministic scalar sources are considered for the hybrid MAC WSN in conjunction with linear minimum MSE (MMSE) and maximum likelihood (ML) estimations, respectively. Specifically, the optimal and suboptimal schemes are developed to allocate sensor power under a total power constraint and these power allocation schemes are found similar in terms of algorithm and achievable estimation performance for both scalar sources. Particularly, closed-form expressions for sensor power are obtained for suboptimal power allocation and two-step procedures are proposed to solve the optimal power allocation problems numerically. Furthermore, asymptotic expressions of MSE under various limiting parametric cases and SOP are analytically derived for suboptimal power allocation under a uniform sensor grouping strategy. It is analytically shown that, under a uniform sensor grouping strategy, the suboptimal power allocation scheme can offer asymptotically the same MSE performance as the optimal power allocation scheme when the number of sensors transmitting through the same coherent channel increases to infinity. Simulation results also show that the suboptimal power allocation scheme provides nearly optimal MSE performance for arbitrary sensor grouping strategies and the uniform sensor grouping is virtually the best grouping strategy. i Moreover, for uniformly grouped hybrid MAC, the SOP for suboptimal power allocation is shown to provide a diversity order equal to the number of orthogonal channels when there are sufficiently many orthogonal channels. The distributed estimation of a deterministic scalar source is considered in Chapter 3 by a power-constrained WSN with a FC where the phase-modulated sensor signals are relayed to the FC over a uniformly grouped hybridMAC. In this case, ML estimate for ideal sensing environments and a two-step estimate are developed for the estimation of source parameter and the corresponding modified Cram’

並列關鍵字

distributed estimation ； distributed detection ； wireless sensor network ； multiple access channel ； mean squared error ； outage probability ； pulse-amplitude modulation ； phase-modulation ； differential modulation ； M-ary phase-shift keying ； detection error probability

參考文獻

estimation in wireless sensor networks with applications in localization,” IEEE

Trans. Veh Technol., vol. 59, no. 6, pp. 2998-3011, Jul. 2010.

[2] A. Cenedese, G. Ortolan, and M. Bertinato, “Low-density wireless sensor networks for

localization and tracking in critical environments,” IEEE Trans. Veh Technol., vol. 59,

energy efficiency in distributed sensing,” IEEE Trans. Signal Process., vol. 55, no. 9,

國際替代計量

應用於混合式無線感測網路之偵測與估計演算法

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