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研究生:李景裕
研究生(外文):Keng-U Lei
論文名稱:分散式估計系統之跨層隨機存取協定
論文名稱(外文):Cross-layered Random Access Protocol for Distributed Estimation
指導教授:祁忠勇洪樂文
指導教授(外文):Chong-Yung ChiYao-Win Hong
學位類別:碩士
校院名稱:國立清華大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:41
中文關鍵詞:分散式估計跨層時槽式ALOHA
外文關鍵詞:Distributed estimationCross-layered slotted-ALOHA
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我們針對無線感測網路中的分散式估計問題提出了一個跨層的時槽式ALOHA協定(cross-layered slotted-ALOHA)。假設在網路中,感測器分別對一個相同的事件進行測量,然後把測量值回傳到一個資料融合中心(fusion center)做最終的估測。在所提出的協定中,每個感測器的傳輸機率將取決於當時所測量值的品質以及傳輸通道的好壞。相對於傳統的ALOHA系統而言,我們的目的不在於提高系統的產出效能(throughput),而是要設計一個控制傳輸機率的函數使估計的表現得到最佳化。從結果顯示,一個能夠使產出效能最佳化的傳輸控制並不能提升估計的表現。我們提出了兩個控制傳輸機率的機制分別為:最大化均方差減量(maximum mean-square-error reduction)機制以及屬於次佳化的雙模式最大化均方差減量(two-mode mean-square-error reduction)機制。最大化均方差減量機制能夠在每一傳輸中最快速地減少估計值的均方差,但它需要融合中心提供一些資訊,例如是活躍於系統中的感測器的數量以及目前估計值的誤差等。相反地,次佳化的雙模式最大化均方差減量機制並不需要這兩項資訊,在這個機制下,感測器在兩個不同時期分別利用兩個控制傳輸的函數來決定它們的傳送機率。這兩個時期的切換取決於在每一個時槽中的平均估計誤差值。在上述的系統中,我們假設感測器在每個時槽內都做一次測量,但因其未能在每個時槽均做傳輸,因此有大量的測量資料被廢棄不用。當感測器不傳輸時所得到的測量值其實是可以透過使用一些分集技術加以利用的。具體來說,我們假設感測器能夠在一個固定大小緩衝器內儲存近期測量的資料使用選擇性分集(selective combining)技術選擇最可靠的資料做傳輸。結合之前的通道導向的傳輸機制,我們將能同時使用到空間上及時間上的多樣性。
A cross-layered slotted ALOHA protocol is proposed for distributed estimation in sensor networks. Suppose that the sensors in the network record local measurements of a common event and report the data back to the fusion center through direct transmission links. We employ a channel-aware transmission control where the transmission probability of each sensor is chosen based on the quality of the local observation and the conditions of the transmission channel in each time slots. In contrast to conventional ALOHA systems, our goal is to design the transmission control functions such that the estimation performance is optimized as opposed to maximizing the system throughput. Two probability assignment schemes are
proposed: the maximum mean-square-error (MSE) reduction (MMR) scheme and the suboptimal two-mode MSE-reduction (TMMR) scheme. The MMR maximizes the reduction in MSE of the estimation in each time slots, but requires knowledge of the number of active sensors and the error of the estimate obtained up to this point. Instead of obtaining a
different transmission control function for each time slot, in TMMR, the sensors switch among two predetermined transmission control functions based on the average estimation. Furthermore, we notice that if new observations are made by the sensors in each time slot, those data can be fully exploited by employing diversity combining techniques when the sensors are not scheduled to transmit.
Specifically, we propose the use of selective combining on the backlogged measurements within a certain time window before each transmission. The transmission control functions are then adjusted accordingly. As a result, we are able to exploit both the spatial and temporal diversity gains inherent in the multi-user system.
1. Introduction
2. System Model
3. Cross-layered Random Access Protocol for Distributed Estimation
3.1 The Maximum MSE-Reduction (MMR) Method
3.1.1 Repeated Transmission Scheme
3.1.2 Transmit Once Scheme
3.2 The Two-mode Maximum MSE-Reduction (TMMR) Method
3.2.1 Repeated Transmission Scheme
3.2.2 Transmit Once Scheme
3.3 Numerical Simulations and Performance Comparisons
4. Maximum MSE Reduction WITH SELECTIVE Combining in Time
4.1 Selective Combining for the RT Scheme
4.2 Selective Combining for the TO Scheme
4.3 Numerical Simulations and Performance Comparisons
5. CONCLUSION
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