Joint multiple parameter estimation and channel decoding for physical-layer network coding and multiuser detection.

本文研究在物理層網絡編碼（PNC）系統和多用戶檢測（MUD）系統中的聯合多參數估計與信道譯碼問題。PNC 與MUD 都是從多個用戶的同時信號傳輸中獲利的技術。然而，多個同時傳輸信號的迭加也對信號處理帶來了若干挑戰。首先一個挑戰是在接收機處的多參數估計問題。另外一個挑戰是，如何同時補償多個參數。本文包括兩部分，每一部分的貢獻分別是在PNC 或MUD 系統中，針對上述問題的解決方案。第一部分: 在本文的第一部分中，我們解決在PNC 系統中的聯合信道估計與信道譯碼問題。在PNC 系統中，多個用戶同時給中繼傳輸信號。PNC 系統的信道譯碼不同於傳統的多用戶系統的信道譯碼。具體地，中繼的目標是譯碼出網絡編碼後的信息而非單獨的每個源信息。雖然之前的研究工作顯示PNC 可以很大程度上提高中繼網絡的吞吐量，但是這個提高的前提假設是能夠獲得精確的信道估計。然而，因為以下原因，PNC系統中的信道估計尤其具有挑戰性：1）多個用戶的信號迭加在一起；2）信道編碼使得數據符號之間非獨立；3）信道是時變的。為解決這些難題，我們將expectation-maximization（EM）算法和belief propagation（BP）算法結合在一個統一的factor graph 框架之下。在這個factor graph 框架下，信道估計由EM subgraph 完成，信道譯碼由建模了和PNC 信道譯碼目標相匹配的虛擬編碼器的BP subgraph 完成。在兩個subgraph 的迭代消息傳輸使得我們可以逐漸逼近信道估計和信道譯碼的最優解。我們提供了大量的模擬結果來說明我們所提出方案的優越性。第二部分: 在本文的第二部分中，我們研究了一個信道編碼的多用戶檢測（MUD）系統。該系統是基於正交頻分複用（OFDM）調製和交織分多址接入（IDMA）技術的。將OFDM與IDMA結合的動機是其可以在頻率選擇多址接入信道環境下獲得多用戶分集增益的能力。然而，為了實現這個能力，我們必須首先解決由多個載波頻率偏移（CFO）所引起的頻率異步問題。論文本部分解決如下挑戰。首先的挑戰是多信道參數（CFO，信道增益等）的估計。考慮到各個用戶的參數估計問題互相影響而導致總的參數估計誤差會隨用戶數目而增長，一個具體地難題是如何克制多個用戶多個參數的估計誤差。第二個挑戰是如何補償多個CFO。一個具體的難題是，不同於只存在一個CFO 的單用戶接收機，我們的多用戶接收機不可能同時補償多個不同的CFO。為解決以上兩個挑戰，我們提出了在一個多用戶系統中聯合、迭代解決多信道參數估計、CFO 補償和信道譯碼的框架。該框架利用了space alternating generalized expectation-maximization（SAGE）算法和expectation-conditional maximization （ECM）算法。我們的研究揭示，在ECM 算法中，將數據符號而非信道參數設置為hidden data 將導致更好的系統性能。進一步地，我們用Gaussian message passing 技術將算法複雜度有效降低。計算機仿真和軟件無線電平臺上的真實實驗表明，和傳統多用戶方法相比，我們方法能獲得非常高的性能增益。總體來說，本文提出了兩個算法框架（EM-BP，SAGE-ECM）來解決聯合多參數估計和信道解碼問題。我們相信，針對多用戶系統中多個信號疊加而帶來的信號處理挑戰，我們所提算法框架是非常具有前景的解決方案。This thesis investigates the joint multiple parameter estimation and channel decoding problem for physical-layer network coding (PNC) and multiuser detection (MUD) systems. Both of PNC and MUD can take advantages from the simultaneous transmissions by multiple users. However, the superimposition of multiple transmissions brings with it new challenges for signal processing. The first major challenge is the estimation of the multiple parameters at the receiver. The second major challenge is how to compensate for system impairments caused by these parameters. This thesis consists of two parts that tackle these challenges: The first part is related to PNC systems and the second part is related to MUD systems.Part I: The first part of this thesis addresses the problem of joint channel estimation and channel decoding in PNC systems. In PNC, multiple users transmit to a relay simultaneously. PNC channel decoding is different from conventional multiuser channel decoding: Specifically, the PNC relay aims to decode a network-coded message rather than the individual messages of the users. Although prior work has shown that PNC can significantly improve the throughput of a relay network, the improvement is predicated on the availability of accurate channel estimates. Channel estimation in PNC, however, can be particularly challenging because of 1) the overlapped signals of multiple users; 2) the correlations among data symbols induced by channel coding; and 3) time-varying channels. We combine the expectation-maximization (EM) algorithm and belief propagation (BP) algorithm on a unified factor-graph framework. In this framework, channel estimation is performed by an EM subgraph, and channel decoding is performed by a BP subgraph that models a virtual encoder matched to the target of PNC channel decoding. Iterative message passing between these two subgraphs allows the optimal solutions for both to be approached progressively. We present extensive simulation results demonstrating the superiority of our PNC receivers over other PNC receivers.Part II: The second part of this thesis investigates a channel-coded MUD system operated with orthogonal frequency division multiplexing (OFDM) and interleaved division multiple-access (IDMA). In general, there are many variations to MUD systems. Our choice of the combination of OFDM and IDMA is motivated by its ability to achieve multiuser diversity gain in frequency-selective multiple-access channels. However, to realize this potential advantage of OFDM-IDMA, we must first solve the frequency asynchrony problem induced by the multiple carrier frequency offsets (CFOs) of the signals of multiple users. This part of the thesis tackles the following two major challenges. The first, as in PNC systems, is the estimation of multiple channel parameters (e.g., CFOs and channel gains). A particular challenge is how to contain the estimation errors of the channel parameters of the multiple users, considering that the overall estimation errors may increase with the number of users because the estimations of their channel parameters are intertwined with each other. The second is how to compensate for the multiple CFOs. A particular difficulty is that, different from a single-user receiver for which there is only one CFO, it is not possible for our multiuser receiver to compensate for all the multiple CFOs simultaneously. To tackle the two challenges, we put forth a framework that solves the joint problem of multiuser channel-parameter estimation, CFO compensation, and channel decoding iteratively by employing the space alternating generalized expectation-maximization (SAGE) and expectation-conditional maximization (ECM) algorithms. Our study reveals that treating the data rather than the channel parameters as the hidden data in ECM will lead to better performance. We further show that Gaussian message passing is an effective complexity reducing technique. Simulations and real experiments based on software-defined radio (SDR) indicate that, compared with other approaches, our approach can achieve significant performance gains.Overall, this thesis puts forth two frameworks (EM-BP for PNC, SAGE-ECM for MUD) to address the problem of multiple parameter estimation and channel decoding. We believe our frameworks are promising solutions for the signal processing challenges arising from the superimposition of multiple transmissions in multiuser systems.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Wang, Taotao.Thesis (Ph.D.) Chinese University of Hong Kong, 2015.Includes bibliographical references (leaves 157-168).Abstracts also in Chinese