No description available.

The well-known code division multiple access (CDMA) decorrelating detector (DD) and minimum mean-square error (MMSE) detector use a bank of correlators, followed by the inverse of the matrix operation to eliminate the multiple access interference (MAI). However, the operation for the inverse of the matrix involves a great deal of computation, especially when the users¡¦ number is large. Therefore, in this thesis, we propose some recursive methods, the least-mean-square (LMS) algorithm and the recursive least-squares (RLS) algorithm, to detect users¡¦ signals adaptively. We make use of the analogy between a traditional Winner filter and the decorrelating detector to construct adaptive implementation schemes of the decorrelating detector and MMSE detector, called decorrelating transversal filter and MMSE transversal filter, respectively. We applied both LMS algorithm and RLS algorithm to the decorrelating transversal filter and MMSE transversal filter, just as the ways to apply the LMS algorithm and RLS algorithm to the Winner filters. With the proposed schemes, we can greatly reduce the computational complexity of a CDMA multi-user detector while maintaining an acceptable performance.

No description available.

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.

In this thesis, we propose a modified blind adaptive approach to MAI cancellation for synchronous DS-CDMA systems, termed PCMA Approach. It has the same advantage as MOE and CMA of suppressing MAI without the explicit knowledge of interference parameters. We study the effect of arrival and departure of new users to the synchronous blind multiuser receivers, namely MOE and CMA. The perturbation caused by this sudden birth or death of interferers is examined from the viewpoint of the change of the cost function under the noiseless assumption. It is shown that in the vicinity of lock convergence, the changes in the CMA and MOE costs are the same if the perturbation is small enough. But it is also observed that the transient behavior of the CMA detector becomes significantly less robust as the existing number of users approaches the processing gain. To overcome this question of the CMA detector, we employ a projection matrix P that can remove the desired signal from the received signal with other interferers unchanged. Finally its performance has been demonstrated by numerical results in comparison with CMOE, ANMMSE, BAMMSE and Linear MMSE. The proposed receiver can combat MAI, approach the performance of Linear MMSE and have a higher capacity and fast convergence rate for a time-varying user population.

Signal processing techniques such as multi user detection (MUD) have the capability of greatly enhancing the performance and capacity of future generation wireless communications systems. Blind adaptive MUD's have many favourable qualities and their application to OS-COMA systems has attracted a lot of attention. The constant modulus algorithm is widely deployed in blind channel equalizations applications. The central premise of this thesis is that the constant modulus cost function is very suitable for the purposes of blind adaptive MUD for future generation wireless communications systems. To prove this point, the adaptive performance of blind (and non-blind) adaptive MUD's is derived analytically for all the schemes that can be made to fit the same generic structure as the constant modulus scheme. For the first time, both the relative and absolute performance levels of the different adaptive algorithms are computed, which gives insights into the performance levels of the different blind adaptive MUD schemes, and demonstrates the merit of the constant modulus based schemes. The adaptive performance of the blind adaptive MUD's is quantified using the excess mean square error (EMSE) as a metric, and is derived for the steady-state, tracking, and transient stages of the adaptive algorithms. If constant modulus based MUD's are suitable for future generation wireless communications systems, then they should also be capable of suppressing multi-rate DS-COMA interference and also demonstrate the ability to suppress narrow band interference (NBI) that arises in overlay systems. Multi-rate DS-COMA provides the capability of transmitting at various bit rates and quality of service levels over the same air interface. Limited spectrum availability may lead to the implementation of overlay systems whereby wide-band COMA signal are collocated with existing narrow band services. Both overlay systems and multi-rate DS-COMA are important features of future generation wireless communications systems. The interference patterns generated by both multi-rate OS-COMA and digital NBI are cyclostationary (or periodically time varying) and traditional MUD techniques do not take this into account and are thus suboptimal. Cyclic MUD's, although suboptimal, do however take the cyclostationarity of the interference into account, but to date there have been no cyclic MUD's based on the constant modulus cost function proposed. This thesis thus derives novel, blind adaptive, cyclic MUD's based on the constant modulus cost function, for direct implementation on the FREquency SHift (FRESH) filter architecture. The FRESH architecture provides a modular and thus flexible implementation (in terms of computational complexity) of a periodically time varying filter. The operation of the blind adaptive MUD on these reduced complexity architectures is also explored.· The robustness of the new cyclic MUD is proven via a rigorous mathematical proof. An alternate architecture to the FRESH filter is the filter bank. Using the previously derived analytical framework for the adaptive performance of MUD's, the relative performance of the adaptive algorithms on the FRESH and filter bank architectures is examined. Prior to this thesis, no conclusions could be drawn as to which architecture would yield superior performance. The performance analysis of the adaptive algorithms is also extended in this thesis in order to consider the effects of timing jitrer at the receiver, signature waveform mismatch, and other pertinent issues that arise in realistic implementation scenarios. Thus, through a careful analytical approach, which is verified by computer simulation results, the suitability of constant modulus based MUD's is established in this thesis. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2004.

No description available.

In a multiuser system, several signals are transmitted simultaneously within the same frequency band. This can result in significant improvements both in spectral efficiency and system capacity. However, a detrimental effect of the shared transmissions (both in time and bandwidth), is that the signal received at the base station (BS) or access point (AP) suffers from cochannel interference (CCI) and inter-symbol interference (ISI). This situation presents challenges to receiver design. To combat the destructive nature of multipath fading, a receiver often employs multiple antennas to collect the faded superimposed versions of the transmitted signals. The multiple signals are combined and processed in such a way that the effects of CCI and ISI are minimized and the desired information is reliably recovered. The situation is even more challenging when the system is operating under overload, i.e. when there are fewer receive antennas than there are transmitted signals. Multiuser detection (MUD) is used to simultaneously estimate the information sent by the transmitters. To do this, the receiver exploits differences among the cochannel signals (through unique spatial signatures in this case). We consider a cochannel communication system where multiple transmitted signals arrive at a receiver (equipped with multiple receive antennas) after propagating through a Rayleigh fading channel. It is assumed that the receiver is operating in an overloaded scenario. For such systems, an optimum maximum a posterior probability (MAP) detector estimates the transmitted signal by maximizing the probability of correct decision. The MAP detector reduces to the maximum likelihood (ML) detector when all the transmitted signals are equiprobable. The computational complexity of both MAP and ML detectors increases exponentially with the number of transmitted signals and the channel memory. For large systems suffering severe CCI and ISI, this is clearly not a good choice for real-time implementation due to the associated computational expenses. The main factors that influence the complexity of MAP / ML detection are: (i) the number of transmitted signals (or equivalently the number of users sharing the system resources), (ii) modulation alphabet size, and (iii) length of the channel memory. On the other hand, linear detection approaches fail to offer acceptable performance while other nonlinear sub-optimum approaches incur high computational costs for reasonably improved system performance and exhibit an irreducible error-floor at medium to high signal to noise ratio (SNR) values. We develop receiver signal processing techniques for the frequency-flat fading channel (where all the multipaths of the transmitted signal arrive at the receiver within a symbol period). We develop an ant colony optimization (ACO) assisted soft iterative detection approach for binary phase-shift keying (BPSK) modulated signals which employs a simplified MAP criteria to extract the most probable signals from the search space. The structure of the receiver is such that it can continue operating under overloaded conditions. The technique achieves near maximum likelihood (ML) performance in critically loaded cases using much lower complexity. For the challenging case of overload it still offers performance close to ML at low to moderate SNR values. Second, an integrated framework comprising of ACO metaheuristic and a recursively defined ML search criteria is developed to handle multilevel modulations. The proposed receiver is capable of achieving near-ML performance for the considered system with significant savings in computational complexity. The receiver framework is independent of the system loading condition, and therefore it remains suitable for overloaded scenarios. Due to the branch and bound nature of the algorithm, an exact expression for the complexity cannot be determined. Instead, an upper bound on computational complexity is developed.

This thesis investigates two systems that exploit multi-user interference in wireless communication in which multiple users transmit simultaneously. The thesis consists of two parts that aim for two different wireless network setups: the first part focuses on the decoding of convolutional-coded physical-layer network coding (PNC) in a two-way relay network (TWRN); the second part presents a scalable multi-user multiple-input multiple-output (MU-MIMO) architecture supporting real-time MIMO decoding. Both systems take advantage of multi-user interference to boost the system performance. The investigations of the two systems involve both theoretical analysis and system prototyping on software radio. / The first part of this thesis investigates the decoding process of asynchronous convolutional-coded PNC systems. Specifically, we put forth a layered decoding framework for convolutional-coded PNC consisting of three layers: symbol realignment layer, codeword realignment layer, and joint channel-decoding network coding (Jt-CNC) decoding layer. Our framework can deal with phase asynchrony (phase offset) and symbol arrival-time asynchrony (symbol misalignment) between the signals simultaneously transmitted by multiple sources. A salient feature of this framework is that it can handle both fractional and integral symbol misalignments. For the decoding layer, instead of Jt-CNC, previously proposed PNC decoding algorithms (e.g., XOR-CD and reduced-state Viterbi algorithms) can also be used with our framework to deal with general symbol misalignments. Our Jt-CNC algorithm based on belief propagation (BP) is BER-optimal for synchronous PNC and near optimal for asynchronous PNC. Extending beyond convolutional codes, we further generalize the Jt-CNC decoding algorithm for all cyclic codes. Our simulations show that Jt-CNC outperforms the previously proposed XOR-CD algorithm and reduced-state Viterbi algorithm by 2 dB for synchronous PNC. For both phase-asynchronous and symbolasynchronous PNC, Jt-CNC performs better than the other two algorithms. Importantly, for real wireless network experimentation, we implemented our decoding algorithm in a PNC prototype built on the USRP software radio platform. Our experiment shows that the proposed Jt-CNC decoder works well in practice. / The second part of the thesis presents BigStation, a scalable architecture that enables real-time signal processing in large-scale MU-MIMO systems with tens or hundreds of antennas. Our strategy to scale the system is to extensively parallelize the MU-MIMO processing on many simple and low-cost commodity computing devices. Our design can incrementally support more antennas by proportionally adding more computing devices. To reduce the overall processing latency, which is a critical constraint for wireless communication, we parallelize the MU-MIMO decoding process with a distributed pipeline based on its computation and communication patterns. At each stage of the pipeline, we further use data partitioning and computation partitioning to speed up the signal processing. As a proof of concept, we have built a BigStation prototype based on commodity PC servers and SORA software radio platform. Our prototype employs 15 PC servers and can support realtime signal processing of 12 software radio antennas. Our results show that the BigStation is able to scale to tens to hundreds of antennas. With 12 antennas, our BigStation prototype can increase wireless throughput by 6.8 x with a low mean latency of 860μs. While this latency is not yet low enough for the 802.11 MAC, it already satisfies the real-time requirements of many existing wireless standards such as LTE and WCDMA. / 本論文探討了兩種可以利用多個無線用戶同時傳輸時所造成的多用戶干擾的無線通信系統。本文由旨在針對不同的無線網絡結構的兩部分構成：第一部分論述了雙向中繼網絡（TWRN）中卷積編碼的非同步物理層網絡編碼（PNC）的解碼; 第二部分介紹了一種支持實時解碼的可擴展的多用戶多輸入多輸出（MU-MIMO）架構。這兩種系統都利用了無線通信中的多用戶干擾以提高系統性能。在對這兩個系統的探討中，我們都進行了理論分析，並搭建了基於軟件無線電的原型系統。 / 論文的第一部分研究卷積編碼的非同步物理層網絡編碼系統的解碼過程。具體來說，我們提出了一種非同步物理層網絡編碼的分層解碼架構。該架構由三層組成：符號對齊層，碼字對齊層，以及聯合信道解碼網絡編碼（Jt-CNC）解碼層。我們的這種架構能夠處理由多個用戶同時發送信號時造成的相位不同步（相位偏移）和符號到達時間不同步（符號偏移）。這種架構的一個顯著特點是，它可以同時處理分數和整數的符號偏移。對於解碼層，除了Jt-CNC算法，先前提出的各種PNC解碼算法（例如，XOR-CD 和減少狀態的Viterbi算法）也可以應用於我們的解碼架構用來解決符號偏移問題。我們基於置信度傳播理論（BP）的Jt-CNC算法，對於同步PNC是比特差錯率（BER）最優的，對於非同步PNC是接近最優的。除了卷積碼以外，我們進一步將Jt-CNC解碼算法推廣到了所有循環碼。我們的仿真表明，對於同步PNC，Jt-CNC的性能超過先前提出的XOR-CD算法和減少狀態Viterbi算法2 dB。對於相位非同步和符號非同步PNC，Jt-CNC均優於前述兩種算法。重要的是，為了進行真實環境下的無線網絡實驗，我們在基於USRP軟件無線電平台的PNC原型系統上實現了我們的解碼算法。我們的實驗顯示，該Jt-CNC解碼器在真實環境下中表現優越。 / 論文的第二部分介紹了BigStation，一個可擴展的MU-MIMO系統架構。該架構支持帶有幾十甚至上百根天線的大規模MU-MIMO系統的實時信號處理。我們的系統擴展策略是將MU-MIMO信號處理並行化到大量簡單廉價的商用計算單元上。我們的這種設計可以通過成比例地增加計算單元以支持更多的天線。為了減少系統的整體處理延遲，我們根據MU-MIMO信號處理的計算和通信模式，利用分布式流水線把解碼過程並行化。在流水線的各級，我們進一步使用數據分割和計算分割來加速信號處理。作為概念驗證，我們基於商用個人電腦和SORA軟件無線電平台開發了BigStation原型系統。我們的原型使用了15 台個人電腦並且可以支持12 根軟件無線電天線的實時解碼。我們的實驗結果表明，BigStation系統能夠擴展到幾十甚至幾百根天線。當有12 根天線時，我們的BigStation原型系統將無線網絡吞吐量提高了6.8 倍，而平均延遲則低至860 μs。雖然這個延遲對於802.11 的MAC 還不足夠低，但它已滿足了許多現有的無線標准諸如LTE和WCDMA的實時性要求。 / Yang, Qing. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2015. / Includes bibliographical references (leaves 101-108). / Abstracts also in Chinese. / Title from PDF title page (viewed on 21, December, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.

In direct-sequence code division multiple access (DS-CDMA) systems, the heuristic optimization algorithms for multiuser detection include genetic algorithms (GA) and simulated annealing (SA) algorithm. In this thesis, we use particle swarm optimization (PSO) algorithms to solve the optimization problem of multiuser detection (MUD). PSO algorithm has several advantages, such as fast convergence, low computational complexity, and good performance in searching optimum solution. In order to enhance the performance and reduce the number of parameters, we propose two modified PSO algorithms, inertia weighting controlled PSO (W-PSO) and reduced-parameter PSO (R-PSO). From simulation results, the performance of our proposed algorithms can achieve that of optimal solution. Furthermore, our proposed algorithms have faster convergence performance and lower complexity when compared with other conventional algorithms.