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.

In this thesis, we consider the uplink of a cooperative code division multiple access (CDMA) network, where users cooperate by relaying each other¡¦s messages to the base station. The sources adopt CDMA to share the informations offered by relays. In general, spreading waveforms of sources are not orthogonal to each others due to pratical design issues of CDMA network. Therefore, the source signals will suffer from multiple access interference (MAI) at the relays and destination. The MAI results in the increase of bit error rate increased, and diminishes the cooperative network diveristy gains. In order to mitigate MAI, the decorrelating multiuser detection and zero-forcing precoder have been commonly adopted. But, the decorrelating multiuser detection causes noise enhancement, while the zero-forcing precoder causes power expansion. In this thesis, we adopt relay-assisted decorrelating multiuser detector (RAD-MUD) to mitigate MAI as proposed in [1].In this scheme, the relays perform half of decorrelating operation and the destination performs the other half. In this way, neither noise enhancement nor power expansion will occur. However, in the reference [1], the authers assume the transmission is synchronous between sources and relays. The assumption is unrealistic, because it is difficult to achieve synchronization between all sources and relays due to various propagation delays. In this thesis, we extend the research in [1] and relax restriction of synchronization between all sources and relays. Besides, we also adopt cooperative strategies such as transmit beamforming or selective relaying to enhance system performance. Compared with other¡¦s multiuser detection schemes, we show that the proposed schemes can effectively reduce the bit error rate.

Frequency reuse scheme is used to enhance the spectral efficiency in a cellular system, but inevitably the system suffers from co-channel interference of other users. Signal-to-interference ratio (SIR) is often used as a quality index of communication links. Several wireless communication algorithms, such as channel assignment, handover and power control, need real-time SIR information. All of these algorithms are under the assumptions that real-time SIR is available, but the methods of obtaining real-time SIR are seldom mentioned with these algorithms. In this thesis, we investigate three simple SIR estimation methods including statistics of spreading chips method, decorrelation detection method, and orthogonal stochastic approximation method. The performance of these SIR estimation methods are evaluated by computer simulations in a WCDMA system.

Smart antenna, which weights are obtained by Wiener solution, would suppress some undesired interference signals in spatial domain. The other interference signals that cannot be suppressed by smart antenna or caused by near-far effect will be post-processed by multiuser detectors. In the proposed algorithm, the cross-correlation matrix of desired signal and received signal from smart antenna algorithm would be applied to multiuser detector to reduce the complexity. From computer simulation results, the proposed algorithm has lower complexity and better BER performance than separate smart antenna or multiuser detection algorithms. Detail derivations of complexity and BER performance are also provided in this thesis.

Over the last decade, access to personal wireless communication networks has evolved to a point of necessity. Attached to the phenomenal growth of the telecommunications industry in recent times is an escalating demand for higher data rates and efficient spectrum utilization. This demand is fuelling the advancement of third generation (3G), as well as future, wireless networks. Current 3G technologies are adding a dimension of mobility to services that have become an integral part of modem everyday life. Wideband code division multiple access (WCDMA) is the standardized multiple access scheme for 3G Universal Mobile Telecommunication System (UMTS). As an air interface solution, CDMA has received considerable interest over the past two decades and a great deal of current research is concerned with improving the application of CDMA in 3G systems. A factoring component of CDMA is multiuser detection (MUD), which is aimed at enhancing system capacity and performance, by optimally demodulating multiple interfering signals that overlap in time and frequency. This is a major research problem in multipoint-to-point communications. Due to the complexity associated with optimal maximum likelihood detection, many different sub-optimal solutions have been proposed. This focus of this dissertation is the application of neural networks for MUD, in a direct sequence CDMA (DS-CDMA) system. Specifically, it explores how the Hopfield recurrent neural network (RNN) can be employed to give yet another suboptimal solution to the optimization problem of MUD. There is great scope for neural networks in fields encompassing communications. This is primarily attributed to their non-linearity, adaptivity and key function as data classifiers. In the context of optimum multiuser detection, neural networks have been successfully employed to solve similar combinatorial optimization problems. The concepts of CDMA and MUD are discussed. The use of a vector-valued transmission model for DS-CDMA is illustrated, and common linear sub-optimal MUD schemes, as well as the maximum likelihood criterion, are reviewed. The performance of these sub-optimal MUD schemes is demonstrated. The Hopfield neural network (HNN) for combinatorial optimization is discussed. Basic concepts and techniques related to the field of statistical mechanics are introduced and it is shown how they may be employed to analyze neural classification. Stochastic techniques are considered in the context of improving the performance of the HNN. A neural-based receiver, which employs a stochastic HNN and a simulated annealing technique, is proposed. Its performance is analyzed in a communication channel that is affected by additive white Gaussian noise (AWGN) by way of simulation. The performance of the proposed scheme is compared to that of the single-user matched filter, linear decorrelating and minimum mean-square error detectors, as well as the classical HNN and the stochastic Hopfield network (SHN) detectors. Concluding, the feasibility of neural networks (in this case the HNN) for MUD in a DS-CDMA system is explored by quantifying the relative performance of the proposed model using simulation results and in view of implementation issues. / Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, 2006.

No description available.

Turbo codes are a class of forward error correction (FEC) codes that offer energy efficiencies close to the limits predicted by information theory. The features of turbo codes include parallel code concatenation, recursive convolutional encoding, nonuniform interleaving, and an associated iterative decoding algorithm. Although the iterative decoding algorithm has been primarily used for the decoding of turbo codes, it represents a solution to a more general class of estimation problems that can be described as follows: a data set directly or indirectly drives the state transitions of two or more Markov processes; the output of one or more of the Markov processes is observed through noise; based on the observations, the original data set is estimated. This dissertation specifically describes the process of encoding and decoding turbo codes. In addition, a more general discussion of iterative decoding is presented. Then, several new applications of iterative decoding are proposed and investigated through computer simulation. The new applications solve two categories of problems: the detection of turbo codes over time-varying channels, and the distributed detection of coded multiple-access signals. Because turbo codes operate at low signal-to-noise ratios, the process of phase estimation and tracking becomes difficult to perform. Additionally, the turbo decoding algorithm requires precise estimates of the channel gain and noise variance. The first significant contribution of this dissertation is a study of several methods of channel estimation suitable specifically for turbo coded systems. The second significant contribution of this dissertation is a proposed method for jointly detecting coded multiple-access signals using observations from several locations, such as spatially separated base stations. The proposed system architecture draws from the concepts of macrodiversity combining, multiuser detection, and iterative decoding. Simulation results show that when the system is applied to the time division multiple-access cellular uplink, a significant improvement in system capacity results. / Ph. D.

The monitoring of traffic signal systems can be of great importance for identifying problems, self-assessment, budgeting, creating the strategy for future steps, etc. Monitoring procedure was developed through a set of dashboards with relevant signal performance and reliability measures. The dashboards were created to reflect performance and reliability of a specific signal system on a weekly or monthly level. The author used data from ATMS.now signal system central software to illustrate how similar dashboards could be developed from any central software to enable operators to promptly and efficiently monitor various parameters of traffic signals. The main outcome of the study is a pair of Excel dashboards accompanied with appropriate user manual. The dashboards represent the tool for monitoring which can be helpful in the process of evaluation for traffic signal systems. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Design and analysis of random-like codes for various multiple-input and multiple-output communication systems are addressed in this work. Random-like codes have drawn significant interest because they offer capacity-achieving performance. We first consider the analysis and design of low-density parity-check (LDPC) codes for turbo multiuser detection in multipath CDMA channels. We develop techniques for computing the probability density function (pdf) of the extrinsic messages at the output of the soft-input soft-output (SISO) multiuser detectors as a function of the pdf of input extrinsic messages, user spreading codes, channel impulse responses, and signal-to-noise ratios. Using these techniques, we are able to accurately compute the thresholds for LDPC codes and design good irregular LDPC codes. We then apply the tools of density evolution with mixture Gaussian approximations to optimize irregular LDPC codes and to compute minimum operational signal-to-noise ratios for ergodic MIMO OFDM channels. In particular, the optimization is done for various MIMO OFDM system configurations which include different number of antennas, different channel models and different demodulation schemes. We also study the coding-spreading tradeoff in LDPC coded CDMA systems employing multiuser joint decoding. We solve the coding-spreading optimization based on the extrinsic information SNR evolution curves for the SISO multiuser detectors and the SISO LDPC decoders. Both single-cell and multi-cell scenarios will be considered. For each of these cases, we will characterize the extrinsic information for both finite-size systems and the so-called large systems where asymptotic performance results must be evoked. Finally, we consider the design optimization of irregular repeat accumulate (IRA) codes for MIMO communication systems employing iterative receivers. We present the density evolution-based procedure with Gaussian approximation for optimizing the IRA code ensemble. We adopt an approximation method based on linear programming to design an IRA code with the extrinsic information transfer (EXIT) chart matched to that of the soft MIMO demodulator.