56 research outputs found

    Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

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    Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

    High Capacity CDMA and Collaborative Techniques

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    The thesis investigates new approaches to increase the user capacity and improve the error performance of Code Division Multiple Access (CDMA) by employing adaptive interference cancellation and collaborative spreading and space diversity techniques. Collaborative Coding Multiple Access (CCMA) is also investigated as a separate technique and combined with CDMA. The advantages and shortcomings of CDMA and CCMA are analysed and new techniques for both the uplink and downlink are proposed and evaluated. Multiple access interference (MAI) problem in the uplink of CDMA is investigated first. The practical issues of multiuser detection (MUD) techniques are reviewed and a novel blind adaptive approach to interference cancellation (IC) is proposed. It exploits the constant modulus (CM) property of digital signals to blindly suppress interference during the despreading process and obtain amplitude estimation with minimum mean squared error for use in cancellation stages. Two new blind adaptive receiver designs employing successive and parallel interference cancellation architectures using the CM algorithm (CMA) referred to as ‘CMA-SIC’ and ‘BA-PIC’, respectively, are presented. These techniques have shown to offer near single user performance for large number of users. It is shown to increase the user capacity by approximately two fold compared with conventional IC receivers. The spectral efficiency analysis of the techniques based on output signal-to interference-and-noise ratio (SINR) also shows significant gain in data rate. Furthermore, an effective and low complexity blind adaptive subcarrier combining (BASC) technique using a simple gradient descent based algorithm is proposed for Multicarrier-CDMA. It suppresses MAI without any knowledge of channel amplitudes and allows large number of users compared with equal gain and maximum ratio combining techniques normally used in practice. New user collaborative schemes are proposed and analysed theoretically and by simulations in different channel conditions to achieve spatial diversity for uplink of CCMA and CDMA. First, a simple transmitter diversity and its equivalent user collaborative diversity techniques for CCMA are designed and analysed. Next, a new user collaborative scheme with successive interference cancellation for uplink of CDMA referred to as collaborative SIC (C-SIC) is investigated to reduce MAI and achieve improved diversity. To further improve the performance of C-SIC under high system loading conditions, Collaborative Blind Adaptive SIC (C-BASIC) scheme is proposed. It is shown to minimize the residual MAI, leading to improved user capacity and a more robust system. It is known that collaborative diversity schemes incur loss in throughput due to the need of orthogonal time/frequency slots for relaying source’s data. To address this problem, finally a novel near-unity-rate scheme also referred to as bandwidth efficient collaborative diversity (BECD) is proposed and evaluated for CDMA. Under this scheme, pairs of users share a single spreading sequence to exchange and forward their data employing a simple superposition or space-time encoding methods. At the receiver collaborative joint detection is performed to separate each paired users’ data. It is shown that the scheme can achieve full diversity gain at no extra bandwidth as inter-user channel SNR becomes high. A novel approach of ‘User Collaboration’ is introduced to increase the user capacity of CDMA for both the downlink and uplink. First, collaborative group spreading technique for the downlink of overloaded CDMA system is introduced. It allows the sharing of the same single spreading sequence for more than one user belonging to the same group. This technique is referred to as Collaborative Spreading CDMA downlink (CS-CDMA-DL). In this technique T-user collaborative coding is used for each group to form a composite codeword signal of the users and then a single orthogonal sequence is used for the group. At each user’s receiver, decoding of composite codeword is carried out to extract the user’s own information while maintaining a high SINR performance. To improve the bit error performance of CS-CDMA-DL in Rayleigh fading conditions, Collaborative Space-time Spreading (C-STS) technique is proposed by combining the collaborative coding multiple access and space-time coding principles. A new scheme for uplink of CDMA using the ‘User Collaboration’ approach, referred to as CS-CDMA-UL is presented next. When users’ channels are independent (uncorrelated), significantly higher user capacity can be achieved by grouping multiple users to share the same spreading sequence and performing MUD on per group basis followed by a low complexity ML decoding at the receiver. This approach has shown to support much higher number of users than the available sequences while also maintaining the low receiver complexity. For improved performance under highly correlated channel conditions, T-user collaborative coding is also investigated within the CS-CDMA-UL system

    Blind Equalization and Channel Estimation in Coherent Optical Communications Using Variational Autoencoders

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    We investigate the potential of adaptive blind equalizers based on variational inference for carrier recovery in optical communications. These equalizers are based on a low-complexity approximation of maximum likelihood channel estimation. We generalize the concept of variational autoencoder (VAE) equalizers to higher order modulation formats encompassing probabilistic constellation shaping (PCS), ubiquitous in optical communications, oversampling at the receiver, and dual-polarization transmission. Besides black-box equalizers based on convolutional neural networks, we propose a model-based equalizer based on a linear butterfly filter and train the filter coefficients using the variational inference paradigm. As a byproduct, the VAE also provides a reliable channel estimation. We analyze the VAE in terms of performance and flexibility over a classical additive white Gaussian noise (AWGN) channel with inter-symbol interference (ISI) and over a dispersive linear optical dual-polarization channel. We show that it can extend the application range of blind adaptive equalizers by outperforming the state-of-the-art constant-modulus algorithm (CMA) for PCS for both fixed but also time-varying channels. The evaluation is accompanied with a hyperparameter analysis.Comment: Published (Open Access) in IEEE Journal on Selected Areas in Communications, Sep 202

    Precoder Detection for Cooperative Decode-and-Forward Relaying in OFDMA Systems

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    We consider an LTE network where a secondary user acts as a relay, transmitting data to the primary user using a decode-and-forward mechanism, transparent to the base-station (eNodeB). Clearly, the relay can decode symbols more reliably if the employed precoder matrix indicators (PMIs) are known. However, for closed loop spatial multiplexing (CLSM) transmit mode, this information is not always embedded in the downlink signal, leading to a need for effective methods to determine the PMI. In this thesis, we consider 2x2 MIMO and 4x4 MIMO downlink channels corresponding to CLSM and formulate two techniques to estimate the PMI at the relay using a hypothesis testing framework. We evaluate their performance via simulations for various ITU channel models over a range of SNR and for different channel quality indicators (CQIs). We compare them to the case when the true PMI is known at the relay and show that the performance of the proposed schemes are within 2 dB at 10% block error rate (BLER) in almost all scenarios. Furthermore, the techniques add minimal computational overhead over existent receiver structure. Finally, we also identify scenarios when using the proposed precoder detection algorithms in conjunction with the cooperative decode-and-forward relaying mechanism benefits the PUE and improves the BLER performance for the PUE. Therefore, we conclude from this that the proposed algorithms as well as the cooperative relaying mechanism at the CMR can be gainfully employed in a variety of real-life scenarios in LTE networks

    Advanced optimization algorithms for sensor arrays and multi-antenna communications

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    Optimization problems arise frequently in sensor array and multi-channel signal processing applications. Often, optimization needs to be performed subject to a matrix constraint. In particular, unitary matrices play a crucial role in communications and sensor array signal processing. They are involved in almost all modern multi-antenna transceiver techniques, as well as sensor array applications in biomedicine, machine learning and vision, astronomy and radars. In this thesis, algorithms for optimization under unitary matrix constraint stemming from Riemannian geometry are developed. Steepest descent (SD) and conjugate gradient (CG) algorithms operating on the Lie group of unitary matrices are derived. They have the ability to find the optimal solution in a numerically efficient manner and satisfy the constraint accurately. Novel line search methods specially tailored for this type of optimization are also introduced. The proposed approaches exploit the geometrical properties of the constraint space in order to reduce the computational complexity. Array and multi-channel signal processing techniques are key technologies in wireless communication systems. High capacity and link reliability may be achieved by using multiple transmit and receive antennas. Combining multi-antenna techniques with multicarrier transmission leads to high the spectral efficiency and helps to cope with severe multipath propagation. The problem of channel equalization in MIMO-OFDM systems is also addressed in this thesis. A blind algorithm that optimizes of a combined criterion in order to be cancel both inter-symbol and co-channel interference is proposed. The algorithm local converge properties are established as well

    Energy-Efficient Digital Signal Processing for Fiber-Optic Communication Systems

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    Modern fiber-optic communication systems rely on complex digital signal processing (DSP) and forward error correction (FEC), which contribute to a significant amount of the over-all link power dissipation. Bandwidth demands are evergrowing and circuit technology scaling will due to fundamental reasons come to an end; energy-efficient design of DSP is thus necessary both from a sustainability perspective and a technical perspective. This thesis explores energy-efficient design of the sub-systems that are estimated to contribute to the majority of the receiver application-specific integrated-circuit power dissipation: chromatic-dispersion compensation, dynamic equalization, nonlinearity mitigation, and forward error correction. With a focus on real-time-processing circuit implementation of the considered algorithms, aspects such as finite-precision effects, pipelining, and parallel processing are explored, the impact on compensation and correction performance is investigated, and energy-efficient circuit implementations are developed. The sub-systems are investigated both individually, and in a system context. DSP designs showing significant energy-efficiency improvements are presented, as well as very high-throughput, energy-efficient, FEC designs. The subsystems are also considered in the context of datacenter interconnect links, and it is shown that DSP-based coherent systems are feasible even in power constrained settings
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