748 research outputs found

    Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

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    New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure

    Blind separation for intermittent sources via sparse dictionary learning

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    Radio frequency sources are observed at a fusion center via sensor measurements made over slow flat-fading channels. The number of sources may be larger than the number of sensors, but their activity is sparse and intermittent with bursty transmission patterns. To account for this, sources are modeled as hidden Markov models with known or unknown parameters. The problem of blind source estimation in the absence of channel state information is tackled via a novel algorithm, consisting of a dictionary learning (DL) stage and a per-source stochastic filtering (PSF) stage. The two stages work in tandem, with the latter operating on the output produced by the former. Both stages are designed so as to account for the sparsity and memory of the sources. To this end, smooth LASSO is integrated with DL, while the forward-backward algorithm and Expectation Maximization (EM) algorithm are leveraged for PSF. It is shown that the proposed algorithm can enhance the detection and the estimation performance of the sources, and that it is robust to the sparsity level and average duration of transmission of the source signals

    Software and hardware implementation techniques for digital communications-related algorithms

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    There are essentially three areas addressed in the body of this thesis. (a) The first is a theoretical investigation into the design and development of a practically realizable implementation of a maximum-likelihood detection process to deal with digital data transmission over HF radio links. These links exhibit multipath properties with delay spreads that can easily extend over 12 to 15 milliseconds. The project was sponsored by the Ministry of Defence through the auspices of the Science and Engineering Research Council. The primary objective was to transmit voice band data at a minimum rate of 2.4 kb/s continuously for long periods of time during the day or night. Computer simulation models of HF propagation channels were created to simulate atmospheric and multipath effects of transmission from London to Washington DC, Ankara, and as far as Melbourne, Australia. Investigations into HF channel estimation are not the subject of this thesis. The detection process assumed accurate knowledge of the channel. [Continues.

    Lattice Sphere Detection Techniques In Block Data Transmission And Multiuser Wireless Systems

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    Recent years have witnessed an increase in demand for higher transmission data rates for wireless multimedia communications applications. Block Data Transmission Systems (BDTS) and Code Division Multiple Access (CDMA) are considered as efficient techniques for high data rate transmission and found in the coming generation of mobile and wireless technologies such as Long Term Evolution (LTE) systems. The Exhaustive Search (ES) detector is the optimum. Owing to its high computational load, lattice sphere detection (LSD) technique and its variants had been proposed. For the system designer, the main objective is to achieve an attractive performance-complexity tradeoff. In this research, LSD based detectors are designed for BDTS and Multi-User Detection (MUD) system. LSD searches lattice points in a sphere within a predetermined radius. In LSD, when the initial radius increases, the performance and complexity increased. This research produces exact expression for the sphere radius used in LSD technique which depends on the lattice dimension and average received power. It is well known fact that a small condition number results in a better detection performance. This research aims to reduce the condition number value to its smallest possible using the regularization methods (L1-regularization and L2-regularization), and utilizing special matrices (i.e., Hankel and Toeplitz). Sequentially, this research proposed a new detection technique which called as a near-An-LSD technique. Exact relationships between the LSD performance and condition number, and the relationship between the radius and condition number had been derived

    A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements

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    Multipath propagation causes major impairments to Global Positioning System (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this work, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step

    Timing and Carrier Synchronization in Wireless Communication Systems: A Survey and Classification of Research in the Last 5 Years

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    Timing and carrier synchronization is a fundamental requirement for any wireless communication system to work properly. Timing synchronization is the process by which a receiver node determines the correct instants of time at which to sample the incoming signal. Carrier synchronization is the process by which a receiver adapts the frequency and phase of its local carrier oscillator with those of the received signal. In this paper, we survey the literature over the last 5 years (2010–2014) and present a comprehensive literature review and classification of the recent research progress in achieving timing and carrier synchronization in single-input single-output (SISO), multiple-input multiple-output (MIMO), cooperative relaying, and multiuser/multicell interference networks. Considering both single-carrier and multi-carrier communication systems, we survey and categorize the timing and carrier synchronization techniques proposed for the different communication systems focusing on the system model assumptions for synchronization, the synchronization challenges, and the state-of-the-art synchronization solutions and their limitations. Finally, we envision some future research directions
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