1,857 research outputs found

    An improved channel estimation approach for MIMO-OFDM systems

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    University of Technology, Sydney. Faculty of Engineering and Information Technology.In wireless environments, signals bounce off many obstacles such as mountains, buildings, trees, etc. as they propagate between transmitters and receivers. The resultant signal at the receive antenna is, therefore, often the sum of the attenuated transmitted signal and one or more delayed versions of the transmitted signal. The received signal also suffers from intersymbol interference which degrades the quality of signal to a certain extent. However, MIMO-OFDM systems are designed to take advantage of the multi-path properties in wireless communications and are capable of improving transmission rate, range and reliability simultaneously. MIMO-OFDM attracts a good deal of research and commercial interest because of the perceived benefits, and has been adopted in many wireless standards such as IEEE 802.1 In, IEEE 802.16e. Such systems are also potential candidates for fourth-generation (4G) systems. However, practical problems still exist in implementing MIMO-OFDM, for example, in the estimation of channel state information (CS1). This thesis studies the issues of MIMO, OFDM and the relevant techniques of MIMO-OFDM, and focuses on proposing a practical, low complexity and accurate channel estimation method for such systems. In a MIMO-OFDM system, CSI is required at the receiver to perform space-time decoding or diversity combining. In many practical wireless applications, the propagation environment is both complex and time-variant, leading to CSI estimation errors and overall system performance degradation. A variety of channel estimation approaches have been proposed in the literature to address this problem. One of the most important parameters of CSI is the number of significant or dominant propagation paths, also referred to as the number of channel taps. However, in most existing estimation schemes for MIMO-OFDM, there is an assumption that the number of channel taps is known at the receiver. In reality, in order to perform space-time decoding, the receiver needs to estimate the number of channel taps from the received signal with this estimation process sometimes aided by the insertion of pilot tones into the transmitted signal. In this thesis, a pilot-assisted, conditional model-order estimation (CME) based channel estimation algorithm is presented. The approach can be utilised to detect both the number of channel resolvable paths and channel gains for MIMO-OFDM systems. The performance of the proposed algorithm is compared with the commonly used minimum description length (MDL) algorithm by mean of simulation in the context of a 2x2 MIMO-OFDM system. Results indicate that the new algorithm is superior to the MDL algorithm in channel order estimation over an unknown, noisy, multipah fading channel with limited pilot assistance. Furthermore, the proposed scheme is tested in both fixed and mobile broadband MIMO-OFDM systems based on WiMAX techniques in Matlab simulation, and its capacity is verified again for those near practical broadband MIMO- OFDM systems in the absence of prior knowledge of model parameters. Finally, with the purpose to “make the thing work in practice”, a 2x2 MIMO baseband platform is built in order to demonstrate the proposed scheme. The platform consists of two DSP based, real-time development boards called SignalWAVe, produced by Lyrtech. Given the existing hardware components, the whole platform is built based on a fixed MIMO-OFDM system according to WiMAX standard, and the results demonstrate that the proposed algorithm is a valid approach in practice

    Near-Instantaneously Adaptive HSDPA-Style OFDM Versus MC-CDMA Transceivers for WIFI, WIMAX, and Next-Generation Cellular Systems

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    Burts-by-burst (BbB) adaptive high-speed downlink packet access (HSDPA) style multicarrier systems are reviewed, identifying their most critical design aspects. These systems exhibit numerous attractive features, rendering them eminently eligible for employment in next-generation wireless systems. It is argued that BbB-adaptive or symbol-by-symbol adaptive orthogonal frequency division multiplex (OFDM) modems counteract the near instantaneous channel quality variations and hence attain an increased throughput or robustness in comparison to their fixed-mode counterparts. Although they act quite differently, various diversity techniques, such as Rake receivers and space-time block coding (STBC) are also capable of mitigating the channel quality variations in their effort to reduce the bit error ratio (BER), provided that the individual antenna elements experience independent fading. By contrast, in the presence of correlated fading imposed by shadowing or time-variant multiuser interference, the benefits of space-time coding erode and it is unrealistic to expect that a fixed-mode space-time coded system remains capable of maintaining a near-constant BER

    Iterative joint channel and data estimation for rank-deficient MIMO-OFDM

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    In this paper we propose a turbo-detected multi-antenna-multi-carrier receiver scheme. Following the philosophy of the turbo processing, our turbo MIMO-OFDM receiver comprises a succession of detection modules, namely the channel estimator, the space-time detector and the decoder, which iteratively exchange soft bit-related information and thus facilitate a substantial improvement of the overall system performance. In this paper we analyze the achievable performance of the iterative system proposed with the aim of documenting the various design trade-offs, such as the achievable error-rate performance, the attainable data-rate as well as the associated computational complexity. Specifically, we report a virtually error-free performance for a rate-1/2 turbo-coded 8x8-QPSK-OFDM system, exhibiting an effective throughput of 8*2/2=8 bits/sec/Hz and having a pilot overhead of only 10%, at SNR of 7.5dB and normalized Doppler frequency of 0.003, which corresponds to a mobile terminal speed of about 65 km/h

    Multiuser MIMO-OFDM for Next-Generation Wireless Systems

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    This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems

    Receiver Architectures for MIMO-OFDM Based on a Combined VMP-SP Algorithm

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    Iterative information processing, either based on heuristics or analytical frameworks, has been shown to be a very powerful tool for the design of efficient, yet feasible, wireless receiver architectures. Within this context, algorithms performing message-passing on a probabilistic graph, such as the sum-product (SP) and variational message passing (VMP) algorithms, have become increasingly popular. In this contribution, we apply a combined VMP-SP message-passing technique to the design of receivers for MIMO-ODFM systems. The message-passing equations of the combined scheme can be obtained from the equations of the stationary points of a constrained region-based free energy approximation. When applied to a MIMO-OFDM probabilistic model, we obtain a generic receiver architecture performing iterative channel weight and noise precision estimation, equalization and data decoding. We show that this generic scheme can be particularized to a variety of different receiver structures, ranging from high-performance iterative structures to low complexity receivers. This allows for a flexible design of the signal processing specially tailored for the requirements of each specific application. The numerical assessment of our solutions, based on Monte Carlo simulations, corroborates the high performance of the proposed algorithms and their superiority to heuristic approaches

    On the distribution of an effective channel estimator for multi-cell massive MIMO

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    Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10

    Two-tier channel estimation aided near-capacity MIMO transceivers relying on norm-based joint transmit and receive antenna selection

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    We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains, while dispensing with the idealised simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximumlikelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process

    Joint Decision-Directed Channel and Noise-Variance Estimation for MIMO OFDM/SDMA Systems Based on Expectation-Conditional Maximization

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    A joint channel impulse response (CIR) and noise-variance estimation scheme is proposed for multiuser multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing/space-division multiple access (OFDM/SDMA) systems, which is based on the expectation-conditional maximization (ECM) algorithm. Multiple users communicating over fading channels exhibiting a range of different characteristics are considered in this paper. Channel estimation becomes quite challenging in this scenario since an increased number of independent transmitter–receiver links having different statistical characteristics have to be simultaneously estimated for each subcarrier. To cope with this scenario, we design an ECM-based joint CIR and noise-variance estimator for multiuser MIMO OFDM/SDMA systems, which is capable of simultaneously estimating diverse CIRs and noise variance. Furthermore, we propose a forward error code (FEC)-aided decision-directed channel estimation scheme based on the ECM algorithm, which further improves the ECM algorithm by exploiting the error correction capability of an FEC decoder for iteratively exchanging information between the decoder and the ECM algorithm
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