Maximum-likelihood joint channel estimation and data detection for space time block coded MIMO systems (original) (raw)
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Closed-form blind MIMO channel estimation for orthogonal space-time block codes
IEEE Transactions on Signal Processing, 2000
In this paper, a new computationally simple approach to blind decoding of orthogonal space-time block codes (OSTBCs) is proposed. Using specific properties of OSTBCs, the authors' approach estimates the channel matrix in a closed form and in a fully blind fashion. This channel estimate is then used in the maximum-likelihood (ML) receiver to decode the information symbols. The proposed estimation technique provides consistent channel estimates, and, as a result, the performance of the authors' blind ML receiver approaches that of the coherent ML receiver, which exploits the exact channel state information (CSI). Simulation results demonstrate the performance improvements achieved by the proposed blind decoding algorithm relative to the popular differential space-time modulation scheme.
Blind and semi-blind ML detection for space-time block-coded OFDM wireless systems
EURASIP Journal on Advances in Signal Processing, 2014
This paper investigates the joint maximum likelihood (ML) data detection and channel estimation problem for Alamouti space-time block-coded (STBC) orthogonal frequency-division multiplexing (OFDM) wireless systems. The joint ML estimation and data detection is generally considered a hard combinatorial optimization problem. We propose an efficient low-complexity algorithm based on branch-estimate-bound strategy that renders exact joint ML solution. However, the computational complexity of blind algorithm becomes critical at low signal-to-noise ratio (SNR) as the number of OFDM carriers and constellation size are increased especially in multiple-antenna systems. To overcome this problem, a semi-blind algorithm based on a new framework for reducing the complexity is proposed by relying on subcarrier reordering and decoding the carriers with different levels of confidence using a suitable reliability criterion. In addition, it is shown that by utilizing the inherent structure of Alamouti coding, the estimation performance improvement or the complexity reduction can be achieved. The proposed algorithms can reliably track the wireless Rayleigh fading channel without requiring any channel statistics. Simulation results presented against the perfect coherent detection demonstrate the effectiveness of blind and semi-blind algorithms over frequency-selective channels with different fading characteristics.
Joint Maximum Likelihood Channel Estimation and Data Detection for MIMO Systems
2007 IEEE International Conference on Communications, 2007
Blind and semiblind adaptive schemes are proposed for joint maximum likelihood (ML) channel estimation and data detection for multiple-input multiple-output (MIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative two-level optimisation loop. An efficient global optimisation search algorithm called the repeated weighted boosting search is employed at the upper level to identify the unknown MIMO channel model while an enhanced ML sphere detector called the optimised hierarchy reduced search algorithm aided ML detector is used at the lower level to perform the ML detection of the transmitted data. A simulation example is included to demonstrate the effectiveness of these two schemes.
Circuits, Systems, and Signal Processing, 2013
Due to the scarcity of the electromagnetic spectrum, multidimensional signaling schemes that take into account several signal dimensions such as space, time, frequency and constellation, are good candidates for increasing the data rate and/or improving the link reliability in future communication systems. This work addresses the problem of joint channel estimation and data detection in precoded multi-layered space-frequency codes (MLSFC) in multiple input multiple output (MIMO) systems based on orthogonal frequency division multiplexing (OFDM). First, we consider a modified (precoded) MLSFC transmit structure that consists in extending the constellation rotation across multiple OFDM symbols. By recasting the received signal as trilinear model, we propose a low-complexity blind receiver based on the least squares Khatri-Rao factorization (LSKRF). Our proposed LSKRF receiver is a closed-form solution and it outperforms the classical alternating least squares (ALS) receiver while being less complex since no iteration is need. Our results attest to the benefits of the proposed receiver in a variety of MLSFC schemes in comparison with the non-blind zero-forcing-based MLSFC receiver that assumes perfect channel knowledge.
This paper investigates the joint maximum-likelihood (ML) data detection and channel estimation problem for space-time-block-coded (STBC) OFDM wireless systems with general constellation modulations. An efficient low-complexity algorithm is proposed based on recursive least squares (RLS) that renders exact ML estimates of both channel and the data. The wireless channel is assumed to be stationary within two OFDM symbols but is allowed to vary after every two OFDM symbols; thus making it suitable for fast fading scenarios. The proposed methodology combines the advantages of both STBC, which gives full diversity gain and OFDM which circumvents the inter-symbol-interference (ISI) problem. The computational complexity of algorithm becomes critical at low signal-to-noise-ratio (SNR) as the number of OFDM carriers and constellation size are increased especially in multiple antenna systems. A new framework for reducing the complexity is proposed based on subcarrier reordering and decoding the carriers with different levels of confidence using a suitable reliability criterion. This newly devised approach enables the algorithm to reliably track the wireless Rayleigh fading channel in semi-blind fashion without requiring any channel statistics to be known. Simulation results demonstrate the effectiveness of blind and semi-blind algorithms over frequency selective channels with different fading characteristics.
Semi-blind Joint Maximum Likelihood Channel Estimation and Data Detection for MIMO Systems
IEEE Signal Processing Letters, 2000
Semi-blind joint maximum likelihood (ML) channel estimation and data detection is proposed for multiple-input multiple-output (MIMO) systems. The joint ML optimization over channel and data is decomposed into an iterative two-level optimization loop. An efficient optimization search algorithm referred to as the repeated weighted boosting search (RWBS) is employed at the upper level to identify the unknown MIMO channel while an enhanced ML sphere detector termed as the optimized hierarchy reduced search algorithm is used at the lower level to perform ML detection of the transmitted data. Only a minimum pilot overhead is required to aid the RWBS channel estimator's initial operation, which not only speeds up convergence but also avoids ambiguities inherent in blind joint estimation of both the channel and data.
Blind ML detection of orthogonal space-time block codes: Efficient high-performance implementations
2006
Abstract Orthogonal space-time block codes (OSTBCs) have attracted much attention owing to their simple code construction, maximal diversity gain, and low maximum-likelihood (ML) detection complexity when channel state information (CSI) is available at the receiver. This paper addresses the problem of ML OSTBC detection with unknown CSI. Focusing on the binary and quaternary PSK constellations, we show that blind ML OSTBC detection can be simplified to a Boolean quadratic program (BQP).
2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop, 2008
In this paper, we use a 4 x 4 MIMO testbed to investigate the experimental performance of the blind channel estimation technique presented in [1]. The operating frequency throughout all experiments was selected to be 2.47 GHz and the transmission bandwidth was 20 MHz. Our experimental results show that the performance of the blind technique can be very close to that of non-blind training based receiver which uses a significant bandwidth overhead as compared to the blind approach developed in [1].
IEEE Transactions on Signal Processing, 2008
In this paper, the problem of joint channel and carrier frequency offset (CFO) estimation is studied in the context of multiple-input multiple-output (MIMO) communications based on orthogonal space-time-block codes (OSTBCs). A new blind approach is proposed to jointly estimate the channel matrix and the CFO parameters using a relaxed maximum likelihood (ML) estimator that, for the sake of simplicity, ignores the finite alphabet constraint. Although the proposed technique can be applied to the majority of OSTBCs, there are, however, a few codes that suffer from an intrinsic ambiguity in the joint channel, CFO, and symbol estimates. For such specific OSTBCs, a semiblind modification of the proposed approach is developed that resolves the aforementioned estimation ambiguity. Our simulation results demonstrate that although the finite alphabet constraint is relaxed, the performance of the proposed techniques approaches that of the informed (fully frequency-synchronized and coherent) receiver, provided that a sufficient number of data blocks is available for each channel realization. Index Terms-Blind channel and carrier frequency offset estimation, multiple-input multiple-output (MIMO) communications, orthogonal space-time block codes. I. INTRODUCTION S PACE-TIME coding has recently gained much interest because of its ability to combat fading by means of exploiting spatial diversity provided by multiple-input multiple-output (MIMO) communication channels [1]-[3]. Among different space-time coding techniques proposed so far, orthogonal space-time codes (OSTBCs) are of great interest as they collect full diversity at low decoding complexity. The optimal ML decoder for OSTBCs amounts to a simple linear matched filter (MF) receiver followed by a symbol-by-symbol decoder. It has recently been shown in the literature that for the majority of OSTBCs, the MIMO channel is blindly identifiable Manuscript
On implementing the blind ML receiver for orthogonal space-time block codes
2005
Abstract We consider the problem of blind maximum-likelihood (ML) detection for the orthogonal space-time block code (OSTBC) scheme. Our previous work has shown that the problem can be simplified to a Boolean quadratic program (BQP). This sequel focuses on effective optimization methods for that BQP, which, from an optimization viewpoint, is still a computationally hard problem.