Whitening-rotation-based semi-blind MIMO channel estimation

2009 IEEE/SP 15th Workshop on Statistical Signal Processing

Wang

et al. 2009

A novel scheme of semi-blind joint maximum likelihood (ML) channel estimation and data detection is proposed for multipleinput multiple-output (MIMO) systems by decomposing the joint ML optimisation over channel and data into an iterative two-level optimisation loop. Particle swarm optimisation (PSO) is invoked at the upper level to identify the unknown MIMO channel while an enhanced ML sphere detector is used at the lower level to detect the transmitted data. The scheme is semi-blind as a minimum pilot overhead is employed to aid the initialisation of the PSO based channel estimator.

“…, that represent potential solutions are initialised asȞ (8) where η is the uniformly distributed random variable defined in the range [−α, α]. Each particleȞ…”

Section: Outer-level Optimisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Particle swarm optimisation aided semi-blind joint maximum likelihood channel estimation and data detection for MIMO systems

Palally

2009 IEEE/SP 15th Workshop on Statistical Signal Processing

Wang

et al. 2009

“…The number of pilot symbols used was . The achievable performance was assessed in the simulation using three metrics, and these were the mse defined in (6), the mean channel error (MCE) defined as (10) with denoting the true MIMO channel matrix, and the bit error ratio (BER). The three algorithmic parameters of the RWBS were found empirically and the values used were , and .…”

Section: Simulation Studymentioning

confidence: 99%

“…In the schemes of [6] are used to provide an initial MIMO channel estimate, and the channel estimator as well as the data detector iteratively exchange their information, where the channel estimator relies on decision-directed adaptation. In [10], the MIMO channel matrix is decomposed into the product of a whitening matrix and a rotational unitary matrix. The first matrix is estimated blindly while the second is estimated with the aid of training symbols.…”

Section: Introductionmentioning

confidence: 99%

Semi-blind Joint Maximum Likelihood Channel Estimation and Data Detection for MIMO Systems

Abuthinien

IEEE Signal Process. Lett.

Hanzo

2008

Abstract-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.Index Terms-Channel estimation, data detection, joint maximum likelihood estimation, multiple-input multiple-output.

“…In the schemes of [6], [7], [8], [9], a few training symbols are used to provide an initial MIMO channel estimate, and the channel estimator as well as the data detector iteratively exchange their information, where the channel estimator relies on decision-directed adaptation. In [10], the MIMO channel matrix is decomposed into the product of a whitening matrix and a rotational unitary matrix. The first matrix is estimated blindly while the second is estimated with the aid of training symbols.…”

Section: Introductionmentioning

confidence: 99%

Semi-Blind Spatial Equalisation for MIMO Channels with Quadrature Amplitude Modulation

2008 IEEE International Conference on Communications

Hanzo

Wang

2008

Abstract-Semi-blind spatial equalisation is considered for multiple-input multiple-output (MIMO) systems that employ high-throughput quadrature amplitude modulation scheme. A minimum number of training symbols, equal to the number of transmitters, are first utilised to provide a rough least squares channel estimate of the system's MIMO channel matrix for the initialisation of the spatial equalisers' weight vectors. A constant modulus algorithm aided soft decision-directed blind algorithm is then employed to adapt the spatial equalisers. This semiblind scheme has a very-low computational complexity, and it converges fast to the minimum mean-square-error spatial equalisation solution as demonstrated in our simulation study.