Joint Cooperative-Transmit and Receive FDE for Single-Carrier Incremental Relaying

Adachi, Kôichi; Takeda, Kazuhisa; Sun, Sumei; Adachi, Fumiyuki

doi:10.1109/tvt.2012.2219566

Cited by 9 publications

(1 citation statement)

References 21 publications

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“…Simulation results showed that the proposed ASCE methods with ZA-LMS/F and RZA-LMS/F algorithms achieve better performance than any sparse LMS method in point-to-point systems. In addition, the proposed method can also be applied in cooperative systems [15]. …”

Section: Regularization Parameter Selection For Sparse Lms/f Algormentioning

confidence: 99%

Least mean square/fourth algorithm for adaptive sparse channel estimation

Gui

Mehbodniya

Adachi

2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

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Abstract-Broadband signal transmission over frequencyselective fading channel often requires accurate channel state information at receiver. One of the most attracting adaptive channel estimation (ACE) methods is least mean square (LMS) algorithm. However, its performance is often degraded by random scaling of input training signal. To overcome this degradation, in this paper we consider the use of standard least mean square/fourth (LMS/F) algorithm. Since the broadband channel is often described by sparse channel model, such sparsity could be exploited as prior information. First, we propose an adaptive sparse channel estimation (ASCE) method with zeroattracting LMS/F (ZA-LMS/F) algorithm by introducing annorm sparse constraint into the cost function. Then, to exploit the sparsity more effectively, an improved ASCE with reweighted zero-attracting LMS/F (RZA-LMS/F) algorithm is proposed. For different channel sparsity, we propose a Monte Carlo method for a regularization parameter selection in RA-LMS/F and RZA-LMS/F to achieve better steady-state estimation performance. Simulation results show that the proposed ASCE methods achieve better estimation performance than the conventional one.

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Section: Regularization Parameter Selection For Sparse Lms/f Algormentioning

confidence: 99%