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In the 3D massive MIMO system, both the transmitter encoding and the receiver signal detection require channel state information. The accuracy of channel state information will directly affect the overall performance of the system. Therefore, accurate channel estimation is the key to reliable communication in the 3D massive MIMO system. Due to the channel sparsity of 3D massive MIMO, existing works...
A robust diffusion adaptive filtering algorithm, called the diffusion recursive least lp-norm (DRLP), is developed for distributed estimation over network. The new algorithm aims at recursively minimizing the lp-norm of error, and can offer a more stable and robust solution than traditional adaptive filtering schemes based on minimization of the squared error, such as the diffusion recursive least...
Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions...
Adaptive sparse system identification (ASIDE) techniques have been successfully applied in many applications, such as sparse channel estimation and radar target detection. Normalized least mean fourth (NLMF)-type algorithms are considered as one of the stable ASIDE techniques even at low signal-to-noise ratio (SNR). However, the convergence capability of sparse NLMF algorithms is severely decreased...
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