Channel Estimation Capacity Enhancement for Multigroup Multicasting Multimedia Networks With DnCNN

Channel Estimation Capacity Enhancement for Multigroup Multicasting Multimedia Networks With DnCNN

Blog Article

In Time Domain Duplex (TDD) massive MIMO systems, multi-group multi-casting becomes a promising technology since it supports services of mass content distribution.Based on the nature of transmitting common message to groups of users simultaneously, there exists a rich literature discussing the resource allocation under various constraints.However, the practical acquisition of CSI has not been fully explored when the number of multi-groups is large and the band is narrow.The insufficient sounding reference signal resources lead to the limited Channel Estimation Capacity (CEC).

Under this case, even with Multi-User (MU) Quercetin channel estimation techniques, some users still cannot be estimated in-timely, which introduces degradation.Aiming at this problem, in this paper we provide a preliminary exploration on CEC enhancement.Based on Denoising Convolutional Neuron Network (DnCNN), which is recently proposed and has succeeded in image denoising, we propose MU-DnCNN Channel Estimation (M-DnCNN CE).M-DnCNN CE includes three parts.

First, we modify the utilization of LEISURE COOKMASTER CK100G232K 100CM FULL GAS RANGE COOKER LPG CONVERTIBLE BLACK SRS sequences.Then we establish the feature maps and propose M-DnCNN to denoise the signals.Finally, a matched channel restoration method is provided.The practical 3-D MIMO channel model is utilized to evaluate the performance.

Compared with DFT-based and conjugated separation methods, results show that the performance of M-DnCNN CE is robust and superior, and the CEC is remarkably improved on the premise of satisfying latency constraint.