CNN Aided Weighted Interpolation for Channel Estimation in Vehicular Communications

A Survey on Deep Learning Based Channel Estimation in Doubly Dispersive Environments

“…Optimized SR-CNN and DN-CNN are employed in this context. The investigations conducted in [32] reveal that both SR-CNN and DN-CNN networks have similar performance in low mobility scenarios, whereas DN-CNN outperforms SR-CNN in high mobility scenarios. Figure 7 and Table 3 illustrate the block diagram as well as configured parameters of the studied CNN-based channel estimators, respectively.…”

“…To overcome the limitations of the ChannelNet and TS-ChannelNet estimators, weighted interpolation (WI)-CNN estimator has been proposed in [32]. In this method, the frame structure is adapted in accordance with the mobility condition employing varied pilot allocation schemes.…”

“…In this survey, we examine the recently proposed DL-based channel estimation schemes in doubly-dispersive environments, where DL algorithms are utilized in two different manners: (i) feed-forward neural networks (FNNs) with different architectures and configurations are employed on top of the conventional SBS channel estimators [27]- [29]. (ii) convolutional neural networks (CNNs) processing is employed where the estimated channel for the entire frame is modeled as a 2D low-resolution noisy image, whereas CNN-based processing is implemented as super resolution and denoising techniques [30]- [32].…”

Performance Comparison of IEEE 802.11p, 802.11bd-draft and a Unique-Word-based PHY in Doubly-Dispersive Channels

“…In addition, further classical and Bayesian approaches of the DPA scheme has been studied in [2] and references therein. Recently, different machine learning approaches based on deep neural networks (DNN) have been studied in order to improve the channel estimation performance for 802.11p [3], [4], [5], [6]. In general however, if the wireless channel is harshly doubly-dispersive, the DPA based derivations of the channel estimation become less effective, because the adjacent subcarriers and symbols tend to lose their spectral and temporal correlations, and also erroneous demapping of initial symbols causes the error to propagate through the entire transmit frame.…”

IRS for Multi-Access Edge Computing in 6G Networks

“…The last three decades have witnessed an exponential growth and tremendous developments in wireless technologies and techniques, and their associated applications. These include indoor localization techniques and related aspects [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], terahertz communications and signal processing applications [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], and antenna design and propagation characteristics .…”