… ⇥H/s⇥W/s, where K = 32 is the number of channels in the latent space, and s = 8 is the total
spatial … The final layer is a convolution with filter size 3, stride 2, and 32 output channels. The …

… multiplexed users accessing the single channel with different channel gains. NOMA may be
… [143], an auto-encoder in the physical layer [144], an auto-encoder for sparse code multiple …

… of ongoing standardization activities, trends in the industry and major … autoencoder is trained
offline on a stochastic channel model which should closely approximate the actual channel. …

… in this section, as the current trend opts for DL applications in order to … An autoencoder can
be trained with noisy images and … Moreover, we can distinguish beamforming and channel …

… Finally, some important challenges, trends and outlooks are … , Boltzmann’s family,
Autoencoders, sparse coding and explored … Challenges, trends and outlooks are also described …

… To illustrate, consider a CNN model with 128 channels in each of the two hidden layers.
These two layers can be combined channel-wise to obtain an output with 256 channels. The …

… 1) For a conventional communication channel, it is a challenge to overcome the dynamic
interference caused by multiple users with a predetermined mathematical model. In this work, …

… features based on all 20 channels [61]. Then, to … Autoencoders [83], which are designed for
data reconstruction, have been explored in the context of MI task classification. Autoencoders …

… and hence remove redundant spectral channels. To achieve this, dimension… autoencoders
is their effectiveness in handling noisy input data. As illustrated in [77], a stacked autoencoder …

… The authors proposed an extension to the simple autoencoder by adding dense layers with
… mitigate the SFO issue of autoencoders and simplifies equalization over multipath channels. …