Method and apparatus for transceiving signal using artificial intelligence in wireless communication system

A method of transmitting or receiving a signal by a base station (BS) in a mobile communication system, the method comprising:

• selecting a neural network model corresponding to an autoencoder among a plurality of autoencoders based on at least one parameter associated with a wireless link between a user equipment (UE) and the BS, the at least one parameter includes an operating frequency, a UE bandwidth, and antenna configurations of the UE and the BS, wherein the neural network model includes a transmission (Tx) neural network disposed in the UE and a reception (Rx) neural network disposed in the BS;
• receiving, from the UE, a new connection weight of the Rx neural network, based on a result of a learning for a connection weight of the neural network model,
• transmitting, to the UE, an acknowledgement (ACK) signal indicating that the new connection weight is received by the BS;
• updating a weight of the Rx neural network based on the new connection weight after the ACK signal is transmitted to the UE, wherein a weight of the Tx neural network is updated based on the result of the learning;
• and
• receiving, from the UE, channel state information (CSI) feedback based on the updated weight of the Rx neural network,
• wherein the learning is performed by a shadow neural network disposed in the UE based on a channel matrix, the channel matrix being estimated by the UE or being transmitted to the UE by the BS, and
• wherein a first neural network corresponding to the Tx neural network and a second neural network corresponding to the Rx neural network are included in the shadow neural network for shadow training.

A user equipment (UE) which transmits or receives a signal in a mobile communication system, the UE comprising:

• a transceiver configured to transmit or receive a signal;
• and
• a controller configured to:
• select a neural network model corresponding to an autoencoder among a plurality of autoencoders based on at least one parameter associated with a wireless link between the UE and a base station (BS), the at least one parameter includes an operating frequency, a UE bandwidth, and antenna configurations of the UE and the BS, wherein the neural network model includes a transmission (Tx) neural network disposed in the UE and a reception (Rx) neural network disposed in the BS,
• learn a connection weight of the neural network model using a shadow neural network disposed in the UE based on a channel matrix, the channel matrix being estimated by the UE_or being received from the BS, wherein a first neural network corresponding to the Tx neural network and a second neural network corresponding to the Rx neural network are included in the shadow neural network for shadow training,
• transmit, to the BS, a new connection weight of the Rx neural network based on a result of the learning,
• receive, from the BS, an acknowledgement (ACK) signal indicating that the new connection weight is received by the BS, wherein a weight of the Rx neural network is updated based on the new connection weight,
• update a weight of the Tx neural network based on the result of the learning after the ACK signal is received from the BS, and
• transmit, to the BS, channel state information (CSI) feedback information based on the updated weight of the Tx neural network.

A base station (BS) which transmits or receives a signal in a mobile communication system, the BS comprising:

• a transceiver configured to transmit or receive a signal;
• and
• a controller configured to:
• select a neural network model corresponding to an autoencoder among a plurality of autoencoders based on at least one parameter associated with a wireless link between a user equipment (UE) and the BS, the at least one parameter includes an operating frequency, a UE bandwidth, and antenna configurations of the UE and the BS, wherein the neural network model includes a transmission (Tx) neural network disposed in the UE and a reception (Rx) neural network disposed in the BS,
• receive, from the UE, a new connection weight of the Rx neural network, based on a result of a learning for a connection weight of the neural network model,
• transmit, to the UE, an acknowledgement (ACK) signal indicating that the new connection weight is received by the BS,
• update a weight of the Rx neural network based on the new connection weight after the ACK signal is transmitted to the UE, wherein a weight of the Tx neural network is updated based on the result of the learning, and
• receive, from the UE, channel state information (CSI) feedback information based on the updated weight of the Rx neural network,
• wherein the learning is performed by a shadow neural network disposed in the UE based on a channel matrix, the channel matrix being estimated by the UE or being transmitted to the UE by the BS, and
• wherein a first neural network corresponding to the Tx neural network and a second neural network corresponding to the Rx neural network are included in the shadow neural network for shadow training.