Representing underlying logical constructs related to temporal sensing and measuring of a radio environment

An apparatus comprising:

• at least one processor coupled with a non-transitory computer-readable medium storing instructions, where when the instructions executed by the at least one processor, cause the apparatus to:
• obtain a library function based on a measurement vector, wherein the measurement vector represents a plurality of measurements of a wireless communication environment and the library function represents a set of candidate non-linear functions of columns of the measurement vector;
• obtain a trained sparse coefficient matrix by optimizing a training target by:
• obtaining, using an input data vector and first phase training of a deep neural network autoencoder, an output data vector;
• wherein the first phase training comprises learning an encoder gradient for an encoder part of the deep neural network autoencoder and learning a decoder gradient for a decoder part of the deep neural network autoencoder, and
• wherein, the optimizing the training target comprises minimizing a loss function;
• obtain, via second phase training of the deep neural network autoencoder, a primary predicted derivative of the input data vector, wherein the second phase training uses:
• the encoder gradient;
• the decoder gradient;
• and
• a derivative of the input data vector;
• obtain, via third phase training of the deep neural network autoencoder, a secondary predicted derivative of the input data vector, wherein the third phase training uses:
• the library function;
• the sparse coefficient matrix;
• and
• the decoder gradient;
• obtain, based on a combination of the sparse coefficient matrix and the library function, a sparse equation;
• and
• transmit, to a device in the wireless communication environment, an indication of an adjustment to a parameter in use in the wireless communication environment, the adjustment to the parameter determined using the sparse equation.

A non-transitory computer readable medium storing instructions thereon, where when the instructions are executed by at least one processor, cause the at least one processor to:

• obtain a library function based on a measurement vector, wherein the measurement vector represents a plurality of measurements of a wireless communication environment and the measurement vector;
• obtain a trained sparse coefficient matrix by optimizing a training target by:
• obtaining, using an input data vector and first phase training of a deep neural network autoencoder, an output data vector;
• wherein the first phase training comprises learning an encoder gradient for an encoder part of the deep neural network autoencoder and learning a decoder gradient for a decoder part of the deep neural network autoencoder, and
• wherein, the optimizing the training target comprises minimizing a loss function;
• obtain, via second phase training of the deep neural network autoencoder, a primary predicted derivative of the input data vector, wherein the second phase training uses:
• the encoder gradient;
• the decoder gradient;
• and
• a derivative of the input data vector;
• obtain, via third phase training of the deep neural network autoencoder, a secondary predicted derivative of the input data vector, wherein the third phase training uses:
• the library function;
• the sparse coefficient matrix;
• and
• the decoder gradient;
• obtain, based on a combination of the sparse coefficient matrix and the library function, a sparse equation;
• and
• transmit, to a device in the wireless communication environment, an indication of an adjustment to a parameter in use in the wireless communication environment, the adjustment to the parameter determined using the sparse equation.