Method and apparatus for downlink channel estimation in massive MIMO

A base station supporting massive multiple-input and multiple-output (MIMO), comprising:

• a processor;
• and
• a non-transitory computer readable storage medium storing programming for execution by the processor, the programming comprising instructions to:
• determine a first dictionary based on at least one of a channel quality indicator (CQI) and antenna array geometry;
• transmit information for constructing the first dictionary to a first user equipment (UE) for compressed sensing (CS)-based downlink (DL) channel estimation of a massive multiple-input and multiple-output (MIMO) channel between the base station and the first UE;
• receive feedback information from the first UE, the feedback information comprising information about a first sparsified DL channel vector used with the first dictionary for the CS-based DL channel estimation, and the first dictionary corresponding to a first representation of the massive MIMO channel;
• determine a second dictionary based on the feedback information, the second dictionary corresponding to a second representation of the massive MIMO channel, the second dictionary being used with a second sparsified DL channel vector for the CS-based DL channel estimation, and the second representation being sparser than the first representation such that a number of non-zero entries in the second representation is less than a number of non-zero entries in the first representation;
• and
• transmit information for constructing the second dictionary to the first UE, the second dictionary being used by the first UE to perform more accurate CS-based DL channel estimation of the massive MIMO channel.

A method, comprising:

• receiving, by a user equipment, information for constructing a first dictionary used for compressed sensing (CS)-based downlink (DL) channel estimation of a base station, the first dictionary being determined by the base station based on at least one of a channel quality indicator (CQI) and antenna array geometry;
• performing, by the user equipment, CS-based DL channel estimation of a massive multiple-input and multiple-output (MIMO). channel between the base station and the user equipment using the first dictionary constructed from the information, the first dictionary corresponding to a first representation of the massive MIMO channel;
• transmitting, by the user equipment, feedback information to the base station, the feedback information comprising information about a first sparsified DL channel vector used with the first dictionary for the CS-based DL channel estimation, the feedback information useful for determination of a second dictionary by the base station, the second dictionary being used with a second sparsified DL channel vector for the CS-based DL channel estimation, the second dictionary corresponding to a second representation of the massive MIMO channel, and the second representation being sparser than the first representation such that a number of non-zero entries in the second representation is less than a number of non-zero entries in the first representation;
• receiving, by the user equipment, information for constructing the second dictionary;
• and
• performing, by the user equipment, more accurate CS-based DL channel estimation of the massive MIMO channel using the second dictionary with the second sparsified DL channel vector.

A user equipment, comprising:

• a processor;
• and
• a non-transitory computer readable storage medium storing programming for execution by the processor, the programming comprising instructions to:
• receive information for constructing a first dictionary used for compressed sensing (CS)-based downlink (DL) channel estimation of a base station, the first dictionary being determined by the base station based on at least one of a channel quality indicator (CQI) and antenna array geometry;
• perform CS-based DL channel estimation of a massive multiple-input and multiple-output (MIMO) channel between the base station and the user equipment using the first dictionary constructed from the information for constructing the first dictionary, the first dictionary corresponding to a first representation of the massive MIMO channel;
• transmit feedback information, the feedback information comprising information about a first sparsified DL channel vector used with the first dictionary for the CS-based DL channel estimation, the feedback information useful for determination of a second dictionary by the base station, the second dictionary being used with a second sparsified DL channel vector for the CS-based DL channel estimation, the second dictionary corresponding to a second representation of the massive MIMO channel, and the second representation being sparser than the first representation such that a number of non-zero entries in the second representation is less than a number of non-zero entries in the first representation;
• receive information for constructing the second dictionary;
• and
• perform more accurate CS-based DL channel estimation of the massive MIMO channel using the second dictionary with the second sparsified DL channel vector.

A method for wireless communications, comprising:

• determining, by a base station, candidate dictionaries based on at least one of a channel quality indicator (CQI) and antenna array geometry, the base station supporting massive multiple-input and multiple-output (MIMO);
• transmitting, by the base station to a first user equipment (UE), information for constructing the candidate dictionaries, the candidate dictionaries being used for compressed sensing (CS)-based downlink (DL) channel estimations;
• and
• receiving, by the base station, feedback information from the first UE, the feedback information comprising information corresponding to a selected dictionary of the candidate dictionaries, the selected dictionary being selected by the first UE based on the CS-based DL channel estimations of a massive multiple-input and multiple-output (MIMO) channel between the base station and the first UE, and the selected dictionary corresponding to a representation of the massive MIMO channel, and the representation corresponding to the selected dictionary is the sparsest among representations of the massive MIMO channel corresponding to the candidate dictionaries such that a number of non-zero entries in the representation corresponding to the selected dictionary is the least, the selected dictionary being used with a sparsified DL channel vector for CS-based DL channel estimation.