Method and system for compressed sensing joint channel estimation in an LTE cellular communications network

A system for performing time domain channel estimation in a multi-user multiple input multiple output (“MIMO”) wireless network operating according to the Long Term Evolution (“LTE”) standard, the system comprising:

• a signal receiving unit configured to receive data corresponding to transmission of training signal from a plurality of users to a base station over a MIMO wireless network;
• and
• a signal processing unit configured to
• estimate a maximum delay spread for the received data according to a coherence bandwidth of the received data,
• determine a limited data set by limiting the received data in a time domain according to the estimated maximum delay spread,
• select an active tap from the limited data set,
• determine a number of active taps in the LTE wireless network,
• assume that positions of the active taps do not vary in time,
• form a well-conditioned low rank training matrix by identifying an LTE channel model,
• estimate the active tap from the formed well-conditioned low rank training matrix, and
• subtract a contribution of the selected active tap from the limited data set.

A non-transitory computer readable medium storing a computer program, executed by a machine, for performing time domain channel estimation in a multi-user multiple input multiple output (“MIMO”) wireless network operating according to the Long Term Evolution (“LTE”) standard, the computer program comprising executable instructions for:

• receiving data corresponding to transmission of training signal from a plurality of users to a base station over a MIMO wireless network;
• estimating a maximum delay spread for the received data according to a coherence bandwidth of the received data;
• determining a limited data set by limiting the received data in a time domain according to the estimated maximum delay spread;
• selecting an active tap from the limited data set:
• determining a number of active taps in the LTE wireless network;
• assuming that positions of the active taps do not vary in time;
• forming a well-conditioned low rank training matrix by identifying an LTE channel model;
• estimating the active tap from the formed well-conditioned low rank training matrix;
• and
• subtracting a contribution of the selected active tap from the limited data set.