PROJECT TITLE :

Frequency-Domain Compressive Channel Estimation for Frequency-Selective Hybrid Millimeter Wave MIMO Systems - 2018

ABSTRACT:

Channel estimation is helpful in millimeter wave (mm-wave) MIMO Communication systems. Channel state data allows optimized styles of precoders and combiners underneath completely different metrics, like mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are typically hybrid, since this architecture provides a suggests that to trade-off power consumption and achievable rate. Channel estimation is difficult when using these architectures, but, since there is no direct access to the outputs of the various antenna elements in the array. The MIMO channel will only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the previous work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this Project, we have a tendency to take into account a frequency-selective mm-wave channel and propose compressed sensing-primarily based strategies to estimate the channel within the frequency domain. We have a tendency to evaluate totally different algorithms and compute their complexity to show tradeoffs in complexity overhead performance as compared with those of previous approaches.