Abstract
We study the maximum likelihood problem for the blind estimation of massive mmWave MIMO channels while taking into account their underlying sparse structure, the temporal shifts across antennas in the broadband regime, and ultimately one-bit quantization at the receiver. The sparsity in the angular domain is exploited as a key property to enable the unambiguous blind separation between user's channels. The main advantage of this approach is the fact that the overhead due to pilot sequences can be dramatically reduced especially when operating at low SNR per antenna. In addition, as sparsity is the only assumption made about the channel, the proposed method is robust with respect to the statistical properties of the channel and data and allows the channel estimation and the separation of interfering users from adjacent base stations to be performed in rapidly time-varying scenarios. For the case of one-bit receivers, a blind channel estimation is proposed that relies on the Expectation Maximization (EM) algorithm. Additionally, performance limits are derived based on the clairvoyant Cramer Rao lower bound. Simulation results demonstrate that this maximum likelihood formulation yields superior estimation accuracy in the narrowband as well as the wideband regime with reasonable computational complexity and limited model assumptions.
Highlights
Channel estimation is recognized as one of the key issues in developing the fifth generation of wireless communication systems [2]
We show that the proposed non-convex optimization approach with appropriate initialization for the blind estimation of weakly sparse channels can be applied for general cases while remaining robust to certain model assumptions, as for instance the type of modulation alphabet, as well as the time and frequency synchronization
We present a new maximum likelihood formulation for blind channel estimation based on l1 regularization with ideal as well as with one-bit receivers
Summary
Channel estimation is recognized as one of the key issues in developing the fifth generation of wireless communication systems [2]. Joint Bayesian channeland-data estimation has been considered and analyzed in [26]–[28] and shown to yield a large improvement compared to training-based methods This approach requires an iterative message passing algorithm applied to a sufficiently large system with significant complexity and generally strict assumptions on the prior distributions of the channel and data, and on the time and frequency synchronization, while convergence and optimality still cannot be guaranteed. To address this issue, we present a maximum likelihood approach for blind mmWave channel estimation that, unlike [25], takes into account the sparsity of these channels. X[n] is a time domain signal and x[m] is the corresponding frequency domain signal
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
