| Paper ID | SPCOM-4.1 |
| Paper Title |
ITERATIVE REWEIGHTED ALGORITHMS FOR JOINT USER IDENTIFICATION AND CHANNEL ESTIMATION IN SPATIALLY CORRELATED MASSIVE MTC |
| Authors |
Hamza Djelouat, Markus Leinonen, Markku Juntti, University of Oulu, Finland |
| Session | SPCOM-4: Channel Estimation for MIMO and Multiuser Systems |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation Time: | Thursday, 10 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPC-MIMO] Multiple-Input Multiple-Output |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Joint user identification and channel estimation (JUICE) is a main challenge in grant-free massive machine-type communications (mMTC). The sparse pattern in users' activity allows to solve the JUICE as a compressed sensing problem in a multiple measurement vector (MMV) setup. This paper addresses the JUICE under the practical spatially correlated fading channel. We formulate the JUICE as an iterative reweighted $\ell_{2,1}$-norm optimization. We develop a computationally efficient alternating direction method of multipliers (ADMM) approach to solve it. In particular, by leveraging the second-order statistics of the channels, we reformulate the JUICE problem to exploit the covariance information and we derive its ADMM-based solution. The simulation results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances. |
