| Paper ID | SPCOM-5.2 |
| Paper Title |
AN EFFICIENT ACTIVE SET ALGORITHM FOR COVARIANCE BASED JOINT DATA AND ACTIVITY DETECTION FOR MASSIVE RANDOM ACCESS WITH MASSIVE MIMO |
| Authors |
Ziyue Wang, University of Chinese Academy of Sciences, China; Zhilin Chen, University of Toronto, Canada; Ya-Feng Liu, Chinese Academy of Sciences, China; Foad Sohrabi, Wei Yu, University of Toronto, Canada |
| Session | SPCOM-5: Detection and Decoding |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation Time: | Friday, 11 June, 11:30 - 12:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing for Communications and Networking: [SPC-MOD] Modulation, demodulation, encoding and decoding |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This paper proposes a computationally efficient algorithm to solve the joint data and activity detection problem for massive random access with massive multiple-input multiple-output (MIMO). The BS acquires the active devices and their data by detecting the transmitted preassigned nonorthogonal signature sequences. This paper employs a covariance based approach that formulates the detection problem as a maximum likelihood estimation (MLE) problem. To efficiently solve the problem, this paper designs a novel iterative algorithm with low complexity in the regime where the device activity pattern is sparse -- a key feature that existing algorithmic designs have not previously exploited for reducing complexity. Specifically, at each iteration, the proposed algorithm focuses on only a small subset of all potential sequences, namely the active set, which contains a few most likely active sequences (i.e., transmitted sequences by all active devices), and performs the detection for the sequences in the active set. The active set is carefully selected at each iteration based on the current detection result and the first-order optimality condition of the MLE problem. Simulation results show that the proposed active set algorithm enjoys significantly better computational efficiency (in terms of the CPU time) than the state-of-the-art algorithms. |
