.png){kind=icon}
.png){kind=icon}
Return
A Low Complexity Symbol-Wise ML Detection Algorithm for User-Centric C-RAN
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2022.3153986.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This letter considers an optimal signal detection problem for uplink user-centric cloud radio access network (C-RAN). In user-centric C-RAN, users communicate with nearby remote radio heads (RRHs), and RRHs are connected to baseband unit (BBU) pool. Then, the baseband processing functionalities are migrated to the BBU pool to provide joint signal detection for a number of users. However, joint signal processing will encounter high computational complexity as the network scale grows. Especially for optimal signal detection, the computational complexity increases exponentially as the number of users increases. To address this issue, we propose a symbol-wise maximum likelihood (ML) detection algorithm to achieve optimal performance with low complexity. The key to realize the symbol-by-symbol separate detection is that it makes full use of the sparsity of the channel matrix in user-centric C-RAN. We further provide the theoretical derivation to clarify the superiority of the proposed algorithm in this sparse channel scenario. Numerical experiments show that the proposed algorithm can achieve optimal performance with low complexity in user-centric C-RAN.
Keywords:
Detection algorithms
Sparse matrices
Signal processing algorithms
Signal detection
Detectors
Iterative algorithms
Computational complexity
C-RAN
user-centric
signal detection
ML detector
optimal performance
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W