Deep learning based energy-efficient transmission control for STAR-RIS aided cell-free massive MIMO networks
Recently, the simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) has been gaining attention as a key enabler for sixth-generation networks, providing additional links with reduction in power consumption. This paper investigates the STAR-RIS’s potential in a cell...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | ICT Express |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959525000116 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Recently, the simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) has been gaining attention as a key enabler for sixth-generation networks, providing additional links with reduction in power consumption. This paper investigates the STAR-RIS’s potential in a cell-free (CF) massive multiple-input multiple-output (mMIMO) network, where distributed APs serve user over the same time/frequency. We propose a deep deterministic policy gradient framework satisfying system-specific and per-user spectral efficiency constraints, exploiting a post-normalization and a penalized reward. From the simulations, it is revealed the proposed algorithm provides better energy performance than benchmarks, highlighting the benefits of STAR-RIS in the CF network. |
|---|---|
| ISSN: | 2405-9595 |