Tu, Haiyan;
Bellavista, Paolo;
Zhao, Liqiang;
Liang, Kai;
Zheng, Gan;
Wong, Kai-Kit;
(2025)
Independent Deep Reinforcement Learning for Optimization of RSMA-enabled hybrid RAN Slicing.
IEEE Transactions on Vehicular Technology
pp. 1-13.
10.1109/TVT.2025.3591037.
(In press).
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Abstract
RAN slicing has been widely studied for providing ultra-reliability low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC) services in 5G. However, the existing RAN slicing networks have not been explored to support the hybrid services, such as massive URLLC (mULC) and ubiquitous eMBB (uMBB) services. In this paper, we propose a novel rate splitting multi-access (RSMA)-enabled RAN slicing system to facilitate the runtime support of mULC and uMBB services. Firstly, three typical slices, i.e., URLLC, eMBB, and mMTC slices are constructed. Then, a multi-connection scheme is proposed by using RSMA technology, i.e., the users can be connected with two typical slices to obtain mULC and uMBB services. Specifically, the transmitted data of each mULC/uMBB user will be split into the common mMTC data and the private URLLC/eMBB data, which will be encoded into the corresponding traffic flows and served by corresponding slices. Next, a system-wide utility optimization problem is proposed to optimize heterogeneous requirements for mULC and uMBB services by joint user grouping, bandwidth allocation, and power control. Finally, a two independent agent DDPG (2IADDPG) algorithm is customized to solve the formulated problem, wherein two independent agents are responsible for independent decision-making. The reported numerical results show that the RSMA scheme outperforms the benchmarks, and in the meanwhile our proposed 2IADDPG algorithm can achieve faster convergence rate compared with the multi-agent DDPG algorithm and other comparison algorithms.
| Type: | Article |
|---|---|
| Title: | Independent Deep Reinforcement Learning for Optimization of RSMA-enabled hybrid RAN Slicing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TVT.2025.3591037 |
| Publisher version: | https://doi.org/10.1109/tvt.2025.3591037 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Rate splitting multi-access, hybrid services, independent Q learning, deep deterministic policy gradient |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10212706 |
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