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- Publisher Website: 10.1109/GLOBECOM42002.2020.9348009
- Scopus: eid_2-s2.0-85100892336
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Conference Paper: Resource Allocation in IRSs Aided MISO-NOMA Networks: A Machine Learning Approach
Title | Resource Allocation in IRSs Aided MISO-NOMA Networks: A Machine Learning Approach |
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Authors | |
Keywords | Deep reinforcement learning Gaussian mixture model (GMM) Intelligent reflecting surface (IRS) Non-orthogonal multiple access (NOMA) |
Issue Date | 2020 |
Citation | Proceedings - IEEE Global Communications Conference, GLOBECOM, 2020, v. 2020-January, article no. 9348009 How to Cite? |
Abstract | A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order and power allocation coefficient vector, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the performance of IRS-NOMA system is better than IRS-OMA system. |
Persistent Identifier | http://hdl.handle.net/10722/349530 |
ISSN |
DC Field | Value | Language |
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dc.contributor.author | Gao, Xinyu | - |
dc.contributor.author | Liu, Yuanwei | - |
dc.contributor.author | Liu, Xiao | - |
dc.contributor.author | Qin, Zhijin | - |
dc.date.accessioned | 2024-10-17T06:59:09Z | - |
dc.date.available | 2024-10-17T06:59:09Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Proceedings - IEEE Global Communications Conference, GLOBECOM, 2020, v. 2020-January, article no. 9348009 | - |
dc.identifier.issn | 2334-0983 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349530 | - |
dc.description.abstract | A novel framework of intelligent reflecting surface (IRS)-aided multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) network is proposed, where a base station (BS) serves multiple clusters with unfixed number of users in each cluster. The goal is to maximize the sum rate of all users by jointly optimizing the passive beamforming vector at the IRS, decoding order and power allocation coefficient vector, subject to the rate requirements of users. In order to tackle the formulated problem, a three-step approach is proposed. More particularly, a long short-term memory (LSTM) based algorithm is first adopted for predicting the mobility of users. Secondly, a K-means based Gaussian mixture model (K-GMM) algorithm is proposed for user clustering. Thirdly, a deep Q-network (DQN) based algorithm is invoked for jointly determining the phase shift matrix and power allocation policy. Simulation results are provided for demonstrating that the proposed algorithm outperforms the benchmarks, while the performance of IRS-NOMA system is better than IRS-OMA system. | - |
dc.language | eng | - |
dc.relation.ispartof | Proceedings - IEEE Global Communications Conference, GLOBECOM | - |
dc.subject | Deep reinforcement learning | - |
dc.subject | Gaussian mixture model (GMM) | - |
dc.subject | Intelligent reflecting surface (IRS) | - |
dc.subject | Non-orthogonal multiple access (NOMA) | - |
dc.title | Resource Allocation in IRSs Aided MISO-NOMA Networks: A Machine Learning Approach | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/GLOBECOM42002.2020.9348009 | - |
dc.identifier.scopus | eid_2-s2.0-85100892336 | - |
dc.identifier.volume | 2020-January | - |
dc.identifier.spage | article no. 9348009 | - |
dc.identifier.epage | article no. 9348009 | - |
dc.identifier.eissn | 2576-6813 | - |