Enhancing xURLLC with RSMA-Assisted Massive-MIMO Networks: Performance Analysis and Optimization
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| Format: | Preprint |
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2024
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| _version_ | 1866917597454270464 |
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| author | Chen, Yuang Lu, Hancheng Zhang, Chenwu Deng, Yansha Nallanathan, Arumugam |
| author_facet | Chen, Yuang Lu, Hancheng Zhang, Chenwu Deng, Yansha Nallanathan, Arumugam |
| contents | Massive interconnection has sparked people's envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently appeals to large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) network architecture tailored to accommodate xURLLC's critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity joint iterative algorithm for efficient optimization. Extensive simulations substantiate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture improves the total ETR by 15.3% and 41.91%, respectively, as well as accommodates larger-scale access. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_16027 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing xURLLC with RSMA-Assisted Massive-MIMO Networks: Performance Analysis and Optimization Chen, Yuang Lu, Hancheng Zhang, Chenwu Deng, Yansha Nallanathan, Arumugam Information Theory Signal Processing Massive interconnection has sparked people's envisioning for next-generation ultra-reliable and low-latency communications (xURLLC), prompting the design of customized next-generation advanced transceivers (NGAT). Rate-splitting multiple access (RSMA) has emerged as a pivotal technology for NGAT design, given its robustness to imperfect channel state information (CSI) and resilience to quality of service (QoS). Additionally, xURLLC urgently appeals to large-scale access techniques, thus massive multiple-input multiple-output (mMIMO) is anticipated to integrate with RSMA to enhance xURLLC. In this paper, we develop an innovative RSMA-assisted massive-MIMO xURLLC (RSMA-mMIMO-xURLLC) network architecture tailored to accommodate xURLLC's critical QoS constraints in finite blocklength (FBL) regimes. Leveraging uplink pilot training under imperfect CSI at the transmitter, we estimate channel gains and customize linear precoders for efficient downlink short-packet data transmission. Subsequently, we formulate a joint rate-splitting, beamforming, and transmit antenna selection optimization problem to maximize the total effective transmission rate (ETR). Addressing this multi-variable coupled non-convex problem, we decompose it into three corresponding subproblems and propose a low-complexity joint iterative algorithm for efficient optimization. Extensive simulations substantiate that compared with non-orthogonal multiple access (NOMA) and space division multiple access (SDMA), the developed architecture improves the total ETR by 15.3% and 41.91%, respectively, as well as accommodates larger-scale access. |
| title | Enhancing xURLLC with RSMA-Assisted Massive-MIMO Networks: Performance Analysis and Optimization |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2402.16027 |