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Enhancing CoMP-RSMA Performance With Movable Antennas: A Meta-Learning Optimization Framework
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DOI：10.1109/TCOMM.2026.3657455.png)
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摘要
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This study investigates a downlink rate-splitting multiple access (RSMA) scenario in which multiple base stations (BSs), employing a coordinated multi-point (CoMP) transmission scheme, serve users equipped with movable antenna (MA) technology. Unlike traditional fixed-position antennas (FPAs), which are subject to random variations in wireless channels, MAs can be strategically repositioned to locations with more favorable channel conditions, thereby achieving enhanced spatial diversity gains. To leverage these advantages and maximize the achievable sum rate, we formulate an optimization problem that jointly determines the optimal transmit beamforming vectors at the BSs, the common stream allocation for different users, and the optimal positioning of the MAs, all while ensuring compliance with quality of service (QoS) constraints. However, the formulated problem is non-convex and computationally challenging due to the strong interdependence among the optimization variables. Traditional methods for solving large-scale optimization problems typically incur prohibitively high computational complexity. To address the above challenge, we propose a gradient-based meta-learning (GML) algorithm that operates without pre-training and is well-suited for handling large-scale optimization tasks. Numerical results demonstrate the effectiveness and accuracy of the proposed approach, achieving near-optimal performance (exceeding 97% compared to the optimal solution). Moreover, the MA-enabled CoMP-RSMA model significantly outperforms conventional benchmark schemes, yielding performance gains of up to 190% over the spatial division multiple access (SDMA)-based scheme and 80% over the RSMA FPA-based model. Finally, the proposed approach is shown to mitigate the sum-rate upper-bound limitations imposed by interference in SDMA, achieving superior performance with fewer BSs.
Keyword:
MA
RSMA
CoMP
beamforming
meta-learning
期刊
IF:
8.3
论文数:
1.2W
被引数:
3.6W
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