Trade-offs in Reliability and Performance Using Selective Beamforming for Ultra-Massive MIMO
This paper addresses the optimization challenges in Ultra-Massive MIMO communication systems, focusing on array selection and beamforming in dynamic and diverse operational contexts. We introduce a novel array selection criterion that incorporates antenna health information into the optimization process, distinguishing our approach from traditional methods. Our methodology employs dual proximal-gradient ascent to effectively tackle the constrained non-convex and non-smooth nature of sparse array selection problems. A central feature of our strategy is the implementation of proportional fairness among communication users, aligning with system resource limitations while ensuring minimum rate requirements for all users. This approach not only enhances system efficiency and responsiveness but also ensures equitable resource distribution. Extensive simulations validate the effectiveness of the proposed solutions in optimizing Ultra-Massive MIMO system performance, demonstrating their applicability in complex communication scenarios. Our findings reveal key trade-offs influenced by the sparsity promotion weight (\(\gamma\)). As \(\gamma\) increases, spectral efficiency (SE) and communication rate (Ri) decrease, while beamforming matrix density (BMD) reduces and antenna reliability (RL) significantly improves. These results highlight the critical balance between performance and reliability, essential for the practical deployment of Ultra-Massive MIMO systems. This work advances the field by providing innovative solutions and new insights into array selection and beamforming optimization, setting a foundation for future research in Ultra-Massive MIMO communication systems.
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