Beamforming and Resource Allocation for Heterogeneous Bands in 6G
Abstract
The proliferation of diverse frequency bands, from millimeter-wave to terahertz, in 6G networks brings both opportunities and challenges. The use of adaptive beamforming and resource allocation algorithms is necessary to optimize the benefits of each band while minimizing its drawbacks. This study addresses several machine learning-based methods that dynamically choose frequency bands, modify beamforming patterns, and distribute resources according to real-time data analysis, channel circumstances and user requests. In comparison with traditional resource allocation schemes, remarkable enhancement in user experience and network efficiency has been established by previous researchers. By utilizing channel modeling and mitigation methods for this high-frequency range, the challenge of terahertz communication has been covered. Our results indicate the efficiency of resource allocation and dynamic, data-driven beamforming in enabling heterogeneous 6G networks to reach their maximum potential.
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