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Recently, a versatile limited feedback scheme based on a Gaussian mixturemodel (GMM) was proposed for frequency division duplex (FDD) systems. Thisscheme provides high flexibility regarding various system parameters and isapplicable to both point-to-point multiple-input multiple-output (MIMO) andmulti-user MIMO (MU-MIMO) communications. The GMM is learned to cover theoperation of all mobile terminals (MTs) located inside the base station (BS)cell, and each MT only needs to evaluate its strongest mixture component asfeedback, eliminating the need for channel estimation at the MT. In this work,we extend the GMM-based feedback scheme to variable feedback lengths byleveraging a single learned GMM through merging or pruning of dispensablemixture components. Additionally, the GMM covariances are restricted toToeplitz or circulant structure through model-based insights. These extensionssignificantly reduce the offloading amount and enhance the clustering abilityof the GMM which, in turn, leads to an improved system performance. Simulationresults for both point-to-point and multi-user systems demonstrate theeffectiveness of the proposed extensions.

Enhanced Low-Complexity FDD System Feedback with Variable Bit Lengths via Generative Modeling