Limited feedback scheme using tensor decompositions for FDD massive MIMO systems




Joe, Kevin Jinho

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We propose a novel limited feedback scheme for massive multiple-input multiple-output (MIMO) systems in frequency-division duplexing (FDD) wideband system. We assume that the user (UE) has knowledge of a downlink (DL) channel estimate. In order for massive MIMO systems to achieve high capacity, the base station (BS) must have the DL channel state information. Traditional feedback methods cannot work because channels for massive MIMO systems are usually too large to feedback within the coherence time. Our goal is to feedback the DL channel estimate from the UE back to the BS with as little information as possible. Our method uses two different tensor decompositions, the canonical polyadic decomposition (CPD) and the rank-(L [subscript r], L [subscript r], 1) or LL-1 block decomposition, on the DL frequency channel to estimate its parameters. By feeding back only the channel parameters, we show through simulations that our method is able to efficiently and accurately reconstruct the DL channel.


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