Hybrid Massive MIMO Precoding in Cloud-RAN by Tho Le-Ngoc & Ruikai Mai

Hybrid Massive MIMO Precoding in Cloud-RAN by Tho Le-Ngoc & Ruikai Mai

Author:Tho Le-Ngoc & Ruikai Mai
Language: eng
Format: epub
ISBN: 9783030021580
Publisher: Springer International Publishing


where we note that optimization of the precoders F BB,M and F RF,M for the Mth ARQ round depends on the previous precoders . The problem of interest is accordingly formulated as

(4.3)

In view of the difficulty with jointly solving for {F RF,M, F BB,M} subject to the constraint , we propose a two-step solution technique. In the first step, we choose the RF precoder F RF,M to be one of the feasible solutions from the set , say G RF,M, and conditioned on this choice, we derive the closed-form solution for the optimal baseband precoder . This procedure is repeated for each feasible G RF,M to generate a set of , from which the pair that yields the maximum mutual information in (4.3) is declared as the solution to the original problem. We remark that unlike the one-shot approach based on matrix reconstruction in [1], the proposed two-step technique obviates the need for knowing the fully digital solution, and enjoys the flexibility of performing waterfilling-based power loading at baseband.

Clearly, the set of constant-modulus RF precoders contains an unlimited number of elements. To facilitate the RF precoding optimization, we consider two suboptimal but effective alternatives, where the columns of the RF precoder F RF,M are assumed to be chosen either from the set of N cl N ray transmit array response vectors [1] or from the N t columns of the N t-dimensional DFT matrix [2]. The rationale for constraining the columns of the RF precoder to the finite sets and for the ARQ retransmission is threefold: (1) on one hand, the optimal progressive digital precoder has been known to be a function of a unitary matrix whose columns span the row space of the channel H M [7]. On the other hand, it has been observed in [1] that under certain conditions, the set of transmit array response vectors serves as another basis for the row space of the channel H M; (2) as indicated by the definition (4.1), the transmit array response vector , consists of constant-modulus entries; (3) when angle-domain quantization is considered as an option to reduce the overhead of estimating the complete AoD information, the array response vector-based RF precoding design can still be directly applied. One extreme case is the DFT-based codebook, which represents blind uniform quantization of the azimuth AoDs. Interestingly, in the large-scale array regime, the DFT matrix becomes an asymptotically good approximation for the channel eigen-space [8]. Our proposed approach to generate the hybrid RF-baseband precoder for the Mth ARQ retransmission is summarized in Algorithm 1.



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