Optimal channel feedback in FDD massive MIMO systems using an angle-of-departure (AoD)

Since channel feedback plays an increasingly important role in FDD massive MIMO systems, and MIMO, which is a key technology for 5G wireless communication systems, dramatically mitigates the inter-user interference with simple low complexity precoders and develops rapidly, more reliable and efficient channel feedback techniques are required to perform better forwarding. In FDD massive MIMO systems, due to the fact that feedback gain cannot be met without the knowledge of channel state information at the transmitter (CSIT), it is necessary to reduce feedback overhead reasonably to offer high spectral efficiency for next generation cellular systems. However, in the traditional techniques of channel feedback, the codebook is designed based on channel statistics and the feedback overhead scales linearly with the number of BS antennas to guarantee the capacity loss with an acceptable level. Therefore, as the number of BS antennas in massive MIMO systems is much larger than that of current systems, the feedback overhead will be overwhelming. In order to balance and minimize the feedback overhead, a channel feedback based on AoD-adaptive subspace codebook in FDD massive MIMO systems is presented. In this research, collaborated with the concept of angle coherence time and the observation that path AoDs vary more slowly than path gains, the proposed technique utilizes the AoD information to accomplish the scalability and adaptability of the exactly distributed quantization vectors in the channel subspace. By providing the performance analysis of the proposed AoD-adaptive subspace codebook in a large-scale regime, the required number of feedback bits only scales linearly with the number of dominant paths, not with the number of BS antennas. Simulation was adopted using MATLAB. The simulation results show that the proposed codebook efficiently identifies the redundant antennas and users and is able to quantize the channel vector in a more accurate way while improving the performance and reducing the feedback overhead.

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