1,267 research outputs found
On the Number of RF Chains and Phase Shifters, and Scheduling Design with Hybrid Analog-Digital Beamforming
This paper considers hybrid beamforming (HB) for downlink multiuser massive
multiple input multiple output (MIMO) systems with frequency selective
channels. For this system, first we determine the required number of radio
frequency (RF) chains and phase shifters (PSs) such that the proposed HB
achieves the same performance as that of the digital beamforming (DB) which
utilizes (number of transmitter antennas) RF chains. We show that the
performance of the DB can be achieved with our HB just by utilizing RF
chains and PSs, where is the rank of the
combined digital precoder matrices of all sub-carriers. Second, we provide a
simple and novel approach to reduce the number of PSs with only a negligible
performance degradation. Numerical results reveal that only PSs per RF
chain are sufficient for practically relevant parameter settings. Finally, for
the scenario where the deployed number of RF chains is less than ,
we propose a simple user scheduling algorithm to select the best set of users
in each sub-carrier. Simulation results validate theoretical expressions, and
demonstrate the superiority of the proposed HB design over the existing HB
designs in both flat fading and frequency selective channels.Comment: IEEE Transactions on Wireless Communications (Minor Revision
Machine Learning Inspired Energy-Efficient Hybrid Precoding for MmWave Massive MIMO Systems
Hybrid precoding is a promising technique for mmWave massive MIMO systems, as
it can considerably reduce the number of required radio-frequency (RF) chains
without obvious performance loss. However, most of the existing hybrid
precoding schemes require a complicated phase shifter network, which still
involves high energy consumption. In this paper, we propose an energy-efficient
hybrid precoding architecture, where the analog part is realized by a small
number of switches and inverters instead of a large number of high-resolution
phase shifters. Our analysis proves that the performance gap between the
proposed hybrid precoding architecture and the traditional one is small and
keeps constant when the number of antennas goes to infinity. Then, inspired by
the cross-entropy (CE) optimization developed in machine learning, we propose
an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture.
It aims to adaptively update the probability distributions of the elements in
hybrid precoder by minimizing the CE, which can generate a solution close to
the optimal one with a sufficiently high probability. Simulation results verify
that our scheme can achieve the near-optimal sum-rate performance and much
higher energy efficiency than traditional schemes.Comment: This paper has been accepted by IEEE ICC 2017. The simulation codes
are provided to reproduce the results in this paper at:
http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.htm
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