17 research outputs found
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
NOMA Meets Finite Resolution Analog Beamforming in Massive MIMO and Millimeter-Wave Networks
Finite resolution analog beamforming (FRAB) has been recognized as an
effective approach to reduce hardware costs in massive multiple-input
multiple-output (MIMO) and millimeter-wave networks. However, the use of FRAB
means that the beamformers are not perfectly aligned with the users' channels
and multiple users may be assigned similar or even identifical beamformers.
This letter shows how non-orthogonal multiple access (NOMA) can be used to
exploit this feature of FRAB, where a single FRAB based beamformer is shared by
multiple users. Both analytical and simulation results are provided to
demonstrate the excellent performance achieved by this new NOMA transmission
scheme
Energy Efficiency of Generalized Spatial Modulation Aided Massive MIMO Systems
One of focuses in green communication studies is the energy efficiency (EE)
of massive multiple-input multiple-output (MIMO) systems. Although the massive
MIMO technology can improve the spectral efficiency (SE) of cellular networks
by configuring a large number of antennas at base stations (BSs), the energy
consumption of radio frequency (RF) chains increases dramatically. The
increment of energy consumption is caused by the increase of RF chain number to
match the antenna number in massive MIMO communication systems. To overcome
this problem, a generalized spatial modulation (GSM) solution is presented to
simultaneously reduce the number of RF chains and maintain the SE of massive
MIMO communication systems. A EE model is proposed to estimate the transmission
and computation power of massive MIMO communication systems with GSM.
Simulation results demonstrate that the EE of massive MIMO communication
systems with GSM outperforms the massive MIMO communication systems without
GSM. Besides, the computation power consumed by massive MIMO communication
systems with GSM is effectively reduced
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hybrid beamforming is a promising technology for 5G millimetre-wave
communications. However, its implementation is challenging in practical
multiple-input multiple-output (MIMO) systems because non-convex optimization
problems have to be solved, introducing additional latency and energy
consumption. In addition, the channel-state information (CSI) must be either
estimated from pilot signals or fed back through dedicated channels,
introducing a large signaling overhead. In this paper, a hybrid precoder is
designed based only on received signal strength indicator (RSSI) feedback from
each user. A deep learning method is proposed to perform the associated
optimization with reasonable complexity. Results demonstrate that the obtained
sum-rates are very close to the ones obtained with full-CSI optimal but complex
solutions. Finally, the proposed solution allows to greatly increase the
spectral efficiency of the system when compared to existing techniques, as
minimal CSI feedback is required.Comment: Published in IEEE-ICC202