3,029 research outputs found
Transfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
This paper studies fast adaptive beamforming optimization for the
signal-to-interference-plus-noise ratio balancing problem in a multiuser
multiple-input single-output downlink system. Existing deep learning based
approaches to predict beamforming rely on the assumption that the training and
testing channels follow the same distribution which may not hold in practice.
As a result, a trained model may lead to performance deterioration when the
testing network environment changes. To deal with this task mismatch issue, we
propose two offline adaptive algorithms based on deep transfer learning and
meta-learning, which are able to achieve fast adaptation with the limited new
labelled data when the testing wireless environment changes. Furthermore, we
propose an online algorithm to enhance the adaptation capability of the offline
meta algorithm in realistic non-stationary environments. Simulation results
demonstrate that the proposed adaptive algorithms achieve much better
performance than the direct deep learning algorithm without adaptation in new
environments. The meta-learning algorithm outperforms the deep transfer
learning algorithm and achieves near optimal performance. In addition, compared
to the offline meta-learning algorithm, the proposed online meta-learning
algorithm shows superior adaption performance in changing environments
Fast Meta Learning for Adaptive Beamforming
This paper studies the deep learning based adaptive downlink beamforming solution for the signal-to-interference-plus-noise ratio balancing problem. Adaptive beamforming is an important approach to enhance the performance in dynamic wireless environments in which testing channels have different distributions from training channels. We propose an adaptive method to achieve fast adaptation of beamforming based on the principle of meta learning. Specifically, our method first learns an embedding model by training a deep neural network as a transferable feature extractor. In the adaptation stage, it fits a support vector regression model using the extracted features and testing data of the new environment. Simulation results demonstrate that compared to the state of the art meta learning method, our proposed algorithm reduces the complexities in both training and adaptation processes by more than an order of magnitude, while achieving better adaptation performance
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
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