530 research outputs found
High Performance Interference Suppression in Multi-User Massive MIMO Detector
In this paper, we propose a new nonlinear detector with improved interference
suppression in Multi-User Multiple Input, Multiple Output (MU-MIMO) system. The
proposed detector is a combination of the following parts: QR decomposition
(QRD), low complexity users sorting before QRD, sorting-reduced (SR) K-best
method and minimum mean square error (MMSE) pre-processing. Our method
outperforms a linear interference rejection combining (IRC, i.e. MMSE
naturally) method significantly in both strong interference and additive white
noise scenarios with both ideal and real channel estimations. This result has
wide application importance for scenarios with strong interference, i.e. when
co-located users utilize the internet in stadium, highway, shopping center,
etc. Simulation results are presented for the non-line of sight 3D-UMa model of
5G QuaDRiGa 2.0 channel for 16 highly correlated single-antenna users with
QAM16 modulation in 64 antennas of Massive MIMO system. The performance was
compared with MMSE and other detection approaches.Comment: Accepted for presentation at the VTC2020-Spring conferenc
Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO
In this paper, we present a new performance bound for uplink channel
estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO)
system. The proposed approach is based on noise power prediction after the CE
unit. Our method outperforms the accuracy of a well-known Cramer-Rao lower
bound (CRLB) due to considering more statistics since performance strongly
depends on a number of channel taps and power ratio between them. Simulation
results are presented for the non-line of sight (NLOS) 3D-UMa model of 5G
QuaDRiGa 2.0 channel and compared with CRLB and state-of-the-art CE algorithms.Comment: accepted for presentation in a poster session at the ICASSP 2020
conferenc
Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless Telecommunication Performance
The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users' location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and deep
learning, in implementing contextual beamforming techniques that leverage user
location information. Based on the comprehensive review, the results suggest
that the combination of MRT and Zero forcing (ZF) techniques, alongside deep
neural networks (DNN) employing Bayesian Optimization (BO), represents the most
promising approach for contextual beamforming. Furthermore, the study discusses
the future potential of programmable switches, such as Tofino, in enabling
location-aware beamforming
An Orthogonal-SGD based Learning Approach for MIMO Detection under Multiple Channel Models
In this paper, an orthogonal stochastic gradient descent (O-SGD) based
learning approach is proposed to tackle the wireless channel over-training
problem inherent in artificial neural network (ANN)-assisted MIMO signal
detection. Our basic idea lies in the discovery and exploitation of the
training-sample orthogonality between the current training epoch and past
training epochs. Unlike the conventional SGD that updates the neural network
simply based upon current training samples, O-SGD discovers the correlation
between current training samples and historical training data, and then updates
the neural network with those uncorrelated components. The network updating
occurs only in those identified null subspaces. By such means, the neural
network can understand and memorize uncorrelated components between different
wireless channels, and thus is more robust to wireless channel variations. This
hypothesis is confirmed through our extensive computer simulations as well as
performance comparison with the conventional SGD approach.Comment: 6 pages, 4 figures, conferenc
- …