My publications

Publications

Li Ang, Ma Yi, Xue Songyan, Yi Na, Tafazolli Rahim (2018) A Carrier-Frequency-Offset Resilient OFDMA Receiver Designed Through Machine Deep Learning,Proceedings of the 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC): IEEE PIMRC 2018 Institute of Electrical and Electronics Engineers (IEEE)
The aim of this paper is to handle the multifrequency
synchronization problem inherent in orthogonal
frequency-division multiple access (OFDMA) uplink
communications, where the carrier frequency offset (CFO)
for each user may be different, and they can be hardly
compensated at the receiver side. Our major contribution
lies in the development of a novel OFDM receiver that
is resilient to unknown random CFO thanks to the use
of a CFO-compensator bank. Specifically, the whole CFO
range is evenly divided into a set of sub-ranges, with
each being supported by a dedicated CFO compensator.
Given the optimization for CFO compensator a NP-hard
problem, a machine deep-learning approach is proposed
to yield a good sub-optimal solution. It is shown that the
proposed receiver is able to offer inter-carrier interference
free performance for OFDMA systems operating at a wide
range of SNRs.
Xue Songyan, Ma Yi, Tafazolli Rahim (2018) Unsupervised Deep Learning for MU-SIMO Joint Transmitter and Noncoherent Receiver Design,IEEE Wireless Communications Letters Institute of Electrical and Electronics Engineers (IEEE)
This work aims to handle the joint transmitter
and noncoherent receiver optimization for multiuser single-input
multiple-output (MU-SIMO) communications through unsupervised
deep learning. It is shown that MU-SIMO can be modeled
as a deep neural network with three essential layers, which
include a partially-connected linear layer for joint multiuser
waveform design at the transmitter side, and two nonlinear layers
for the noncoherent signal detection. The proposed approach
demonstrates remarkable MU-SIMO noncoherent communication
performance in Rayleigh fading channels.
Li Ang, Ma Yi, Xue Songyan, Tafazolli Rahim, Dodgson Terence E (2019) Unsupervised Deep Learning for Blind Multiuser Frequency Synchronization in OFDMA Uplink,IEEE Communications Society IEEE Communications Society
In this paper, a novel unsupervised deep learning
approach is proposed to tackle the multiuser frequency synchronization
problem inherent in orthogonal frequency-division
multiple-access (OFDMA) uplink communications. The key idea
lies in the use of the feed-forward deep neural network (FF-DNN)
for multiuser interference (MUI) cancellation taking advantage
of their strong classification capability. Basically, the proposed
FF-DNN consists of two essential functional layers. One is
called carrier-frequency-offsets (CFOs) classification layer that
is responsible for identifying the users? CFO range, and another
is called MUI-cancellation layer responsible for joint multiuser
detection (MUD) and frequency synchronization. By such means,
the proposed FF-DNN approach showcases remarkable MUIcancellation
performances without the need of multiuser CFO
estimation. In addition, we also exhibit an interesting phenomenon
occurred at the CFO-classification stage, where the
CFO-classification performance get improved exponentially with
the increase of the number of users. This is called multiuser
diversity gain in the CFO-classification stage, which is carefully
studied in this paper.
Xue Songyan, Ma Yi, Li Ang, Yi Na, Tafazolli Rahim (2019) On Unsupervised Deep Learning Solutions for Coherent MU-SIMO Detection in Fading Channels,IEEE Communications Society IEEE Communications Society
In this paper, unsupervised deep learning solutions
for multiuser single-input multiple-output (MU-SIMO) coherent
detection are extensively investigated. According to the ways
of utilizing the channel state information at the receiver side
(CSIR), deep learning solutions are divided into two groups.
One group is called equalization and learning, which utilizes the
CSIR for channel equalization and then employ deep learning for
multiuser detection (MUD). The other is called direct learning,
which directly feeds the CSIR, together with the received signal,
into deep neural networks (DNN) to conduct the MUD. It is found
that the direct learning solutions outperform the equalizationand-
learning solutions due to their better exploitation of the
sequence detection gain. On the other hand, the direct learning
solutions are not scalable to the size of SIMO networks, as
current DNN architectures cannot efficiently handle many cochannel
interferences. Motivated by this observation, we propose
a novel direct learning approach, which can combine the merits
of feedforward DNN and parallel interference cancellation. It is
shown that the proposed approach trades off the complexity for
the learning scalability, and the complexity can be managed due
to the parallel network architecture.
Wang Jinfei, Ma Yi, Xue Songyan, Yi Na, Tafazolli Rahim, Dodgson Terence E. (2019) Parallel Decoding for Non-recursive Convolutional Codes and Its Enhancement Through Artificial Neural Networks,Proceedings of the 20th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2019) Institute of Electrical and Electronics Engineers (IEEE)
This paper presents a parallel computing approach
that is employed to reconstruct original information bits from
a non-recursive convolutional codeword in noise, with the goal
of reducing the decoding latency without compromising the
performance. This goal is achieved by means of cutting a
received codeword into a number of sub-codewords (SCWs)
and feeding them into a two-stage decoder. At the first stage,
SCWs are decoded in parallel using the Viterbi algorithm or
equivalently the brute force algorithm. Major challenge arises
when determining the initial state of the trellis diagram for each
SCW, which is uncertain except for the first one; and such results
in multiple decoding outcomes for every SCW. To eliminate or
more precisely exploit the uncertainty, an Euclidean-distance
minimization algorithm is employed to merge neighboring SCWs;
and this is called the merging stage, which can also run in
parallel. Our work reveals that the proposed two-stage decoder
is optimal and has its latency growing logarithmically, instead
of linearly as for the Viterbi algorithm, with respect to the
codeword length. Moreover, it is shown that the decoding latency
can be further reduced by employing artificial neural networks
for the SCW decoding. Computer simulations are conducted
for two typical convolutional codes, and the results confirm our
theoretical analysis.
Xue Songyan, Li Ang, Wang Jinfei, Yi Na, Ma Yi, Tafazolli Rahim, Dodgson Terrence (2019) To Learn or Not to Learn: Deep Learning Assisted Wireless Modem Design,ZTE Communications ZTE
Deep learning is driving a radical paradigm shift in wireless communications, all the way from the application layer down to the physical layer. Despite this, there is an ongoing debate as to what additional values artificial intelligence (or machine learning) could bring to us,
particularly on the physical layer design; and what penalties there may have? These questions motivate a fundamental rethinking of the wireless modem design in the artificial intelligence era. Through several physical-layer case studies, we argue for a significant role that machine learning could play, for instance in parallel error-control coding and decoding, channel equalization, interference cancellation,
as well as multiuser and multiantenna detection. In addition, we will also discuss the fundamental bottlenecks of machine learning as
well as their potential solutions in this paper.
Xue Songyan, Ma Yi, Tafazolli Rahim (2020) An Orthogonal-SGD based Learning Approach for MIMO
Detection under Multiple Channel Models
,
IEEE ICC'20 Workshop - 5GLTEIC
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.