33 research outputs found
Application of End-to-End Deep Learning in Wireless Communications Systems
Deep learning is a potential paradigm changer for the design of wireless
communications systems (WCS), from conventional handcrafted schemes based on
sophisticated mathematical models with assumptions to autonomous schemes based
on the end-to-end deep learning using a large number of data. In this article,
we present a basic concept of the deep learning and its application to WCS by
investigating the resource allocation (RA) scheme based on a deep neural
network (DNN) where multiple goals with various constraints can be satisfied
through the end-to-end deep learning. Especially, the optimality and
feasibility of the DNN based RA are verified through simulation. Then, we
discuss the technical challenges regarding the application of deep learning in
WCS.Comment: This work has been submitted to the IEEE for possible publicatio
Non-Orthogonal Multiple Access: Common Myths and Critical Questions
Non-orthogonal multiple access (NOMA) has received tremendous attention for
the design of radio access techniques for fifth generation (5G) wireless
networks and beyond. The basic concept behind NOMA is to serve more than one
user in the same resource block, e.g., a time slot, subcarrier, spreading code,
or space. With this, NOMA promotes massive connectivity, lowers latency,
improves user fairness and spectral efficiency, and increases reliability
compared to orthogonal multiple access (OMA) techniques. While NOMA has gained
significant attention from the communications community, it has also been
subject to several widespread misunderstandings, such as The above statements are actually false, and this paper aims at
identifying such common myths about NOMA and clarifying why they are not true.
We also pose critical questions that are important for the effective adoption
of NOMA in 5G and beyond and identify promising research directions for NOMA,
which will require intense investigation in the future.Comment: To appear in the IEEE Wireless Communication
An Enhanced SCMA Detector Enabled by Deep Neural Network
In this paper, we propose a learning approach for sparse code multiple access
(SCMA) signal detection by using a deep neural network via unfolding the
procedure of message passing algorithm (MPA). The MPA can be converted to a
sparsely connected neural network if we treat the weights as the parameters of
a neural network. The neural network can be trained off-line and then deployed
for online detection. By further refining the network weights corresponding to
the edges of a factor graph, the proposed method achieves a better performance.
Moreover, the deep neural network based detection is a computationally
efficient since highly paralleled computations in the network are enabled in
emerging Artificial Intelligence (AI) chips
Cascade-Net: a New Deep Learning Architecture for OFDM Detection
In this paper, we consider using deep neural network for OFDM symbol
detection and demonstrate its performance advantages in combating large Doppler
Shift. In particular, a new architecture named Cascade-Net is proposed for
detection, where deep neural network is cascading with a zero-forcing
preprocessor to prevent the network stucking in a saddle point or a local
minimum point. In addition, we propose a sliding detection approach in order to
detect OFDM symbols with large number of subcarriers. We evaluate this new
architecture, as well as the sliding algorithm, using the Rayleigh channel with
large Doppler spread, which could degrade detection performance in an OFDM
system and is especially severe for high frequency band and mmWave
communications. The numerical results of OFDM detection in SISO scenario show
that cascade-net can achieve better performance than zero-forcing method while
providing robustness against ill conditioned channels. We also show the better
performance of the sliding cascade network (SCN) compared to sliding
zero-forcing detector through numerical simulation.Comment: 5 pages,5 figure
Decision Directed Channel Estimation Based on Deep Neural Network k-step Predictor for MIMO Communications in 5G
We consider the use of deep neural network (DNN) to develop a
decision-directed (DD)-channel estimation (CE) algorithm for multiple-input
multiple-output (MIMO)-space-time block coded systems in highly dynamic
vehicular environments. We propose the use of DNN for k-step channel prediction
for space-time block code (STBC)s, and show that deep learning (DL)-based DD-CE
can removes the need for Doppler spread estimation in fast time-varying quasi
stationary channels, where the Doppler spread varies from one packet to
another. Doppler spread estimation in this kind of vehicular channels is
remarkably challenging and requires a large number of pilots and preambles,
leading to lower power and spectral efficiency. We train two DNNs which learn
real and imaginary parts of the MIMO fading channels over a wide range of
Doppler spreads. We demonstrate that by those DNNs, DD-CE can be realized with
only rough priori knowledge about Doppler spread range. For the proposed DD-CE
algorithm, we also analytically derive the maximum likelihood (ML) decoding
algorithm for STBC transmission. The proposed DL-based DD-CE is a promising
solution for reliable communication over the vehicular MIMO fading channels
without accurate mathematical models. This is because DNN can intelligently
learn the statistics of the fading channels. Our simulation results show that
the proposed DL-based DD-CE algorithm exhibits lower propagation error compared
to existing DD-CE algorithms while the latters require perfect knowledge of the
Doppler rate
Deep Receiver Design for Multi-carrier Waveforms Using CNNs
In this paper, a deep learning based receiver is proposed for a collection of
multi-carrier wave-forms including both current and next-generation wireless
communication systems. In particular, we propose to use a convolutional neural
network (CNN) for jointly detection and demodulation of the received signal at
the receiver in wireless environments. We compare our proposed architecture to
the classical methods and demonstrate that our proposed CNN-based architecture
can perform better on different multi-carrier forms including OFDM and GFDM in
various simulations. Furthermore, we compare the total number of required
parameters for each network for memory requirements.Comment: PrePrint for TSP Conferenc
A Deep-learning-based Joint Inference for Secure Spatial Modulation Receiver
As a green and secure wireless transmission way, secure spatial modulation
(SM) is becoming a hot research area. Its basic idea is to exploit both the
index of activated transmit antenna and amplitude phase modulation (APM) signal
to carry messages, improve security, and save energy. In this paper, we
reviewed its crucial techniques: transmit antenna selection (TAS), artificial
noise (AN) projection, power allocation (PA), and joint detection at desired
receiver. To achieve the optimal performance of maximum likelihood (ML)
detector, a deep-neural-network (DNN) joint detector is proposed to jointly
infer the index of transmit antenna and signal constellation point with a
lower-complexity. Here, each layer of DNN is redesigned to optimize the joint
inference performance of two distinct types of information: transmit antenna
index and signal constellation point. Simulation results show that the proposed
DNN method performs 3dB better than the conventional DNN structure and is close
to ML detection in the low and medium signal-to-noise ratio regions in terms of
the bit error rate (BER) performance, but its complexity is far
lower-complexity compared to ML. Finally, three key techniques TAS, PA, and AN
projection at transmitter can be combined to make SM a true secure modulation
Deep Learning-Based Decoding for Constrained Sequence Codes
Constrained sequence codes have been widely used in modern communication and
data storage systems. Sequences encoded with constrained sequence codes satisfy
constraints imposed by the physical channel, hence enabling efficient and
reliable transmission of coded symbols. Traditional encoding and decoding of
constrained sequence codes rely on table look-up, which is prone to errors that
occur during transmission. In this paper, we introduce constrained sequence
decoding based on deep learning. With multiple layer perception (MLP) networks
and convolutional neural networks (CNNs), we are able to achieve low bit error
rates that are close to maximum a posteriori probability (MAP) decoding as well
as improve the system throughput. Moreover, implementation of
capacity-achieving fixed-length codes, where the complexity is prohibitively
high with table look-up decoding, becomes practical with deep learning-based
decoding.Comment: 7 pages, 6 figures, accepted by IEEE Global Communications Conference
Workshop - Machine learning for communication
Building Encoder and Decoder with Deep Neural Networks: On the Way to Reality
Deep learning has been a groundbreaking technology in various fields as well
as in communications systems. In spite of the notable advancements of deep
neural network (DNN) based technologies in recent years, the high computational
complexity has been a major obstacle to apply DNN in practical communications
systems which require real-time operation. In this sense, challenges regarding
practical implementation must be addressed before the proliferation of
DNN-based intelligent communications becomes a reality. To the best of the
authors' knowledge, for the first time, this article presents an efficient
learning architecture and design strategies including link level verification
through digital circuit implementations using hardware description language
(HDL) to mitigate this challenge and to deduce feasibility and potential of DNN
for communications systems. In particular, DNN is applied for an encoder and a
decoder to enable flexible adaptation with respect to the system environments
without needing any domain specific information. Extensive investigations and
interdisciplinary design considerations including the DNN-based autoencoder
structure, learning framework, and low-complexity digital circuit
implementations for real-time operation are taken into account by the authors
which ascertains the use of DNN-based communications in practice.Comment: This work has been submitted to the IEEE for possible publicatio
Trainable Projected Gradient Detector for Sparsely Spread Code Division Multiple Access
Sparsely spread code division multiple access (SCDMA) is a promising
non-orthogonal multiple access technique for future wireless communications. In
this paper, we propose a novel trainable multiuser detector called sparse
trainable projected gradient (STPG) detector, which is based on the notion of
deep unfolding. In the STPG detector, trainable parameters are embedded to a
projected gradient descent algorithm, which can be trained by standard deep
learning techniques such as back propagation and stochastic gradient descent.
Advantages of the detector are its low computational cost and small number of
trainable parameters, which enables us to treat massive SCDMA systems. In
particular, its computational cost is smaller than a conventional belief
propagation (BP) detector while the STPG detector exhibits nearly same
detection performance with a BP detector. We also propose a scalable joint
learning of signature sequences and the STPG detector for signature design.
Numerical results show that the joint learning improves multiuser detection
performance particular in the low SNR regime.Comment: 6 pages, 5 figure