23 research outputs found
Massive Access in Media Modulation Based Massive Machine-Type Communications
The massive machine-type communications (mMTC) paradigm based on media
modulation in conjunction with massive MIMO base stations (BSs) is emerging as
a viable solution to support the massive connectivity for the future
Internet-of-Things, in which the inherent massive access at the BSs poses
significant challenges for device activity and data detection (DADD). This
paper considers the DADD problem for both uncoded and coded media modulation
based mMTC with a slotted access frame structure, where the device activity
remains unchanged within one frame. Specifically, due to the slotted access
frame structure and the adopted media modulated symbols, the access signals
exhibit a doubly structured sparsity in both the time domain and the modulation
domain. Inspired by this, a doubly structured approximate message passing
(DS-AMP) algorithm is proposed for reliable DADD in the uncoded case. Also, we
derive the state evolution of the DS-AMP algorithm to theoretically
characterize its performance. As for the coded case, we develop a
bit-interleaved coded media modulation scheme and propose an iterative DS-AMP
(IDS-AMP) algorithm based on successive inference cancellation (SIC), where the
signal components associated with the detected active devices are successively
subtracted to improve the data decoding performance. In addition, the channel
estimation problem for media modulation based mMTC is discussed and an
efficient data-aided channel state information (CSI) update strategy is
developed to reduce the training overhead in block fading channels. Finally,
simulation results and computational complexity analysis verify the superiority
of the proposed algorithms in both uncoded and coded cases. Also, our results
verify the validity of the proposed data-aided CSI update strategy.Comment: Accepted by IEEE Transactions on Wireless Communications. The codes
and some other materials about this work may be available at
https://gaozhen16.github.i
Signal Processing and Learning for Next Generation Multiple Access in 6G
Wireless communication systems to date primarily rely on the orthogonality of
resources to facilitate the design and implementation, from user access to data
transmission. Emerging applications and scenarios in the sixth generation (6G)
wireless systems will require massive connectivity and transmission of a deluge
of data, which calls for more flexibility in the design concept that goes
beyond orthogonality. Furthermore, recent advances in signal processing and
learning have attracted considerable attention, as they provide promising
approaches to various complex and previously intractable problems of signal
processing in many fields. This article provides an overview of research
efforts to date in the field of signal processing and learning for
next-generation multiple access, with an emphasis on massive random access and
non-orthogonal multiple access. The promising interplay with new technologies
and the challenges in learning-based NGMA are discussed
Compressive Sensing-Based Grant-Free Massive Access for 6G Massive Communication
The advent of the sixth-generation (6G) of wireless communications has given
rise to the necessity to connect vast quantities of heterogeneous wireless
devices, which requires advanced system capabilities far beyond existing
network architectures. In particular, such massive communication has been
recognized as a prime driver that can empower the 6G vision of future
ubiquitous connectivity, supporting Internet of Human-Machine-Things for which
massive access is critical. This paper surveys the most recent advances toward
massive access in both academic and industry communities, focusing primarily on
the promising compressive sensing-based grant-free massive access paradigm. We
first specify the limitations of existing random access schemes and reveal that
the practical implementation of massive communication relies on a dramatically
different random access paradigm from the current ones mainly designed for
human-centric communications. Then, a compressive sensing-based grant-free
massive access roadmap is presented, where the evolutions from single-antenna
to large-scale antenna array-based base stations, from single-station to
cooperative massive multiple-input multiple-output systems, and from unsourced
to sourced random access scenarios are detailed. Finally, we discuss the key
challenges and open issues to shed light on the potential future research
directions of grant-free massive access.Comment: Accepted by IEEE IoT Journa
Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access
In the massive machine-type communication (mMTC) scenario, a large number of
devices with sporadic traffic need to access the network on limited radio
resources. While grant-free random access has emerged as a promising mechanism
for massive access, its potential has not been fully unleashed. In particular,
the common sparsity pattern in the received pilot and data signal has been
ignored in most existing studies, and auxiliary information of channel decoding
has not been utilized for user activity detection. This paper endeavors to
develop advanced receivers in a holistic manner for joint activity detection,
channel estimation, and data decoding. In particular, a turbo receiver based on
the bilinear generalized approximate message passing (BiG-AMP) algorithm is
developed. In this receiver, all the received symbols will be utilized to
jointly estimate the channel state, user activity, and soft data symbols, which
effectively exploits the common sparsity pattern. Meanwhile, the extrinsic
information from the channel decoder will assist the joint channel estimation
and data detection. To reduce the complexity, a low-cost side information-aided
receiver is also proposed, where the channel decoder provides side information
to update the estimates on whether a user is active or not. Simulation results
show that the turbo receiver is able to reduce the activity detection, channel
estimation, and data decoding errors effectively, while the side
information-aided receiver notably outperforms the conventional method with a
relatively low complexity
PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach
PhD ThesisRecently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with
cognition which can be developed by implementing Artificial Intelligence (AI) techniques.
Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum
abnormalities, can be effectively implemented as shown by the proposed research. One important
application is PHY-layer security since it is essential to establish secure wireless communications
against external jamming attacks.
In this framework, signals are non-stationary and features from such kind of dynamic
spectrum, with multiple high sampling rate signals, are then extracted through the Stockwell
Transform (ST) with dual-resolution which has been proposed and validated in this work as
part of spectrum sensing techniques.
Afterwards, analysis of the state-of-the-art about learning dynamic models from observed
features describes theoretical aspects of Machine Learning (ML). In particular, following the
recent advances of ML, learning deep generative models with several layers of non-linear
processing has been selected as AI method for the proposed spectrum abnormality detection
in CR for a brain-inspired, data-driven SA.
In the proposed approach, the features extracted from the ST representation of the wideband
spectrum are organized in a high-dimensional generalized state vector and, then, a generative
model is learned and employed to detect any deviation from normal situations in the analysed
spectrum (abnormal signals or behaviours). Specifically, conditional GAN (C-GAN),
auxiliary classifier GAN (AC-GAN), and deep VAE have been considered as deep generative
models.
A dataset of a dynamic spectrum with multi-OFDM signals has been generated by using
the National Instruments mm-Wave Transceiver which operates at 28 GHz (central carrier frequency)
with 800 MHz frequency range. Training of the deep generative model is performed
on the generalized state vector representing the mmWave spectrum with normality pattern
without any malicious activity. Testing is based on new and independent data samples corresponding
to abnormality pattern where the moving signal follows a different behaviour which
has not been observed during training.
An abnormality indicator is measured and used for the binary classification (normality hypothesis
otherwise abnormality hypothesis), while the performance of the generative models
is evaluated and compared through ROC curves and accuracy metrics
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society. This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering