137 research outputs found
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
Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications
A fundamental challenge of the large-scale Internet-of-Things lies in how to
support massive machine-type communications (mMTC). This letter proposes a
media modulation based mMTC solution for increasing the throughput, where a
massive multi-input multi-output based base station (BS) is used for enhancing
the detection performance. For such a mMTC scenario, the reliable active device
detection and data decoding pose a serious challenge. By leveraging the
sparsity of the uplink access signals of mMTC received at the BS, a compressive
sensing based massive access solution is proposed for tackling this challenge.
Specifically, we propose a block sparsity adaptive matching pursuit algorithm
for detecting the active devices, whereby the block-sparsity of the uplink
access signals exhibited across the successive time slots and the structured
sparsity of media modulated symbols are exploited for enhancing the detection
performance. Moreover, a successive interference cancellation based structured
subspace pursuit algorithm is conceived for data demodulation of the active
devices, whereby the structured sparsity of media modulation based symbols
found in each time slot is exploited for improving the detection performance.
Finally, our simulation results verify the superiority of the proposed scheme
over state-of-the-art solutions.Comment: submitted to IEEE Transactions on Vehicular Technology [Major
Revision
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
Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach
A key challenge of massive MTC (mMTC), is the joint detection of device
activity and decoding of data. The sparse characteristics of mMTC makes
compressed sensing (CS) approaches a promising solution to the device detection
problem. However, utilizing CS-based approaches for device detection along with
channel estimation, and using the acquired estimates for coherent data
transmission is suboptimal, especially when the goal is to convey only a few
bits of data.
First, we focus on the coherent transmission and demonstrate that it is
possible to obtain more accurate channel state information by combining
conventional estimators with CS-based techniques. Moreover, we illustrate that
even simple power control techniques can enhance the device detection
performance in mMTC setups.
Second, we devise a new non-coherent transmission scheme for mMTC and
specifically for grant-free random access. We design an algorithm that jointly
detects device activity along with embedded information bits. The approach
leverages elements from the approximate message passing (AMP) algorithm, and
exploits the structured sparsity introduced by the non-coherent transmission
scheme. Our analysis reveals that the proposed approach has superior
performance compared to application of the original AMP approach.Comment: Submitted to IEEE Transactions on Communication
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
Energy-Efficient System Design for Future Wireless Communications
The exponential growth of wireless data traffic has caused a significant increase in the power consumption of wireless communications systems due to the higher complexity of the transceiver structures required to establish the communication links. For this reason, in this Thesis we propose and characterize technologies for improving the energy efficiency of multiple-antenna wireless communications. This Thesis firstly focuses on energy-efficient transmission schemes and commences by introducing a scheme for alleviating the power loss experienced by the Tomlinson-Harashima precoder, by aligning the interference of a number of users with the symbols to transmit. Subsequently, a strategy for improving the performance of space shift keying transmission via symbol pre-scaling is presented. This scheme re-formulates complex optimization problems via semidefinite relaxation to yield problem formulations that can be efficiently solved. In a similar line, this Thesis designs a signal detection scheme based on compressive sensing to improve the energy efficiency of spatial modulation systems in multiple access channels. The proposed technique relies on exploiting the particular structure and sparsity that spatial modulation systems inherently possess to enhance performance. This Thesis also presents research carried out with the aim of reducing the hardware complexity and associated power consumption of large scale multiple-antenna base stations. In this context, the employment of incomplete channel state information is proposed to achieve the above-mentioned objective in correlated communication channels. The candidate’s work developed in Bell Labs is also presented, where the feasibility of simplified hardware architectures for massive antenna systems is assessed with real channel measurements. Moreover, a strategy for reducing the hardware complexity of antenna selection schemes by simplifying the design of the switching procedure is also analyzed. Overall, extensive theoretical and simulation results support the improved energy efficiency and complexity of the proposed schemes, towards green wireless communications systems
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