185 research outputs found
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
Five Facets of 6G: Research Challenges and Opportunities
Whilst the fifth-generation (5G) systems are being rolled out across the
globe, researchers have turned their attention to the exploration of radical
next-generation solutions. At this early evolutionary stage we survey five main
research facets of this field, namely {\em Facet~1: next-generation
architectures, spectrum and services, Facet~2: next-generation networking,
Facet~3: Internet of Things (IoT), Facet~4: wireless positioning and sensing,
as well as Facet~5: applications of deep learning in 6G networks.} In this
paper, we have provided a critical appraisal of the literature of promising
techniques ranging from the associated architectures, networking, applications
as well as designs. We have portrayed a plethora of heterogeneous architectures
relying on cooperative hybrid networks supported by diverse access and
transmission mechanisms. The vulnerabilities of these techniques are also
addressed and carefully considered for highlighting the most of promising
future research directions. Additionally, we have listed a rich suite of
learning-driven optimization techniques. We conclude by observing the
evolutionary paradigm-shift that has taken place from pure single-component
bandwidth-efficiency, power-efficiency or delay-optimization towards
multi-component designs, as exemplified by the twin-component ultra-reliable
low-latency mode of the 5G system. We advocate a further evolutionary step
towards multi-component Pareto optimization, which requires the exploration of
the entire Pareto front of all optiomal solutions, where none of the components
of the objective function may be improved without degrading at least one of the
other components
Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO
This paper proposes a grant-free massive access scheme based on the
millimeter wave (mmWave) extra-large-scale multiple-input multiple-output
(XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency,
high data rate, and high localization accuracy in the upcoming sixth-generation
(6G) networks. The XL-MIMO consists of multiple antenna subarrays that are
widely spaced over the service area to ensure line-of-sight (LoS)
transmissions. First, we establish the XL-MIMO-based massive access model
considering the near-field spatial non-stationary (SNS) property. Then, by
exploiting the block sparsity of subarrays and the SNS property, we propose a
structured block orthogonal matching pursuit algorithm for efficient active
user detection (AUD) and channel estimation (CE). Furthermore, different
sensing matrices are applied in different pilot subcarriers for exploiting the
diversity gains. Additionally, a multi-subarray collaborative localization
algorithm is designed for localization. In particular, the angle of arrival
(AoA) and time difference of arrival (TDoA) of the LoS links between active
users and related subarrays are extracted from the estimated XL-MIMO channels,
and then the coordinates of active users are acquired by jointly utilizing the
AoAs and TDoAs. Simulation results show that the proposed algorithms outperform
existing algorithms in terms of AUD and CE performance and can achieve
centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision.
Codes will be open to all on https://gaozhen16.github.io/ soo
Smart Surface Radio Environments
This Roadmap takes the reader on a journey through the research in electromagnetic wave propagation control via reconfigurable intelligent surfaces. Metasurface modelling and design methods are reviewed along with physical realisation techniques. Several wireless applications are discussed, including beam-forming, focusing, imaging, localisation, and sensing, some rooted in novel architectures for future mobile communications networks towards 6G
Compressive Sensing Based Grant-Free Communication
Grant-free communication, where each user can transmit data without following the strict access grant process, is a promising technique to reduce latency and support massive users. In this thesis, compressive sensing (CS), which exploits signal sparsity to recover data from a small sample, is investigated for user activity detection (UAD), channel estimation, and signal detection in grant-free communication, in order to extract information from the signals received by base station (BS). First, CS aided UAD is investigated by utilizing the property of quasi-time-invariant channel tap delays as the prior information for the burst users in internet of things (IoT). Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. In particular, g-TIDCS and m-TIDCS do not require any prior knowledge of the number of active users like the existing approaches and therefore are more practical. Second, periodic communication as one of the salient features of IoT is considered. Two schemes, namely periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), are proposed to exploit the non-continuous temporal correlation of the received signal for joint UAD, channel estimation, and signal detection. The theoretical analysis and simulation results show that the PBOMP and PBSBL outperform the existing schemes in terms of the success rate of UAD, bit error rate (BER), and accuracy in period estimation and channel estimation. Third, UAD and channel estimation for grant-free communication in the presence of massive users that are actively connected to the BS is studied. An iteratively UAD and signal detection approach for the burst users is proposed, where the interference of the connected users on the burst users is reduced by applying a preconditioning matrix to the received signals at the BS. The proposed approach is capable of providing significant performance gains over the existing algorithms in terms of the success of UAD and BER. Last but not least, since the physical layer security becomes a critical issue for grant-free communication, the channel reciprocity in time-division duplex systems is utilized to design environment-aware (EA) pilots derived from transmission channels to prevent eavesdroppers from acquiring users’ channel information. The proposed EA-pilots based approach possesses a high level of security by scrambling the eavesdropper’s normalized mean square error performance of channel estimation
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