229 research outputs found
Reweighted lp Constraint LMS-Based Adaptive Sparse Channel Estimation for Cooperative Communication System
This paper studies the issue of sparsity adaptive channel reconstruction in time-varying cooperative
communication networks through the amplify-and-forward transmission scheme. A new sparsity adaptive system
identification method is proposed, namely reweighted norm ( < < ) penalized least mean square(LMS)algorithm.
The main idea of the algorithm is to add a norm penalty of sparsity into the cost function of the LMS algorithm. By doing
so, the weight factor becomes a balance parameter of the associated norm adaptive sparse system identification.
Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper
bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted norm.
With the upper bounds, we prove that the ( < < ) norm sparsity inducing cost function is superior to the
reweighted norm. An optimal selection of for the norm problem is studied to recover various sparse channel
vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus
demonstrate that the proposed algorithm has a better convergence speed and better steady state behavior than other LMS
algorithms
Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms
This paper studies massive access in cell-free massive multi-input
multi-output (MIMO) based Internet of Things and solves the challenging active
user detection (AUD) and channel estimation (CE) problems. For the uplink
transmission, we propose an advanced frame structure design to reduce the
access latency. Moreover, by considering the cooperation of all access points
(APs), we investigate two processing paradigms at the receiver for massive
access: cloud computing and edge computing. For cloud computing, all APs are
connected to a centralized processing unit (CPU), and the signals received at
all APs are centrally processed at the CPU. While for edge computing, the
central processing is offloaded to part of APs equipped with distributed
processing units, so that the AUD and CE can be performed in a distributed
processing strategy. Furthermore, by leveraging the structured sparsity of the
channel matrix, we develop a structured sparsity-based generalized approximated
message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the
quantization accuracy of the processed signals is taken into account. Based on
the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE
scheme is further developed under two paradigms for reduced access latency.
Simulation results validate the superiority of the proposed approach over the
state-of-the-art baseline schemes. Besides, the results reveal that the edge
computing can achieve the similar massive access performance as the cloud
computing, and the edge computing is capable of alleviating the burden on CPU,
having a faster access response, and supporting more flexible AP cooperation.Comment: 17 pages, 16 figures. The current version has been accepted by IEEE
Journal on Selected Areas in Communications (JSAC) Special Issue on Massive
Access for 5G and Beyon
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 Massive Access for Internet of Things: Cloud Computing or Fog Computing?
This paper considers the support of grant-free massive access and solves the
challenge of active user detection and channel estimation in the case of a
massive number of users. By exploiting the sparsity of user activities, the
concerned problems are formulated as a compressive sensing problem, whose
solution is acquired by approximate message passing (AMP) algorithm.
Considering the cooperation of multiple access points, for the deployment of
AMP algorithm, we compare two processing paradigms, cloud computing and fog
computing, in terms of their effectiveness in guaranteeing ultra reliable
low-latency access. For cloud computing, the access points are connected in a
cloud radio access network (C-RAN) manner, and the signals received at all
access points are concentrated and jointly processed in the cloud baseband
unit. While for fog computing, based on fog radio access network (F-RAN), the
estimation of user activity and corresponding channels for the whole network is
split, and the related processing tasks are performed at the access points and
fog processing units in proximity to users. Compared to the cloud computing
paradigm based on traditional C-RAN, simulation results demonstrate the
superiority of the proposed fog computing deployment based on F-RAN.Comment: 7 pages, 7 figures, accepted by IEEE International Conference on
Communications (ICC) 2020, Dublin, Irelan
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
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