8 research outputs found
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
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 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 sensingbased grant-free massive access roadmap is presented, where the evolutions from single-antenna to large-scale antenna arraybasedbase 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