Grant-Free Rateless Multiple Access: A Novel Massive Access Scheme for Internet of Things

Zhang, Zhaoyang; Wang, Xianbin; Zhang, Yu; Chen, Yan

doi:10.1109/lcomm.2016.2593447

Cited by 69 publications

(18 citation statements)

References 13 publications

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“…In [123], a low complexity threshold-based message passing algorithm (MPA) is proposed to achieve sub-optimal performance when a belief threshold is properly set to rapidly select credible codewords. In [124], a grant-free rateless multiple access scheme is proposed to reduce latency remarkably by utilizing the inherent pseudo-random pattern. In [125], the authors propose a grantfree sparse code multiple access to significantly reduce the transmission latency in heavy overloading scenarios.…”

Section: Resource-reuse-based Techniquesmentioning

confidence: 99%

Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

Sutton

Zeng

Liu

et al. 2019

IEEE Commun. Surv. Tutorials

211

163

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Future 5th generation (5G) networks are expected to enable three key services -enhanced mobile broadband (eMBB), massive machine type communications (mMTC) and ultra-reliable and low latency communications (URLLC). As per the 3rd generation partnership project (3GPP) URLLC requirements, it is expected that the reliability of one transmission of a 32 byte packet will be at least 99.999% and the latency will be at most 1 ms. This unprecedented level of reliability and latency will yield various new applications such as smart grids, industrial automation and intelligent transport systems. In this survey we present potential future URLLC applications, and summarize the corresponding reliability and latency requirements. We provide a comprehensive discussion on physical (PHY) and medium access control (MAC) layer techniques that enable URLLC, addressing both licensed and unlicensed bands. The paper evaluates the relevant PHY and MAC techniques for their ability to improve the reliability and reduce the latency. We identify that enabling long-term evolution (LTE) to coexist in the unlicensed spectrum is also a potential enabler of URLLC in the unlicensed band, and provide numerical evaluations. Lastly, the paper discusses the potential future research directions and challenges in achieving the URLLC requirements.

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Section: Resource-reuse-based Techniquesmentioning

confidence: 99%

Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

Sutton

Zeng

Liu

et al. 2019

IEEE Commun. Surv. Tutorials

211

163

View full text Add to dashboard Cite

show abstract

“…We refer to the jointly structured sparsity in (8) and (9) as the angular-frequency structured sparsity of massive MIMO channels. Additionally, we further define the virtual angular domain channel matrix as…”

Section: Angular-frequency Structured Sparsity In Massive Mimomentioning

confidence: 99%

Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

Gao

et al. 2020

IEEE Trans. Signal Process.

324

302

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This paper considers massive access in massive multiple-input multiple-output (MIMO) systems and proposes an adaptive active user detection and channel estimation scheme based on compressive sensing. By exploiting the sporadic traffic of massive connected user equipments and the virtual angular domain sparsity of massive MIMO channels, the proposed scheme can support massive access with dramatically reduced access latency. Specifically, we design non-orthogonal pseudo-random pilots for uplink broadband massive access, and formulate the active user detection and channel estimation problems as a generalized multiple measurement vector compressive sensing problem. Furthermore, by leveraging the structured sparsity of the uplink channel matrix, we propose an efficient generalized multiple measurement vector approximate message passing (GMMV-AMP) algorithm to realize simultaneous active user detection and channel estimation based on a spatial domain or an angular domain channel model. To jointly exploit the channel sparsity presented in both the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP algorithm is developed for detecting the active users and estimating their channels in an alternating manner. Finally, an adaptive access scheme is proposed, which adapts the access latency to guarantee reliable massive access for practical systems with unknown channel sparsity level. Additionally, the state evolution of the proposed GMMV-AMP algorithm is derived to predict its performance. Simulation results demonstrate the superiority of the proposed active user detection and channel estimation schemes compared to several baseline schemes.

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“…Other improved random access schemes include irregular repetition slotted ALOHA (IRSA) [ 30 ], coded slotted ALOHA (CSA) [ 31 ], rateless multiple access (RMA) [ 32 ] and grant-free RMA [ 33 ] etc. Although these schemes can improve throughput to a certain extent, they also increase implementation complexity and are thus difficult to apply to S-IoT.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things

Fei

Jiang

et al. 2021

Sensors

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Random access is one of the most competitive multiple access schemes for future space-based Internet of Things (S-IoT) due to its support for massive connections and grant-free transmission, as well as its ease of implementation. However, firstly, existing random access schemes are highly sensitive to load: once the load exceeds a certain critical value, the throughput will drop sharply due to the increased probability of data collision. Moreover, due to variable satellite coverage and bursty traffic, the network load of S-IoT changes dynamically; therefore, when existing random access schemes are applied directly to the S-IoT environment, the actual throughput is far below the theoretical maximum. Accordingly, this paper proposes an intelligent load control-based random access scheme based on CRDSA++, which is an enhanced version of the contention resolution diversity slotted ALOHA (CRDSA) and extends the CRDSA concept to more than two replicas. The proposed scheme is dubbed load control-based three-replica contention resolution diversity slotted ALOHA (LC-CRDSA3). LC-CRDSA3 actively controls network load. When the load threatens to exceed the critical value, only certain nodes are allowed to send data, and the load is controlled to be near the critical value, thereby effectively improving the throughput. In order to accurately carry out load control, we innovatively propose a maximum likelihood estimation (MLE)-based load estimation algorithm, which estimates the load value of each received frame by making full use of the number of time slots in different states. On this basis, LC-CRDSA3 adopts computational intelligence-based time series forecasting technology to predict the load values of future frames using the historical load values. We evaluated the performance of LC-CRDSA3 through a series of simulation experiments and compared it with CRDSA++. Our experimental results demonstrate that in S-IoT contexts where the load changes dynamically, LC-CRDSA3 can obtain network throughput that is close to the theoretical maximum across a wide load range through accurate load control.

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Grant-Free Rateless Multiple Access: A Novel Massive Access Scheme for Internet of Things

Cited by 69 publications

References 13 publications

Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

Enabling Technologies for Ultra-Reliable and Low Latency Communications: From PHY and MAC Layer Perspectives

Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO

An Intelligent Load Control-Based Random Access Scheme for Space-Based Internet of Things

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