Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO Versus Cooperative MIMO

Chen, Zhilin; Sohrabi, Foad; Yu, Wei

doi:10.1109/twc.2019.2920823

Cited by 99 publications

(77 citation statements)

References 27 publications

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“…The ones described in [18] and [19] are iterative and belong to the class of Bayesian inference algorithms. There are also solutions based on approximate message passing (AMP) [20], as [23] and [24], while the works in [25] and [26] developed a solution based on AMP and expectation minimization (EM) [27].…”

Section: A Relevant Prior Artmentioning

confidence: 99%

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

et al. 2020

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Massive machine-type communications (mMTC) is one of the key application scenarios of fifth generation (5G) and beyond cellular networks. Bringing the unique technical challenge of supporting a huge number of MTC devices (MTCD) in cellular networks, how to efficiently estimate the channel, detect the active users and data in this scenario is an open research topic. In this regard, this paper aims to present an overview of different techniques to address the problem of channel estimation, activity and data detection specifically for the mMTC scenario. In order to highlight potential solutions and to propose new research directions, we discuss the performance of the state-of-the-art techniques in the literature using a unified evaluation framework. INDEX TERMS 5G, channel estimation, detection, massive access, mMTC, random access.

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Section: A Relevant Prior Artmentioning

confidence: 99%

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

et al. 2020

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“…Indeed, since each AP updates the gradient with probabilityp at each iteration, all these updates add up to a dense one. By substituting (22) into (12) and calculating the proximal step of g(γ b ), we can obtain the following intermediate recursion…”

Section: Derivation Of Algorithm Recursionsmentioning

confidence: 99%

“…For clarity, the pseudo-code of the cooperative activity detection (CAD) algorithm for sourced random access is summarized in Algorithm 1. Different from the cooperative MIMO [22], where each BS performs signal processing locally, the vectors of log-likelihood ratio are then forwarded to the central unit for decision making, the proposed CAD algorithm exchanges device state vector iteratively among neighboring APs. In this context, the access latency in cooperative MIMO is longer than that of the proposed CAD algorithm.…”

Section: Derivation Of Algorithm Recursionsmentioning

confidence: 99%

“…To overcome these challenges in sourced and unsourced random access, cooperative activity detection with multiple APs can be applied to detect the active device and the active columns of the coding matrix. Inspired by this, the authors in [22] considered the active device detection in multi-cell massive multiple-input-multiple-output (MIMO) systems and cooperative MIMO systems using the AMP algorithm. For multi-cell massive MIMO, the authors assumed that each AP operates independently to detect the active devices of its own cell.…”

Section: Introductionmentioning

confidence: 99%

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Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms

Shao

Chen

et al. 2020

IEEE Trans. Signal Process.

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This paper investigates the issue of cooperative activity detection for grant-free random access in the sixthgeneration (6G) cell-free wireless networks with sourced and unsourced paradigms. First, we propose a cooperative framework for solving the problem of device activity detection in sourced random access. In particular, multiple access points (APs) cooperatively detect the device activity via exchanging low-dimensional intermediate information with their neighbors. This is enabled by the proposed covariance-based algorithm via exploiting both the sparsity-promoting and similarity-promoting terms of the device state vectors among neighboring APs. A decentralized approximate separating approach is introduced based on the forward-backward splitting strategy for addressing the formulated problem. Then, the proposed activity detection algorithm is adopted as a decoder of cooperative unsourced random access, where the multiple APs cooperatively detect the list of transmitted messages regardless of the identity of the transmitting devices. Finally, we provide sufficient conditions on the step sizes that ensure the convergence of the proposed algorithm in the sense of Bregman divergence. Simulation results show that the proposed algorithm is efficient for addressing both sourced and unsourced massive random access problems, while requires a shorter signature sequence and accommodates a significantly larger number of active devices with a reasonable antenna array size, compared with the state-of-art algorithms.

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“…For instance, accommodating 1000-time increase in the volume of data traffic has been suggested by many organizations and standard bodies. To this end, in order to obtain the extra transmission bandwidth, millimeter-wave communication technology was proposed in [1]; additionally, to enhance the spectral efficiency, a massive multiple-input multiple-output (MMIMO) method was proposed in [2]. However, since the extra spectrum will not be sufficient to support the fast-growing data traffic, in the long term, the number of small cells will grow rapidly.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Clustering Algorithm for Interference Management in Ultra-Dense Small Cell Networks

et al. 2020

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Ultra-dense small cell networks (UD-SCNs) will be an integral part of next generation network (NGN). How to deal with serious interference is one of the important challenges in a UD-SCN. In this paper, the cooperative interference management problem for a UD-SCN is explored by allowing small cell base stations (SBSs) to collaborate with their neighbors. The proposed coalitional structure generation among SBSs can mitigate the co-tier interference within a coalition, thus improving the network capacity. Specifically, a cooperative scheme among the neighboring SBSs is formulated as a coalitional structure generation with characteristic forms. Furthermore, the relative sub-channel resources are allocated in the process of cluster generation. Compared with the existing SBS cooperative schemes, the novelty of the proposed SBS cooperation method, which uses a hierarchical clustering algorithm (SC-HCA) to compute the pairs of members, is demonstrated. The computation can enhance the efficiency of the proposed algorithm and is especially suitable for UD-SCN scenarios with tens and even hundreds of cells. The simulation results show that the proposed SC-HCA achieves a 422.13% system data rate improvement relative to that of the non-cooperative scheme in a UD-SCN scenario. INDEX TERMS Coalitional structure generation, hierarchical clustering, interference management, ultra-dense small cell networks.

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Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO Versus Cooperative MIMO

Cited by 99 publications

References 27 publications

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

Detection Techniques for Massive Machine-Type Communications: Challenges and Solutions

Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms

A Hierarchical Clustering Algorithm for Interference Management in Ultra-Dense Small Cell Networks

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