Channel Estimation and User Activity Identification in Massive Grant-Free Multiple-Access

Zhang, Jiatian; Pan, Peng; Yang, Lie‐Liang

doi:10.1109/ojvt.2020.3020228

Cited by 14 publications

(4 citation statements)

References 49 publications

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“…When multiple users attempt random access simultaneously, multiple packets are transmitted using the same time/frequency channel. At the base station, first the received preamble signal is used to detect the set of active users and estimate its channel [24]. In random access, the user index (user ID) of the user who transmits a packet is unknown in advance, and active user detection means detecting the set of RAIDs, M det , each of which is used in a received packet.…”

Section: Noma-based Random Access For Multi-packet Reception and Its ...mentioning

confidence: 99%

“…In NOMA-based random access, the base station can recognize the RAID of the received packet through active user detection and channel estimation [24,25] using a RAID-specific preamble added to the received packet. However, the user index (user ID) is written in the data part of the packet, so it will be recognized only when the received packet is correctly decoded.…”

Section: Introductionmentioning

confidence: 99%

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HARQ Using Hierarchical Tree-Structured Random Access Identifiers in NOMA-Based Random Access

ASADA

Nonaka

Higuchi

2023

IEICE Trans. Commun.

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We propose an efficient hybrid automatic repeat request (HARQ) method that simultaneously achieves packet combining and resolution of the collisions of random access identifiers (RAIDs) during retransmission in a non-orthogonal multiple access (NOMA)-based random access system. Here, the RAID functions as a separator for simultaneously received packets that use the same channel in NOMA. An example of this is a scrambling code used in 4G and 5G systems. Since users independently select a RAID from the candidate set prepared by the system, the decoding of received packets fails when multiple users select the same RAID. Random RAID reselection by each user when attempting retransmission can resolve a RAID collision; however, packet combining between the previous and retransmitted packets is not possible in this case because the base station receiver does not know the relationship between the RAID of the previously transmitted packet and that of the retransmitted packet. To address this problem, we propose a HARQ method that employs novel hierarchical tree-structured RAID groups in which the RAID for the previous packet transmission has a one-to-one relationship with the set of RAIDs for retransmission. The proposed method resolves RAID collisions at retransmission by randomly reselecting for each user a RAID from the dedicated RAID set from the previous transmission. Since the relationship between the RAIDs at the previous transmission and retransmission is known at the base station, packet combining is achieved simultaneously. Computer simulation results show the effectiveness of the proposed method.

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Section: Noma-based Random Access For Multi-packet Reception and Its ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HARQ Using Hierarchical Tree-Structured Random Access Identifiers in NOMA-Based Random Access

ASADA

Nonaka

Higuchi

2023

IEICE Trans. Commun.

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“…In random access, to suppress the packet loss due to collision and to achieve multi-packet reception, accurate active RAID (user) detection, in which the set of RAIDs that are used in the transmitted packets is identified, and channel estimation for detected RAIDs are essential [18]. The active RAID detection and channel estimation are conducted by using a preamble attached to the packet.…”

Section: Introductionmentioning

confidence: 99%

Random Access Identifier-Linked Receiver Beamforming with Transmitter Filtering in TDD-Based Random Access

Yuto

Murakami

Kishiyama

et al. 2022

IEICE Trans. Commun.

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This paper proposes a novel random access identifier (RAID)-linked receiver beamforming method for time division duplex (TDD)-based random access. When the number of receiver antennas at the base station is large in a massive multiple-input multiple-output (MIMO) scenario, the channel estimation accuracy per receiver antenna at the base station receiver is degraded due to the limited received signal power per antenna from the user terminal. This results in degradation in the receiver beamforming (BF) or antenna diversity combining and active RAID detection. The purpose of the proposed method is to achieve accurate active RAID detection and channel estimation with a reasonable level of computational complexity at the base station receiver. In the proposed method, a unique receiver BF vector applied at the base station is linked to each of the M RAIDs prepared by the system. The user terminal selects an appropriate pair comprising a receiver BF vector and a RAID in advance based on the channel estimation results in the downlink assuming channel reciprocity in a TDD system. Therefore, per-receiver antenna channel estimation for receiver BF is not necessary in the proposed method. Furthermore, in order to utilize fully the knowledge of the channel at the user transmitter, we propose applying transmitter filtering (TF) to the proposed method for effective channel shortening in order to increase the orthogonal preambles for active RAID detection and channel estimation prepared for each RAID. Computer simulation results show that the proposed method greatly improves the accuracy of active RAID detection and channel estimation. This results in lower error rates than that for the conventional method performing channel estimation at each antenna in a massive MIMO environment.

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“…In particular, BiMSGAMP updates messages according to the activity user detection (AUD) and the residual belief propagation (RBP), metrics already available in the factor graph. Additionally, we examine the mMTC overhead issues described before by considering in our framework non-orthogonal pilots and investigate the asynchronous mMTC scenario [65], [68], [69], [56], [70]. Therefore, BiMSGAMP departs from the common synchronous transmissions and addresses the problem without requiring frame-level synchronization.…”

Section: Introductionmentioning

confidence: 99%

Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications

Renna¹,

Lamare²

2022

Preprint

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In this work, we present a joint channel estimation, activity detection and data decoding scheme for massive machinetype communications. By including the channel and the a priori activity factor in the factor graph, we present the bilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution that uses the channel decoder beliefs to refine the activity detection and data decoding. We include two messagescheduling strategies based on the residual belief propagation and the activity user detection in which messages are evaluated and scheduled in every new iteration. An analysis of the convergence of BiMSGAMP along with a study of its computational complexity is carried out. Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms, highlighting the gains achieved by using the dynamic scheduling strategies and the effects of the channel decoding part in the system.

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Channel Estimation and User Activity Identification in Massive Grant-Free Multiple-Access

Cited by 14 publications

References 49 publications

HARQ Using Hierarchical Tree-Structured Random Access Identifiers in NOMA-Based Random Access

HARQ Using Hierarchical Tree-Structured Random Access Identifiers in NOMA-Based Random Access

Random Access Identifier-Linked Receiver Beamforming with Transmitter Filtering in TDD-Based Random Access

Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications

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