Grant-free Random Access Research Articles

Grant-Free Random Access With Backscattering Self-Conjugating Metasurfaces

Grant-Free Random Access With Backscattering Self-Conjugating Metasurfaces

Confidence based compressed sensing approach with QoS differentiation for reliable grant-free access

With the rapid development of 5G technology, numerous intelligent terminals can be successively connected to a network. This brings significant challenges to existing network access because it increases the collision probability. It is essential for Grant-free random access (GF-RA) to ensure low delay and reliable transmission. Due to multiuser interference and varying environments, user detection can be complicated, which leads to unreliable transmission. To address the limitations above, research on the GF-RA method based on compressed sensing is carried out and it includes the following: (1) The confidence-weighted orthogonal matching pursuit method is used for estimating the number of active users and overcomes the constraint of sparse condition. (2) The confidence-weighted simultaneous hard thresholding (SHT) method is used for active user detection (AUD), which can improve the performance of the SHT algorithm. (3) We conduct collision probability analysis to not only provide the performance upper bound using the multiple preambles method but also infer the collision probability relationship with different priorities. This is helpful to realize diverse quality of service requirements. The numerical results prove that the proposed method is valid for non sparse signals and can realize a high success access rate.

Grant-Free Random Access Enhanced by Massive MIMO and Non-Orthogonal Preambles

Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However, the bottleneck of mGFRA is mainly dominated by the orthogonal preamble collisions, since the orthogonal preamble pool is small and of a fixed-sized. In this paper, we explore the potential of non-orthogonal preambles to overcome limitations and enhance the success probability of mGFRA without extending the length of the preamble. Given the RA procedure of mGFRA, we analyze the factors influencing the success rate of mGFRA with non-orthogonal preamble and propose to use two types of sequences, namely Gold sequence and Gaussian distribution sequence, as the preambles for mGFRA. Simulation results demonstrate the effectiveness of these two types pf non-orthogonal preambles in improving the success probability of mGFRA. Moreover, the system parameters’ impact on the performance of mGFRA with non-orthogonal preambles is examined and deliberated.

Asynchronous Grant-Free Random Access: Receiver Design With Partially Uni-Directional Message Passing and Interference Suppression Analysis

Asynchronous Grant-Free Random Access: Receiver Design With Partially Uni-Directional Message Passing and Interference Suppression Analysis

A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.

$K$-Repetition for Grant-Free Random Access in Cell-Free Massive MIMO Networks

$K$-Repetition for Grant-Free Random Access in Cell-Free Massive MIMO Networks

Precise Analysis of Covariance Identifiability for Activity Detection in Grant-Free Random Access

Precise Analysis of Covariance Identifiability for Activity Detection in Grant-Free Random Access

OTFCS-Modulated Waveform Design for Joint Grant-Free Random Access and Positioning in C-V2X

OTFCS-Modulated Waveform Design for Joint Grant-Free Random Access and Positioning in C-V2X

Delay-Constrained Grant-Free Random Access in MIMO Systems: Distributed Pilot Allocation and Power Control

Delay-Constrained Grant-Free Random Access in MIMO Systems: Distributed Pilot Allocation and Power Control

Joint User Detection and Channel Estimation in Grant-Free Random Access for Massive MIMO Systems

Grant-free random access (RA) utilizing massive multiple-input multiple-output (MIMO) technology has attracted considerable attention in recent years due to its potential to enhance spectral efficiency. This paper introduces an innovative and advanced approach for the joint detection of users and estimation of channels in grant-free RA. The approach incorporates two distinct preamble structures: the single orthogonal preamble (SOP) and the concatenated orthogonal preamble (COP). The proposed algorithms make full use of the inherent quasiorthogonal characteristic of massive MIMO, thereby enabling the accurate estimation of user channels while effectively avoiding collisions in the preambles. As a result, these algorithms generate highly precise estimations of user channels. To substantiate the effectiveness of the proposed algorithms, this paper provides an extensive theoretical analysis and presents a comprehensive set of experimental results. These findings offer robust evidence for the efficacy of the algorithms in substantially bolstering the performance of grant-free RA. Additionally, we have conducted further research and analysis, which has led to additional insights and refinements in our proposed approach. Moreover, the experimental results validate the statistical significance and reliability of the performance enhancements achieved by these algorithms. Moreover, the proposed approach exhibits robustness in scenarios with different levels of user density and varying channel conditions. Through a thorough analysis of these scenarios, we showcase the versatility and applicability of our algorithms in real-world environments.

A Scalable Deep-Learning-Based Active User Detection Approach for SEU-Assisted Cell-Free Massive MIMO Systems

Massive ultra-reliable and low-latency communications (mURLLC) is an emerging and dominate traffic service in 6G. To reduce the signaling overhead and access delay, grant-free random access (GFRA) is widely used in mURLLC. As the first step in GFRA, active user detection (AUD) is aimed to identify the set of active users accurately and timely. Conventional AUD schemes relying on iterative computations over massive users bring redundant computing overload and processing delay, which seriously affect the system scalability in the mURLLC scenario. Considering the near-real-time requirement of mURLLC, we propose a scalable deep learning-based AUD approach utilizing similar channel sparsity in cell-free (CF) massive multiple input multiple-output (mMIMO) systems. By exploiting the distributed computing unit, i.e., space expansion unit (SEU), we design a SEU-assisted CF mMIMO to improve the scalability of the traditional centralized CF computing architecture. In the proposed system, all access points (APs) are divided into several clusters, and the SEU in each cluster provides a reliable distributed AUD scheme through a one-dimensional convolutional network (1D CNN). In addition, a transfer learning-based ensemble model is established at the CPU to achieve a better global detection decision. Simulation results demonstrate the superiority of our scalable deep learning-based approach, and reveal that through the transfer learning-based model fusion at the CPU, our proposed scalable SEU-assisted approach can obtain success probability close to that of the centralized CF computing scheme with less access delay. In addition, our scheme requires fewer pilots than other CS-based schemes.

Tandem Spreading Multiple Access With MIMO

With the massive deployment of 5G commercial applications, the Internet of Everything promotes the transformation and upgrading of the society production mode. The Internet of Things (IoT) is supported by the massive machine-type communications (mMTC), which is one of the three major application scenarios of 5G. Recently, a novel spreading based non-orthogonal multiple access (NOMA) scheme named tandem spreading multiple access (TSMA) has been proposed for grant-free random access in mMTC. However, TSMA only considers the case of single antenna, the connectivity expansion on spatial domain has not been considered. In this article, a multi-antenna system scheme of TSMA (MIMO-TSMA) is proposed to scale up user connections. In this scheme, spectrum efficiency can be promoted by sharing the time frequency resources in different beams. In the meantime, scheme against channel deep fading is considered in this work. The simulation results show that MIMO-TSMA can effectively take advantage of MIMO and TSMA to enhance the mMTC system performance.

Toward Massive Active Connectivity: Performance Analysis and Near-Optimal Detectors for Grant-Free Random Access Systems

Toward Massive Active Connectivity: Performance Analysis and Near-Optimal Detectors for Grant-Free Random Access Systems

Composite Preambles Based on Differential Phase Rotations for Grant-Free Random Access Systems

With the advantages of low signaling overhead and latency, grant-free random access (GFRA) becomes a promising technology for supporting massive machine-type communications (mMTC), but poses new challenges for active user detection (AUD) and channel estimation (CE), whose performance mainly depends on the preamble detection. In this paper, we design the composite preamble based on differential phase rotations by aggregating orthogonal Zadoff-Chu (ZC) sequences and multiple root ZC sequences with differential phase rotations to reduce the probability of preamble collisions, thereby improving the performance of AUD and CE. In particular, differential phase rotations extend the preamble set size so that users colliding in orthogonal sequences can be distinguished by phase rotations. In addition, it also reduces non-orthogonal interference and thus reduces CE errors. The preamble detection algorithm and CE scheme are proposed, along with the theoretical analysis of AUD and CE performance to verify the effectiveness of the designed preamble. In addition, the proposed preamble is extended to combine phase rotations with cyclic shifts to further enlarge the preamble set size with low non-orthogonality. Simulation results show that the proposed composite preamble outperforms existing preambles in terms of the probability of detection and CE accuracy.

Grant-free random access in pilot-reused multiceli massive MIMO systems with backoff mechanisms

Grant-free random access (RA) is attractive for future network due to the minimized access delay. In this paper, we investigate the grantfree RA in multicell massive multiple-input multiple-output (MIMO) systems with pilot reuse. With backoff mechanism, user equipments (UEs) in each cell are randomly activated, and active UEs randomly select orthogonal pilots from a predefined pilot pool, which results in a random pilot contamination among cells. With the help of indicators that capture the uncertainties of UE activation and pilot selection, we derive a closed-form approximation of the spectral efficiency per cell after averaging over the channel fading as well as UEs' random behaviors. Based on the analysis, the optimal backoff parameter and pilot length that maximize the spectral efficiency can be obtained. We find that the backoff mechanism is necessary for the system with large number of UEs, as it can bring significant gains on the spectral efficiency. Moreover, as UE number grows, more backoff time is needed.

Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-Free Massive Random Access

In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. Recently, grant-free random access has emerged as a promising mechanism for this challenging scenario, but its potential has not been fully unleashed. In particular, the available auxiliary information has not been fully exploited, including the common sparsity pattern in the received pilot and data signal, as well as the channel decoding information. This paper develops advanced receivers in a holistic manner to improve the massive access performance by jointly designing activity detection, channel estimation, and data decoding. To tackle the algorithmic and computational challenges, a turbo structure is adopted at the joint receiver. For performance enhancement, all the received symbols are utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information (SI)-aided receiver is also proposed, where the channel decoder provides side information to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, supporting twice as many active users compared with a separate design that disregards the common sparsity. In addition, the SI-aided receiver notably outperforms the conventional methods with a relatively low complexity.

On the Grant-Free Random Access in Multicell Massive MIMO Systems: Spatiotemporal Modeling and Backoff Scheme Optimization

Grant-free random access (GFRA) becomes attractive in Internet-of-Things (IoT) due to its low signaling overhead and short access latency. In this paper, we invetigate GFRA in a multicel massive multiple-input-multiple-output (MIMO) system. As the IoT device usually has sporadic traffic, we set a packet buffer for each device to describe its temporal traffic, and also use the stochastic geometry to describe the randomness of devices’ spatial locations. With backoff mechanism, only devices with a non-empty buffer and a successful backoff are activated and allowed to request access. Unlike previous works that regard all devices selecting the same pilot (i.e., the colliding devices) as undetectable, we give a more accurate model that the base station (BS) can detect colliding devices when they locate far away from each other, and we set a unique collision area for each device to quantify the boundary that the collision can be ignored. A tight approximation for the number of packets successfully transmitted at the unit area and time slot, named packet throughput, is derived. Based on it, we obtain the optimal backoff parameter that maximizes the packet throughput under devices’ delay constraints. It is shown that when pilots are insufficient or device packet traffic is heavy, a long backoff time is needed. However, as the pilot grows or the packet traffic turns light, devices should gradually reduce the backoff time. In particular, if pilots are surplus, cheap detectors can be equipped on the BS without an obvious packet throughput reduction.

Code-Domain Collision Resolution Grant-Free Random Access for Massive Access in IoT

Code-Domain Collision Resolution Grant-Free Random Access for Massive Access in IoT

Variational Bayesian Inference Clustering-Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC

Tailor-made for massive connectivity and sporadic access, grant-free random access has become a promising candidate access protocol for massive machine-type communications (mMTC). Compared with conventional grant-based protocols, grant-free random access skips the exchange of scheduling information to reduce the signaling overhead, and facilitates sharing of access resources to enhance access efficiency. However, some challenges remain to be addressed in the receiver design, such as unknown identity of active users and multi-user interference (MUI) on shared access resources. In this work, we deal with the problem of joint user activity and data detection for grant-free random access. Specifically, the approximate message passing (AMP) algorithm is first employed to mitigate MUI and decouple the signals of different users. Then, we extend the data symbol alphabet to incorporate the null symbols from inactive users. In this way, the joint user activity and data detection problem is formulated as a clustering problem under the Gaussian mixture model. Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem. Simulation results show that, compared with state-of-art solutions, the proposed AMP-combined VBIC (AMP-VBIC) algorithm achieves a significant performance gain in detection accuracy.

Grant-Free Random Access in Cell-Free Massive MIMO Systems With UE Detection Thresholds: A Stochastic Geometry Approach

In this paper, we analyze the grant-free random access (GFRA) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Unlike previous works on GFRA that regard all user equipments (UEs) selecting the same pilot as undetectable, we give a more accurate model that the access point (AP) can detect the strongest UE when its signal-to-interference-plus-noise ratio (SINR) exceeds a threshold. On top of this, we also define the concept of effective access that a UE is successfully detected by APs around it within a limited area. With tools from stochastic geometry, the probability of UE effective access and the minimum required AP spatial density that satisfies UEs' access delay constraints are derived. It is found that increasing the AP antenna number from 1 to 2 can significantly improve the UE access probability and reduce the required AP number, while this improvement decays quickly as the antenna number keeps growing, which works more obviously when the access delay constraint is strict or the pilot length is short.