Grant-free Access Research Articles

Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks

Multi-Agent Multi-Armed Bandit Learning for Grant-Free Access in Ultra-Dense IoT Networks

Joint Activity and Data Detection for Massive Grant-Free Access Using Deterministic Non-Orthogonal Signatures

Joint Activity and Data Detection for Massive Grant-Free Access Using Deterministic Non-Orthogonal Signatures

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.

Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

Grant-free access, in which each Internet-of-Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant-free access with multi-packet reception capabilities, with an emphasis on ultra-low-end IoT applications with small data sizes, sporadic activity, and energy usage constraints. We propose a power allocation scheme aimed at maximizing throughput while minimizing power consumption by considering the traffic and energy constraints of IoT devices. Our approach employs a stochastic geometry framework and mean-field game theory to model and analyze the mutual interference among active IoT devices. Additionally, we utilize a Markov chain model to capture and track the queue length of IoT devices, enabling the derivation of the transmission success probability at steady-state. The simulation results illustrate the optimal power allocation strategy and evaluate the proposed approach’s performance in terms of packet transmission success probability and average delay.

On AoI of Grant-Free Access With HARQ

On AoI of Grant-Free Access With HARQ

MLE-based Device Activity Detection under Rician Fading for Massive Grant-free Access with Perfect and Imperfect Synchronization

MLE-based Device Activity Detection under Rician Fading for Massive Grant-free Access with Perfect and Imperfect Synchronization

A HARQ Assisted Grant-Free Access Scheme in Cell-Free Massive MIMO System

A HARQ Assisted Grant-Free Access Scheme in Cell-Free Massive MIMO System

Joint beamforming and compressed sensing for uplink grant-free access

Joint beamforming and compressed sensing for uplink grant-free access

Grant-Free SCMA Design Using SDN/NFV With Physical Layer Security

In 5G and beyond, the end-user broadcasts data into the radio domain; therefore, protecting the physical layer security (PLS) is a more challenging issue. Here, Sparse Code Multiple Access (SCMA) handles PLS naturally. Because multiple physical resources are encoded in different codes, these codes cannot be easily decoded without sharing a mother codebook and mapping matrix. However, there are a few studies about security implementation in the physical layer against the challenges of the three subdomains of end users, base stations, and the core network. Moreover, there are two new research questions for resource allocation in SCMA: How do codebooks affect the Bit Error Rate (BER) performance of end-users? In the presence or absence of a jammer or interference, how should codebooks be assigned to users? We believe that it can only be overcome by using some new paradigms together: Software-defined radio (SDR), Software-defined networking (SDN), and network function virtualization (NFV). We consider the PLS implementation on SCMA-based 5G by investigating codebook assignment in the presence of noise/jammer with a novel Quality of Service (QoS) parameter (ϕ), which is the ratio of the received power by the user to overloading (λ). In the SDN/NFV controller, the QoS metric-based codebook assignment algorithm prioritizes users into newly defined PEACE, NORMAL, and WAR states. In the OpenFlow table of SDR, grant-free access can be easily implemented. As a result, the BERs for different overloading are the same for the same ratio of energy rate per bit and spectral noise density. The BER is lower when the mother codebook is 150% than when the codebook is 200%. An existing codebook of one eMBB can be utilized to serve one URLLC user and two MTC users with an acceptable BER.

Intelligent Resource Allocation for Grant-Free Access: A Reinforcement Learning Approach

Intelligent Resource Allocation for Grant-Free Access: A Reinforcement Learning Approach

Statistical Learning-Based Adaptive Network Access for the Industrial Internet of Things

Industrial Internet-of-Things (IIoT) applications generate data in varying amounts with diverse quality of service requirements. The adaptive network access approach and distributed resource management in IIoT networks can reduce the communication overheads caused by centralized resource management approaches. In this regard, statistical learning is a promising tool for addressing decision-making problems in a dynamic environment. This paper considers uplink dominant IIoT networks in which massive devices generate delay-sensitive and delay-tolerant data and communicate over shared radio resources. We propose a novel grant-free access scheme using a statistical learning approach that enables IIoT entities to perform delay-sensitive and delay-tolerant transmissions over dynamically partitioned resources in a prioritized manner. In order to improve utilization of available radio resources, we design an adaptive network access mechanism operating in a semi-distributed manner. This mechanism enables end devices to use their transmission history to choose between static and dynamic resource allocation-based grant-free schemes in a dynamic environment. Simulation results show that average latency and resource utilization vary in grant-free access schemes employing static and dynamic resource allocations. Thus, compared to a single transmission scheme, the proposed adaptive network access offers better channel utilization while meeting the application-specific latency bound in IIoT networks.

Traffic Load Learning Towards Early Detection of Intrusion in Industrial mMTC Networks

The fifth generation (5G) network offers a powerful infrastructure for Industrial IoT in that its support on massive machine-type communications (mMTC). Recently, grant-free (GF) access has been recognized as the promising new access approach for mMTC, however, it also introduces the potential risk. To timely discover intrusion, in this paper we propose a learning network by extracting traffic-load information from the states (success, collision, and idle) of access resources observed at media access control (MAC) and physical (PHY) layers. In particular, our learning network consists of three concatenated function modules, i.e., traffic-load estimation, traffic-load prediction, and intrusion identification. Moreover, our proposed learning network is able to identify two types of intrusion: false data dissemination and congestion attack. Simulation results indicate that the proposed scheme can effectively capture the number of active devices, provide reasonable prediction by using history records, and eventually, achieve more accurate detection compared with baseline approaches.

Proactive Scheduling for Zero-Energy Device Networks With Fast Uplink Grant

To address the inefficiency in terms of resource utilization of grant-free (GF) protocols, the fast uplink (FU) grant medium-access protocol has been proposed. In this work, we design a proactive wireless power transfer (WPT) framework for FU grant zero-energy massive machine-type communication. Specifically, zero-energy devices (ZEDs) first harvest energy during the WPT phase and then transmit data based on the FU grant protocol. Moreover, a multi-arm bandit traffic prediction scheme is adopted. Then, considering that due to the FU grant protocol ZEDs may harvest energy for several WPT rounds prior to data transmission, we extract the ZEDs’ outage probability during uplink. Based on the derived expressions, a chance-constrained optimization problem is formulated, which minimizes the energy consumption for WPT, while guaranteeing an acceptable outage probability for the ZEDs during data transmission. Simulations validate the theoretical analysis and illustrate that the proposed scheme outperforms GF access.

Statistical Device Activity Detection for OFDM-Based Massive Grant-Free Access

Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of things (IoT), mainly concentrate on narrow band systems under flat fading. In contrast, this paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grant-free access scheme. Then, we propose two different but equivalent models for the received pilot signal. Specifically, one directly models the received signal for actual devices, whereas the other can be interpreted as a signal model for virtual devices. The two signal models are insightful and essential for designing various device activity detection and channel estimation methods for OFDM-based massive grant-free access. Next, we systematically investigate statistical device activity detection under frequency-selective Rayleigh fading based on the two signal models. In particular, in the case without prior knowledge of device activities, we model device activities as deterministic but unknown binary constants and propose three maximum likelihood (ML) estimation-based device activity detection methods with different detection accuracies and computation times. In the case with prior knowledge of device activities, we model device activities as realizations of Bernoulli random variables with a known joint distribution, which appropriately incorporates the prior knowledge, and propose three maximum a posterior probability (MAP) estimation-based device activity methods, which further enhance the accuracies of the corresponding ML estimation-based methods at the cost of increased computational complexities. The proposed methods can meet diverse practical needs for OFDM-based massive grant-free access.

A Primer on Rate-Splitting Multiple Access: Tutorial, Myths, and Frequently Asked Questions

Rate-Splitting Multiple Access (RSMA) has emerged as a powerful multiple access, interference management, and multi-user strategy for next generation communication systems. In this tutorial, we depart from the orthogonal multiple access (OMA) versus non-orthogonal multiple access (NOMA) discussion held in 5G, and the conventional multi-user linear precoding approach used in space-division multiple access (SDMA), multi-user and massive MIMO in 4G and 5G, and show how multi-user communications and multiple access design for 6G and beyond should be intimately related to the fundamental problem of interference management. We start from foundational principles of interference management and rate-splitting, and progressively delineate RSMA frameworks for downlink, uplink, and multi-cell networks. We show that, in contrast to past generations of multiple access techniques (OMA, NOMA, SDMA), RSMA offers numerous benefits: 1) enhanced spectral, energy and computation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">efficiency</i> ; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">universality</i> by unifying and generalizing OMA, SDMA, NOMA, physical-layer multicasting, multi-user MIMO under a single framework that holds for any number of antennas at each node (SISO, SIMO, MISO, and MIMO settings); 3) flexibility by coping with any interference levels (from very weak to very strong), network loads (underloaded, overloaded), services (unicast, multicast), traffic, user deployments (channel directions and strengths); 4) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robustness</i> to inaccurate channel state information (CSI) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">resilience</i> to mixed-critical quality of service; 5) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reliability</i> under short channel codes and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">low latency</i> . We then discuss how those benefits translate into numerous opportunities for RSMA in over forty different applications and scenarios of 6G, e.g., multi-user MIMO with statistical/quantized CSI, FDD/TDD/cell-free massive MIMO, millimeter wave and terahertz, cooperative relaying, physical layer security, reconfigurable intelligent surfaces, cloud-radio access network, internet-of-things, massive access, joint communication and jamming, non-orthogonal unicast and multicast, multigroup multicast, multibeam satellite, space-air-ground integrated networks, unmanned aerial vehicles, integrated sensing and communications, grant-free access, network slicing, cognitive radio, optical/visible light communications, mobile edge computing, machine/federated learning, etc. We finally address common myths and answer frequently asked questions, opening the discussions to interesting future research avenues. Supported by the numerous benefits and applications, the tutorial concludes on the underpinning role played by RSMA in next generation networks, which should inspire future research, development, and standardization of RSMA-aided communication for 6G.

Semi-Probabilistic Repetition Schemes for Sporadic URLLC Traffic in Multiuser Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) assisted grant-free access is a compelling approach to support uplink ultra-reliable low-latency communications (URLLCs). It is a natural idea to combine massive MIMO and repetition transmission to further improve the reliability. However, excessive repetitions may increase the collision probability in multiuser scenarios and thus limit the reliability improvement. In this paper, we propose a novel semi-probabilistic repetition (SPRe) scheme for the massive multiuser MIMO (MU-MIMO) systems with grant-free access. By introducing a transmission probability for the replicas, the SPRe is more flexible than the popular repetition scheme, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition, and covers the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition as a special case. Considering practical MU-MIMO systems with finite block-length coding, channel estimation errors, pilot collisions and the sporadic arrivals of URLLC traffic, the reliability of the proposed SPRe is thoroughly analyzed and derived in closed form. Through further asymptotic analysis, the optimal transmission probability to maximize the reliability of MU-MIMO is derived for the high-SNR regime and low-load scenarios. Moreover, the diversity gain of the proposed SPRe is also analyzed and it is proved that the optimal SPRe is superior to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -repetition in terms of reliability. The analysis and superiority of the SPRe are finally verified through computer simulations.

Improved Grant-Free Access for URLLC via Multi-Tier-Driven Computing: Network-Load Learning, Prediction, and Resource Allocation

Grant-Free (GF) access has been recognized as a promising candidate for Ultra-Reliable and Low-Latency Communications (URLLC). However, even with GF access, URLLC still may not effectively gain high reliability and millimeter-level latency, simultaneously. This is because the network load is typically time-varying and not known to the base station (BS), and thus, the resource allocated for GF access cannot well adapt to variations of the network load, resulting in low resource utilization efficiency under light network load and leading to severe collisions under heavy network load. To tackle this problem, we propose a multi-tier-driven computing framework and the associated algorithms for URLLC to support users with different QoS requirements. Especially, we concentrate on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K $ </tex-math></inline-formula> - repetition GF access in light of its simplicity and well-balanced performance for practical systems. In particular, our framework consists of three tiers of computation, namely network-load learning, network-load prediction, and adaptive resource allocation. In the first tier, the BS can learn the network-load information from the states (success, collision, and idle) of random-access resources in terms of resource blocks (RB) and time slots. In the second tier, the network-load variation is effectively predicted based on estimation results from the first tier. Finally, in the third tier, by deriving and weighing the failure probabilities of different groups of users, their QoS requirements, and the predicted network loads, the BS is able to dynamically allocate sufficient resources accommodating the varying network loads. Simulation results show that our proposed approach can estimate the network load more accurately compared with the baseline schemes. Moreover, with the assistance of network-load prediction, our adaptive resource allocation offers an effective way to enhance the QoS for different URLLC services, simultaneously.

Digital Twin-Aided Orchestration of Mobile Edge Computing With Grant-Free Access

Digital twin-aided (DT) edge computing is investigated, where users utilize grant-free random access with adaptive rate to offload their tasks to the edge server. A novel, with lower implementation complexity, probabilistic partial offloading scheme is introduced, while each device is assumed to have an infinite buffer to store its tasks. The aim of the proposed work is to minimize the average delay of the partial offloading. To that end, the average delay of waiting in the queue, the delay of offloading, and the local computation delay are extracted by using queuing theory tools. Then, the non-convex problem of minimizing the average delay of all clients is formulated, while taking into account DT imperfections. Successive convex approximation (SCA), alternating optimization (AO), and various algebraic manipulations are utilized to transform the problem into an equivalent convex problem with tractable solution. Finally, simulation results showcase the value of the proposed analysis and offer important insights for the proposed DT-aided edge network. Specifically, the proposed partial offloading scheme is shown to be more delay efficient compared to both local computing and full offloading, particularly, for greater task generation rates at the users. Also, the impact of the DT imperfections at the average delay is shown to be more notable as the number of users, or the tasks’ size, increases.

Joint Device Activity Detection, Channel Estimation and Signal Detection for Massive Grant-Free Access via BiGAMP

Massive access has been challenging for the fifth generation (5 G) and beyond since the abundance of devices causes communication overload to skyrocket. In an uplink massive access scenario, device traffic is sporadic in any given coherence time. Thus, channels across the antennas of each device exhibit correlation, which can be characterized by the row sparse channel matrix structure. In this work, we develop a bilinear generalized approximate message passing (BiGAMP) algorithm based on the row sparse channel matrix structure. This algorithm can jointly detect device activities, estimate channels, and detect signals in massive multiple-input multiple-output (MIMO) systems by alternating updates between channel matrices and signal matrices. The signal observation provides additional information for performance improvement compared to the existing algorithms. We further analyze state evolution (SE) to measure the performance of the proposed algorithm and characterize the convergence condition for SE. Moreover, we perform theoretical analysis on the error probability of device activity detection, the mean square error of channel estimation, and the symbol error rate of signal detection. The numerical results demonstrate the superiority of the proposed algorithm over the state-of-the-art methods in DAD-CE-SD, and the numerical results are relatively close to the theoretical analysis results.

Estimation of user activity prior for active user detection in massive machine type communications

To enable massive-scale connectivity with low latency and efficient resource usage for massive machine type communications, the grant free access scheme has been proposed, which allows users to communicate with low signaling overhead and without the need for any prior resource allocation procedure. Due to the sporadic nature of packet transmission and non-orthogonal multiplexing, access points need to perform active user detection (AUD) to identify which users have sent the packets based on the received data. In this paper, we propose an enhanced AUD algorithm, which exploits each user’s activity pattern for detecting active users. We assume that each user randomly sends its own packet with different probability distributions parameterized by the user activity probability (UAP). In our work, such UAPs are inferred from the trajectory of the measurements collected over a certain period of time. Then, the estimated UAPs are incorporated into the compressed sensing-based algorithm for the joint AUD and channel estimation. Using the expectation-maximization algorithm, our method can efficiently find the maximum likelihood estimate of the UAPs for all users. We also present an on-line algorithm that sequentially updates the UAP estimates for each measurement. Our numerical evaluation demonstrates the benefit of the UAP estimation for compressed sensing-based AUD methods.