Grant-free Access Research Articles

Grant-Free Non-Orthogonal Multiple Access in Single-Carrier Frequency-Selective Fading Channels

Grant-free (GF) access is one key technology for massive machine-type communication (mMTC) in Internet-of-things. Single-carrier (SC) modulation has higher power-efficiency than multicarrier modulation such as orthogonal frequency division multiplexing, due to lower peak-to-average power ratio, and thus is specifically suitable for low-power-supply machine-type devices. In this paper, we study both the coherent and non-coherent grant-free non-orthogonal multiple access (GF-NOMA) schemes where SC modulation is employed in frequency selective fading channels. For coherent SC-GF-NOMA, the linear input-output relation is first presented, and two user-activity detection (UAD) algorithms, block covariance-based maximum-likelihood detection (CB-MLD) and turbo approximated message passing (Turbo AMP), are proposed. In block CB-MLD, a block update strategy is introduced in the coordinate descent iteration to utilize the special block-sparsity signal property inherent in coherent SC-GF-NOMA. Moreover, Turbo AMP consists of two components, AMP and message passing (MP), and turbo iteration between them. Concretely, AMP is designed for UAD-CE with fixed active user probability, and MP is designed to update the active user probability by utilizing the block sparsity. Moreover, the performance of Turbo AMP is analyzed by state-evolution (SE) formalism. For non-coherent SC-GF-NOMA, the linear input-output relation is first derived from which the hierarchical block-sparsity signal property is observed, and then block CB-MLD-based UAD algorithm is presented to utilize this signal property. Finally, computer simulation results are given to demonstrate the effectiveness of the proposed algorithms in both coherent and non-coherent SC-GF-NOMA.

Wirtinger Flow for Nonconvex Blind Demixing With Optimal Step Size

The prospect of massive deployment of devices for Internet-of-Things (IoT) motivates grant-free access for simultaneously uplink transmission by multiple nodes. Blind demixing represents a promising technique for recovering multiple such source signals over unknown channels. Recent studies show Wirtinger Flow (WF) algorithm can be effective in blind demixing. However, existing theoretical results on WF step size selection tend to be conservative and slow down convergence rates. To overcome this limitation, we propose an improved WF (WF-OPT) by optimizing its step size in each iteration and expediting the convergence. We provide a theoretical guarantee on the strict contraction of WF-OPT and present the upper bounds of the contraction ratio. Simulation results demonstrate the expected convergence gains.

Optimal Probabilistic Repetition for Massive MIMO-Aided Grant-Free Short-Packet Transmissions

In this letter, we propose a novel probabilistic repetition scheme for the uplink massive MIMO system with grant-free access. Compared with canonical K-repetition, the proposed scheme is more flexible and covers the K-repetition as a special case. With the consideration of multiple practical factors, including the sporadic arrivals, the short-packet transmissions, probabilistic repetition, and channel estimation error, the reliability and throughput of the proposed probabilistic repetition assisted grant-free access scheme are theoretically analyzed. It is also proved that the optimal transmission probability for reliability maximization exists and can be found through gradient descent method. Simulation results validate the accuracy of our theoretical analysis, and also show that the proposed Optimal Probabilistic Repetition assisted grant-free Access scheme (OPRA) is superior to the popular modern grant-free random access with repetition transmission, that is, irregular repetition slotted ALOHA (IRSA).

Massive Grant-Free Access With Massive MIMO and Spatially Coupled Replicas

Massive multiple access schemes, capable of serving a large number of uncoordinated devices while fulfilling reliability and latency constraints, are proposed. The schemes belong to the class of grant-free coded random access protocols and are tailored to massive multiple input multiple output (MIMO) base station processing. High reliability is obtained owing to an intra-frame spatial coupling effect, triggered by a simple device access protocol combined with acknowledgements (ACKs) from the base station. To provide system design guidelines, analytical bounds on error floor and latency are also derived. The proposed schemes are particularly interesting to address the challenges of massive machine-type communications in the framework of next generation massive multiple access systems.

A Study of Obstacles in Plagiarism Software Subscribing by Colleges in Tamil Nadu

This article attempts to comprehend the current issues and hurdles that Indian colleges affiliated with Tamil Nadu State Universities encounter when trying to subscribe to a software that detects plagiarism. The study’s goals are to determine whether colleges employ anti-plagiarism software, whether they ensure that their student-given assignments are free of copyright infringement, whether tutors teach about academic misconduct, and what people seem to think of anti-plagiarism software. We surveyed for this study and distributed the questionnaires among college administrators, principals, and librarians. The study respondents are 15.9 per cent principals, 64.2 per cent library professionals, and 19.9 per cent college administrators. The survey study report shows that 70.9 per cent of the majority of the colleges did not subscribe. 41.7 per cent gave the reason it is too expensive, and 30.5 per cent of respondents thought that for their college, it is unnecessary to subscribe. However, nobody has confirmed whether or not all colleges possess access to plagiarism detection software. Thus, according to this investigation, further Indian states must be involved in this research to understand the specific context fully. This report advises the UGC to enforce the requirement that colleges have plagiarism detection software; they either provide colleges additional money to subscribe to such software, or the university must grant free access to the affiliated colleges.

Kronecker-Based Non-Orthogonal Sequences of Small Phases for Uplink Grant-Free Access Using the Genetic Algorithm

In massive machine-type communications (mMTC), grant-free access is a key enabler for a massive number of users to be connected to a base station with low signaling overhead and low latency. In this letter, we present a set of Kronecker-based non-orthogonal sequences and study the performance of uplink grant-free access using the sequences. The user-specific, non-orthogonal, unimodular sequences of small phases are designed by subsampling the Kronecker product of Hadamard and Fourier matrices, where we use the genetic algorithm (GA) to find the subsampling index set. Simulation results demonstrate that the Kronecker-based non-orthogonal sequences exhibit excellent performance for compressed sensing based joint activity detection and channel estimation in uplink grant-free access. Consequently, the Kronecker-based sequences can be a good option for mMTC, thanks to the superior performance and the practical benefits of arbitrary sequence length and small phases.

New Constructions of Binary Golay Spreading Sequences for Uplink Grant-Free NOMA

Non-orthogonal multiple access (NOMA) is a promising technology for massive machine-type communications (mMTC). This letter focuses on constructing spreading sequences with low coherence as well as low peak-to-average power ratio (PAPR) for uplink grant-free NOMA. Recently, a framework of spreading matrices from Boolean functions was provided by Yu. Exploiting this framework, we first show that optimum coherence of the constructed spreading matrices can be achieved if the difference of any two distinct Boolean functions is a bent (or almost bent) function. With this result, explicit constructions of Boolean functions are then developed, which lead to new binary Golay spreading matrices with optimum coherence and yielding PAPR of each spreading sequence at most 2. Simulation results demonstrate that such spreading sequences are suitable for uplink grant-free access. Moreover, the proposed Golay spreading sequences are capable of accommodating up to 400% overloaded devices, which can not be achieved from the existing algebraic methods.

Joint Activity and Channel Estimation for Extra-Large MIMO Systems

Extra large MIMO (XL-MIMO) systems are subject to spatial non-stationarity forming visibility regions (VRs), which leads to a sub-array-wise sparse structure of the channel matrix. When XL-MIMO systems operate in grant-free access mode, in which only a fraction of the potential users are active during a given time slot, it follows that the channel matrix possesses a doubly-sparse and user-specific structure such that the activity of each user and each sub-array can be jointly modeled by a nested Bernoulli-Gaussian distribution. This article considers the joint activity and channel estimation (JACE) problem in XL-MIMO systems subject to this so-defined spatial non-stationarity, tackling this challenging inference problem. Our main contributions are 1) to introduce the novel Bernoulli-Gaussian model to simultaneously capture the aforementioned two distinct structured sparsities, and 2) a new bilinear Bayesian inference algorithm capable of jointly estimating the associated channel coefficients, user activity patterns, sub-array activity patterns ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a.k.a$ </tex-math></inline-formula> . spatial non-stationarity), boosted by expectation maximization (EM)-based auto-parameterization. In addition, to shed light on a realistic modeling of VRs, we also introduce a Matérn-cluster point process (MCPP)-based approach to imitate the clustered activity pattern due to spatial non-stationarity. The efficacy of the proposed bilinear JACE algorithm is confirmed by numerical simulations, which show that the proposed method not only significantly outperforms the state-of-the-art (SotA) but also can reach the performance of a genie-aided scheme over wide signal-to-noise-ratio (SNR) ranges, in both uniformly-random and MCPP-based sub-array activity scenarios.

Grant-Free Access for mMTC: A Performance Analysis Based on Number of Preambles, Repetitions, and Retransmissions

Grant-free (GF) access is a multiple access mechanism being considered for supporting high user density in massive machine-type communication (mMTC). Since the GF schemes are predominantly contention based, a major challenge would be to handle user packet collisions. Additionally, users experiencing collision will retransmit their packets, leading to an increased load on an already dense network. Thus, the probability of successful transmission of an individual user and that of all users become the key performance indicators (KPIs) to analyze such systems. In this article, a GF uplink system is modeled for cases when the number of users is—1) fixed and 2) random (following a Poisson distribution). Analytical closed-form expressions for all-user and per-user success probabilities are derived in both the cases. Conventionally, GF access schemes have been analyzed using a fixed number of preambles. In this work, it is proposed to choose the number of preambles in the GF system such that the probability of success of at least “all but two” users is greater than or equal to 99%. It is shown that such a choice limits the maximum number of colliding users to be within four, thereby reducing the number of retransmissions. The analysis is extended to the case of transmission using multiple repetitions and numerical simulations are performed to validate the theoretical analysis. Finally, a tradeoff between the number of preambles, repetitions, and retransmissions is presented in terms of the per-user success probability, transmission latency, and energy consumption.

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> -norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

Random Interleaving Pattern Identification From Interleaved Reed-Solomon Code Symbols

Random interleavers are widely employed in digital communication systems to combat channel fading and burst errors. In applications such as grant-free access by Internet of Things (IoT) devices, accurately identifying a specific irregular interleaving pattern within an interleaver period is vital to both terminal recognition and data recovery. In this work, we investigate effective approaches for random interleaving pattern identification in Reed-Solomon (RS) coded data streams. We first propose an algorithm of low computational complexity to detect positions of code symbols belonging to the same RS codeword group (RSCG) under modest bit error rate. We further develop another low-complexity algorithm to successfully identify random interleaving patterns for RS code symbols within each RS codeword under moderate to high error rate applications. Our theoretical analysis and simulation results corroborate to demonstrate the effectiveness of our algorithms.

Active User Detection and Channel Estimation for Massive Machine-Type Communication: Deep Learning Approach

Recently, massive machine-type communications (mMTCs) have become one of key use cases for 5G. In order to support massive users transmitting small data packets at low rates, grant-free (GF) access and nonorthogonal multiple access (NOMA) have been suggested. Since each device transmits information without scheduling in the GF-NOMA systems, the device identification process, called active user detection (AUD), is required at the base station (BS). For the NOMA-based systems, the channel estimation (CE), an operation after the AUD, is a challenging task since multiple devices’ transmit signals and channels are superimposed in the same wireless resources. In this article, we propose a deep learning (DL)-based AUD and CE in the GF-NOMA systems. In our work, DL figures out the direct mapping between the received NOMA signal and the indices of active devices and associated channels using the long short-term memory (LSTM). From numerical experiments, we show that the proposed scheme is effective in handling the AUD and CE in the mMTC environments.

Perspectives on adapting a mobile application for pain self-management in neurofibromatosis type 1: results of online focus group discussions with individuals living with neurofibromatosis type 1 and pain management experts

ObjectiveNeurofibromatosis type 1 (NF1) is a genetic disorder in which chronic pain commonly occurs. The study sought to understand the needs of individuals with NF1 and pain management experts when...

Hash Access in Blockchain Radio Access Networks: Characterization and Optimization

Blockchain radio access network (B-RAN) is a decentralized, trustworthy wireless networking paradigm spurred by distributed ledger technologies (DLTs). In B-RAN, even though blockchain builds trust in upper layers, the absence of trust between client devices still causes the problem with open access, or the so-called Rogue’s Dilemma, and degrades the network performance. Therefore, Hash Access was proposed for B-RAN to address the trust issue between clients and enforce client devices to obey the grant-free access rule. However, the characteristics and performance of Hash Access in B-RAN remain unclear. In this work, we dive deep into the Rogue’s Dilemma from a game-theoretic model to emphasize the necessity of Hash Access. We establish an analytical model to comprehensively evaluate the performance of B-RAN using Hash Access regarding transmission success probability, access delay, and network throughput. Based on the analytical model, we further optimize the Hash Access protocol for network throughput and provide useful practical guidelines. Simulation results are presented to validate our proposed model and insights.

Blind Multiple Measurement Vector AMP Based on Expectation Maximization for Grant-Free NOMA

We consider a new approach to perform active user detection and channel estimation in massive grant-free access without requiring prior knowledge of the wireless channels, such as information on large-scale fading coefficients. To this end, we propose a multiple measurement vector approximate message passing (MMV-AMP) with expectation-maximization (EM)-based hyperparameter update, i.e., EM-MMV-AMP. Moreover, we revisited the decision rule for active user detection for EM-MMV-AMP. The numerical results indicate that the performance of the proposed scheme is superior to those of conventional schemes.

Rainbow-Link: Beam-Alignment-Free and Grant-Free mmW Multiple Access Using True-Time-Delay Array

The millimeter-wave (mmW) communications is a key enabling technology in 5G to provide ultra-high throughput. Current mmW technologies rely on analog phased arrays to realize beamforming gain and overcome high path loss. However, due to a limited number of simultaneous beams that can be created with analog/hybrid phased antenna arrays, the overheads of beam training and beam scheduling become a bottleneck for emerging networks that need to support a large number of users and low latency applications. This paper introduces rainbow-link, a novel multiple access protocol, that can achieve low latency and massive connectivity by exploiting wide bandwidth at mmW frequencies and novel analog true-time-delay array architecture with frequency dependent beamforming capability. In the proposed design, the network infrastructure is equipped with the true-time-delay array to simultaneously steer different frequency resource blocks towards distinct directions covering the entire cell sector. Users or devices, equipped with a narrowband receiver and either a single antenna or small phased antenna array, connect to the network based on their angular positions by selecting frequency resources within their rainbow beam allocation. Rainbow-link is combined with a contention-based grant-free access to eliminate the explicit beam training and user scheduling. The proposed design and analysis show that rainbow-link grant-free access is a potential candidate for latency-critical use cases within massive connectivity. Our results show that, given less than 1e-5 probability of packet loss, a rainbow-link cell, over 1 GHz bandwidth using 64 element antenna array, attains sub-millisecond user-plane latency and Mbps user rates with an approximate 400m line-of-sight coverage and a density of up to 5 active single antenna users per second per meter square.

Statistical Learning-Based Grant-Free Access for Delay-Sensitive Internet of Things Applications

Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.

Analyzing Novel Grant-Based and Grant-Free Access Schemes for Small Data Transmission

Fifth Generation (5G) New Radio (NR) does not support data transmission during random access (RA) procedures, which results in unnecessary control signalling overhead and power consumption, especially for small data transmission (SDT). Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare the 4/2-step SDT RA schemes with the benchmark 4/2-step RA schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability and average throughput in each RACH attempt. We also derive the average energy consumption in each RACH attempt. Our results show that 2-step SDT RA scheme provides the highest overall packet transmission success probability, and the lowest average energy consumption, but the performance gain decreases with the increase of device intensity.

DDI: A Novel Architecture for Joint Active User Detection and IoT Device Identification in Grant-Free NOMA Systems for 6G and Beyond Networks

Nonorthogonal multiple access (NOMA) with a grant-free access has received a lot of attention due to its support to massive machine-type communication (mMTC) devices. The devices in grant-free systems are allowed to transmit information without undergoing an authentication process. Therefore, in such systems, the base station needs to distinguish between active and nonactive devices, called the active user detection (AUD) process. This process is challenging as the active device needs to be detected from the received signals that are superimposed. Furthermore, the identification of the Internet of Things (IoT) devices from these signals also poses a great challenge, which could help allocate resources in future generation communication systems. Motivated from the aforementioned facts, this article proposes a device detection and identification (DDI) architecture for joint AUD and IoT device identification from the received superimposed signals. The architecture extracts the Fourier patterns as the representative feature vector, which results in an improved detection and identification process. Experimental results show that the architecture not only outperforms the conventional schemes and deep neural network-based approaches in terms of success probability for the AUD task but also yields lower computational complexity. The evaluation of the DDI architecture for IoT device identification problems has also been performed and compared to various shallow learning methods to prove its efficacy.

Achieving Energy-Efficient Uplink URLLC With MIMO-Aided Grant-Free Access

The optimal design of the energy-efficient multiple-input multiple-output (MIMO) aided uplink ultra-reliable low-latency communications (URLLC) system is an important but unsolved problem. For such a system, we propose a novel absorbing-Markov-chain-based analysis framework to shed light on the puzzling relationship between the delay and reliability, as well as to quantify the system energy efficiency. We derive the transition probabilities of the absorbing Markov chain considering the Rayleigh fading, the channel estimation error, the zero-forcing multi-user-detection (ZF-MUD), the grant-free access, the ACK-enabled retransmissions within the delay bound and the interactions among these technical ingredients. Then, the delay-constrained reliability and the system energy efficiency are derived based on the absorbing Markov chain formulated. Finally, we study the optimal number of user equipments (UEs) and the optimal number of receiving antennas that maximize the system energy efficiency, while satisfying the reliability and latency requirements of URLLC simultaneously. Simulation results demonstrate the accuracy of our theoretical analysis and the effectiveness of massive MIMO in supporting large-scale URLLC systems.