Non-orthogonal Random Access Research Articles

MARL-Based Access Control for Grant-Free Nonorthogonal Random Access in UDN

MARL-Based Access Control for Grant-Free Nonorthogonal Random Access in UDN

Minimizing Age of Information in Nonorthogonal Random Access Networks

Minimizing Age of Information in Nonorthogonal Random Access Networks

OFDM-Based Massive Connectivity for LEO Satellite Internet of Things

Low earth orbit (LEO) satellite has been considered as a potential supplement for the terrestrial Internet of Things (IoT). In this paper, we consider grant-free non-orthogonal random access (GF-NORA) in the orthogonal frequency division multiplexing (OFDM) system to increase access capacity and reduce access latency for LEO satellite-IoT. We focus on the joint device activity detection (DAD) and channel estimation (CE) problem at the satellite access point. The delay and the Doppler effect of the LEO satellite channel are assumed to be partially compensated. We propose an OFDM-symbol repetition technique to better distinguish the residual Doppler frequency shifts, and present a grid-based parametric probability model to characterize channel sparsity in the delay-Doppler-user domain, as well as to characterize the relationship between the channel states and the device activity. Based on that, we develop a robust Bay esian message-passing algorithm named modified variance state propagation (MVSP) for joint DAD and CE. Moreover, to tackle the mismatch between the real channel and its on-grid representation, an expectation–maximization (EM) framework is proposed to learn the grid parameters. Simulation results demonstrate that our proposed algorithms significantly outperform the existing approaches in both activity detection probability and channel estimation accuracy.

Massive Access in 5G and Beyond Ultra-Dense Networks: An MARL-Based NORA Scheme

Power-domain Non-Orthogonal Multiple Access (NOMA) and Ultra-Dense Network (UDN) are promising candidates to cope with the massive access challenge of Machine-Type Communications (MTC). The power level pool is crucial for NOMA to bring performance gains. The existing related literatures rarely consider the power level pool design problem, or only resolve it in the single-cell scenario. However, this problem in multi-cell scenario is more complex and difficult to solve due to the presence of inter-cell interference. Towards this end, we propose a Non-Orthogonal Random Access (NORA) scheme to enable the coexistence of Human-Type Communications (HTC) and MTC for 5G and beyond UDN, where the power level pool design problem in multi-cell scenario is our focus. In order to deal with the complexity caused by multiple optimization objectives and inter-cell interference, we present a Multi-Agent Reinforcement Learning (MARL)-based solution to solve this problem, where each small base station acts as an agent to learn a suitable gap between adjacent power levels. Extensive numerical comparisons demonstrate the superior performances of our proposed scheme in multiple perspectives.

Non-Orthogonal Random Access (NORA) in Large Cellular Networks

Non-orthogonal random access (NORA) is promising to alleviate random access (RA) collision and increase RA throughout. However, the application of NORA in multi-cell scenarios is challenging due to uncertain spatial distribution of NORA UEs and complex inter-cell interference. In this letter, an analytical framework for NORA in multi-cell scenarios is proposed using stochastic geometry. In particular, the approximate spatial distributions of NORA UEs are given. The analytical model of NORA gain is derived taking into account inter-cell interference. Analytical and simulation results indicate the preferred power control scheme and demonstrate that the base station (BS) deployment scheme should be adjusted according to given wireless channel characteristics.

User Scheduling for Opportunistic Nonorthogonal Random Access in Finite Blocklength IoT Networks

We propose a user-scheduling scheme for uplink opportunistic nonorthogonal random access in a finite blocklength Internet-of-Things (IoT) network. The considered system consists of high-priority IoT (HP-IoT) devices requiring strict quality-of-service (QoS) and low-priority IoT (LP-IoT) devices with delay-tolerance. Active LP-IoT devices opportunistically transmit their data subject to the QoS constraints of the HP-IoTs. We first analyze the sum-throughput achieved by the LP-IoT devices. Next, we devise greedy algorithm and Kuhn–Munkres algorithm-based schemes for user scheduling to maximize the throughput. Numerical results are provided to demonstrate the effectiveness of our proposed scheme over the conventional approach.

Adaptive–Persistent Nonorthogonal Random Access Scheme for URLL Massive IoT Networks

Critical massive Internet of Things networks are emerging technologies that face new challenges in designing transmission protocols for beyond 5G communication systems. Conventional transmission schemes are ill-suited to provide ultrareliable, lowlatency, and scalability requirements of IoT networks with the massive number of nodes having sporadic data traffic behavior. This article overcomes such challenges with proposing a random access transmission scheme that exploits nonorthogonal multiple access (NOMA) with short-packet transmissions and automatic request and repeat (ARQ) strategy with the limited number of retransmissions. To utilize the spectrum further and to meet ultrareliable low-latency requirement, an adaptive–persistent technique is proposed in which each node distributively controls its transmission based on the number of active devices without extra signaling. Since the nodes’ data traffic behavior is assumed sporadic, NOMA-based clustering is performed dynamically at each frame, avoiding additional signaling overhead. Network metrics, such as reliability, effective sum rate, and the distribution of packet latency, are analytically derived. Furthermore, the effects of different network parameters, such as blocklength, the maximum number of packet replicas, number of nodes, and number of resource blocks on network metrics, are investigated and compared with S-ALOHA-ARQ. The analysis show that the proposed scheme outperforms conventional schemes in terms of effective sum rate, reliability, and average packet latency.

Deep Learning Approach for Outage-Constrained Non-Orthogonal Random Access

Deep Learning Approach for Outage-Constrained Non-Orthogonal Random Access

Performance Study of Cybertwin-Assisted Random Access NOMA

In this article, a cybertwin-assisted nonorthogonal random access (RA) system is presented, where the cybertwins of the physical devices at the access point (AP) collect the devices’ information and decide the transmission parameters on behalf of the devices to achieve the maximum system performance. Specifically, the system performance of a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -persistent slotted CSMA system with nonorthogonal multiple access (NOMA) is analyzed, in which wireless devices transmit data to the ensure the received signal strength at the AP side is either high power or low power with certain probabilities. We first develop an analytical framework to quantify the successful transmission probability and the sum data rate as a function of the above probabilities. Accordingly, the feasible region of the number of high-power and low-power devices to ensure successful transmission is derived. With the analysis, nonconvex optimization problems are then formulated to maximize successful transmission probability and the sum data rate, respectively. To tackle the nonconvexity, an effective and fast-convergent iterative algorithm is designed to obtain the optimal transmission probabilities for the devices. Extensive simulations are conducted to validate our analytical results and demonstrate the benefits of NOMA in RA networks.

A Successive Interference Cancellation Based Random Access Channel Mechanism for Machine-to-Machine Communications in Cellular Internet-of-Things

In Cellular Internet-of-Things, random access channel (RACH) mechanism is used by machine-to-machine communication (M2M) devices to connect to a base station (BS) for any information exchange. However, increase in the number of M2M devices increases the network contention and reduces the number of RACH successes. In order to address this problem, we propose a successive interference cancellation based non-orthogonal random access (SIC-NORA) mechanism. In the proposed mechanism, each M2M device is allowed to repeat its transmission in a finite number of time slots within a radio frame. The messages of two devices that collide in the same slot can be decoded if the difference in the arrival time of their messages is greater than the predetermined threshold. Upon the successful RACH from an M2M device, the BS applies SIC in the current and all previous slots of the radio frame to decode messages of other devices for enhanced RACH success. A Markov chain model of the proposed mechanism is developed and the corresponding steady-state probabilities are derived. Through extensive numerical results, we show that the proposed mechanism performs better than state-of-the-art RACH mechanisms in terms of number of RACH successes and average access delay. Further, the proposed mechanism improves the success rate by 65.3% and 30.3% as compared to NORA and SIC-based RACH mechanism, respectively. Moreover, there is 29.6% and 15% reduction in the number of time slots required for 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> devices to get success with the proposed mechanism as compared to NORA and SIC-based RACH mechanism, respectively, at the corresponding optimal value of radio frame length.

Triangular Non-Orthogonal Random Access in mMIMO Systems

Massive multiple-input multiple-output (mMIMO) based grant-free random access (RA) (mGFRA) has been studied for massive machine-type communication (mMTC). In the existing works of mGFRA, it is implicitly assumed that each RA user equipment (UE) in mMTC has the same data-packet size. However, this may not be the case in practice as RA UEs in different applications can have different numbers of bits to send, which may result in data packets of different sizes. In addition to that, preamble collision is a key issue that degrades the performance of mGFRA when orthogonal preamble is used. In this article, we aim to address the preamble collision issue under a practical mGFRA scenario that consists of two different types of RA UEs (with short and large data packets). Specifically, by exploiting different sizes of data packets, we propose a triangular non-orthogonal RA (T-NORA) scheme, where the preamble transmission of RA UEs with small-sized data packet is embedded into the data transmission of RA UEs with large-sized data packet so that the preamble collision between two different types of RA UEs can be avoided. As a result, an improved performance over conventional mGFRA can be achieved by T-NORA with the assistance of mMIMO. Through analytical and simulation results, we can confirm that the proposed T-NORA scheme outperforms the conventional mGFRA scheme.

An Efficient Resource Allocation for Massive MTC in NOMA-OFDMA Based Cellular Networks

To alleviate random access congestion and support massive-connections with less energy consumption for machine-type communications (MTC) in the 5G cellular network, we propose an efficient resource allocation for massive MTC (mMTC) with hybrid non-orthogonal multiple access (NOMA)-orthogonal frequency division multiple access (OFDMA). First, a hybrid multiple access scheme, including the NOMA-based congestion-alleviating access scheme (NCAS) and OFDMA-based congestion-alleviating access scheme (OCAS), is proposed, in which the NOMA based devices coexist with OFDMA based ones. Then, aiming at maximizing the system access capacity, a traffic-aware resource blocks (RBs) allocation is investigated to optimize RBs allocation for preamble transmission and data packets transmission, as well as to optimize the RBs allocation between NCAS and OCAS for the RBs usage efficiency improvement. Next, aiming at the problem of high computational complexity and improving energy efficiency in hybrid NOMA-OFDMA based cellular M2M communications, this paper proposes an improved low complexity power allocation algorithm. The feasibility conditions of power allocation solution under the maximum transmit power constraints and quality of service (QoS) requirements of the devices is investigated. The original non-convex optimization problem is solved under the feasibility conditions by two iterative algorithms. Finally, a device clustering scheme is proposed based on the channel gain difference and feasible condition of power allocation solution, by which NOMA based devices and OFDMA based devices can be determined. Simulation results show that compared with non-orthogonal random access and transmission (NORA-DT), the proposed resource allocation scheme for hybrid NOMA-OFDMA systems can efficiently improve the performance of access capacity and energy efficiency.

Multiple Delay Estimation Using Genetic Algorithm-Based MCMC in Non-Orthogonal Random Access

In machine-type communication (MTC), since multiple active devices are located randomly in a cell, the arrival time of multiple signals can be different. A priori information on the arrival time of multiple signals is helpful to estimate more specific parameters such as the number of signals, which is essential to employ the successive interference cancellation (SIC) to improve throughput of a non-orthogonal random access (NORA) system. In this letter, we propose an approach to estimate the round-trip delay (RTD) of multiple signals based on Markov chain Monte Carlo (MCMC) and Genetic Algorithm (GA). Simulation results show our proposed GA-based MCMC method can achieve high performance with low complexity.

Multiple Delay Estimation for Collision Resolution in Non-Orthogonal Random Access

In machine-type communications (MTC), contention-based random access is employed to support a number of MTC devices with a limited number of resource blocks (RBs). Since multiple active devices may transmit signals in the same RB or channel, the collision caused by the presence of multiple signals is inevitable and the detection of collision becomes important. Furthermore, if the arrival time and the number of multiple signals can be estimated, successive interference cancellation (SIC) can be employed in time domain to improve the throughput. In this paper, we focus on the estimation of round-trip delays (RTD) of multiple signals in non-orthogonal random access (NORA) based on the maximum likelihood (ML) criterion. Since the computational complexity of the ML approach is high, we propose a low-complexity approach based on variational inference, which is widely used in machine learning. We also show that the number of signals can be reliably estimated from the estimated RTDs.

Non-Orthogonal Random Access and Data Transmission Scheme for Machine-to-Machine Communications in Cellular Networks

In order to address the signalling overhead and resource allocation problems of Machine-to-Machine (M2M) communications with non-orthogonal multiple access (NOMA), we propose a hybrid non-orthogonal random access and data transmission (NORA-DT) scheme. A novel design of NORA-DT protocol for M2M communications in cellular networks is firstly proposed. A power back-off scheme is introduced to adjust machine-type communications device (MTCD)'s target arrived power, and a closed-form analytic formula for the relation of MTCD's transmission power is derived. Based on the transmission power relation, the devices are clustered into a set of NOMA clusters. In the hybrid NORA-DT protocol, the cluster center MTCD transmits a extended preamble on behalf of the MTCDs in the same NOMA cluster on the physical random access channel (PRACH) for connection request. Base station (BS) can perfectly detect the preamble collisions in advance and schedules physical uplink shared channel (PUSCH) only to the NOMA clusters without collision. Then the MTCDs in the same NOMA clusters transmit data packets right after preamble transmission on the PUSCH to reduce the signalling overhead. By finding the optimal power allocation, we propose a low-complexity energy efficiency maximization problem for NORA-DT scheme. Due to the relation of MTCD's transmission power, we transform the problem into the function of cluster center MTCD's transmission power and solve it by difference of convex (DC) programming under the maximum transmission power constraints and minimum rate requirements at the MTCDs. A computationally efficient adaptive resource allocation scheme is finally proposed to improve the system throughput and resource usage. The optimal resource allocation between PRACH and PUSCH for any number of MTCDs can be learned by BS in advance, which avoids frequent computation. The analytic model is validated by simulation results. We demonstrate that the proposed NORA-DT scheme can significantly improve the system throughput, resource efficiency and energy efficiency performance.

Backoff-Based Coded Random Access for Intelligent Connected Vehicles

The rapid growth in the scale of the intelligent connected vehicles (ICVs) brings about the need for novel random access schemes that realize collision avoidance and reliable communication. The coded random access (CRA) scheme utilizes successive interference cancellation (SIC) across slots to enable the extraction of multiple users' packets from the collision slots and proves to be an efficient solution to collision resolution. In this paper, we propose a random backoff-based CRA scheme to achieve a tradeoff between the access success probability and access delay for CRA. Specifically, we partition the traditional CRA frame into subframes, so that the user access delay is reduced to the same scale of the subframe length. In addition, the backoff operation among subframes is introduced so that access failures originated from the subframe length limit can be reduced with the retransmission in subsequent subframes. In particular, we consider practical Nakagami-m fading channel in the CRA performance analysis and take into account the capture effect in the iterative interference cancellation process. Simulation results show that backoff-based CRA can significantly reduce the average access delay without severe negative effect on the access success probability for moderate system load compared with traditional CRA. Comparisons with the state-of-the-art non-orthogonal random access (NORA) scheme proposed for 5G indicates that backoff-based CRA provides a feasible solution for random access of large-scale ICVs.

Throughput-Oriented Non-Orthogonal Random Access Scheme for Massive MTC Networks

Machine-type communications (MTC) technology, which enables direct communications among devices, plays an important role in realizing Internet-of-Things. However, a large number of MTC devices can cause severe collisions. As a result, the network throughput is decreased and the access delay is increased. To address this issue, a throughput-oriented non-orthogonal random access (NORA) scheme is proposed for massive machine-type communications (mMTC) networks. Specifically, by employing the technique of tagged preambles (PAs), multiple MTC devices (MTCDs) choosing the same PA can be distinguished and regarded as a non-orthogonal multiple access (NOMA) group, which enables multiple MTCDs to share the same physical uplink shared channel for transmissions by multiplexing in the power domain. The Sukhatme's classic theory and the characteristic function approach are adopted to formulate an optimization problem. The aim is to maximize the throughput subject to the constraints on the power back-off factor, the number of MTCDs included in a NOMA group, and the successful transmission probability. Based on the particle swarm optimization (PSO) algorithm, the formulated optimization problem is efficiently solved. The derived solution can be used to adjust the access class barring factor such that more MTCDs can obtain the access opportunities. Moreover, a low-complexity suboptimal solution is also developed, which can achieve near-PSO performance under high data rate requirement. Simulation results show that the proposed scheme can efficiently improve the network performance and comparison is made with the existing schemes.

A Non-orthogonal Random Access Scheme Based on NB-IoT

Narrowband Internet of Things (NB-IoT) is a Quasi-5G technology that will provide coverage for massive number of low-power consumption applications. Hence, the potential massive random access (RA) users call for a new RA scheme. In this paper, based on the key ideas of non-orthogonal multiple access and successive interference cancellation, the NB-IoT non-orthogonal random access (NB-NORA) scheme has been proposed to provide a more effective access performance for NB-IoT system. Further, the RA preamble (MSG1) transmission collision probability and initial layer 3 message (MSG3) outage performances are analyzed based on stochastic geometry, which is compared with the traditional NB-IoT orthogonal random access (NB-ORA) scheme. Simulation results show that the maximum throughput of MSG1 within NB-NORA scheme is double that within NB-ORA scheme, and the successful RA probability of NB-NORA is increased by about 75%.

DNN-Aided Block Sparse Bayesian Learning for User Activity Detection and Channel Estimation in Grant-Free Non-Orthogonal Random Access

In the upcoming Internet-of-Things (IoT) era, the communication is often featured by massive connection, sporadic transmission, and small-sized data packets, which poses new requirements on the delay expectation and resource allocation efficiency of the Random Access (RA) mechanisms of the IoT communication stack. A grant-free non-orthogonal random access (NORA) system is considered in this paper, which could simultaneously reduce the access delay and support more Machine Type Communication (MTC) devices with limited resources. In order to address the joint user activity detection (UAD) and channel estimation (CE) problem in the grant-free NORA system, we propose a deep neural network-aided message passing-based block sparse Bayesian learning (DNN-MP-BSBL) algorithm. In the DNN-MP-BSBL algorithm, the iterative message passing process is transferred from a factor graph to a deep neural network (DNN). Weights are imposed on the messages in the DNN and trained to minimize the estimation error. It is shown that the trained weights could alleviate the convergence problem of the MP-BSBL algorithm, especially on crowded RA scenarios. Simulation results show that the proposed DNN-MP-BSBL algorithm could improve the UAD and CE accuracy with a smaller number of iterations, indicating its advantages for low-latency grant-free NORA systems.

Deep Learning-Based Power Control for Non-Orthogonal Random Access

This letter presents deep learning (DL) based non-orthogonal random access (NORA) where multiple nodes utilizing the identical preamble simultaneously transmit data over the same time-frequency resources. Effective power control algorithms are essential for the NORA, however, only partial information of channels such as the timing advance (TA) is available. This poses challenges for existing algorithms requiring full channel knowledge. We propose unsupervised DL-based power control schemes which maximize the minimum rate based only on the TA information. Numerical results verify the effectiveness of the proposed DL-based NORA over conventional methods.