Random Access Scheme Research Articles

Random Access and Detection Performance of Internet of Things for Smart Ocean

Over the last decade, the Internet of Things (IoT) has been employed as an enabling technology for the smart ocean. As one of the key technologies in the IoT, machine-type communication (MTC) has been considered to support devices’ connectivity. In the MTC, random access is introduced for devices to share a common access channel during the packet transmission with low signaling overhead. However, the collision caused by the presence of multiple devices is inevitable. Since maritime sensors have limited energy sources, in this article, we propose a relay-aided random access (RARA) scheme for the smart ocean, where retransmissions are carried out by maritime buoys with the relay function, to deal with collisions. In the RARA scheme, a base station (BS) is able to recover multiple collided signal packets simultaneously by using multiuser detection with multiple copies of collided signals forwarded by buoy nodes. As a result, our proposed scheme becomes energy efficient and reliable to be suitable for the smart ocean. Theoretical and simulation results show that a high throughput and a low outage probability can be achieved with a large number of buoy nodes.

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.

On the SINR Distribution of Grant-Free Random Access With Moderate MIMO

With a limited bandwidth to support massive connectivity in machine-type communication (MTC), various massive multiple-input multiple-output (MIMO) based random access schemes have been studied. Among those, in particular, grant-free random access with massive MIMO for short message delivery from devices and sensors is promising thanks to low signaling overhead. While asymptotic analysis becomes straightforward with the number of antennas approaching infinity, in practice, the number of antennas is limited, making asymptotic analysis useless. For non-asymptotic analysis, in this letter, we derive an approximate closed-form expression for the distribution of the signal-to-interference-plus-noise ratio (SINR) in grant-free random access with moderate (not massive) MIMO.

Two-Tier Communication for UAV-Enabled Massive IoT Systems: Performance Analysis and Joint Design of Trajectory and Resource Allocation

In this article, we propose a two-tier communication strategy to facilitate data collection in unmanned aerial vehicle (UAV)-enabled massive Internet of Things (IoT) systems through introducing ground access points (APs) to serve between the UAV and IoT devices. In the first tier of our proposed strategy, all IoT devices transmit their packets to their local APs via a multi-channel ALOHA-based random access scheme, while in the second tier, APs deliver their aggregated data to the UAV through coordinated time division multiple access. Thus, our introduced APs not only liberate the UAV from the potential massive IoT congestion but also facilitate the design of UAV's trajectory based on the location of APs. To examine the performance of our strategy, we propose a tractable framework to analyze the average system throughput. We reveal that the average two-tier throughput of each AP monotonically increases with its maximum achievable throughput in the second tier, while the increasing slope becomes steeper with a higher traffic load mean in the first tier. Then, we formulate the joint design of UAV's trajectory and resource allocation as a non-convex optimization problem to maximize the average system throughput while considering the heterogeneous quality of service requirement of each AP. To solve this problem, a low-complexity iterative algorithm is devised based on successive convex approximation. Numerical results demonstrate the substantial average system throughput gain achieved by our proposed strategy and design in the context of massive access, compared to the baseline schemes in the literature.

Adaptive algorithm for spreading factor selection in LoRaWAN networks with multiple gateways

Recently, LoRaWAN has been considered a promising technology for large-scale IoT applications owing to its ability to achieve low power and long range communications. However, LoRaWAN is limited using Aloha random access scheme. When in dense scenarios, such scheme leads to a high number of collisions, thus severely impacts the reliability and scalability of LoRaWAN. In this paper, we investigate the impact of scalability and densification of nodes and gateways on the system reliability taking into account the capture effect. We propose an optimization problem to derive the node distribution at different spreading factors (SF) in LoRaWAN networks with multiple gateways. We then introduce an adaptive algorithm that enables to easily implement SF optimization by adjusting the signal-to-noise ratio thresholds. Moreover, the performance of the proposed algorithm is compared with the performance of legacy LoRaWAN and relevant algorithms from the state-of-the-art. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms, and improves the throughput and packet delivery ratio of the network.

User Activity Detection and Channel Estimation for Grant-Free Random Access in LEO Satellite-Enabled Internet of Things

With recent advances on the dense low-Earth orbit (LEO) constellation, the LEO satellite network has become one promising solution for providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli–Rician message passing with expectation–maximization (BR-MP-EM) algorithm is proposed for this terrestrial–satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation–maximization (EM) method is employed in the outer iterations to update the hyperparameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

Unequal Access Latency Random Access Protocol for Massive Machine-Type Communications

In this paper, we propose a novel multi-slot pilot allocation (MSPA) random access scheme with unequal access latency (UAL) protection for user equipments (UEs) in massive machine-type communications (mMTC). In order to provide UAL protection, we allocate the UEs into different groups according to their UAL requirements, where the higher priority groups can access in a stringent latency under the required access failure probability (AFP) requirement; while the lower priority groups are able to access with predetermined AFP by allocated multi-slot. Specifically, our generalized UAL-MSPA random access protocol is incorporated into two considered UAL protection schemes, i.e., the independent UAL scheme and the expanded UAL scheme. We derive the closed-form expressions to the AFP and the sum throughout for the investigated UAL schemes. By utilizing these analytical results, a joint parameter optimization problem is formulated for obtaining the minimum access latency under the desired AFP requirements. Detailed simulations validate our analytical results and confirm the efficiency of our proposed UAL schemes. Finally, the UAL-MSPA random access protocol with joint optimized parameters outperforms the existing protocols in terms of achieving high sum throughput and shortening the access latency of low priority group.

Achieving per-flow satisfaction with multi-path D2D

Device-to-Device (D2D) communication allows for users placed in a cell to establish direct connections with each other using several connection modes. In this paper, we propose Multi-Path D2D (MPD2D), a mathematical optimization framework that accounts for the availability of D2D modes under the requirements dictated by a process of flow requests. MPD2D selects the combination of cellular and D2D links that boosts network performance as much as possible. We consider Underlay and Overlay as Inband D2D modes reusing cellular frequencies with scheduled resources and the Outband D2D mode exploiting WLAN frequencies and the 802.11 random access scheme to complement cellular resources. We model throughput, energy consumption, interference, and per-flow network requirements, so to define a network utility function that accounts for throughput and power efficiency. Moreover, we formulate a user satisfaction metric that accounts for the history of users within the cell. Integrating such a metric in a throughput optimization problem is lightweight yet very effective to drive towards almost complete fairness. Our optimization scheme is formulated as a Binary Non-Linear Program, which results in higher throughput performance in comparison to other state-of-the-art solutions we have tested. Finally, we propose two effective heuristics, whose performance is near-optimal, whereas their complexity scales polynomially with the number of users.

Iterative Joint Channel Estimation, User Activity Tracking, and Data Detection for FTN-NOMA Systems Supporting Random Access

Given the requirements of increased data rate and massive connectivity in the Internet-of-things (IoT) applications of the fifth-generation communication systems (5G), non-orthogonal multiple access (NOMA) was shown to be capable of supporting more users than OMA. As a further potential enhancement, the faster-than-Nyquist (FTN) signaling is also capable of increasing the symbol rate. Since NOMA and FTN signaling impose non-orthogonalities from different perspectives, it is possible to achieve further increased spectral efficiency by exploiting both. Hence we investigate the FTN-NOMA uplink in the context of random access. Although random access schemes reduce the signaling overheads as well as latency, they require the base station to identify active users before performing data detection. As both inter-symbol and inter-user interferences exist, performing optimal detection requires a prohibitively high complexity. Moreover, in typical mobile communication environments, the channel envelope of users fluctuates violently, which imposes challenges on the receiver design. To tackle this problem, we propose a joint user activity tracking and data detection algorithm based on the factor graph framework, which relies on a sophisticated amalgam of expectation maximization (EM) and hybrid message passing algorithms. The complexity of the algorithm advocated only increases linearly with the number of active users. Our simulation results show that the proposed algorithm is effective in tracking user activity and detecting data symbols in dynamic random access systems.

On Throughput of Compressive Random Access for One Short Message Delivery in IoT

In this article, we study the compressive random access (CRA) with two stages for machine-type communication (MTC) in cellular Internet of Things (IoT). In particular, we consider the case that each user (IoT device or sensor) has only one short message (of the same length) when it is activated to send data in IoT applications. Two different CRA-based random access schemes are discussed (one is conventional and the other is new based on a simplified handshaking process). Based on the throughput analysis, we show that the CRA-based random access scheme with simplified handshaking process can outperform as its length of the payload is adaptively decided depending on the number of active users. The simulation results confirm that the derived throughput expressions agree with them and can be used to design a random access system for MTC with each active device or sensor that has one short message.

Model-Aware Collision Resolution for High-Order Orthogonal Modulations

A novel method is proposed to resolve collisions in slotted-ALOHA random access schemes that rely on orthogonal modulations at the physical layer, a common choice in wireless sensor networks. The algorithm includes solutions from machine learning, regularized optimization and transportation theory to exploit the symmetry structure that characterizes the signal model at a reasonable computational cost. Simulation results corroborate the effectiveness of the new method as compared to previous solutions.

Adaptive Multi-Receiver Coded Slotted ALOHA for Indoor Optical Wireless Communications

In this paper, we design a novel high-throughput random access scheme for an indoor optical wireless communication (OWC) massive Internet of Things (IoT) scenario. Due to the large density of both IoT devices and OWC access points (APs), we base the proposed scheme on multi-receiver coded slotted ALOHA. In this scenario, collisions at APs are resolved by a centralized interference cancellation decoder that may exploit both spatial and temporal diversity. By applying adaptive control of each OWC AP field of view (FOV), the proposed system is able to dynamically adapt to different IoT device activation rates, in order to maintain a high total throughput. Using illustrative simulation results, we demonstrate the design methodology and performance possibilities of the proposed method.

A Grant-Free Random Access Scheme for M2M Communication in Massive MIMO Systems

A novel grant-free random access scheme is proposed to support massive connectivity with low access delay and overhead for machine-to-machine communication in massive multiple-input–multiple-output systems. This scheme allows all active user equipments (UEs) to transmit their pilots and uplink messages via the same time–frequency resource and performs the joint active UEs detection and uplink message decoding without channel estimation in one shot by utilizing the proposed ensemble independent component analysis (EICA) decoding algorithm. We call the proposed scheme the EICA-based pilot random access (EICA-PA). We analyze the successful access probability, probability of missed detection, and uplink throughput of the EICA-PA scheme. Numerical results show that the EICA-PA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability and provides low-frame error rate at the same time.

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.

A Hybrid-Grant Random Access Scheme in Massive MIMO Systems for IoT

The grant-free random access (RA) can minimize the access delay but also brings severe data transmission interferences. To overcome this defect, we propose a new RA scheme that inserts a base station (BS) broadcasting message after user equipments (UEs) transmitting pilots. In this scheme, UEs can determine whether they have colliders by resolving the broadcasting message, and only non-colliding UEs can transmit data in the following step while colliding UEs keep silent. By doing this, the data interferences from colliding UEs are eliminated without costing much extra time. Since this BS broadcasting message is also used in the legacy grant-based RA, we call the new RA scheme as a hybrid-grant RA. We investigate the hybrid-grant RA in massive multiple-input multiple-output (MIMO) systems and obtain a tight closed-form approximation of the spectral efficiency with maximum-ratio-combing (MRC) and zero-forcing (ZF) receivers, respectively. Via simulation, we find that our proposed hybrid-grant RA can obtain a significant gain on the spectral efficiency compared with grant-free RA, especially for ZF receivers. In particular, this gain grows rapidly as the UE number goes up, which means the hybrid-grant RA is more suitable for the system with large amount of UEs and it is a typical scenario in future communications networks. Moreover, we also analyze the optimal pilot length and UE activation probability that maximize the spectral efficiency, which can be used as references for the practical application of the proposed hybrid-grant RA.

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.

Performance Analysis and Fair Coexistence of Heterogeneous Radio Networks for Multiple Devices With Different Channel Access Schemes

Growing demands and key requirements for Machine-to-Machine (M2M) communications have raised concerns about the coexistence of various networks in unlicensed bands. Currently in unlicensed bands, Short Range Devices (SRD) are performing a part of the services that M2M aims to implement. SRD comply with European standards and therein two random access schemes, Duty Cycle (DC) Limit and Listen Before Talk (LBT), are specified. In fact, DC Limit and LBT can be regarded as pure ALOHA and non-persistent CSMA, respectively. Thus, it is likely that radio devices using ALOHA and CSMA coexist in a single network. However, in spite of the simple concepts of ALOHA and CSMA, there have been few research works on the accurate performance analysis for such heterogeneous networks due to high correlations among devices. To tackle this issue, we rigorously investigate the types of collisions that can occur in such networks and derive accurate formulas for related probabilities. We also provide new and accurate closed form expressions for the throughput and success probability associated with each channel access scheme. The accuracy of our results is validated by simulation. Taking advantage of the accuracy of our results, we provide a method to configure the network for optimal network throughput. Furthermore, we devise a distributed dynamic channel access mechanism in which each device adjusts its access scheme by learning the network state to achieve near-maximum network throughput and high fairness.

Randomly Pre-Coded Packets Based Random Access Scheme for IoT-Oriented Satellite Networks

In this article, a novel random pre-coding (RPC) based random access (RA) scheme is proposed to improve the system performance in the Internet of Things-oriented satellite networks. In this scheme, each device randomly selects a non-orthogonal multi-level complex sequence from a pre-defined pre-coding sequence set to pre-code the transmitted packets in a sub-frame, by which multiple superimposed packets in the sub-frame can be successfully decoded by using the minimum mean squared error (MMSE) based successive interference cancellation (SIC) detection algorithm. Besides, by combining the proposed RPC with the existing contention resolution diversity slotted ALOHA (CRDSA) scheme, which the RPC-CRDSA scheme is generated. The SIC processing should be carried out across sub-frames to remove the received signals that are already recovered, which can further significantly improve the system throughput. The performance of the RPC-RA scheme is evaluated via both theoretical analysis and computer simulation. The collision probabilities of both preamble and pre-coding sequences and an upper bound of throughput are derived, respectively. Simulation results show that the peak normalized throughput of RPC-CRDSA with the pre-coding sequence length $L = 8$ can reach 1.87 bits/symbol, which largely outperforms traditional schemes.

Dynamic Backoff Collision Resolution for Massive M2M Random Access in Cellular IoT Networks

The deployment of machine-to-machine (M2M) communications on cellular networks provides ubiquitous services to Internet-of-Things (IoT) systems. Cellular networks have been chosen as the best infrastructure for M2M communications due to the wide coverage and spectral efficiency. However, with the increased number of devices connecting to the network, massive number of devices are expected to simultaneously access the network resources. This massive access results in excessive congestion and collisions in the random access channel (RACH) which causes major degradation in systems performance. This article focuses on resolving the RACH collisions during the massive access scenarios for cellular M2M communications. We propose a collision resolution scheme using the backoff procedure which dynamically adjusts the backoff indicator (BI) based on the number of backlog devices and the available resources. The proposed scheme is integrated with three well-known random access schemes; standard random access (SRA), static access class barring (ACB) and dynamic access class barring (DAB). Furthermore, the paper presents an analysis for access success probability based on the dynamic backoff procedure. The optimal value of BI that achieves the highest access success probability is derived for the three different schemes. The analysis and simulation results indicate that the dynamic value of BI achieves approximately 99.9% access success rate with a slight increase in access delay of around 10%, which is considered a reasonable increment for delay-tolerant applications during the massive arrivals scenarios.

Energy-Constrained Uncoordinated Multiple Access for Next-Generation Networks

This article analyzes the performance of a distributed joint power/packet diversity random access scheme in the presence of energy requirements. In particular, the two main approaches separately developed in the recent years for exploiting the Interference Cancellation (IC) capabilities of modern receivers, that is, Non-Orthogonal Multiple Access (NOMA) and Packet Repetition (PR), are firstly compared and then combined by imposing a constraint on the total available power. This constraint, which is alternative to the commonly adopted one based on the maximum power allowed for a transmission, results much more practical, since it enables to better infer the energy efficiency of a scheme, be it a pure NOMA, PR or combined NOMA/PR one. Both theoretical derivations and numerical simulations are carried out to evaluate the success probability and the throughput of the considered schemes by accounting for evolved reception criteria and different fading scenarios. Furthermore, the influence of several nonidealities, including imperfect IC and packet overhead, is discussed together with the impact of the system parameters, such as the user rate, the average signal to noise ratio, and the number of slots that compose a random access frame.