Access Success Probability Research Articles

Two-Step Random Access Optimization for 5G-and-Beyond LEO Satellite Communication System: A TD3-Based MsgA Channel Allocation Strategy

For 2-step random access of 5G-and-beyond low earth orbit (LEO) satellite communication system, the large coverage of satellite causes the serious collisions on MsgA channel which reduce the access success probability seriously, and introduces the problem of high dimensionality. To maximize the access success probability of the system, we propose a dynamic MsgA channel allocation (MCA) strategy. The strategy is realized by flexibly pre-configuring the channels mapping relationship on MsgA via the perception of historical access information. To mitigate the curse of dimensionality for the large action space, we establish an improved twin delayed deep deterministic policy gradient (TD3) based deep reinforcement learning (DRL) model for the strategy. Simulation result shows that the proposed strategy can increase the access success probability of the system by 39.12%.

Is cerebral palsy associated with successful ureteral access during the initial attempt at ureteroscopy for urolithiasis in children and young adults?

Ureteroscopy is a common treatment for urolithiasis, but initial ureteral access is not always possible, particularly in pediatrics. Clinical experience suggests that neuromuscular conditions such as cerebral palsy (CP) may facilitate access, thus avoiding the need for pre-stenting and staged procedures. We sought to determine if probability of successful ureteral access (SUA) during initial attempted ureteroscopy (IAU) is higher in pediatric patients with CP vs. without CP. We reviewed IAU cases for urolithiasis (2010-2021) at our center. Patients with pre-stenting, prior ureteroscopy, or urologic surgical history were excluded. CP was defined using ICD-10 codes. SUA was defined as scope access to urinary tract level sufficient to reach stone. Association of CP and other factors with SUA were evaluated. 230 patients (45.7% male, median age: 16 years [IQR: 12-18y], 8.7% had CP) underwent IAU, with SUA in 183 (79.6%). SUA occurred in 90.0% of patients with CP vs. 78.6% of those without CP (p=0.38). SUA was 81.7% in patients >12 years (vs. 73.8% in those <12), and the highest SUA was in those >12 years with CP (93.3%), but these differences were not statistically significant. Renal stone location was significantly associated with lower SUA (p=0.007). Among patients with renal stone only, SUA in those with CP was 85.7% vs. 68.9% in those without CP (p=0.33). SUA did not differ significantly by gender or BMI. CP may facilitate ureteral access during IAU in pediatric patients, but we were unable to show a statistically significant difference. Further study of larger cohorts may demonstrate whether CP or other patient factors are associated with successful initial access. Improved understanding of such factors would help preoperative counseling and surgical planning for children with urolithiasis.

Grant-Free Random Access With Channel Differentiation in Massive MIMO Systems

This paper investigates the uplink access for massive machine-type communication (mMTC) in massive multiple input multiple output (mMIMO) systems with coexisting line-of-sight (LOS) and non-LOS (NLOS) channels. A grant-free random access scheme with channel differentiation (CD-GFRA) is proposed to handle the massive access for this scenario, in which machine-type devices (MTDs) attempt to access the network with two different strategies according to their channel conditions. In CD-GFRA, MTDs with angle-dependent LOS channels access the network directly, while NLOS-channel MTDs transmit with the assistance of random relays. Moreover, an angle of arrival (AOA) based channel estimation is further proposed to improve the access capacity of LOS-channel MTDs. Numerical results show that the proposed CD-GFRA significantly improves the access success probability of MTDs in poor coverage areas and achieves almost triple the throughput of the conventional scheme for a high MTD density.

A Usage Aware Dynamic Spectrum Access Scheme for Interweave Cognitive Radio Network by Exploiting Deep Reinforcement Learning.

Future-generation wireless networks should accommodate surging growth in mobile data traffic and support an increasingly high density of wireless devices. Consequently, as the demand for spectrum continues to skyrocket, a severe shortage of spectrum resources for wireless networks will reach unprecedented levels of challenge in the near future. To deal with the emerging spectrum-shortage problem, dynamic spectrum access techniques have attracted a great deal of attention in both academia and industry. By exploiting the cognitive radio techniques, secondary users (SUs) are capable of accessing the underutilized spectrum holes of the primary users (PUs) to increase the whole system’s spectral efficiency with minimum interference violations. In this paper, we mathematically formulate the spectrum access problem for interweave cognitive radio networks, and propose a usage-aware deep reinforcement learning based scheme to solve it, which exploits the historical channel usage data to learn the time correlation and channel correlation of the PU channels. We evaluated the performance of the proposed approach by extensive simulations in both uncorrelated and correlated PU channel usage cases. The evaluation results validate the superiority of the proposed scheme in terms of channel access success probability and SU-PU interference probability, by comparing it with ideal results and existing methods.

A GCICA Grant-Free Random Access Scheme for M2M Communications in Crowded Massive MIMO Systems

A novel grant-free random access scheme with a high success rate is proposed to support massive access for machine-to-machine communications in massive multiple-input&#x2013;multiple-output (MIMO) systems. This scheme allows active user equipments (UEs) to transmit their modulated uplink messages and super pilots consisting of multiple subpilots to a base station (BS). Then, the BS performs channel state information (CSI) estimation and uplink message decoding by utilizing a proposed graph combined clustering independent component analysis (GCICA) decoding algorithm and then employs the estimated CSIs to detect active UEs by using the characteristic of asymptotic favorable propagation of massive MIMO channel. We call this proposed scheme as the GCICA-based random access (GCICA-RA) scheme. We analyze the successful access probability, missed detection probability, and uplink throughput of the GCICA-RA scheme. Numerical results show that the GCICA-RA scheme significantly improves the successful access probability and uplink throughput, decreases missed detection probability, and provides low CSI estimation error at the same time.

Uplink OFDMA-Based Random Access Mechanism With Bursty Arrivals for IEEE 802.11ax Systems

IEEE 802.11ax defines a new channel access scheme of OFDMA-based random access (UORA) for varying number of associated stations (STAs) to send their requests or data to the access point (AP) in a contention manner. This letter presents an analytical model to investigate the performance of UORA in transient condition under bursty arrivals with arbitrary distribution. The key performance metrics of access success probability; average access delay; cumulative distribution function (CDF) for the number of transmissions; and utilization of random access resource units (RA-RUs) were derived. Simulation results verified the accuracy of the proposed analytical model.

A Novel Grant-Based Pilot Access Scheme for Crowded Massive MIMO Systems

We propose a novel grant-based pilot access scheme to tackle the pilot collision in crowded massive multiple-input multiple-output (MIMO) systems. The proposed scheme divides the cell into several annuluses. Then, weaker user equipments (UEs) in each annulus, judged by a decision rule in a strongest-user collision resolution (SUCRe) scheme, send their reselected or allocated idle pilots to a base station (BS). We call this scheme enhanced SUCRe combined idle pilots access (E-SUCR-IPA). We analyze the number of successful UEs and the successful access probability of one UE with respect to its distance to the BS for the E-SUCR-IPA scheme. Numerical results show that the E-SUCR-IPA scheme significantly increases the number of successful UEs while ensuring fairness among UEs compared to baselines.

Exploiting an Optimal Delay-Collision Tradeoff in CSMA-Based High-Dense Wireless Systems

A novel carrier sense multiple access strategy with collision avoidance (CSMA/CA) balancing contention probability and channel access time is proposed. The approach can be applied to any context where the computational simplicity of the MAC must be preferred to the complexity of the channel access strategy. Our MAC, called Delay-Collision CSMA (DC-CSMA), is a slotted nonpersistent CSMA/CA with nonuniform contention probability distribution, designed to reduce at the same time latency of contenders and preserve a high successful access probability. An utility function aiming at equalizing the effects of these two performance metrics is introduced, and the related theoretical properties and optimal distribution are derived. DC-CSMA is insensitive to the number of contenders and very robust with respect to contention window size, packet length, and impairments such as frame synchronization errors and hidden terminals, and it does not require any adaptive tuning to optimize its performance. Current technologies such as WSN, RFID, IoT devices can benefit from such a simple access technique. The numerical evaluation has been led out considering latency, successful probability and throughput, and DC-CSMA has been compared with other classical strategies such as CSMA with uniformly distributed contention probability, CSMA/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p^{\ast}$ </tex-math></inline-formula> and Sift distribution.

Random Access Based on Maximum Average Distance Code for Massive MTC in Cellular IoT Networks

Code-expanded Random Access (CeRA) is a promising technique for supporting massive machine-type communications in cellular IoT networks. However, its potentiality is limited by code ambiguity, which results from the inference of a larger number of codewords than those actually transmitted. In this letter, we propose a random access (RA) scheme to alleviate this problem by allowing devices to select the preambles to be transmitted considering a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${q}$ </tex-math></inline-formula> -ary code with maximum average distance. Moreover, a CeRA decoding approach based on hypergraphs is proposed and an analytical model is derived. Numerical results show that the proposed scheme significantly increases the probability of successful channel access as well as resource utilization.

Random Access Using Deep Reinforcement Learning in Dense Mobile Networks.

5G and Beyond 5G mobile networks use several high-frequency spectrum bands such as the millimeter-wave (mmWave) bands to alleviate the problem of bandwidth scarcity. However high-frequency bands do not cover larger distances. The coverage problem is addressed by using a heterogeneous network which comprises numerous small and macrocells, defined by transmission and reception points (TRxPs). For such a network, random access is considered a challenging function in which users attempt to select an efficient TRxP by random access within a given time. Ideally, an efficient TRxP is less congested, minimizing delays in users’ random access. However, owing to the nature of random access, it is not feasible to deploy a centralized controller estimating the congestion level of each cell and deliver this information back to users during random access. To solve this problem, we establish an optimization problem and employ a reinforcement-learning-based scheme. The proposed scheme estimates congestion of TRxPs in service and selects the optimal access point. Mathematically, this approach is beneficial in approximating and minimizing a random access delay function. Through simulation, we demonstrate that our proposed deep learning-based algorithm improves performance on random access. Notably, the average access delay is improved by 58.89% from the original 3GPP algorithm, and the probability of successful access also improved.

RAN Slicing for Massive IoT and Bursty URLLC Service Multiplexing: Analysis and Optimization

Future wireless networks are envisioned to serve massive Internet of Things (mIoT) via some radio access technologies, where the random access channel (RACH) procedure should be exploited for IoT devices to access the networks. However, the theoretical analysis of the RACH procedure for massive IoT devices is challenging. To address this challenge, we first correlate the RACH request of an IoT device with the status of its maintained queue and analyze the evolution of the queue status by the probability theory. Based on the analysis result, we then derive the closed-form expression of the random access (RA) success probability, which is a significant indicator characterizing the RACH procedure of the device by the stochastic geometry theory. Besides, considering the agreement on converging different services onto a shared infrastructure, we investigate the radio access network (RAN) slicing for mIoT and bursty ultrareliable and low-latency communication (URLLC) service multiplexing. Specifically, we formulate the RAN slicing problem as an optimization one to maximize the total RA success probabilities of all IoT devices and provide URLLC services for URLLC devices in an energy-efficient way. A slice resource optimization (SRO) algorithm, exploiting relaxation and approximation with provable tightness and error bound, is then proposed to mitigate the optimization problem. Simulation results demonstrate that the proposed SRO algorithm can effectively implement the service multiplexing of mIoT and bursty URLLC traffic.

Deep Reinforcement Learning Based Massive Access Management for Ultra-Reliable Low-Latency Communications

With the rapid deployment of the Internet of Things (IoT), fifth-generation (5G) and beyond 5G networks are required to support massive access of a huge number of devices over limited radio spectrum radio. In wireless networks, different devices have various quality-of-service (QoS) requirements, ranging from ultra-reliable low latency communications (URLLC) to high transmission data rates. In this context, we present a joint energy-efficient subchannel assignment and power control approach to manage massive access requests while maximizing network energy efficiency (EE) and guaranteeing different QoS requirements. The latency constraint is transformed into a data rate constraint which makes the optimization problem tractable before modelling it as a multi-agent reinforcement learning problem. A distributed cooperative massive access approach based on deep reinforcement learning (DRL) is proposed to address the problem while meeting both reliability and latency constraints on URLLC services in massive access scenario. In addition, transfer learning and cooperative learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. Simulation results clearly show that the proposed distributed cooperative learning approach outperforms other existing approaches in terms of meeting EE and improving the transmission success probability in massive access scenario.

Delay-aware dynamic access control for mMTC in wireless networks using deep reinforcement learning

The success of the applications based on the Internet of Things (IoT) relies heavily on the ability to process large amounts of data with different Quality-of-Service (QoS) requirements. Access control remains an important issue in scenarios where massive Machine-Type Communications (mMTC) prevail, and as a consequence, several mechanisms such as Access Class Barring (ACB) have been designed aiming at reducing congestion. Although this mechanism can effectively increase the total number of User Equipments (UEs) that can access the system, it can also harm the access delay, limiting its usability in some scenarios. In this work, we propose a delay-aware double deep reinforcement learning mechanism that can dynamically adapt two parameters of the system in order to enhance the probability of successful access using ACB, while at the same time reducing the expected delay by modifying the Random Access Opportunity (RAO) periodicity. Results show that our system can accept a simultaneously massive number of machine-type and human-type UEs while at the same time reducing the mean delay when compared to previously known solutions. This mechanism can work adequately under varying load conditions and can be trained with real data traces, which facilitates its implementation in real scenarios.

Deep-Reinforcement-Learning-Based Energy-Efficient Resource Management for Social and Cognitive Internet of Things

Internet of Things (IoT) has attracted much interest due to its wide applications, such as smart city, manufacturing, transportation, and healthcare. Social and cognitive IoT is capable of exploiting social networking characteristics to optimize network performance. Considering the fact that the IoT devices have different Quality-of-Service (QoS) requirements [ranging from ultrareliable and low-latency communications (URLLCs) to minimum data rate], this article presents a QoS-driven social-aware-enhanced device-to-device (D2D) communication network model for social and cognitive IoT by utilizing social orientation information. We model the optimization problem as a multiagent reinforcement learning formulation, and a novel coordinated multiagent deep-reinforcement-learning-based resource management approach is proposed to optimize the joint radio block assignment and the transmission power control strategy. Meanwhile, the prioritized experience replay (PER) and the coordinated learning mechanisms are employed to enable communication links to work cooperatively in a distributed manner, which enhances the network performance and access success probability. The simulation results corroborate the superiority in the performance of the presented resource management approach, and it outperforms other existing approaches in terms of meeting the energy efficiency and the QoS requirements.

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.

Performance Analysis of LTE Random Access Protocol With an Energy Harvesting M2M Scenario

In this article, we analyze the performance of the long-term evolution random access procedure with the Third Generation Partnership Project's access class barring (ACB) mechanism in an energy harvesting (EH) machine-to-machine (M2M) scenario. To circumvent the state-space explosion in the conventional Markov-chain-based analysis due to time-dependent traffic pattern and data and energy buffer status, we develop an analytical model that combines mean-value analysis with the Markov-based analysis. Based on the analytical model, the random access success probability, the access delay of the network, and the average time duration between two successive successful transmissions are derived. Our analysis suggests that in the EH scenario, despite the lower number of the contending nodes in comparison with the non-EH scenario, the ACB parameters must be chosen in a more conservative way to avoid excessive collisions. The ACB parameters include access barring rate and mean barring duration. We also study an energy threshold-based activation policy and investigate the joint effects of this policy and the ACB mechanism on the random access success probability. The extensive simulations were conducted to evaluate the accuracy of the analytical model.

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.

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.

Deep reinforcement learning mechanism for dynamic access control in wireless networks handling mMTC

One important issue that needs to be addressed in order to provide effective massive deployments of IoT devices is access control. In 5G cellular networks, the Access Class Barring (ACB) method aims at increasing the total successful access probability by delaying randomly access requests. This mechanism can be controlled through the barring rate, which can be easily adapted in networks where Human-to-Human (H2H) communications are prevalent. However, in scenarios with massive deployments such as those found in IoT applications, it is not evident how this parameter should be set, and how it should adapt to dynamic traffic conditions. We propose a double deep reinforcement learning mechanism to adapt the barring rate of ACB under dynamic conditions. The algorithm is trained with simultaneous H2H and Machine-to-Machine (M2M) traffic, but we perform a separate performance evaluation for each type of traffic. The results show that our proposed mechanism is able to reach a successful access rate of 100 % for both H2H and M2M UEs and reduce the mean number of preamble transmissions while slightly affecting the mean access delay, even for scenarios with very high load. Also, its performance remains stable under the variation of different parameters.

Low Latency Random Access for Sporadic MTC Devices in Internet of Things

This paper proposes a compressed sensing-based random access protocol (CS-RACH), which is suitable for servicing a large number of machine-type communication devices in Internet of Things (IoT) network. In CS-RACH, we utilize a larger number of unique preambles compared to conventional LTE-RACH, however, the compressed sensing technique makes it possible to simultaneously detect the users with high accuracy. Compared to the user detection in conventional LTE-RACH, the proposed user detection can get rid of preamble collisions and decrease the collision probability, thereby the overall access latency is significantly reduced. To prove the benefits of the proposed CS-RACH, we mathematically analyze and compare access latency performance of LTE-RACH and CS-RACH. In particular, based on the least absolute shrinkage and selection operator approach, we derive a normalized throughput, access success probability, and average access latency. Our simulation results also exhibit that the proposed CS-RACH considerably reduces the access latency under reasonable conditions in IoT environments.