Quality Of Service Requirements Research Articles

Challenges in meeting QoS requirements toward 6G wireless networks: A state of the art survey

AbstractThe 6G wireless network with its superior capabilities must be considered to accommodate the much more stringent quality of service (QoS) requirements of the numerous emerging applications and services in the future. Attempting to adhere these constraints raise various challenges that are required to be addressed and solved in order to realize the vision of a fully intelligent and automated future society. This article provides the key enabling technologies and future applications for 6G networks followed by a discussion on QoS. It also presents a state‐of‐the‐art survey on the challenges faced while trying to meet the QoS requirements regarding the time latency, throughput, connectivity, packet loss, and bandwidth, together with possible solutions to handle various constraints. A discussion on user satisfaction estimation from QoS analysis is also included. Finally, the article concludes with future research opportunities in meeting the QoS constraints of the 6G network.

QoS-Aware Joint Task Scheduling and Resource Allocation in Vehicular Edge Computing

Vehicular edge computing (VEC) has emerged in the Internet of Vehicles (IoV) as a new paradigm that offloads computation tasks to Road Side Units (RSU), aiming to thereby reduce the processing delay and resource consumption of vehicles. Ideal computation offloading policies for VEC are expected to achieve both low latency and low energy consumption. Although existing works have made great contributions, they rarely consider the coordination of multiple RSUs and the individual Quality of Service (QoS) requirements of different applications, resulting in suboptimal offloading policies. In this paper we present FEVEC, a Fast and Energy-efficient VEC framework, with the objective of realizing an optimal offloading strategy that minimizes both delay and energy consumption. FEVEC coordinates multiple RSUs and considers the application-specific QoS requirements. We formalize the computation offloading problem as a multi-objective optimization problem by jointly optimizing offloading decisions and resource allocation, which is a mixed-integer nonlinear programming (MINLP) problem and NP-hard. We propose MOV, a Multi-Objective computing offloading method for VEC. First, vehicle prejudgment is proposed to meet the requirements of different applications by considering the maximum tolerance delay related to the current vehicle speed. Second, an improved Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is adopted to obtain the Pareto-optimal solutions with low complexity. Finally, the optimal offloading strategy is selected for QoS maximization. Extensive evaluation results based on real and simulated vehicle trajectories verify that the average QoS value of MOV is improved by 20% compared with the state-of-the-art VEC mechanism.

An Energy-Efficient Method for Recurrent Neural Network Inference in Edge Cloud Computing

Recurrent neural networks (RNNs) are widely used to process sequence-related tasks such as natural language processing. Edge cloud computing systems are in an asymmetric structure, where task managers allocate tasks to the asymmetric edge and cloud computing systems based on computation requirements. In such a computing system, cloud servers have no energy limitations, since they have unlimited energy resources. Edge computing systems, however, are resource-constrained, and the energy consumption is thus expensive, which requires an energy-efficient method for RNN job processing. In this paper, we propose a low-overhead, energy-aware runtime manager to process tasks in edge cloud computing. The RNN task latency is defined as the quality of service (QoS) requirement. Based on the QoS requirements, the runtime manager dynamically assigns RNN inference tasks to edge and cloud computing systems and performs energy optimization on edge systems using dynamic voltage and frequency scaling (DVFS) techniques. Experimental results on a real edge cloud system indicate that in edge systems, our method can reduce the energy up to 45% compared with the state-of-the-art approach.

Multisignal Cooperative Processing Method for Internet of Vehicles Based on Data-Driven Edge Computing Method

With the rapid development of Internet of Vehicles applications, more and more data are generated. How to effectively distribute content in the Internet of Vehicles to meet the service quality requirements of users has become one of the industry pain points in the field of smart cars and autonomous driving. In order to solve the shortage of local computing resources of vehicles, a vehicle edge network is proposed, which uses data-driven edge computing to offload vehicle tasks to a mobile edge computing server to reduce overall network energy consumption and meet task latency requirements. In addition, in order to reduce the end-to-end delay, caching technology is adopted at the network edge, which can reduce the content transmission delay. This paper focuses on the problem of computing offloading in the data-driven edge computing method in the context of the Internet of Vehicles. Simulation experiments have proved its superiority compared with the traditional offloading method, and the delay and energy consumption are better than the traditional method. First, the basic concepts of the Internet of Vehicles and MEC are introduced; second, the TOAI algorithm flow chart and the computing tasks of the offloading work of the Internet of Vehicles are introduced; then, based on MEC and partial offloading, the task offloading problem is modeled and solved; the problem of unloading collaborative content under networking is solved, and the simulation results are analyzed and verified. The simulation experiment not only shows that the proposed algorithm optimizes the efficiency of the task under the average unit but also shows the effectiveness of this method, which lays a foundation for the engineering implementation of the algorithm. The experimental results show that the average task completion rate is increased by 0.58%, and the average unit task energy consumption is increased by 0.32%, which improves the practicability of the system.

Adaptive QoS-aware multipath congestion control for live streaming

Nowadays, the popularization of live streaming bring challenges in providing customized Quality of Service (QoS) and satisfying diverse Quality of Experience (QoE) requirements. Multipath TCP (MPTCP), designed for bandwidth and reliability in aggregating transmission, has the potential to improve live streaming performance with multiple simultaneously transmitting paths. However, the existing multipath congestion control algorithms (CCAs) of MPTCP fail to adapt to diverse network environments and different QoS requirements. We study the problem with extensive experiments to observe the limitations of current multipath CCAs. To tackle these problems and support stable and customized live streaming, we propose ACCeSS, an adaptive QoS-aware multipath congestion control framework. ACCeSS is able to promptly adapt to network changes and QoS requirements with a novel control policy optimization phase. In order to adjust and stimulate improvement on the preferred performance metric, ACCeSS exploits Random Forest Regressing (RFR) method to perform QoS-specific utility function optimization. Besides, ACCeSS is implemented and deployed in a multipath live streaming system. We compare it with other multipath CCAs in the Linux kernel and evaluate their performance in both emulated and real-world networks. It is revealed that ACCeSS outperforms classic multipath CCAs and the state-of-the-art learning-based multipath CCA, with better environment adaptive capability of QoS and higher QoE for live streaming.

Double QoS Guarantee for NOMA-Enabled Massive MTC Networks

Massive connections and diverse Quality of Service (QoS) requirements pose a major challenge for machine-type communication (MTC) networks. In this article, to satisfy the various QoS requirements of a massive number of MTC devices (MTCDs), the devices are divided into multiple clusters based on the QoS characteristics. The cluster access control and intracluster resource allocation problems are studied to satisfy the double delay requirements in the access and data transmission phases in a cross-layer approach. Specifically, we formulate an access control problem to maximize the access efficiency with constraints on access and transmission delays. An efficient algorithm is proposed to adaptively adjust the access time intervals and backoff factors of the clusters for different numbers of active MTCDs and transmission rates. Given the access parameters, nonorthogonal multiple access is adopted in resource allocation to maximize the system utility function while guaranteeing the delay requirements for each accessed MTCD. An efficient sequential convex programming iterative algorithm is proposed to solve the NP-hard nonconvex problem with two typical utility objectives: 1) total throughput and 2) consumed power. Simulation results show that the proposed scheme can achieve better performance in terms of access efficiency, delay, throughput, and consumed power than other schemes. The impacts of various parameters, including delay and traffic rate, on the performance, are disclosed.

A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud–Fog Computing

Cloud–fog computing is a large-scale service environment developed to deliver fast, scalable services to clients. The fog nodes of such environments are distributed in diverse places and operate independently by deciding on which data to process locally and which data to send remotely to the cloud for further analysis, in which a Service-Level Agreement (SLA) is employed to govern Quality of Service (QoS) requirements of the cloud provider to such nodes. The provider experiences varying incoming workloads that come from heterogeneous fog and Internet of Things (IoT) devices, each of which submits jobs that entail various service characteristics and QoS requirements. To execute fog workloads and meet their SLA obligations, the provider allocates appropriate resources and utilizes load scheduling strategies that effectively manage the executions of fog jobs on cloud resources. Failing to fulfill such demands causes extra network bottlenecks, service delays, and energy constraints that are difficult to maintain at run-time. This paper proposes a joint energy- and QoS-optimized performance framework that tolerates delay and energy risks on the cost performance of the cloud provider. The framework employs scheduling mechanisms that consider the SLA penalty and energy impacts of data communication, service, and waiting performance metrics on cost reduction. The findings prove the framework’s effectiveness in mitigating energy consumption due to QoS penalties and therefore reducing the gross scheduling cost.

A Novel Approach to Enhance the End-to-End Quality of Service for Avionic Wireless Sensor Networks

Going wireless is one of the key industrial trends, which assists the emergence of new manufacturing and maintenance processes by reducing the complexity and cost of physical equipment. However, the adoption of Wireless Sensor Networks (WSNs) in production environments is limited due to the strict Quality of Service (QoS) requirements of industrial applications. In particular, Wireless Avionics Intra-Communication (WAIC) systems operating in 4.3 GHz band are designed for intra-aircraft use cases with considerable restrictions on the transmission power of sensors, which results in multi-hop topologies, complicating a guaranteed QoS. The Internet Engineering Task Force (IETF) has developed the protocol stack IPv6 over the Time Slotted Channel Hopping (TSCH) mode of IEEE 802.15.4 (6TiSCH) based on the IEEE 802.15.4 Standard for Low-Rate Wireless Networks, which combines the TSCH reliability with ubiquitous IPv6 connectivity and with the robust Routing Protocol for Low-Power and Lossy Networks (RPL). The Scheduling Function (SF) is a core IPv6 over the TSCH mode of IEEE 802.15.4 (6TiSCH) component, but the specification of the SF is an open research topic: numerous scientific articles investigated how QoS for a wide range of applications can be met by developing specialized SFs. However, no full-scale information exchange between the layers of the 6TiSCH stack was considered to optimize the SFs and to improve the network performance. In this work, we propose a novel solution named 6TiSCH-CLX to satisfy demanding QoS requirements using cross-layer communication. It is an extension of the 6TiSCH framework at the network and Medium Access Control (MAC) layers, addressing latency and reliability challenges agnostic of the physical layer. 6TiSCH-CLX is evaluated both analytically and in simulations for several safety-critical avionic intra-communication use cases in WAIC. Preliminary results indicate considerable improvements to latency, while maintaining almost 100% Packet Delivery Ratio (PDR) without retransmissions and they highlight the capability of the cross-layer approach compared to existing solutions.

A MEC architecture for a better quality of service in an Autonomous Vehicular Network

Designing 5G networks and related applications such as the Internet of Things (IoT), Cellular and autonomous vehicular networks (AVNET) is a challenge. Indeed, these networks are subjected to a number of constraints that play a crucial role in the network’s quality of service (QoS). Among these constraints, we have the management of computing, storage, bandwidth resources and low latency requirements. If in the past cloud computing has been used to avoid networks being affected by some of the previous constraints, that technology negatively affects the low latency requirements required by AVNETs for example. Multi-access Edge Computing (MEC) has recently emerged as a palliative solution to cloud computing. MEC aims to bring computing and storage resources from Cloud Data Center to Edge Data Center nearer to the User Equipment (UEs) in order to reduce the UEs’ requests latency for an improvement of the network’s QoS. Many MEC architectures have been proposed for AVNET. These solutions make use of Software Defined Networking (SDN), Network Function Virtualization (NFV), Service Function Chaining (SFC) or Network Slicing (NS) technologies. Some of these techniques combine partially these technologies while others do not. But to the best of our knowledge, none of them combines all these technologies to obtain a better QoS. In this paper, we combine the SDN, NFV, SFC and NS technologies to efficiently manage the MEC server resources for guaranteeing the QoS requirements in AVNETs. The QoS parameters we consider are latency, computing, storage and bandwidth resources. In that way, we first present a MEC server mathematical resource management model, then, we propose a new MEC architecture adapted to AVNETs that uses the aforementioned technologies also as the mathematical model we first present to manage the bandwidth, computing, and storage resources. The simulation results show that these resources are well managed, resulting to a low end to end latency for autonomous vehicles (AVs) requests executing on edge/cloud servers when the direct communication of AVs with cloud server takes long end to end delays, ensuring a better QoS for the AVNET.

Computation Resource Configuration With Adaptive QoS Requirements for Vehicular Edge Computing: A Fluid-Model Based Approach

In this paper, the computation resource configuration for vehicular edge computing is investigated in this study. Dissimilar to a large portion of the current literature, we center around the problem of determining the optimal edge computing resource allocation to vehicles with computation offloading requests for maximizing the long-term management profit of the network operator (i.e., the road-side unit of the vehicular network) under the randomness of vehicular traffics and task processing. A multi-type management framework is used to characterize the heterogeneities among different vehicles in terms of their edge computing quality-of-service (QoS) requirements. A novel fluid model is proposed that facilitates the formulation of the corresponding resource optimization problem by taking into account the system’s steady state characteristics with dynamic evolutions. In addition, rather than considering fixed QoS requirements in long-run, we explore the impact of the resulted service quality on the QoS requirements determined by vehicles. The QoS requirements of each vehicle is allowed to change adaptively according to the service quality fed back by the system. Based on this, we propose a simple but efficient approach, called threshold-based computation resource configuration scheme (TCRCS). The proposed solution’s performance is assessed by theoretical analysis and simulations, which show that it outperforms competitors.

Resource Optimization of D2D-Assisted CR Network With NOMA for 5G and Beyond Systems

Device-to-device (D2D) communication has emerged as a potential candidate for 5G and beyond networks to optimize spectrum utilization, reduce power consumption, and support higher data rates. The energy and spectral efficiency of D2D communication can be enhanced further by adapting cognitive radio (CR), nonorthogonal multiple access (NOMA), and radio-frequency energy harvesting (RF-EH) technologies. Therefore, this article proposes a sum-rate maximization problem for a D2D-assisted CR network with NOMA, considering the RF-EH mechanism. A joint optimization problem is modeled to maximize the sum throughput of cellular and D2D nodes by considering power assignments, radio resource allocation, user pairing, and transmission time ratio assignment. The problem is then converted into a standard convex optimization problem subject to power allocations at individual nodes, interference temperature limits, individual data rate, and secrecy capacity. The duality theory is adopted to decompose the problem into multiple subproblems, and Karush–Kuhn–Tucker (KKT) conditions are applied to provide the solution. Finally, the proposed schemes are validated through simulation results. The proposed schemes outperform the existing schemes in terms of sum throughput and energy harvesting under different quality of service requirements.

A Novel Congestion Controller With Prescribed Settling Time for TCP/AQM Network System

This paper studies the congestion control problem with prescribed settling time of nonlinear TCP/AQM network system. With the continuous improvement of users' requirements for network quality of service, it is a challenging task to quickly recover from the network congestion state to the normal network state. In the framework of backstepping, this paper studies how to realize congestion control according to the network state. The basic idea is to introduce a new nonautonomous differential equation to compensate the shortcomings of the existing arbitrary convergence time controller, and integrate it into the design of congestion controller to realize free-will arbitrary time congestion control. In order to reduce the packet loss rate and avoid reducing network efficiency caused by a large number of packet retransmissions, a finite-time performance function is introduced in the design process. The results show that using the proposed congestion mechanism, the congestion control problem with prescribed settling time can be solved by a nonpiecewise continuous arbitrary convergence time control law. The arbitrary time convergence of the system state is proved theoretically. Finally, the effectiveness of the proposed controller is verified by simulations and compared with related work.

Joint Node Activation, Beamforming and Phase-Shifting Control in IoT Sensor Network Assisted by Reconfigurable Intelligent Surface

Power saving and battery-life extension have always been a critical concern for IoT network deployment. One effective solution is to switch wireless devices into sleep mode to save power. This paper considers the power control in an IoT network via jointly activating IoT sensors and designing their transmit beamforming. Besides, inspired by the great potential of reconfigurable intelligent surface (RIS) in energy saving, we additionally introduce RIS to further lower the sensors’ power consumption. The considered problem is highly challenging due to its combinatorial nature, the highly non-convex quality-of-service (QoS) constraint and the hardware restrictions from the RIS. By exploiting the cutting-the-edge majorization minimization (MM) and the penalty dual decomposition (PDD) frameworks, we have successfully developed highly efficient solutions to tackle this problem. Our proposed solutions can achieve nearly identical performance with that of the exhaustive search but with a much lower complexity. Besides, as revealed by the numerical experiments, our proposed sensor activation scheme can switch off a large portion of sensors under mild QoS requirements, which significantly reduces power expenditure. Moreover, the deployment of RIS can bring an additional 45% – 70% power saving compared to the no-RIS case.

MOR: Multi-objective routing for underwater acoustic wireless sensor networks

Underwater acoustic wireless sensor networks (UAWSN) are becoming more and more extensive, and different application scenarios have different service quality requirements. Routing protocols that can meet different traffic transmission requirements are vital to the application of UAWSN in different scenarios. Due to the harsh environment of the underwater acoustic channel, the routing protocol of the UAWSN should consider the characteristics of the underwater channels. In this paper, a multi-objective routing (MOR) scheme is proposed for UAWSN applications, and relay selection algorithms (RSA) are devised for delay-sensitive (DS) and delay-insensitive (DIS) scenarios to meet different quality-of-service (QoS). For DS applications, RSA selects the relays taking into account of the energy and delay affected mainly by MAC time, node congestion and hops of the nodes for connecting from potential candidate node to sink, while the RSA mainly takes energy consumption and reliability into consideration for DIS case to choose desired relay. The results demonstrated that the MOR outperforms VBF and QELAR in terms of delay and energy consumption for DS applications, while it is performs better in terms of delay, energy consumption, packet delivery rate and lifetime for DIS applications.

Reinforcement Learning-Aided Edge Intelligence Framework for Delay-Sensitive Industrial Applications.

With the advancement in next-generation communication technologies, the so-called Tactile Internet is getting more attention due to its smart applications, such as haptic-enabled teleoperation systems. The stringent requirements such as delay, jitter, and packet loss of these delay-sensitive and loss-intolerant applications make it more challenging to ensure the Quality of Service (QoS) and Quality of Experience (QoE). In this regard, different haptic codec and control schemes were proposed for QoS and QoE provisioning in the Tactile Internet. However, they maximize the QoE while degrading the system’s stability under varying delays and high packet rates. In this paper, we present a reinforcement learning-based Intelligent Tactile Edge (ITE) framework to ensure both transparency and stability of teleoperation systems with high packet rates and variable time delay communication networks. The proposed ITE first estimates the network challenges, including communication delay, jitter, and packet loss, and then utilizes a Q-learning algorithm to select the optimal haptic codec scheme to reduce network load. The proposed framework aims to explore the optimal relationship between QoS and QoE parameters and make the tradeoff between stability and transparency during teleoperations. The simulation result indicates that the proposed strategy chooses the optimal scheme under different network impairments corresponding to the congestion level in the communication network while improving the QoS and maximizing the QoE. The end-to-end performance of throughput (1.5 Mbps) and average RTT (70 ms) during haptic communication is achieved with a learning rate and discounted factor value of 0.5 and 0.8, respectively. The results indicate that the communication system can successfully achieve the QoS and QoE requirements by employing the proposed ITE framework.

Intelligent Content Precaching Scheme for Platoon-Based Edge Vehicular Networks

To provide various onboard entertainment services, the ever-increased Internet contents to be exchanged among remote data centers, roadside units (RSUs), and vehicles demand reliable and fast content dissemination in the vehicular networks. Edge precaching technology is expected to provide flexible and low-latency content dissemination by allowing edge nodes (i.e., RSUs and vehicles) to precache contents. However, the content dissemination process of edge precaching still suffers from high mobility and highly dynamic topology of vehicular networks. The recently proposed platoon-based vehicular network has potentials to mitigate the mobility challenges, but need to deal with multihop wireless content dissemination’s latency and reliability issues. Additionally, the network resources are limited in edge nodes, whereas various onboard Internet services with different Quality-of-Service (QoS) requirements share the same resource pool by the same network resource scheduling policy, thereby decaying the network performance. Based on the above observations, to cope with the challenging content precaching problem under diverse QoS requirements in a platoon-based edge vehicular network, we first abstract two isolated virtual content service slices with different QoS requirements based on network slicing technology to provide on-demand customized services. Then, we propose an intelligent deep reinforcement learning (DRL)-based content precaching scheme, which optimally matches the available communication resources and limited caching capacities in the edge vehicular network. The scheme jointly considers the impacts of content precaching policy and multihop wireless transmission on the content precaching performance. Simulation results show that our proposed DRL-based content precaching scheme achieves a competitive performance of reliability and latency comparing with other state-of-the-art algorithms.

Energy-efficient subchannel assignment and power allocation in VLC-IoT systems with SLIPT.

In this paper, we investigate joint subchannel assignment and power allocation for the energy efficiency (EE) optimization in an indoor visible light communication (VLC)-Internet of Things (IoT) system with simultaneous lightwave information and power transfer (SLIPT). A mixed-integer fractional nonlinear programming problem that is challenging to solve is formulated. To tackle this problem, the optimal transmission scheme is decomposed into subchannel assignment and power allocation. Firstly, a virtual cell formation and subchannel assignment (VCF-SA) scheme is proposed, which considers the location of IoT devices, quality of service requirements, and energy states. Secondly, a quadratic transformation-based power allocation (QTBPA) scheme is designed to transform the fractional nonlinear programming problem into a series of convex problems that can be solved iteratively. Simulation results illustrate the effectiveness of the proposed scheme in terms of system EE compared to the benchmarks. The impact of system parameters on the EE is also analyzed.

Review and analysis of secure energy efficient resource optimization approaches for virtual machine migration in cloud computing

Computing operations such as databases, networks, hardware, programs, analytics, and so on are all part of the cloud computing service. As such, it may serve as an option to in-house hardware and software. However, dynamic consolidation of VMs is required to improve power consumption, load balance, the frequency of migrations, Quality of Service (QoS), and the rate at which SLA violations are addressed, all of which contribute to better resource use. VM technology has quickly become a pillar of data centers and cluster systems because to its utility in partitioning, consolidating, and moving workloads. VM placement is another thing that affects the quality of consolidation. It is important to design a system that improves energy efficiency by allocating resources to applications in a smart way while still meeting QoS requirements for applications. Moreover, the security of information that is being processed during the migration process is also one of the important tasks. Hence, in this paper, review and analysis based on parameters and metrics for the VM migration under the influence of energy optimization, consolidation, and security have been investigated in a wider manner. Moreover, some open issues that are still prevalent in the current field have also been highlighted.

Robust Congestion Control for Demand-Based Optimization in Precoded Multi-Beam High Throughput Satellite Communications

High-throughput satellite communication systems are growing in strategic importance thanks to their role in delivering broadband services to mobile platforms and residences and/or businesses in rural and remote regions globally. Although precoding has emerged as a prominent technique to meet ever-increasing user demands, there is a lack of studies dealing with congestion control. This paper enhances the performance of multi-beam high throughput geostationary satellite systems under congestion, where the users’ quality of service (QoS) demands cannot be fully satisfied with limited resources. In particular, we propose congestion control strategies, relying on simple power control schemes. We formulate a multi-objective optimization framework balancing the system sum-rate and the number of users satisfying their QoS requirements. Next, we propose two novel approaches that effectively handle the proposed multi-objective optimization problem. The former is a model-based approach that relies on the weighted sum method to enrich the number of satisfied users by solving a series of the sum-rate optimization problems in an iterative manner. The latter is a data-driven approach that offers a low-cost solution by utilizing supervised learning and exploiting the optimization structures as continuous mappings. The proposed general framework is evaluated for different linear precoding techniques, for which the low computational complexity algorithms are designed. Numerical results manifest that our proposed framework effectively handles the congestion issue and brings superior improvements of rate satisfaction to many users than previous works. Furthermore, the proposed algorithms show low run-time and make them realistic for practical systems.

Deep Reinforcement Learning-Based Multichannel Access for Industrial Wireless Networks With Dynamic Multiuser Priority

In Industry 4.0, massive heterogeneous industrial devices generate a great deal of data with different quality of service requirements, and communicate via industrial wireless networks (IWNs). However, the limited time-frequency resources of IWNs cannot well support the high concurrent access of massive industrial devices with strict real-time and reliable communication requirements. To address this problem, a deep reinforcement learning-based dynamic priority multichannel access (DRL-DPMCA) algorithm is proposed in this article. Firstly, according to the time-sensitivity of industrial data, industrial devices are assigned with different priorities, based on which their channel access probabilities are dynamically adjusted. Then, the Markov decision process is utilized to model the dynamic priority multichannel access problem. To cope with the explosion of state space caused by the multichannel access of massive industrial devices with dynamic priorities, DRL is used to establish the mapping from states to actions. Next, the long-term cumulative reward is maximized to obtain an effective policy. Especially, with joint consideration of the access reward and priority reward, a compound reward for multichannel access and dynamic priority is designed. For breaking the time correlation of training data while accelerating the convergence of DRL-DPMCA, an experience replay with experience-weight is proposed to store and sample experiences categorically. Besides, the gated recurrent unit, dueling architecture and step-by-step <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> -greedy method are employed to make states more comprehensive and reduce model oscillation. Extensive experiments show that, compared with slotted-Aloha and deep Q network algorithms, DRL-DPMCA converges quickly, and guarantees the highest channel access probability and the minimum queuing delay for high-priority industrial devices in the context of minimum access conflict and nearly 100% channel utilization.