Deterministic low-latency communication is critical for emerging applications such as industrial automation and autonomous driving. Cell-free (CF) is a promising network architecture for deterministic delay communications, owing to its user-centric cooperative transmission. In this paper, we address the joint optimization problem of resource allocation and beam selection for deterministic delay in the CF network. Specifically, we propose a delay-aware hierarchical distributed deep reinforcement learning (DRL) framework that enables distributed decision-making and improves scalability in the CF network. This framework incorporates a safe reinforcement learning (RL) algorithm to effectively address the delay constraint. Simulation results demonstrate that the proposed scheme improves spectral efficiency and reduces the delay violation ratio, achieving a delay violation ratio of 10−4at a system load of around 90%, an order of magnitude lower than the 10−3achieved by the modified largest weighted delay first (MLWDF) scheme.

In this paper, a two-stage intelligent scheduler is proposed to minimize the packet-level delay jitter while guaranteeing delay bound. Firstly, Lyapunov technology is employed to transform the delay-violation constraint into a sequential slot-level queue stability problem. Secondly, a hierarchical scheme is proposed to solve the resource allocation between multiple base stations and users, where the multi-agent reinforcement learning (MARL) gives the user priority and the number of scheduled packets, while the underlying scheduler allocates the resource. Our proposed scheme achieves lower delay jitter and delay violation rate than the Round-Robin Earliest Deadline First algorithm and MARL with delay violation penalty.

With the rapid development of 5 G, managing resources to achieve high throughput and low latency has become an increasingly prominent challenge. In this paper, we propose an alternating deep reinforcement learning (ADRL) framework for dynamic resource allocation in cell-free systems, aiming to enhance the sum-rate and reduce delay violation probability by optimizing the association between access points (APs) and user equipments (UEs), along with subcarrier allocation, beamforming, and power control. To achieve efficient resource management while simplifying decision-making, the ADRL framework employs diverse deep reinforcement learning (DRL) networks to alternately optimize specific resources at different time granularities. Simulation results demonstrate improvements in both throughput and delay performance, highlighting the potential of the proposed framework in advancing industrial wireless communication systems.

Ultra-reliable and low-latency communication (URLLC) with statistical quality-of-service (QoS) guarantees has garnered increasing attention. Cell-free massive multiple-input multiple-output (CF-mMIMO) emerges as a promising network architecture for such applications. This paper analyzes the reliability and latency-constrained performance in CF-mMIMO uplink transmission. First, we derive the closed-form expression for the signal-to-plus-noise ratio (SNR) distribution with maximum ratio combining (MRC) under independent non-identically distributed (i.n.i.d.) Rayleigh fading channels, precisely characterizing large-scale fading effects. Subsequently, we develop closed-form approximations for both decoding error probability and effective capacity. Finally, the QoS exponent is obtained by solving the effective bandwidth-effective capacity equation under Poisson traffic arrivals, thereby providing the delay violation probability expression. Monte Carlo simulations validate the theoretical results and demonstrate that the derived results achieve accurate approximations for both decoding error probabilities and delay violation probabilities. Furthermore, they highlight significant enhancements in reliability and latency assurance enabled by CF-mMIMO architectures.

In this paper, we investigate the hybrid traffic scheduling problem in 5G-TSN integrated systems, to achieve the endogenous deterministic communication for 5G. A novel strategy namely time-slot orchestration is proposed, and a latencyand jitter-aware traffic scheduling problem with the objective of maximizing the resource utilization is further formulated. To the best of our knowledge, it is the first time for such a problem being studied in the context of 5G-TSN integration. To solve this complex combinatorial optimization problem, a low-complexity heuristic scheduling algorithm is elaborately designed and extensively evaluated. Experimental results show that, compared with the optimal method derived from the genetic algorithm (GA) and the method based on the classical earliest deadline first (EDF) scheduling, the proposed method can efficiently enhance the endogenous deterministic communication capability of 5G by orders of magnitude, especially under high loads.

In the context of Ultra-Reliable Low Latency Communication (URLLC) scenarios, 5G incorporates numerous enhancements, with link adaptation (LA) being one of them. In the pursuit of reliability, a measurement-prediction-decision approach can be considered to enhance the accuracy of Modulation and Coding Scheme (MCS) decisions during LA, specifically by forecasting interference. In this paper, a two-stage uplink interference prediction algorithm is proposed. In the first stage, complex uplink interference values are decomposed to extract inherent patterns. In the second stage, leveraging the prior knowledge provided by the first stage, which enhances the algorithm's robustness and accuracy, inference is made. The experimental results demonstrate that the proposed interference prediction algorithm not only exhibits a significant improvement in accuracy but also contributes to a substantial enhancement in the performance of the communication system.

A fundamental issue in multiaccess edge computing (MEC) is efficiently offloading multiple tasks to multiple helper nodes (MTMH), i.e., MEC servers. However, most of the existing decentralized schemes do not consider interuser interference or merely adopt time division multiple access (TDMA) as the multiple access scheme for MTMH in the heterogeneous scenario, leading to a large latency. To address these issues, we propose DOT, a novel Decentralized Offloading of Tasks scheme in orthogonal frequency division multiple access (OFDMA)-based heterogeneous MEC, to minimize the sum cost in terms of energy consumption and delay. Specifically, we first formulate DOT as an optimization problem considering the interuser interference and dynamics in communication and computation resource allocation. Then, considering the huge dimension of potential offloading decisions and conflicting objectives of different users, the total cost of each user is minimized in a distributed manner by modeling the offloading problem as a potential game. The formulated potential game is proved to be an ordinal potential game and thus admits a Nash equilibrium (NE). Further, we develop an offloading algorithm to achieve the NE by exploiting the finite improvement property. Finally, simulation results demonstrate that DOT can achieve a lower cost compared with other baselines.

Abstract The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

Fog computing has been promoted to support delay-sensitive applications in future Internet of Things (IoT). For a general heterogeneous fog network consisting of many dispersive fog nodes (FNs), it may well happen that some of them have delay-sensitive tasks to process, i.e., task nodes (TNs), and some have spare resources to help the TNs to process tasks, i.e., helper nodes (HNs). It remains a fundamental challenge to effectively map multiple tasks or TNs into multiple HNs to minimize every task's service delay in a distributed manner, i.e., the multitask multihelper (MTMH) problem. The problem becomes more challenging as tasks are splittable, i.e., tasks can be divided into multiple subtasks and offloaded to multiple HNs to further reduce the service delay via the scheme similar to distributed computing, because it introduces the more complicated task division problem which results in a much larger and more complex solution space. To tackle this challenge, in this article, a generalized Nash equilibrium problem (GNEP), called parallel offloading of splittable tasks (POST), is formulated and studied thoroughly. The structural properties of the problem are characterized and thus the existence of generalized Nash equilibrium (GNE) is proven via the fixed-point theorem. Furthermore, the corresponding distributed task offloading algorithm is developed via the Gauss-Seidel-type method. The simulation results show that the proposed POST algorithm can offer much better performance in terms of the system average delay, individual delay, delay reduction ratio (DRR), and number of beneficial TNs, compared with the existing solution to the counterpart problem for nonsplittable tasks.

Fog computing (FC) has the potential to process computation-intensive tasks in Industrial Internet of Things (IIoT) systems. In parallel with the development of FC, non-orthogonal multiple access (NOMA) has been recognized as a promising technique to significantly improve the spectrum efficiency. In this paper, a NOMA-based FC framework for IIoT systems is considered, where multiple task nodes offload their tasks via NOMA to multiple nearby helper nodes for execution. We formulate a joint task scheduling and subcarrier allocation problem, with an objective to minimize the total cost in terms of the delay and energy consumption, while taking into account the practical communication and computation constraints. Note that the task scheduling includes task, computation resource, and power allocations. Since the task and subcarrier allocations involve binary variables, it is challenging to obtain an optimal solution for such a combinatorial problem. To this end, we solve the task scheduling and subcarrier allocation problem in an online learning fashion. During the online learning process, we propose an iterative algorithm to jointly optimize the subcarrier allocation and task scheduling in each time episode. Simulation results show that the proposed scheme can significantly reduce the sum cost compared to the baseline schemes.

Fog computing has been promoted to support delay-sensitive applications in future Internet of Things (IoT) and wireless networks. For a general heterogeneous fog network consisting of many dispersive Fog Nodes (FNs) with diverse resources and capabilities, some of them have delay-sensitive tasks to process, i.e., Task Nodes (TNs), while some have spare resources to help their neighboring TNs to process tasks, i.e., Helper Nodes (HNs). How to effectively map multiple tasks or TNs into multiple HNs to minimize every task's service delay in a distributed manner is a fundamental challenge, which is key to reap the full benefits of fog computing. The problem becomes more challenging when tasks can be divided into multiple subtasks to further reduce the service delay via distributed computing. To tackle this challenge, in this paper, a generalized nash equilibrium (NE) game called Parallel Scheduling of Multiple Tasks (PSMT) is formulated and studied. The structure properties of the problem are deduced and thus the existence of NE is proven by the fixed point theorem. Further, the corresponding distributed task scheduling algorithm/mechanism is developed via Gauss-Seidel-type method. Simulation results show that the proposed PSMT algorithm can converge in a fast way and offer much better performance in system average delay and number of beneficial TNs, comparing to the Paired Offloading of Multiple Tasks (POMT) solution to the counterpart problem not supporting distributed computing.

Fog computing has risen as an evolving architecture to support delay-sensitive applications in Internet of Things (IoT) and next generation mobile networks. For a typical heterogeneous fog network consisting of many fog nodes, some of them have different computation tasks while some have spare computation resources, which forms a multi-task multi-helper (MTMH) network. How to effectively map multiple tasks into multiple helper nodes to reduce the service delay is a key issue to be resolved. To tackle this issue, a computation offloading problem minimizing every task's delay is considered, from the perspective of individuals. This problem is further formulated into a non-cooperative game, i.e., MTMH computation offloading (MTMHCO) game, to model the competition among tasks for helpers. The existence of Nash equilibrium (NE) is guaranteed and an efficient distributed algorithm is developed to achieve an NE for the MTMHCO game. Theoretical analysis and simulation results show that the proposed algorithm can offer the nearoptimal performance in system average delay and achieve more number of beneficial task nodes, at two orders of magnitude lower complexity than a centralized optimal algorithm.

Fog computing has risen as a promising architecture for future Internet of Things, 5G and embedded artificial intelligence applications with stringent service delay requirements along the cloud to things continuum. For a typical fog network consisting of heterogeneous fog nodes (FNs) with different computing resources and communication capabilities, how to effectively schedule complex computation tasks to multiple FNs in the neighborhood to achieve minimal service delay is a fundamental challenge. To tackle this problem, a new concept named processing efficiency (PE) is first defined to incorporate computing resources and communication capacities. Further, to minimize service delay in heterogeneous fog networks, a scalable, stable, and decentralized algorithm, namely dispersive stable task scheduling (DATS), is proposed and evaluated, which consists of two key components: 1) a PE-based progressive computing resources competition and 2) a QoE-oriented synchronized task scheduling. Theoretical proofs and simulation results show that the proposed DATS algorithm can achieve effective tradeoff between computing resources and communication capabilities, thus significantly reducing service delay in heterogeneous fog networks.

By providing shared and flexible communication, computation, and storage resources along the cloud-to-things continuum, fog computing has become an attractive technology to support delay-sensitive applications in Internet of Things (IoT) and future wireless networks. Consider a typical heterogeneous fog network consisting of different types of fog nodes (FNs), wherein some task nodes (TNs) have computation-intensive and delay-sensitive tasks, while some helper nodes (HNs) have spare computation resources for sharing with their neighboring nodes. In order to minimize the delay of every task, these TNs and HNs should be effectively associated in a distributed manner, which is the fundamental multi-task multi-helper (MTMH) problem. To tackle this challenging problem, a potential game called paired offloading of multiple tasks (POMT) is formulated and studied. Theoretical analysis proves the existence of the Nash equilibrium (NE) for this proposed game. Further, the corresponding POMT algorithm is developed for every TN to achieve the NE of the general game. The analytical and simulation results show that our POMT algorithm can offer the near-optimal performance in system average delay and delay reduction ratio (DRR), and achieve more number of beneficial TNs, at two orders of magnitude lower complexity than a centralized optimal algorithm for computation offloading.

Future Internet of Things (IoT) networks enabled with fog computing is promising to achieve lower processing delay and lighter link burden, by effectively offloading the computing tasks of the terminal nodes (TNs) to nearby fog nodes (FNs) at the network edge. Existing researches for the energy consumption in fog-enabled networks mostly focused on the minimization of the overall energy consumed by the task offloading services. However, fair offloading among multiple FNs while maintaining a satisfactory energy efficiency is of great significance for the sustainability of the fog-enabled IoT networks, especially in the scenarios with battery-powered FNs. In this paper, we propose a fair and energy-minimized task offloading (FEMTO) algorithm based on a fairness scheduling metric, taking three important characteristics into consideration, which include the task offloading energy consumption, the FN's historical average energy and the FN priority. The analytical results of the optimal target FN, the optimal TN transmission power, and the optimal subtask size are obtained in a fair and energy-minimized manner. Extensive simulations are carried out for the heterogeneous fog-enabled IoT network, and the numerical results indicate that the proposed FEMTO algorithm effectively determines the FN feasibility and the minimum energy consumption for the task offloading services. Moreover, a high and robust fairness level for the FNs' energy consumptions is obtained by the proposed FEMTO algorithm.

In this paper, we investigate the joint user pairing and resource allocation (UP-RA) problem in uplink multi-user MIMO or virtual MIMO (V-MIMO) systems. Due to the practical transmission constraints, solving such a joint optimization problem is NP-hard. Therefore, we study this problem from a novel perspective, and propose a universal and efficient heuristic algorithm. First, we formulate the UP-RA problem as a special many-to-one matching problem with constraints. Different from most existing researches, we consider a universal scheduling problem, instead of a particular one. Then, we modify the classical deferred acceptance (DA) algorithm which is designed for the common many-to-one matching problem, and develop a lowcomplex heuristic algorithm called adapted deferred acceptance (ADA) algorithm for the UP-RA problem. Numerical results demonstrate that the ADA algorithm achieves robust and better performance compared with other representative algorithms in many scenarios.

Fog computing is seen as a promising approach to perform computation-intensive and latency-critical applications for mobile devices. Existing results mainly focus on power consumption or delay minimization problems which are both from the perspective of end devices. In this work, we further investigate the communication and computation resources minimization problem from the standpoint of the fog server operators instead. In practice, fog server operators must at first decide how many communication resources, e.g., bandwidth, and computation units should be deployed in order to satisfy various requirements from their serving devices. Meanwhile, the operators have to maximize their profits by balancing the cost of the deployed resources and devices' satisfaction. Motivated by such requirements, we formulate the problem as the minimization of the weighted bandwidth and computation resources with per-task delay requirement constraints. We prove that the optimization problem is convex and further derive important properties for the relationship between the required delay and the available resources. It indicates that there is an unachievable region for the completion time, where the task can not be completed within the required delay no matter how large the amount of communication bandwidth and the computation resources at the fog server is. We evaluate the performance of the feasible solutions for the mentioned problem through extensive numerical simulations and the numerical results have verified the analysis and the conclusions.