Collaborative inference among Internet-of-Things (IoT) devices can reduce deep neural network (DNN) inference latency by exploiting the communication and computing resources of IoT devices. However, existing collaborative inference strategies often overlook the padding data integrity in information interaction among devices, leading to the loss of boundary data or redundant communication overhead, thus undermining the inference latency improvements. To address these issues, in this paper, we propose PIDC, a padding-aware IoT device collaboration framework for accelerating DNN inference, which jointly optimizes DNN partitioning and padding interaction among devices to minimize inference latency. First, the minimum amount of data exchanged for padding interaction is analyzed. Then, we formulate the latency minimization problem as a nonlinear integer programming problem, and transform it into a linear programming formulation by introducing auxiliary variables, enabling efficient solution with existing solvers. We implement a prototype using heterogeneous devices to validate the effectiveness of PIDC in real-world settings. Experimental results demonstrate that PIDC achieves significant inference latency reductions, with up to 43.0% latency reductions across different DNN models and datasets compared to the state-of-the-art methods.

Uncrewed aerial vehicle (UAV) assisted wireless network (UAWN) is emerging as a promising architectural innovation for the provisioning of ubiquitous coverage and enhanced connectivity in the forthcoming 6G era. To accommodate the increasingly diversified services of 6G without deploying individual UAWNs for each service type, the integration of network slicing with UAWNs becomes essential. However, unlike terrestrial networks, the dynamic and uncertain network conditions caused by the mobility of the UAVs pose significant challenges to the UAWN slicing problem. In this paper, we investigate the UAWN slicing problem by jointly considering UAV deployment, channel allocation, and power allocation under uncertain network conditions including imperfect channel state information, uncertain user demand, and imprecise user location. As expected, this problem turns out to be a robust nonconvex mixed-integer problem, making it overwhelmingly difficult to solve. In light of the limited computing power of the UAV, we propose a lightweight optimization named RUNs, which jointly exploits problem decomposition, the augmented Lagrange method, and the batch coordinate descent method. We prove that the RUNs framework runs fast in the sense that it converges to the stationary point at a log-linear rate. Meanwhile, the numerical results demonstrate that RUNs has significant performance gains over existing benchmark solutions.

Satellite-terrestrial edge computing network (STECN) is emerging as a novel computation paradigm that enables a fashion of on-orbit computation, accommodating real-time processing of various types of computation tasks within a wider area. However, STECNs are incapable of meeting the diverse Quality-of-Service (QoS) requirements of various computational tasks without deploying dedicated infrastructures tailored to each type of task. Therefore, we studied the problem of joint network slicing and task offloading, which is modeled as a problem with a two-layer structure. At the higher level, slice tenants optimize their profits by determining resource allocations from the STECN, while at the lower layer, the users within each network slice maximize their utilities by deciding their offloading policies. In light of the complexity and the intricate interplay of these two problems, a multileader-disjoint-follower bi-level game is proposed. We show that both the leader’s game and the followers’ game admit at least one Nash equilibrium (NE). We then proposed two distributed algorithms that could prove to converge to the NE of these two games, respectively, without revealing any private information of all stakeholders. We evaluate the performance of our algorithms through extensive simulations, and the results demonstrate that our algorithms converge to the NE rapidly and can achieve superior performance gains compared with some known benchmarks.

Unmanned aerial vehicle (UAV) has been recognized as a key supplement for terrestrial networks to meet the stringent requirements of the forthcoming 6G networks. However, a significant challenge lies in the provisioning of differentiated services through a common UAV network without deploying individual networks for each service type. In this paper, we consider the problem of joint network slicing and UAV placement under dynamic wireless environment as well as the uncertain traffic demands. To overcome the difficulties brought by the network dynamics, we propose an intelligent hierarchical UAV slicing framework that operates at two different time-scales. At the large time-scale, we formulate the problem of inter-slice resource slicing and UAV placement as a mixed integer nonlinear program, which is solved by a decomposition algorithm. At the small time-scale, the problem of intra-slice resource adjustment is modeled as a stochastic game and a distributed learning algorithm is proposed to find the expected Nash Equilibrium. Simulation results demonstrate that the proposed framework is lightweight and outperforms a number of known benchmark algorithms in terms of throughput and transmission delay.

One of the key challenges faced by 5G-Advanced and the forthcoming 6G networks is the provisioning of delay-critical services. Recently, the integration of network slicing and Multi-access Edge Computing (MEC) is regarded as a promising solution for this challenge. However, existing proposals for the integration are far from perfect with various drawbacks, such as rigid slicing, privacy leakage, and high signaling costs. In this paper, we investigate the problem of joint network slicing and computation offloading, which is formulated as a multi-leader-disjoint-follower Stackelberg game. We prove that the game is a potential game which has at least one global Nash Equilibrium (gNE). Then we propose a distributed algorithm that provably converges to a gNE of the game without revealing any private information of all stakeholders. The performance of our proposed algorithm is evaluated through simulations, which demonstrate that the algorithm converges to the gNE rapidly and outperforms a number of benchmark algorithms.

Unmanned aerial vehicle (UAV) has been recognized as a key supplement for terrestrial networks to meet the stringent requirements of the forthcoming 6G networks. However, a significant challenge lies in providing differentiated services through a common UAV network, without the need to deploy individual networks for each service type. In this paper, we consider the problem of joint network slicing and UAV deployment under dynamic wireless environments as well as the uncertain traffic demands. To overcome the challenges posed by the network dynamics, we propose an intelligent hierarchical UAV slicing framework that operates at two different time-scales. At the large time-scale, the problem of inter-slice resource slicing and UAV deployment is formulated as a mixed integer nonlinear program, and a decomposition technique is applied to resolve it. At the small time-scale, the problem of intra-slice resource adjustment is modeled as a stochastic game and a distributed learning algorithm is proposed to find its Nash Equilibrium. Simulation results demonstrate that the proposed framework is lightweight and outperforms a number of known benchmark algorithms in terms of system utility, throughput and transmission delay.

Unmanned aerial vehicle (UAV) assisted wireless network has been recognized as an effective technology to facilitate the formation of a super flexible low-altitude platform for relieving the strain on traditional ground cellular systems. However, the on-demand deployment of the UAV-assisted wireless networks (OWN) becomes an essential yet challenging issue, as the constraints of UAVs’ location, resource provisioning, and demand distribution should be jointly considered. In this work, we investigate the OWN problem by proposing an autonomous learning framework (ALF) consisting of three sequential stages: demand prediction, proactive deployment, and resource allocation fine-tuning, which can be capable of autonomous network planning without reliance on manual operations in an extremely dynamic environment. In the demand prediction stage, we first design a dual transformer network (DTN) to capture the temporal and spatial dependencies of wireless traffic. We further reduce the computational complexity of DTN from quadratic time complexity to log-linear time complexity. In the proactive deployment stage, we jointly optimize the UAVs’ location and resource provisioning by proposing a modified general benders decomposition algorithm with a$\Gamma $-optimal convergence, where a learning-based discerning module is designed to accelerate the algorithm. In the resource allocation fine-tuning stage, we propose a simulated annealing-based algorithm to minimize the transmission rate degradation of users to reduce the bias caused by traffic demand prediction. Extensive numerical results based on an open source dataset demonstrate the effectiveness of the proposed methods in comparison with existing baselines.

With space, air and ground multiple layers, space-air-ground integrated networks (SAGINs) have been emerging as a promising technology to improve coverage and quality of service (QoS) for mobile users. With inhomogeneous access technologies at different layers in SAGINs, the joint gateway selection and user scheduling (GSUS) plays a crucial role to improve QoS and system performance. However, the moving aerial access point leads to highly dynamic inter-layer links, and it is challenging to capture the dynamics when solving the GSUS problem. In this paper, we resort to a non-stationary Markov Decision Process (MDP) formulation to make intelligent GSUS decisions in dynamic SAGINs. Unfortunately, conventional reinforcement learning (RL) is not applicable to solving the non-stationary MDP problem. To this end, we use the Dynamic Parameter Markov Decision Process (DP-MDP) to decompose the non-stationary MDP into a sequence of stationary MDPs, and then encode them with latent parameters, facilitating policy transfer between similar MDPs. Finally, the GSUS problem is solved by using an online learning framework including representation learning and RL. Simulation results demonstrate that the proposed framework outperforms a known benchmark scheme in terms of network throughput and packet drop rate.

Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands and provide profitable business models for future mobile networks. In RAN slicing architecture, self-healing is an important functional module to minimize the impact of network failings on the performance of RAN slices. However, self-healing of burgeoning sliced RAN is vastly different from that of traditional RAN and has been rarely investigated. In this paper, we address the Self-healing of RAN Slice (SRANS) problem by modeling it as a Pareto optimization problem with the aim of maximizing the self-healing utilities of individual RAN slices. To deal with the weakness of diversity maintenance in traditional Pareto optimization methods, we propose a Generative Adversarial Network (GAN) based Pareto Optimization (GPO) framework. Specifically, we employ self-conditioned GANs to replace the offspring reproduction module in the traditional Evolutionary Algorithm (EA), where the insufficiency of diversity maintenance in EAs is effectively overcome. Furthermore, we theoretically prove that GPO framework is guaranteed to converge to the optimal Pareto solution set. Numerical results demonstrate that the convergence of proposed GPO framework can be expedited by enhancing the diversity of solution sets in solving the SRANS problem. Compared with traditional schemes, GPO can achieve significant performance gain in terms of the utilities and isolation level of repaired RAN slices.

Proactive reconfiguration of network slices according to uncertain traffic demands is essential to improve network resource utilization while ensuring service quality in 5G-and-beyond systems. Existing researches on network slice reconfiguration are either model-driven or data-driven methods. However, model-driven methods may cause resource over-provisioning due to a lack of prediction mechanism, while data-driven methods are unrealistic in inter-slice reconfiguration that involves costly and time-consuming operations such as VNF migration. To address these issues, in this paper, we propose a Hybrid Model-Data driven (HMD) framework that intelligently performs inter-slice reconfiguration by leveraging prediction interval and robust optimization. We design a Prediction Interval-oriented Predictor (PIP) to produce a prediction interval that can bracket the future traffic demand with a prespecified probability. Based on the prediction interval, we design an inter-slice reconfiguration scheme (named box optimizer) to perform fast inter-slice reconfigurations. To tackle the over-conservativeness of the box optimizer, we further design the ellipsoid optimizer with better optimality at a cost of increased complexity. Numerical results demonstrate that the proposed framework can provide high robustness with low power consumption. Meanwhile, the trade-off between the power consumption and the realized robustness can be flexibly adjusted according to the type of slice and the level of traffic demand fluctuations.

Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands for future mobile networks. As an essential requirement for RAN slicing, self-healing is to provide services with certain quality requirements by minimizing the impact of mobile network failings. In this paper, we propose a Multi-objective Pareto Optimization based Self-healing (MPOS) scheme to solve the SRANS problem. We model the SRANS problem as a multi-objective optimization problem with aim of maximizing the self-healing profits of individual RAN slices and demonstrate the NP-hardness. In proposed MPOS scheme, we employ self-conditioned GANs to replace the offspring reproduction module in the traditional Multi-Objective Evolutionary Algorithm (MOEA), where the insufficiency of diversity maintenance in MOEA is effectively overcome. Furthermore, we theoretically prove that MPOS framework is guaranteed to converge to the optimal Pareto solution set with probability 1. Numerical results demonstrate that our MPOS scheme is effective in reducing the inverted generational distance of optimal Pareto solutions and achieving high profit and isolation level of RAN slices.

It is widely acknowledged that the agile reconfiguration of network slice according to traffic demand is of vital importance in 5G-and-beyond systems. Existing relevant works make reconfiguration decisions based either on point prediction of the uncertain demand, which lacks indications on how accurate it is, or on handcrafted uncertainty set with robust optimization, which may lead to resource over-provisioning due to the lack of prediction mechanism. To overcome these drawbacks, in this paper, we propose a predictor-optimizer framework that intelligently performs inter-slice reconfiguration with the aim of minimizing the energy consumption of serving these slices. Specifically, the predictor produces a prediction interval comprised of lower and upper bounds that bracket the future traffic demands with a prespecified probability. Then by regarding the prediction interval as the uncertainty set, we formulate the network slice reconfiguration problem as a Robust Mixed Integer Programming (RMIP). We solve this RMIP by using linearization technique and robust optimization. Numerical results demonstrate that the proposed framework outperforms traditional methods in terms of robustness and energy consumption. Meanwhile, the tradeoff between robustness and the energy consumption can be automatically adjusted according to the type of slice and traffic demands.

Due to the explosive growth of data traffic and poor indoor coverage, ultra-dense network has been introduced as a fundamental architectural technology for the 5G-and-beyond systems. As the telecom operator is shifting to the plug-and-play manner in mobile networks, network planning and optimization become difficult, especially in residential small-cell base stations (SBSs) deployment. Under this circumstance, severe inter-cell interference becomes inevitable which deteriorates network performance and the quality of service (QoS) of user equipments (UEs). In this paper, we propose a fully distributed self-learning interference mitigation (SLIM) scheme for autonomous networks under a model-free multi-agent reinforcement learning (MARL) framework. In SLIM, SBSs autonomously perceive surrounding interferences and determine downlink transmit power without necessity of signaling interaction between SBSs for mitigating interferences. To tackle the dimensional disaster of joint action in MARL model, we employ the Mean Field Theory to approximate the action value function, thus to greatly decrease the computational complexity. Simulation results based on 3GPP dual-stripe urban model demonstrate that SLIM outperforms conventional interference coordination schemes in mitigating interference while guaranteeing UEs'QoS.

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond systems need to support. To guarantee performance isolation while maximizing network resource utilization under traffic uncertainty, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by the numerous variables. In this paper, we investigate network slice reconfiguration with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). To address the curse of dimensionality of the problem, we propose to incorporate the Branching Dueling Q-network (BDQ) into DRL, to avoid some unnecessary calculations of Q-value by separating the Q-network into a shared value branch and a number of distributed advantage branches. Furthermore, the value branch and the advantage branch of each dimension are aggregated to derive the corresponding dimension's sub-Q-value. Then the best reconfiguration action is composed of the subactions in individual dimensions which are selected by €-greedy policy. Finally, we design an intelligent online network slice reconfiguration policy based on BDQ and extensive simulation experiments are conducted to validate the effectiveness of the proposed slice reconfiguration policy.

It is widely acknowledged that network slicing can tackle the diverse usage scenarios and connectivity services that the 5G-and-beyond system needs to support. To guarantee performance isolation while maximizing network resource utilization under dynamic traffic load, network slice needs to be reconfigured adaptively. However, it is commonly believed that the fine-grained resource reconfiguration problem is intractable due to the extremely high computational complexity caused by numerous variables. In this article, we investigate the reconfiguration within a core network slice with aim of minimizing long-term resource consumption by exploiting Deep Reinforcement Learning (DRL). This problem is also intractable by using conventional Deep Q Network (DQN), as it has a multi-dimensional discrete action space which is difficult to explore efficiently. To address the curse of dimensionality, we propose to exploit Branching Dueling Q-network which incorporates the action branching architecture into DQN to drastically decrease the number of estimated actions. Based on the discrete BDQ network, we develop an intelligent network slice reconfiguration algorithm (INSRA). Extensive simulation experiments are conducted to evaluate the performance of INSRA and the numerical results reveal that INSRA can minimize the long-term resource consumption and achieve high resource efficiency compared with several benchmark algorithms.