Real-world multivariate time series often exhibit multi-scale temporal dynamics and intricate inter-variable dependencies, making long-term forecasting particularly challenging. In this work, we propose a global token-driven multi-scale forecasting framework with dual-attention fusion. To capture multi-scale periodic patterns, the input sequence is segmented into multi-scale patches based on candidate periods, with the dominant ones derived via fast Fourier transform. Global tokens are then introduced as shared representations to integrate information from the temporal and variable dimensions, and a multi-scale patch-token interaction module is designed to establish interactions between the patches and global tokens, enabling the capture and aggregation of temporal dependencies across different scales. A dual-attention fusion module, employing both self-attention and cross-attention mechanisms, is then proposed to capture intrinsic and context-aware variable correlation among variables. To integrate cross-variable and cross-scale information into patch representations, a global information fusion module is designed. Finally, a period-aware weighting approach is devised to adaptively fuse the multi-scale predictions. Comprehensive experiments demonstrate that the proposed framework achieves state-of-the-art performance across various real-world datasets.

Heterogeneous satellite communication systems consisting of geostationary Earth orbit (GEO) and low Earth orbit (LEO) satellites have attracted significant attention due to their complementary advantages in wide coverage and low-latency transmission. In this paper, we investigate the joint data scheduling and precoding problem in multi-antenna GEO-LEO heterogeneous satellite systems. Jointly considering the dynamic network topology, time-varying channel conditions, stochastic traffic arrivals, and queue dynamics, we model the problem as a long-term average cache queue length minimization problem under quality of service (QoS) constraints. To tackle the formulated mixed-integer non-convex optimization problem, we decompose it into two subproblems, i.e., data scheduling subproblem and precoding subproblem, and design a nested iterative solution framework. To address the data scheduling subproblem, we formulate it as a Markov decision process (MDP) and put forward a proximal policy optimization (PPO)-based data scheduling algorithm. Given the state and action of the MDP, the joint optimization problem is reduced to a precoding subproblem, which can then be transformed into a weighted mean square error minimization problem, and is efficiently solved via the Lagrangian dual method. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

In this paper, we propose a hierarchical scheduling method for enhanced mobile broadband and ultra-reliable low-latency communication slices in mobile networks with non-stationary traffic arrivals, where resource allocation and traffic scheduling are decoupled into intra-slice and inter-slice processes operating on different timescales. An effective bandwidth estimation algorithm is developed, dynamically triggered by traffic fluctuations to determine transmission rates that ensure ultra-reliable delay guarantees under non-stationary arrivals. The intra-slice scheduling is formulated as a generalized assignment problem, for which we design an efficient algorithm that maximizes throughput under transmission rate constraints and guarantees a provable approximation ratio. We propose an inter-slice online resource allocation algorithm to enhance long-term system utility and establish that it admits a sublinear dynamic regret bound in piecewise-stationary environments. Numerical results demonstrate that the proposed method outperforms alternatives, improving system utility while satisfying slice requirements.

We investigate hierarchical scheduling for resource allocation and traffic management of ultra-reliable low-latency communication and enhanced mobile broadband slices operating at different time scales. An intra-slice scheduling method is proposed to maximize slice throughput while satisfying delay constraints and is theoretically shown to achieve a bounded approximation ratio. We develop a Bayesian optimization-based algorithm to learn optimal inter-slice resource allocation decisions online. Numerical results demonstrate that the proposed approach significantly improves system utility while satisfying the service requirements of each slice.

In this work, we explore the task execution problem within unmanned aerial vehicle (UAV)-assisted low Earth orbit (LEO) satellite offloading networks. We define a system cost function that includes both energy consumption and task dropping cost, and formulate the joint power allocation, task offloading and scheduling, and UAV flight trajectory planning problem as a constrained system cost minimization problem. Given that the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we decompose the problem into four subproblems, i.e., IoT device task transmission subproblem, UAV trajectory design subproblem, power allocation subproblem, and task offloading and computing scheduling sub-problem. We propose an iterative algorithm for the first three subproblems and a heuristic for task offloading and computing scheduling subproblem. The simulation results reveal that our proposed method achieves superior performance compared to the existing algorithm.

Uncrewed aerial vehicles (UAVs), due to their inherent flexibility and autonomous operation, are widely used in Internet of Things (IoT) networks for collecting data to facilitate real-time evaluation and monitoring applications. This article investigates a UAV-assisted IoT network, in which the UAV sequentially accesses IoT devices (IoTDs). During hovering, the UAV works on a full-duplex mode while collecting data from target devices, taking into account actual propulsion power consumption. In order to quantify the freshness of devices data, we introduce the concept of Age of Information (AoI). A multiobjective optimization method is proposed to jointly optimize three objectives: 1) maximization of the data rate; 2) minization of AoI; and 3) minization of the UAV energy consumption over a particular mission period. These three objectives partially conflict with each other and provide weight parameters to describe their importance. Since the data uploaded by IoTDs is dynamically changing, the trajectory planning of the UAV is required. Considering that the UAV has no prior knowledge of the network environment, the optimization problem is reformulated as a Markov decision process. Aiming at the learning problem of the UAV control strategies over multiple objectives, a deep reinforcement learning algorithm for multiobjective collaborative optimization is proposed. While training, the agent collects data in time according to the devices priority, and generates optimal strategies under the conditions of giving weights. Experimental results demonstrate that the proposed multiobjective twin delayed deep deterministic policy gradient algorithm jointly optimizes three objectives and can adjust the optimal policy based on the weight parameters of each objective.

In this study, we investigate minimization of energy consumption in multi-UAV assisted networks. We formulate an energy minimization optimization problem with UAV trajectory design, content fetching, power allocation and content placement constraints. The problem is a mixed integer nonlinear programming (MINLP); therefore, we convert the formulated problem into semi-Markov decision process (SMDP). To tackle this SMDP optimization challenge, we introduce an option-based hierarchical deep reinforcement learning (OHDRL) approach. We designate UAV trajectory planning and power allocation as the low level action space, and content placement and content fetching as the high level option space. Through simulations, we demonstrate the effectiveness of the proposed OHDRL method.

In future space-ground integrated networks, a satellite-based core network can reduce frequent signaling interactions between satellites and ground stations, thereby enhancing network architecture and supporting global communications. Users can achieve end-to-end communication through the satellitebased User Plane Function (UPF). However, the high dynamics of Low Earth Orbit (LEO) satellites result in frequent inter-satellite handovers, significantly affecting user service continuity. Existing satellite handover strategies are overly simplistic and fail to ensure the Quality of Service (QoS). Additionally, ground users compete for satellite links based on limited observations, leading to network congestion. This paper proposes a loadbalanced, distributed, multi-agent deep reinforcement learning method for satellite handover. We formulate a combinatorial optimization problem to maximize the total utility of user-satellite associations across various service types. Each user acts based on local information and engages in distributed matching with satellites. Simulation results indicate that our method ensures QoS for various service types, optimizes load balancing, and outperforms basic handover strategies in terms of handover success rate and frequency.

Low Earth Orbit (LEO) satellite systems provide geographically unrestricted services to ground users. However, the conflict between existing resource allocation schemes and the variability of inter-beam traffic is becoming increasingly prominent. To address this issue, this paper proposes a resource allocation strategy for beam-hopping satellite networks based on Rate-Splitting Multiple Access (RSMA) technology, aiming to reduce co-channel interference while improving system resource utilization. First, by analyzing the resource allocation challenges faced by beam-hopping satellite networks, including low spectrum utilization and co-channel interference, the background and motivation for the proposed strategy are provided. Next, RSMA technology is introduced, dividing user messages into common and private parts, and a corresponding resource allocation algorithm is designed to enhance spectrum utilization and reduce co-channel interference. Through the construction of a system model and simulation experiments, the effectiveness and performance advantages of the proposed strategy are verified. This study provides new ideas and methods for resource allocation in beam-hopping satellite networks, which is significant for improving system performance and service quality.

Unmanned aerial vehicles (UAVs) are expected to act as aerial relays which forwards data packets for ground users (GUs) leveraging their advantages of low cost, high flexibility and maneuverability. One challenging problem in UAV-assisted cellular systems is how to design the efficient UAV deployment, GU association and resource allocation strategy which achieves system performance optimization. In this paper, we address the data transmission problem in a UAV-assisted cellular system with the knowledge of statistical GU positions. Stressing the energy consumption of base station (BS) and UAVs, and the cost of UAVs, we formulate the joint UAV deployment, GU association and power allocation problem as a constrained system cost minimization problem. To solve the formulated problem, we decouple it into three subproblems, i.e., UAV deployment, GU association and power allocation subproblem. Then, the UAV deployment subproblem is modeled as a Markov decision process (MDP), and an embedded multi-agent double deep $\mathbf{Q}$ network (DDQN) algorithm is proposed. Specifically, given the state and action of the MDP, we formulate and solve the power allocation subproblem and determine the transmit power of the UAVs by applying the Lagrange dual method-based algorithm. The GU association subproblem is then tackled by utilizing a proposed Kuhn-Munkres (K-M) algorithm-based scheme. Based on the obtained power allocation and GU association strategy, the reward of the MDP can be computed and the UAV deployment strategy is determined which maximizes the long-term average reward. Simulation results demonstrate the effectiveness of the proposed algorithms.

In this paper, we investigate the joint task offloading and computing scheduling in low earth orbit (LEO) satellite networks, which consists of a number of ground terminals (GTs) and multiple LEO satellites. Considering the characteristics of tasks and the energy consumption of task execution, we define a system utility function, and formulate the problem of task offloading and computing scheduling as a constrained system utility function maximization problem. The formulated optimization problem is a mixed integer non-linear programming problem which cannot be solved conveniently. To solve the formulated optimization problem, we first propose local computing scheduling algorithm to determine the task local partition strategy. And then priority-based offloading and computing scheduling algorithm is proposed to determine the task offloading strategy. The effectiveness of the proposed algorithm is verified through simulations in satellite tool kit (STK) and MATLAB.

Unmanned aerial vehicles (UAVs) have attracted widespread attentions for the applications in various fields leveraging their high maneuverability, ease of deployment, and low cost. Trajectory optimization and mission allocation of UAVs are among the most critical issues for achieving flexible and efficient mission execution. In this paper, we study the problem of trajectory planning and mission assignment in a mission execute scenario of multiple UAVs. Considering the reward obtained from mission execution, as well as the energy and resource consumption due to mission execution, we define system utility function and formulate the joint trajectory planning and mission assignment problem as a constrained system long-term utility function maximization problem. To solve the formulated problem, we first model the flight trajectory planing problem of the UAVs as a decentralized partially observable Markov decision process (Dec-POMDP) and propose a multi-agent double deep Q network (DDQN)-based trajectory planning algorithm. Given specific system state and action within the DDQN framework, we then design a greedy method-based mission assignment and execution ratio determination strategy. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

The dynamic edge computing-enabled networks contain various resources, and network parameters and system models are subject to uncertainty. Despite this, there is still a lack of comprehensive online solutions for coordinating wireless, transport, and computing resources. This paper investigates the use of online convex optimization for resource allocation in edge computing-enabled networks with time-varying cost and time-varying constraint functions. Taking into account the uncertainty of wireless status, quality of service requirements, and cost function, the goal is to minimize the long-term cost by optimizing the selection of access points, association of computing nodes, allocation of computing resources, and bandwidth allocation. To address the proposed online resource allocation problem, the modified online saddle-point algorithm is employed and dynamic regret and accumulative constraint violation are defined to measure the performance of the algorithm. To reduce the computational complexity of the projection in the modified online saddle point algorithm, the projection is reformulated as quadratic programs, which can be solved efficiently by convex optimization. Finally, the effectiveness and superiority of the proposed solution are demonstrated through simulation analysis.

In this work, we investigate content delivery and target sensing problem in unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) networks where UAVs are allowed storing user-requested contents, delivering the content to users and performing target sensing as well. To jointly address the performance of content transmission and target sensing, we define utility function and formulate the UAV deployment, communication and sensing precoder design problem as a constrained utility maximization problem. As the formulated problem is a mixed-integer nonlinear programming problem, which cannot be solved conveniently, we transform it into two subproblems, namely, user grouping and UAV deployment subproblem, and communication and sensing precoder design subproblem, and solve the two subproblems by using an alternate iteration-based algorithm. Specifically, we first design a mean-shift-based user grouping strategy which divides users into different groups and then propose a UAV deployment strategy based on successive convex approximation (SCA)-based iterative algorithm and the first order Taylor expansion method. To solve communication and sensing precoder design subproblem, we propose a two-layer penalty-based SCA algorithm. Simulation results demonstrate the effectiveness of the proposed algorithms.

In recent years, unmanned aerial vehicles (UAVs) have experienced rapid development and have been widely used in many fields. Equipped with both communication modules and sensing modules, UAVs are capable of conducting integrated communication and target detection, thus greatly improving spectrum efficiency and system performance. In this article, we consider a scenario where multiple UAVs collaborate to detect targets and transmit the collected data to a central UAV. Addressing the problem of communication and perception scheduling, we first analyze the target detection and communication performance, and then formulate the joint communication and perception scheduling problem as two optimization problems, with the objectives being maximizing the average utility function (MAUF) and minimizing the completion time (MCT), respectively. To solve the formulated problems, we first consider the dynamic characteristics of the environment, and model the problems as two Markov decision processes. Regarding the UAVs as multiple agents, we then propose a multiagent double deep Q-network (DDQN)-based MAUF algorithm and a multiagent DDQN-based MCT algorithm to determine the communication and perception scheduling strategies of the UAVs. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

This paper proposes a novel knowledge-driven approach for resource allocation in wireless networks using the graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach, named UWGNN, incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, by unrolling WMMSE algorithm into a series of interconnected submodules, UWGNN aligns closely with the optimization steps of the algorithm. Our analysis reveals the effectiveness of the deep unrolling method within UWGNN, which decomposes complicated end-to-end mappings, leading to a reduction in model complexity and parameter count. Experimental results demonstrate that UWGNN maintains optimal performance with computation latency 3 to 4 orders of magnitude lower than the WMMSE algorithm and exhibits strong performance and generalization across diverse data distributions and communication topologies without the need for retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.

Multibeam satellite communication systems have been received widespread attention due to their high throughput and efficient resource utilization. In this article, we investigate the beam illumination and resource allocation problem in multibeam satellite communication systems. By jointly considering user position and service characteristics, an optics-based initial user grouping algorithm is proposed. To enhance beam coverage performance, a minimum circle algorithm is proposed to optimally design satellite beam positions and coverage radius. Given the obtained user grouping strategy, we address the difference between random user service demands and service provisioning capability of the system, and define system cost function. The joint beam illumination, subchannel, and power allocation problem is formulated as a system cost function minimization problem. To solve the formulated optimization problem, we introduce aggregate nodes to describe the characteristics of user groups, and address the beam illumination and power allocation problem of user groups. The problem is modeled as a mixed-space Markov decision process (MDP), and a parameterized deep Q-network-based joint beam illumination and power allocation algorithm is proposed. Based on the obtained resource allocation strategy for user groups, we then design user-oriented subchannel and power allocation strategy. To this end, we model the optimization problem as an MDP and propose a double deep Q-network (DDQN) algorithm-based algorithm. To address the concern that the DDQN algorithm may reach a local optimum, proximal policy optimization algorithms with discrete action space and continuous action space are proposed. Simulation results validate the effectiveness of the proposed algorithms.

Multibeam satellite communication systems have received extensive attentions in recent years. By generating multiple spot beams at the transmitter of satellites, high-bandwidth connectivity to specific geographic areas can be achieved. In this paper, the joint beam illumination and resource allocation problem is studied for multibeam satellite communication systems. To address the different resource management granularity levels in the considered multibeam satellite system, we propose a hybrid offline and online resource management algorithm. Specifically, a two-step offline user grouping scheme is proposed under beam coverage and maximum user number constraints. Then, based on the obtained user grouping strategy, the online beam illumination and time-frequency resource allocation problem is studied. To this end, we jointly consider the revenue received from successful packet transmission and the energy consumption of the satellites due to data transmission, and define a system utility function. The problem of joint beam illumination and resource allocation is then formulated as a constrained utility function maximization problem. To solve the optimization problem, we introduce aggregate users (AUs) to represent the service requirements and the transmission characteristics of individual user groups, and design a proportional fairness and virtual Kuhn-Munkres-based beam illumination strategy for the AUs. Given the obtained beam illumination strategy of user groups, a two-level prioritizing scheme is proposed for the GUs and a priority and greedy-based algorithm is designed to assign time and frequency resources to the GUs. Numerical results verify the effectiveness of the proposed algorithm.

Multi-beam satellite communication systems have been widely recognized as an efficient technology for providing reliable and high-speed communication services. In this paper, we consider a multi-beam satellite communication system, which consists of a multi-beam satellite, ground cells and a ground gateway for processing information of the system. We focus on the beam scheduling, subchannel and power allocation problem to improve system performance. To jointly consider data transmission performance and power consumption, we define a utility function as the weighted sum of service queue length and satellite transmit power. To adapt to the dynamic arriving of data packets and the time-varying satellite channels, we formulate the resource allocation problem as a long-term utility function maximization problem. Since the optimization problem is a non-convex mixed integer problem, which cannot be solved using traditional convex optimization tools, we first decouple the original problem into beam scheduling subproblem and joint subchannel and power allocation subproblem. To solve beam scheduling subproblem, two beam scheduling schemes are proposed. Furthermore, three deep reinforcement learning (DRL)-based joint subchannel and power allocation algorithms are proposed to tackle joint subchannel and power allocation subproblem. Numerical results demonstrate the effectiveness of the proposed algorithms.

Deploying edge computing servers on satellites to provide computing services for Internet of thing (IoT) devices will be an indispensable example for satellite IoT (SIoT) systems. In this paper, we study task offloading problem for SIoT systems. Considering the IoT devices and satellites task queue state and satellite-ground channel state, under the conditions of meeting the limited computing resources and transmit power, and the queue stability, the task offloading problem for SIoT systems is formulated as a minimizing the long-term average system energy consumption problem. In order to solve this problem, We first use Lyapunov optimization method to decouple the joint optimization problem into three subproblems, i.e., computing resource allocation subproblem of IoT devices, computing resource allocation subproblem of satellites, joint task offloading and power allocation subproblem. For computing resource allocation subproblems, we apply Lagrange dual algorithm to solve them. To solve joint task offloading and power allocation subproblem, we formulate it as a Markov decision process (MDP), and propose a parameterized deep Q-network (PDQN)-based task offloading and power allocation algorithm. Finally, the simulation results show that the proposed algorithm has good performance in optimizing the long-term average system energy consumption.

In this paper, multi-antenna UAV-enabled joint communication and sensing scenario is examined. Taking into account the flight energy of the UAV, multi-antenna transmission and user service requirements are jointly considered, the problem of UAV communication, sensing precoding and flight trajectory is formulated as a multi-objective optimization problem which jointly maximizes the minimum data rate of communication users and the minimum discovery probability of targets. Since the minimum rate maximization problem of communication users is a non-convex optimization problem, which is difficult to solve directly, we decompose the original optimization problem into communication precoding design subproblem and UAV trajectory design subproblem. Then we solve the two subproblems successively by applying an alternate iteration method. Specifically, a zero-forcing (ZF) algorithm is put forward for solving the communication precoding design subproblem. A successive convex approximation (SCA) algorithm is applied to determine the optimal trajectory of the UAV. Based on the optimal trajectory of the UAV, the sensing positions selection problem is modeled as a weighted distance minimization problem, and then the extensive search algorithm is applied to obtain the optimal locations. Finally, a ZF algorithm-based joint communication and sensing precoding is presented. The effectiveness of the proposed algorithm is verified by simulations.

In recent years, unmanned aerial vehicles (UAVs) have been experienced rapid development and have been widely used in many fields. Equipped with both communication modules and sensing modules, UAVs are capable of achieving integrated communication and target detection, thus greatly improving spectrum efficiency and system performance. In this paper, we consider a scenario where multiple UAVs collaborate to detect targets and transmit the collected data to a central UAV. Addressing the problem of communication and perception scheduling, we first analyze the detection performance and communication performance, and then formulate the joint communication and perception scheduling problem into an average utility function maximization problem. To solve the formulated problem, we first consider the dynamic characteristics of the environment, and model the problem as a Markov decision process. Regarding the UAVs as multiple agents, we propose a multi-agent double deep Q-network based joint communication and perception scheduling algorithm. Simulation results demonstrate the effectiveness and superiority of the algorithm.

Multi-beam satellite communication systems have received considerable attentions in recent years. By generating multiple beams at the transmitting satellites, the coverage areas can be enlarged and the transmission capacity can be improved. In this paper, we study dynamic radio resource allocation problem for multi-beam satellite systems. Considering the dynamic arriving of data packets and signal distortion due to nonlinear amplifying, we formulate the joint beam scheduling, subchannel and power allocation problem of the satellite system as a long-term average utility function maximization problem. To solve the formulated problem, we first propose a cell grouping and beam scheduling scheme. Then, we model the dynamic resource allocation problem of individual cells as a Markov decision process and propose a double deep Q-network (DDQN)-based subchannel and power allocation algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithm.

In this paper, we consider the data collection problem in an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN). To address the energy consumption of the UAV, we formulate joint data scheduling and trajectory planning problem as an average minimization energy consumption. To solve the formulated optimization problem, we first propose a multi-time slot KM based algorithm to determine data scheduling strategy. We then model the UAV trajectory planning problem as a Markov decision process (MDP) and propose a deep Q-network based algorithm. Simulation results demonstrate the effectiveness of the proposed algorithms.

Multi-beam satellite communication systems have received a lot of attentions due to their high throughout and resources utilization. In this paper, we study the problem of user grouping and resource allocation in multi-beam satellite communication systems, and propose a two-stage resource management scheme. Addressing dynamic and diverse user service requirements, we first propose a Voronoi diagram-based iterative user grouping algorithm to achieve load balancing among groups. Then, we formulate the subchannel and power allocation problem as a system average utility function maximization problem. To solve the problem, we regard each beam as an agent, and propose a multi-agent deep Q-network (DQN)-based algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Integrated sensing and communication (ISAC) technology has received considerable attention due to its high spectral efficiency and equipment utilization. In this paper, we consider an unmanned aerial vehicle (UAV)-assisted target detection system, which consists of a UAV, multiple base stations (BSs) and targets. In the considered system, the UAV equipped with an ISAC unit performs target detection and transmits sensory data to the BS. In order to achieve the trade-off between the radar sensing and communication performance of the UAV, we formulate the joint target sensing, data scheduling as well as UAV flight trajectory problem as an average utility maximization problem. To solve the problem, we consider the dynamic characteristics of the sensing and communication scenario and propose a deep double Q-network (DDQN)-based algorithm to determine the joint sensing and communication strategy. To expedite the convergence process of the proposed DDQN-based algorithm, we further propose an improved priority experience replay DDQN (PER-DDQN) algorithm. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

In recent years, unmanned aerial vehicles (UAVs) have been experienced rapid development and have been widely used in many fields. UAVs can serve as aerial base stations to alleviate base station load in cellular systems. In this paper, we study data scheduling and UAV trajectory planing problem in UAV-assisted systems. Stressing the service sensitiveness on queuing delay, we examine the user queue length and formulate joint data scheduling and UAV trajectory planning problem as a longest user queue length minimization problem. To tackle this problem, we first model the problem as a Markov decision process (MDP), and then we propose two deep reinforcement learning (DRL)-based algorithms to determine the joint data scheduling and UAV trajectory planing strategy. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Unmanned aerial vehicles (UAVs) have been regarded as an efficient approach for collecting data in wireless sensor networks (WSNs), benefited from their mobility and flexibility. In this work, we investigate the data collection problem in UAV-assisted WSNs. In order to improve data collection efficiency, we first propose a multi-scenario parallel data collection scheme which allows data packets being transmitted through various modes/links simultaneously. Then, addressing the importance of completing data collection within a short time duration, we formulate a constrained optimization problem which minimizes the data collection time of the sensor nodes (SNs) by jointly designing UAV flight trajectory, cluster head mode selection, SN clustering strategy and UAV velocity. To resolve the optimization problem, we first consider the data transmission performance between SNs and present an SN clustering scheme based on a modified K-means algorithm. Given the clustering strategy, the optimization problem is then converted into three sub-problems, i.e., CH mode selection, UAV trajectory design, and flight velocity optimization. Firstly, jointly considering the data collection time of the cluster heads in various transmission modes and the spectrum resources of the sink node, we propose a greedy method-based CH mode selection scheme. Then, we map the UAV trajectory optimization problem as a traveling salesman problem and propose a simulated annealing-based algorithm to determine the flight trajectory for the UAV. Finally, by applying discrete time segment scheme, the UAV velocity optimization subproblem is transformed into a sequence of convex flight time minimization problems and a segment optimization-based flight velocity control strategy is presented. Numerical results reveal that the proposed data collection algorithm can achieve$25{\mathrm{\% }}$and$12{\mathrm{\% }}$performance gains comparing to the existing algorithms and the benchmark scheme, respectively.

Caching popular contents at the base station (BS) in maritime Internet of Things (IoT) networks makes sensor nodes be free from frequently responding to user requests, which can remarkably save the energy consumption of sensor nodes. However, to ensure the freshness of contents, cached contents need to be updated periodically. Frequent content updating can minimize the age of information (AoI) of contents while increase the energy consumption of sensor nodes. To make a better tradeoff between the AoI and energy consumption, in this paper, both the cache placement and content updating interval are jointly optimized to minimize the weighted sum of AoI of contents and energy consumption of sensor nodes. As the formulated problem is a mixed integer nonlinear programming problem, the cache placement and the content updating interval are alternatively optimized. For the cache placement problem, a local optimal solution is achieved via the binary constraint reformulation and successive convex approximation. For the content updating problem, the optimal solution with semi-closed form is derived. Simulation results show that our proposed algorithm outperforms other benchmarks in terms of the weighted sum of AoI and energy consumption.

The rapid development and wide application of wireless communication technologies advance the integration of heterogeneous access networks. While the heterogeneous networks are expected to offer enhanced data transmission services for mobile terminals (MTs), the heterogeneous characteristics of access technologies, the diverse requirements of user services and various features of user devices pose challenges to network access and handover strategy. In this paper, the network access and handover problem in dynamic heterogeneous network scenarios is investigated. To tackle the variation of performance metrics in different access networks, we define instantaneous quality of service (QoS) metrics. Then, system utility function is introduced to characterize user service experience on access networks. Specifically, by jointly considering network performance, service characteristics and user requirement, system utility function is defined as the weighted sum of user QoS, queue status and handover cost. Aiming to maximize the long-term average utility, network access and handover problem is formulated as a constrained stochastic optimization model. To solve the optimization problem, we regard the network access and handover procedure as a Markov decision process (MDP), and propose a deep Q-network (DQN)-based approach to determine the optimal strategy. Simulation results show that compared to the reference algorithms, the proposed algorithm offers better performance.

Recent years have witnessed the rapid development of wireless mobile communication technologies, which promotes the integration of heterogeneous access networks. While heterogeneous integrated networks are expected to improve the transmission performance of mobile users (MUs), the heterogeneous characteristics of access cells and the diversified demands of user services pose challenges to the design of network selection and handover strategies. This paper studies network selection and handover problem in a dynamically changing heterogeneous network scenario. Jointly considering network performance and user requirements, we define system utility function and formulate network selection and handover problem as a long-term utility function maximization model. To resolve the problem, we first propose a back-propagation neural network (BPNN)-based network performance prediction model which enables accurate network performance prediction. Then, based on the predicted network transmission performance, the long-term utility function maximization problem is transformed into the shortest path problem in a weighted directed graph. The Dijkstra's algorithm is applied to solve the problem to determine the optimal network access and handover strategy. Simulation results demonstrate that our proposed algorithm offers better performance compared to the baseline algorithm.

Traditional access control schemes in Internet of things (IoT) systems have long been faced with security problems such as single point of failure, data tampering and cross-domain access. In this paper, we study the access control problem in an IoT system composed of multiple domains. Applying blockchain technology, we propose a hierarchical blockchain-based access control architecture which consists of two layers of blockchain networks, i.e., global blockchain network and local blockchain network. Based on the proposed architecture, the processes of inter-domain and intra-domain access control are examined, respectively. In order to achieve the secure access of user data in the proposed architecture, we further present a trust-value based access control strategy. To examine the performance of proposed architecture and strategy, we set up an access control simulation platform based on Hyperledger Fabric and verify the feasibility of the proposed architecture and strategy.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users (GUs) in various applications. In this paper, we study UAV deployment problem in an integrated access and backhaul network, where a number of UAVs are deployed as aerial base stations (ABSs) or aerial relays (ARs) to forward GUs’ data packets to the remote gateway via multi-hop transmissions. Aiming at minimizing the system cost, which is defined as the weighted sum of UAV deployment cost and the energy consumption required for data transmission, a constrained system cost minimization problem is formulated, where UAV deployment, GU association and route selection problem are optimized. To solve the formulated non-convex problem, we propose a two-stage heuristic algorithm. In the first stage, we focus on the optimal design of the access links and propose a joint ABS deployment and resource allocation algorithm. Specifically, a modified K-means based clustering scheme is proposed to determine ABS deployment and GU association strategy. Given the obtained ABS deployment strategy, in the second stage, we then design a joint AR deployment, route selection scheme for the backhaul links and propose a minimum circle algorithm-based AR deployment and route selection strategy. Numerical results verify the effectiveness of the proposed algorithm.

A hybrid or coexisting orthogonal frequency-division multiple access (OFDMA) and nonorthogonal multiple access (NOMA) scheme is a promising approach to greatly enhance network capacity and reduce the interference in machine-to-machine (M2M) communication networks. In this article, we study the power allocation and access mode selection problem of machine-type communication devices (MTCDs), which are allowed to transmit their data packets to the base station (BS) in direct transmission mode (DTM) or cluster head forwarding mode (CHFM). Considering the transmit power optimization and data rate maximization of the MTCDs, we formulate the power allocation and access mode selection problem as a sum-rate maximization problem. Since the original maximization problem is a nonlinear fractional problem that cannot be solved conveniently, we transform the optimization problem into two subproblems, i.e., power allocation subproblem and access mode selection subproblem. The power allocation subproblem is solved for both OFDMA and NOMA schemes by applying the Lagrange dual method. To solve the access mode selection subproblem, we further divide the subproblem into DTM subproblem and CHFM subproblem and solve these subproblems successively. In particular, for the solution of the CHFM subproblem, we first propose a greedy method-based algorithm, and then, to tackle the issue of high computational complexity, we present a low complexity heuristic algorithm. In the end, we present simulation results to demonstrate the effectiveness of the proposed algorithms.

As a key technology of intelligent transportation systems, cellular vehicle-to-everything (C-V2X) communication technology has received considerable attentions from both industry and academia. C-V2X communications are expected to improve traffic efficiency and enhance road security by allowing vehicle nodes to interact with their neighboring entities. In recent years, 3GPP has dedicated to designing the technology standard for C-V2X communications, and has released C-V2X R14, which specifies two typical V2X communication modes, i.e., Mode 3 and Mode 4. C-V2X Mode 3 supports centralized management of radio resources using base stations, while in C-V2X Mode 4, vehicles schedule their radio resources in a self-organizing manner. Considering the importance of C-V2X Mode 4 for practical V2X communication scenarios, in this work, we set up a system-level simulation platform for C-V2X Mode 4 based on traffic simulator CarMaker and network simulator (NS)3. In order to create realistic vehicle trajectories, typical lane models and vehicle models are created in CarMaker. In NS3, C-V2X Mode 4 protocol stack, channel model and service model are modified and extended to support C-V2X Mode 4 communication between vehicular nodes. Performance analysis is conducted based on our simulation platform and the impacts of different factors on the transmission performance in terms of packet reception ratio (PRR) and average delay are examined.

In this paper, a hybrid satellite-terrestrial relay network (HSTRN) is considered, which is composed of one satellite, a relay ground station (RGS), a destination ground station (DGS) and a number of cellular users (CUs). We model the satellite links from the satellite to the DGS/RGS as shadowed-Rician (SR) fading channel, and the links between any two terrestrial devices as Nakagami-m fading channel. Considering the satellite links share the same spectrum with the terrestrial links, we analyze the transmission performance of both the direct transmission link as well as the relay forward link. Specifically, we derive the statistic characteristics of the signal to interference plus noise ratio (SINR) of the links, derive the outage probability (OP) of both links and the amount of fading (AoF) of the relay link. Simulation results demonstrate the effectiveness of the analysis.

To meet the rapidly increasing demand for spectrum access, citizens broadband radio service (CBRS) system was proposed as a novel spectrum sharing mechanism. While CBRS system is expected to improve spectrum efficiency and alleviate spectrum scarcity issue, it is subject to a single point of failure problem due to the logically centralized paradigm and is heavily relying on efficient resource management schemes. In this paper, we design a blockchain-based hierarchical spectrum sharing architecture for CBRS system. Jointly considering the consensus requirement of the blockchain network and the utility of CBRS system, we propose a new consensus mechanism called proof-of-utility (PoU) which achieves the tradeoff in utility optimization between local entities and global system while ensuring the consistency of nodes. Then, a heuristic sub channel and power allocation algorithm is presented to achieve efficient resource sharing among entities in CBRS system. Simulation results demonstrate the effectiveness of the proposed consensus mechanism and algorithm.

In this paper, we consider a cooperative cellular mobile edge computing (MEC) system, which consists of a number of MEC servers co-located with base stations (BSs) and a number of mobile users (MUs). We assume that MUs have computation-intensive tasks which can be offloaded to the MEC servers. Further assume that in order to complete task process of the MUs, certain auxiliary information is required. Consider a practical communication scenario where MUs may move continuously. Due to the mobility of MUs, MUs may conduct task migration between multiple MEC servers. Addressing the cooperative task processing of the MUs in the system, we formulate the joint task offloading, migration and auxiliary information caching problem as an overall task processing delay minimization problem. In order to achieve the efficient utilization of the cache space of the MEC servers, we first propose a popularity-based information caching algorithm. Given the obtained information caching strategy, the task partition subproblem is formulated and solved by applying a bisection method. We then apply network virtualization scheme and transform the task execution mode selection subproblem as a one-to-one mapping problem in a bipartite graph and then solve the subproblem by means of the Kuhn-Munkres algorithm. Numerical results demonstrate the effectiveness of the proposed scheme.

Unmanned aerial vehicle (UAV) has been considered as an efficient solution to collect data from wireless sensor networks (WSNs) due to its flexibility and mobility. In this paper, we consider a UAV-assisted WSN, wherein the UAV starts from directly above the sink node to collect data and returns to the initial position. To improve data collection performance, sensor node (SN) can be clustered and the cluster head (CH) is responsible for collecting data of cluster members (CMs) and forwarding the data to the sink node. Two data forwarding modes are considered, i.e., the direct transmission mode from CH to the sink node, and the UAV collection mode from CH to UAV and then to the sink node. A data collection time minimization problem is formulated, where the clustering strategy, CH transmission mode selection, UAV trajectory and UAV velocity are jointly optimized. To solve this non-convex problem, a modified K-means algorithm-based clustering scheme is proposed firstly. Based on which, a CH transmission mode selection strategy is designed, and then a traveling salesman problem (TSP)-based UAV trajectory planning algorithm is proposed. Numerical results demonstrate the effectiveness of the proposed algorithm.

In this paper, we consider a satellite communication system which consists of a number of source low earth orbit (SLEO) satellites, a number of relay low earth orbit (RLEO) satellites and a destination ground station (GS). Under the assumption that data flows can be split while transmitted through the system, a joint route selection and time-slot allocation algorithm is proposed. Considering the constraints on the transmission requirements of data flows, system resource allocation and the amount of data flows, the joint route selection and time-slot allocation problem is formulated as an optimization problem which minimizes total system energy consumption. As the original optimization problem is NP-hard, which cannot be solved conveniently, the optimization problem is equivalently converted into candidate link selection subproblem and route selection subproblem. The two optimization problems are solved by means of the K-shortest path algorithm and the knapsack algorithm, respectively, and the optimal strategy of joint route selection and time-slot allocation is obtained. Simulation results demonstrate the effectiveness of the proposed scheme.

Cellular vehicular-to-everything (C-V2X), as a key technology of Internet of Vehicles (IoVs), is expected to improve road traffic safety and achieve intelligent transportation. C-V2X mainly supports two communication modes, Mode 3 and Mode 4. Mode 3 supports centralized control and management of network resources whereas Mode 4 allows vehicles to select radio resources independently for data transmission without the aid of network infrastructures. In this paper, we present an overview of C-V2X Mode 4 communication and stress several major enhancements compared to long term evolution (LTE), including subframe structure design, synchronization mechanism, resource pool configuration, and resource scheduling mechanism, i.e., sensing-based semi-persistent scheduling (S-SPS), etc. To evaluate the transmission performance of C-V2X, we set up a system-level simulation platform based on the integration of traffic simulator CarMaker and network simulator (NS)-3, where CarMaker is utilized to simulate real vehicle trajectories and NS-3 is applied to implement the communication protocols of C-V2X. Under various simulation scenarios, the transmission performance in terms of packet reception ratio (PRR) is examined and the impacts of resource scheduling parameters, including resource reselection probability, resource reservation interval and channel bandwidth on PRR are evaluated.

As a novel network paradigm, software-defined networking (SDN) is capable of simplifying network management and offering flexible support to various user services. In order to meet the rapidly increasing transmission demands of SDN switches, the controller deployment strategy in an SDN scenario should be designed. In this paper, we investigate the capacitated controller deployment problem for SDN. Consider the signaling transmission and processing performance of switches and address the worst-case performance, we define network response time (NRT) as the maximum control plane response time of switches. Then aiming to achieve the tradeoff between NRT and the cost of controllers, we introduce the concept of network cost which is defined as the weighted sum of NRT and controller cost. The capacitated controller deployment problem is formulated as a constrained network cost minimization problem. To solve the optimization problem, we propose a two-stage heuristic algorithm, which first tackles the controller deployment subproblem under the unlimited capacity constraint, and then solves controller-type matching subproblem. Specifically, during the first stage, a minimum eccentricity-based controller deployment algorithm is designed to determine the number and location of controllers as well as the association strategy between controllers and switches. During the second stage, a greedy method-based controller-type matching strategy is proposed to determine the types of deployed controllers. Extensive simulations are performed and the results certify the effectiveness of the proposed algorithm.

Content caching at base stations (BSs), unmanned aerial vehicles (UAVs) and specific user devices is expected to enhance the performance of content fetching significantly. In this paper, we study the UAV deployment problem in UAV-enabled cellular device-to-device (D2D) systems. Stressing the importance of content fetching delay and energy consumption, as well as the cost for deploying UAVs, we define a cost function and formulate the joint UAV deployment and user association problem as a cost function minimization problem. We then propose a heuristic algorithm to solve the optimization problem and determine the joint strategy. Numerical results verify the effectiveness of the proposed algorithm.

In recent years, machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. In M2M communication systems, machine type communication devices (MTCDs) are capable of communicating with each other intelligently under highly reduced human interventions. Although diverse types of services are expected to be supported for MTCDs, various quality of service (QoS) requirements and network states pose difficulties and challenges to the resource allocation and clustering schemes of M2M communication systems. In this paper, we address the joint resource allocation and clustering problem in M2M communication systems. To achieve the efficient resource management of the MTCDs, we propose a joint resource management architecture, and design a joint resource allocation and clustering algorithm. More specifically, by defining system energy efficiency as the sum of the energy efficiency of the MTCDs, the joint resource allocation and clustering problem is formulated as an energy efficiency maximization problem. As the original optimization problem is a nonlinear fractional programming problem, which cannot be solved conveniently, we transform the optimization problem into power allocation subproblem and clustering subproblem. Applying iterative method-based energy efficiency maximization algorithm, we first obtain the optimal power allocation strategy based on which, we then propose a modified K-means algorithm to obtain the clustering strategy. Numerical results demonstrate the effectiveness of the proposed algorithm.

To overcome the drawbacks of traditional Internet architectures, software-defined networking (SDN) technology has been proposed, which is expected to dramatically simplify network control processes and enable the convenient deployment of sophisticated network functions. To achieve highly efficient resource utilization in SDN and offer users with diverse service requirements, virtual network embedding (VNE), which maps various virtual network requests of users to a given substrate network, should be conducted. In this paper, we study the VNE problem in SDN where the substrate SDN switches and links may be subject to malicious attacks. We first propose a hierarchical virtualization-enabled SDN architecture based on which the VNE strategy can be designed. Then, stressing the importance of network load and reliability of the substrate network, we formulate the VNE problem of SDN as a multi-objective optimization problem which jointly minimizes network load and maximizes embedding reliability under the constraints of virtual network requirements and the resource characteristics of substrate network. As the formulated optimization problem is a complicated multi-objective optimization problem which cannot be solved conveniently, we apply the ideal point method. In particular, we first propose virtual node embedding sub-algorithm and virtual link embedding sub-algorithm to determine the locally optimal solution to the two subproblems, i.e., network load minimization subproblem and embedding reliability maximization subproblem. Then, examining the distance between the feasible solutions and the locally optimal solutions, we formulate a single-objective optimization problem and solve the problem to obtain the global VNE strategy by applying discrete particle swarm optimization (DPSO) algorithm. Numerical results demonstrate the effectiveness of the proposed algorithm.

In this paper, we consider a cellular device-to-device (D2D) system which consists of one base station (BS) deployed with a mobile edge computing (MEC) server, and a number of users. By defining task execution cost as the weighted sum of execution latency and energy consumption, the joint computation offloading and resource allocation problem is formulated as a task execution cost minimization problem under the constraints of task requirement, computation offloading, resource allocation and task partition, etc. As the formulated optimization problem is a mixed integer nonlinear problem, which cannot be solved conveniently, we decompose it into two subproblems, i.e., computation offloading subproblem and resource allocation subproblem, and solve the two subproblems by applying Kuhn-Munkres algorithm and Lagrange dual method, respectively. Numerical results demonstrate the effectiveness of the proposed scheme.

Vehicular ad-hoc networks (VANETs) have received considerable attention from both academia and industry in recent years. In VANETs, source vehicles (SVs) are allowed to connect roadside units, such as the access points (APs) of wireless access networks and conduct information interaction. However, due to the high-speed mobile characteristics of VANETs and the dynamic random characteristics of wireless channels, the direct connection between SVs and APs might be inaccessible. In this case, some neighbor vehicles referred to as relay vehicles (RVs) can be selected as relay nodes and help to forward data packets for the SVs. In this paper, we propose an analytical model for evaluating the transmission performance of RVs in VANETs. In particular, we apply network calculus theory to formulate the arrival curve of SVs and the service model of RVs, respectively, and evaluate the effective throughput of the RVs when forwarding data packets for various SVs. We then propose a joint effective throughput optimization based RV selection algorithm. The optimization problem is formulated and transformed into an optimal matching problem in a bipartite graph, which can then be solved based on Kuhn-Munkres (K-M) algorithm. Numerical results demonstrate that compared to previous algorithms, the proposed algorithm offers better transmission performance.

With the development of the fifth generation communication technology, how to improve system capacity and spectral efficiency is a key issue, which has attracted more and more attention from industry and academia. Heterogeneous network has been considered as a new promising technique for enhancing the quality of service, energy and spectrum efficiency as well as coverage of network due to different radio access technology (RAT) and different network structures. In this paper, the rate maximization resource allocation problem for multiuser cognitive heterogeneous networks is formulated to flexibly use network resource and improve the overall capacity, which simultaneously considers cross-tier interference constraint and maximum transmit power of cognitive microcell base station. The non-convex optimization problem is converted into a geometric programming problem which can be solved by Lagrange dual method in a distributed way. Simulation results are given to show the performance of the proposed algorithm in terms of the achievable system capacity and the interference to the macrocell network.

In recent years, heterogeneous radio access technologies have experienced rapid development and gradually achieved effective coordination and integration, resulting in heterogeneous networks (HetNets). In this paper, we consider the downlink secure transmission of HetNets where the information transmission from base stations (BSs) to legitimate users is subject to the interception of eavesdroppers. In particular, we stress the problem of joint user association and power allocation of the BSs. To achieve data transmission in a secure and energy efficient manner, we introduce the concept of secrecy energy efficiency which is defined as the ratio of the secrecy transmission rate and power consumption of the BSs and formulate the problem of joint user association and power allocation as an optimization problem which maximizes the joint secrecy energy efficiency of all the BSs under the power constraint of the BSs and the minimum data rate constraint of user equipment (UE). By equivalently transforming the optimization problem into two subproblems, that is, power allocation subproblem and user association subproblem of the BSs, and applying iterative method and Kuhn-Munkres (K-M) algorithm to solve the two subproblems, respectively, the optimal user association and power allocation strategies can be obtained. Numerical results demonstrate that the proposed algorithm outperforms previously proposed algorithms.

Cognitive radio networks (CRNs) are expected to improve spectrum utilization significantly by allowing secondary users (SUs) to opportunistically access the licensed spectrum of primary users (PUs). In a CRN scenario consisting of multiple heterogeneously integrated CRNs, the SUs with multiple interfaces may have to conduct spectrum handoff to target subchannels. Jointly considering the interruption delay and the transmission performance of the handoff SUs on target subchannels, the subchannel selection and resource allocation optimization problem which maximizes the energy efficiency of all the interrupted SUs under the quality of service (QoS) constraints is formulated and solved through applying iterative algorithm and Lagrange dual method. Numerical results demonstrate the efficiency of the proposed scheme.

Cognitive radio networks (CRNs) are expected to improve spectrum utilization significantly by allowing secondary users (SUs) to opportunistically access licensed spectrum without affecting the normal communications of primary users (PUs). In a CRN scenario consisting of multiple heterogeneously integrated CRNs, the SUs with multiple interfaces may have to conduct network and spectrum selection for continuous data transmission. In this paper, a spectrum selection scheme is proposed which minimizes the total transmission delay of interrupted SUs in a heterogeneous CRN under the quality of service (QoS) constraints. Applying M/G/1 queue model, the waiting delay of SUs is calculated. Jointly considering the heterogeneous characteristics of CRNs, the transmission performance of target channels and the properties of queue model, the total transmission delay of SUs is formulated, and the candidate channel with the minimum total transmission delay is selected as the target channel. Numerical results demonstrate the efficiency of the proposed scheme.

Wireless Body Area Networks (WBANs) are made up of tiny physiological sensors implanted in/on the human body which detect the health status of human body and transmit the information collected to the remote server via one or more coordinators. The rapid proliferation of WBANs has stimulated enormous research efforts that aim to maximize the lifetime of battery-powered sensor nodes and extend the overall network lifetime through designing optimal power allocation or relay selection schemes. However, previous works fail to jointly optimize the highly related resources of WBANs, thus may severely limit the performance of maximizing the network lifetime of WBANs. In this paper, a network lifetime maximization based joint resource allocation scheme is proposed for WBANs. We show that the problem of optimizing network lifetime is equivalent to maximizing the residual energy of sensor nodes which can be expressed as function of transmission modes of nodes, cooperative nodes, transmission power and time slots of both source and relay nodes, subject to resource allocation constraints. Through solving the optimization problem, an optimal joint transmission mode, relay selection, transmission power and time slot allocation strategy can be obtained. Numerical results demonstrate that the proposed algorithm is capable for extending the lifetime of network as well as guaranteeing user QoS requirements.

As a special form of mobile ad hoc network (MANET), vehicular ad-hoc network (VANET) is expected to support communication between vehicles, and between vehicles and stationary infrastructures, such as access points (APs). However, due to the highly mobile characteristics of VANET, the direct connection between source vehicles (SVs) and APs might be inaccessible. In this case, relay vehicles (RVs) can be applied for supporting multi-hop connection between SVs and APs. In the practical application scenario that multiple candidate RVs are available, the problem of selecting the optimal RV has to be considered. This paper proposes a Game theory-based RV selection algorithm, which jointly considers multiple metrics from various protocol layers, including the characteristics of physical channel, the link status, the bandwidth and delay characteristics of RVs and user service requirement. The payoff functions of both SVs and RVs are modeled. In order to optimize the overall system performance, a joint SV and RV cooperative Game model is established. The optimization problem is formulated and solved based on bipartite matching algorithm. Numerical results demonstrate that compared to previous algorithm, the proposed algorithm offers better performance in terms of throughput, transmission delay and successful transmission rate.

The next generation wireless communication system is expected to provide users with high-speed communication services through integrating multiple wireless access technologies, including cellular system, wireless local area network, worldwide interoperability for microwave access, and ad hoc network, etc. In a heterogeneous wireless network scenario, mobile nodes with multiple interfaces will be capable of choosing different access networks for service accessing and performing vertical handoff among various networks. However, the heterogeneity and incompatibility of wireless access technologies and the varieties of user service types pose difficulties and challenges to efficient vertical handoff scheme design. In this paper, the joint access network utility is modeled based on quadratic utility function and a vertical handoff decision algorithm that achieves the maximization of joint network utility under certain constraints of network load status and user handoff number is proposed. Numerical results demonstrate the efficiency of the proposed algorithm.

User selection for access network is considered to be one of the distinct features of heterogeneous wireless systems, in which users with multi-network interface terminals can freely select access network for better quality of service with lower expense. On the other hand, service providers (SPs) will have to face more intense competition for attracting more subscribers and increasing their profits, which can be achieved through either non-cooperative or cooperative strategies. In this study, the authors propose a unified quantification model for evaluating the access service of heterogeneous systems. The relation between competitive SPs and users is described by different game models, based on general assumptions and practical application scenarios. A novel network selection scheme for maximising user performance–cost ratio (PCR) is proposed. Numerical results demonstrate that all SPs can achieve Nash equilibrium price under non-cooperative game framework and coalition price for cooperative game case to maximise their absolute profits, and the maximal PCR criterion for user network selection scheme is analysed under different scenarios.