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A joint user association and dynamic resource allocation algorithm was proposed for the slicing scenario of ultra dense low earth orbit-radio access network (LEO-RAN) in order to address the efficient utilization of resources of the integrated terrestrial-satellite network for 6G .Considering the constraints of the minimum rate, maximum delay and resource proportion of different slices, a joint optimization problem of user association and resource allocation was established to maximize the weighted sum of the SE and the differentiated SLA of different slices as the optimization objective.A network slicing algorithm based on multi-agent deep deterministic policy gradient (MADDPG) was proposed to determine the proportion of slicing resources, a Lagrange dual based user association algorithm was proposed to determine the optimal user association policy and the resources were allocated to users by using the round-robin scheduling mechanism.The simulation results show that the proposed algorithm can effectively improve SE while satisfying the differentiated SLA of different slices.Compared with MADDPG-RA, MATD3-LG, MATD3-RA, MASAC-LG and MASAC-RA algorithms, the system utility of the proposed algorithm is improved by 2.0%, 2.3%, 5.7%, 8.7% and 9.4%, respectively.

A joint user association and dynamic resource allocation algorithm was proposed for the slicing scenario of ultra dense low earth orbit-radio access network (LEO-RAN) in order to address the efficient utilization of resources of the integrated terrestrial-satellite network for 6G .Considering the constraints of the minimum rate, maximum delay and resource proportion of different slices, a joint optimization problem of user association and resource allocation was established to maximize the weighted sum of the SE and the differentiated SLA of different slices as the optimization objective. A network slicing algorithm based on multi-agent deep deterministic policy gradient (MADDPG) was proposed to determine the proportion of slicing resources, a Lagrange dual based user association algorithm was proposed to determine the optimal user association policy and the resources were allocated to users by using the round-robin scheduling mechanism. The simulation results show that the proposed algorithm can effectively improve SE while satisfying the differentiated SLA of different slices. Compared with MADDPG-RA, MATD3-LG, MATD3-RA, MASAC-LG and MASAC-RA algorithms, the system utility of the proposed algorithm is improved by 2.0%, 2.3%, 5.7%, 8.7% and 9.4%, respectively. [ABSTRACT FROM AUTHOR]

Based on the idea of crowdsourcing,mobile crowdsensing(MCS) collects mobile sensing devices to sense the surroun-ding environment,which can make environment sensing and information collection more flexible,convenient and efficient.Whe-ther the task allocation strategy is reasonable or not directly affects the success of the sensing task.Therefore,formulating a reasonable task allocation strategy is a hotspot and focus in the research of MCS.At present,most of the task allocation methods in MCS systems are offline and targeted at single type tasks.However,in practice,online multi-type task allocation is more common.Therefore,this paper studies the task allocation method in MCS for multiple types of tasks,and proposes an online task allocation strategy oriented to system benefits combined with the characteristics of MCS technology in the military field.In this paper,a long-term,dynamic online task allocation system model is established,and the problem is solved based on Lyapunov optimization theory with the system benefit as the optimization goal,so that the online dynamic control of task admission strategy and task allocation scheme is realized.Experiment shows that the online task allocation algorithm proposed in this paper is effective and feasible.It can reasonably allocate the tasks arriving at the MCS system online,ensure the stability of the task queue,and increase the system utility by adjusting the parameter value.

To effectively meet the differentiated quality of service (QoS) requirements of different slices, a dual time scale network slicing resource allocation algorithm based on dueling double DQN (D3QN) was proposed for B5G multi-service scenarios.The joint resource slicing and scheduling problem was formulated, with the weighted sum of the normalized spectral efficiency (SE) and the QoS of users indices of different slices as the optimization objective.On large time scale, the SDN controller used the D3QN algorithm to pre-allocate resources to different slices based on the resource requirements of each service, and then performed BS-level resource updating based on the load status of BS.On small time scale, the BS scheduled resources to end-users by using the round-robin scheduling algorithm.The simulation results show that the proposed algorithm has excellent performance in ensuring the QoS requirements of slice users, SE and system utility.Compared with the other 4 baseline algorithms, the system utility is improved by 3.22%, 3.81%, 7.48% and 21.14%, respectively.

In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delay-sensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26%and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

To effectively meet the differentiated quality of service (QoS) requirements of different slices, a dual time scale network slicing resource allocation algorithm based on dueling double DQN (D3QN) was proposed for B5G multi-service scenarios. The joint resource slicing and scheduling problem was formulated, with the weighted sum of the normalized spectral efficiency (SE) and the QoS of users indices of different slices as the optimization objective. On large time scale, the SDN controller used the D3QN algorithm to pre-allocate resources to different slices based on the resource requirements of each service, and then performed BS-level resource updating based on the load status of BS. On small time scale, the BS scheduled resources to end-users by using the round-robin scheduling algorithm. The simulation results show that the proposed algorithm has excellent performance in ensuring the QoS requirements of slice users, SE and system utility. Compared with the other 4 baseline algorithms, the system utility is improved by 3.22%, 3.81%, 7.48% and 21.14%, respectively. [ABSTRACT FROM AUTHOR]

Radio access network (RAN) slicing can significantly improve network flexibility and resource utilization efficiency. Generally, deep reinforcement learning (DRL) is a prevailing approach to implementing practicable resource management in RAN slicing. However, this demand-aware resource allocation suffers from long training time and slow execution efficiency. This can lead to the inability to quickly achieve the desired spectral efficiency and service level agreement satisfaction rate. To tackle this issue, we propose a novel RAN slicing resource allocation framework based on a deep hierarchical RL framework for efficient resource scheduling. Structurally, our framework consists of a policy selection network and a policy evaluation network. In particular, a newly built action acceleration unit can achieve quick reward accumulation, thus speeding up the optimal policy search. Extensive simulations show that our proposal has higher system utility and faster convergence compared to the state-of-the-art DRL algorithms. [ABSTRACT FROM AUTHOR]

In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delaysensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26%and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy. [ABSTRACT FROM AUTHOR]

The Canadian banking system, like its Australian counterpart, is often credited for weathering the 2007/8 financial crisis effectively. The oligopolistic rent is overlooked, as a necessary price for the resiliency of the system. In this paper, the authors discuss that the resilience was due to the banks' ability to pass on the cost of the crisis and the following regulations to the end users of their products and services, and their ability to alter products and services to maximise profitability. In a competitive market, positive economic profit is supposed to erode over time, especially following major events such as the 2007/8 crisis. This did not happen for Canadian banks; therefore, arguably, there was an 'implicit bailout' in Canada whose cost was not borne by the taxpayers (as in the case of an explicit bailout) but by the end users of the banking services and products (the payers of the oligopolistic rent). This may reoccur in the aftermath of the COVID crisis, and the perceived resilience may win out over competition. The authors also theorise the diversification benefit vs the systemic risk trade-off with an increasing bank size. The diversification benefit can increase, but at a decreasing rate as a bank grows, whereas systemic risk increases at an increasing rate. Therefore, there exists a point where the systemic risk outweighs the diversification benefit as a bank continues to grow. The authors find that the standardised capital regime not only diminishes the banks' competiveness against their internal ratings-based (IRB) counterparts but also forces them into the riskier segments of the market and diminishes their ability to diversify. The authors make a case that a banking environment that promotes competition is desirable to increase the utility of the system without sacrificing safety and soundness. As a matter of fact, increased competition can increase stability while reducing the undesired implicit public subsidy of private enterprise. [ABSTRACT FROM AUTHOR]

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Owing to the flexibility and controllability, unmanned aerial vehicle (UAV) is frequently integrated into mobile edge computing (MEC) network to improve the system performance. This paper investigates a novel multi-user multi-hotspot MEC network supported by a UAV, where the UAV can help compute the tasks offloaded from end users (EUs) in multiple hotspots. In this network, we consider the task priority and task size are dynamic, due to the EUs' demands. We then propose a task priority-based system utility model to evaluate the network performance, which considers the priorities of tasks based on the urgent or non-urgent level. We further formulate a utility maximization problem that jointly optimizes the UAV's access path and the EUs' offloading strategy, while satisfying the constraints related to the UAV's battery capacity and UAV's duration of flight. Since the formulated problem is a NP-hard problem, we present a deep reinforcement learning (DRL) based scheme as a solution. The DRL scheme utilizes principles from reinforcement learning to address the optimization problem effectively, resulting in a dynamic solution. Simulation results demonstrate that the proposed DRL scheme outperforms alternative benchmark schemes in terms of system utility. [ABSTRACT FROM AUTHOR]

Since all the existing real world networks are evolving, the study of traffic dynamics is a challenging task. Avoidance of traffic congestion, system utility maximization and enhancement of network capacity are prominent issues. Network capacity may be improved either by optimizing network topology or enhancing in routing approach. In this context, we propose and design a model of the time-varying data communication networks (TVCN) based on the dynamics of inflowing links. Traffic congestion can be avoided by using a suitable centrality measure, especially betweenness and Eigen vector centralities. If the nodes coming in user’s route are most betweenness central, then that route will be highly congested. Eigen vector centrality is used to find the influence of a node on others. If a node is most influential, then it will be highly congested and considered as least reputed. For that reason, routes are chosen such that the sum of the centralities of the nodes coming in user’s route should be minimum. Furthermore, Kelly’s optimization formulation for a rate allocation problem is used for obtaining optimal rates of distinct users at different time instants and it is found that the user’s path with lowest betweenness centrality and highest reputation will always give maximum rate at the stable point. [ABSTRACT FROM AUTHOR]

Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO) from two aspects: first, we introduce differential evolution (DE) process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS) process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance. [ABSTRACT FROM AUTHOR]

Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO) from two aspects: first, we introduce differential evolution (DE) process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS) process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

Public service systems are designed to satisfy a public demand on some service. The public is represented here by individual users concentrated at dwelling places situated in a served geographical area. The structure of a public service system is determined by a given number of service center locations, which are to be selected from a finite set of possible locations so that utility perceived by the users be maximal. In this paper, we propose an approach to the public service system design, where the utility perceived by an individual user depends on uncertain parameters. The uncertainty is handled using the theory of fuzzy sets in connection with integer programming tools. The suggested way of public service system designing is accompanied by a computational study. [ABSTRACT FROM AUTHOR]

Prior research (Rai et al, 2002; Seddon and Kiew, 1997) evaluates information systems in their role as information provider. In these, the impact of technology on individual performance is studied (utility of the system) and information quality and facility of the system are considered explanatory factors for utility. However, a system also fulfills the role of auto mating tasks, a role that is not recognized in the aforementioned factors. Based on the impact model by Seddon (1997) and literature about automatization (Kaber and Draper, 2004), this study determines the relation between automatization and the utility of information systems through the construct, intervention level of the system in activities of the individual. Through a questionnaire, data was collected from 246 users from different organizations and functional areas. Structural equations (Bentler, 198) were used for analysis. According to the results, information quality and the intervention level of the system explain utility. In the presence of these factors, the facility ofthe system does not affect utility. Conclusions are that the design and construction of information systems should not only concentrate on offering quality information, it is also key to automate tasks where technology shows relative advantages. [ABSTRACT FROM AUTHOR]

In complex grid environment, a control system should consider all applications and coordinate all layers of grid architecture upon any changes in the system. However, this brings large overhead because any changes will invoke a global coordination. The paper proposes a multiple granularity control scheme in grid computing, which balances control scope and control frequency to improve system performance. Multiple granularity control policies are deployed at different levels: system level control at coarse time granularity and application level control at fine time granularity. System level control considers all applications and coordinates three layers of grid architecture in response to large system changes at coarse time granularity; it exploits the interlayer coupling of fabric layer, collective layer, and application layer to achieve a system-wide optimization based on the user's preferences. Application level control adapts a single application to small changes at fine time granularity. The paper presents a multiple granularity control algorithm (MGCA). Simulations are conducted to test the performance of the control algorithm. [ABSTRACT FROM AUTHOR]

A queuing analytical model is presented to investigate the performances of different sleep and wakeup strategies in a solar-powered wireless sensor/mesh network where a solar cell is used to charge the battery in a sensor/mesh node. While the solar radiation process (and, hence, the energy generation process in a solar cell) is modeled by a stochastic process (i.e., a Markov chain), a linear battery model with relaxation effect is used to model the battery capacity recovery process. Developed based on a multidimensional discrete-time Markov chain, the presented model is used to analyze the performances of different sleep end wakeup strategies in a sensor/mesh node. The packet dropping and packet blocking probabilities at a node are the major performance metrics. The numerical results obtained from the analytical model are validated by extensive simulations. In addition, using the queuing model, based on a game-theoretic formulation, we demonstrate how to obtain the optimal parameters for a particular sleep and wakeup strategy. In this case, we formulate a bargaining game by exploiting the trade-off between packet blocking and packet dropping probabilities due to the steep and wakeup dynamics in a sensor/mesh node. The Nash solution is obtained for the equilibrium point of sleep and wakeup probabilities. The presented queuing model, along with the game-theoretic formulation, would be useful for the design and optimization of energy-efficient protocols for solar-powered wireless sensor/mesh networks under quality-of-service (QoS) constraints. [ABSTRACT FROM AUTHOR]