Resource Allocation in Software Defined Fog Vehicular Networks

As one technique for autonomous driving, vehicular networks can achieve high efficiency with vehicle-and-infrastructure cooperation, bringing high safety and many value-added services. To achieve higher communication efficiency, much effort has been done to cope with the resource allocation issues for vehicular networks. Nevertheless, due to the strong nonconvexity and nonlinearity, the classical joint resource allocation problem in vehicular networks is typically NP-hard. The multiagent reinforcement learning (MARL) has emerged as a promising solution to tackle this challenge but its stability and scalability are not satisfactory when the amount of vehicles gets increased. In this article, we mainly investigate the issue of joint spectrum and power allocation in vehicular communication networks, and carefully consider the interactions between the vehicles and environment by incorporating the cooperative stochastic game theory with MARL, named complete-game MARL (CG-MARL), to achieve a better convergence and stability with the theoretical computational complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{N})$ </tex-math></inline-formula> with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> denoting the dimension of action space and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> denoting the number of V2X Vehicular. Furthermore, the mean-field game (MFG) theory is employed to further enhance the MARL for decreasing the horrible computing resource consumption caused by the CG-MARL to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(n^{2})$ </tex-math></inline-formula> while maintaining an approximate performance. The simulation results demonstrate that the proposed mean-field-aided MARL (MF-MARL) for vehicular network resource allocation can achieve 95% near-optimal performance with much lower complexity, which indicates its significant potentials in the scenarios with massive and dense vehicles.

This paper studies the power allocation of an ultra-dense cellular network consisting of multiple full-duplex (FD) base stations (BSs) serving a large number of half-duplex (HD) user equipments (UEs) located in a wide geographical area. Each BS consists of a baseband unit (BBU) that is responsible for signal processing and BS control, and a radio remote unit (RRU) that corresponds to a radio transceiver remotely built closer to the UEs. We consider a wireless-powered cellular network in which the BBU can periodically charge the RRU. We model the energy efficiency and coverage optimization problem for this network as a mean field game. We consider the weighted energy efficiency of the BS as the main performance metrics and evaluate the optimal strategy that can be adopted by the FD BSs in an ultra-dense network setting. Based on the interference and network energy efficiency models of the mean field game theory, Nash equilibrium of our proposed game is derived. Utilizing solutions of Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations, a new power transmission strategy is developed for the BSs to optimize the energy efficiency of 5G cellular networks with full duplex transmissions. Simulation results indicate that the proposed strategy not only improves the energy efficiency but also ensures the average network coverage probability converges to a stable level.

The Fokker–Planck–Kolmogorov (FPK) equation, as a well investigated partial differential equation, is of great significance to stochastic dynamics due to its theoretical rigor and exactness. However, practical difficulties with the FPK method are encountered when analysis of multi-degree-of-freedom (MDOF) systems with arbitrary nonlinearity is required. In the present paper, a cell renormalized method (CRM) which is based on a numerical determination of response statistical moments of the FPK equation is developed. Specifically, by invoking the concept of equivalence of probability flux, a cell renormalization procedure and a reconstruction scheme of derivative moments are introduced to divide the continuous state space into a discretized region of cells so that numerical derivative moments is derived. Subsequently, the Cell Renormalized FPK (CR-FPK) equation can be solved by a finite difference algorithm. Two numerical examples are included, and the effectiveness of the proposed method is assessed.

This paper considers the network resource allocation problem in multi-service MEC networks, and in particular considers joint spectrum and computing resource allocation in an edge network service system. The resource allocation problem under consideration is formulated as an optimization problem, which takes into account users’ minimum service requirements in terms of the service delay, transmission rate, and computation rate, with an objective to minimize the average service delay of a user’s service request. To solve the problem, we propose a deep-Q-network (DQN) based joint spectrum and computing resource allocation algorithm, which attempts to optimize the resource allocation for each service request by learning a more efficient allocation scheme so as to better adapt to dynamic traffic arrivals, diverse service requirements, and complex environmental conditions. Simulation results show that the proposed resource allocation algorithm can efficiently reduce the average service delay compared with two benchmark algorithms.

In medical vehicular networks, medical vehicles can serve as efficient mobile medical service points to provide necessary and critical medical services for patients while in motion. The delay requirement is very vital for medical services to guarantee service quality and save the lives of patients. Mobile Edge Computing (MEC), as an emerging network paradigm, enables the computation extensive tasks to be offloaded to edge servers, efficiently reducing the delay and bandwidth demands. MEC technology is a promising solution to provide high-quality medical services for users in medical vehicular networks. However, task offloading and resource allocation incurs additional service delay and energy consumption, affecting the overall service performance and Quality of Experience (QoE) of users. Thus, realizing the optimal task offloading and resource allocation in MEC-enabled medical vehicular networks, to reduce task completion time and energy consumption, becomes a potential challenge. To address the challenge, we investigate the joint task offloading and resource allocation problem in MEC-enabled medical vehicular networks to improve the QoE of users. Considering the resource requirements and QoS constraint, we formulate a multi-objective optimization model, with the target of average task completion time and average energy consumption minimization. On this basis, we propose a MOEAD-based task offloading and resource allocation (IMO) algorithm to solve it. Furthermore, in order to obtain the optimal solution and speed up the algorithm convergence, we design a greedy strategy-based population initialization algorithm. The extensive simulations demonstrate that compared to existing algorithms, our proposed IMO algorithm can obtain a smaller average completion time, and achieve better tradeoff between task completion time and energy consumption.

Lots of resource‐consuming intelligent tasks need to be handled in vehicular networks, and traditional resource allocation schemes are hard to meet the intelligent demands. Therefore, this paper proposes a task‐oriented resource allocation scheme for intelligent tasks in vehicular networks. First, we propose a task‐oriented communication system and formulate a resource allocation problem, which is aimed at maximizing the task performance. Second, based on the system model, an intelligent task‐oriented resource allocation optimization criterion is proposed, which is formulated as a mathematical model, and its parameters are solved by the proposed gradient descent‐based algorithm. Third, to solve resource allocation problem, a multiagent deep Q‐network‐ (MADQN‐) based algorithm is proposed, whose convergence and complexity are further analyzed. Last, experiments on real datasets verify the performance advantages of our proposed algorithms.

Mobile Edge Computing (MEC) is a promising computing paradigm, which can provide mobile devices with powerful computing capability and low delay by offloading resource-intensive tasks to the MEC Server. However, due to the varying wireless channel conditions and different task delay requirements, the resource allocation decision made by MEC Server cannot always achieve the lowest system energy consumption. In this paper, we consider resource allocation problem in a multi-user MEC system. The objective is to achieve minimum system energy consumption under delay constraint by suitably allocating communication and computing resources. We solve the communication resources (subcarrier) allocation problem through regression algorithm, make use of three regression algorithms to predict subcarrier allocation. And then calculate the computing resources (CPU frequency) allocation and system energy consumption. Simulation results show that machine learning can achieve near-optimal system energy consumption, where the system energy consumption of the ANN(Artificial Neural Network), Xgboost (Extreme Gradient Boosting), and RF(Random Forest) algorithms is 1.30%, 7.49%, and 23.29% higher than the optimal system energy consumption respectively. In the view of prediction loss, ANN and Xgboost perform better than RF.

Considering the proliferation in the number of cloud users on an everyday basis, the task of resource provisioning in order to support all these users becomes a challenging problem. When resource allocation is non-optimal, users may face high costs or performance issues. So, in order to maximize profit and resource utilization while satisfying all client requests, it is essential for Cloud Service Providers to come up with ways to allocate resources adaptively for diverse conditions. This is a constrained optimization problem. Each client that submits a request to the cloud has its own best interests in mind. But each of these clients competes with other clients in the quest to obtain required quantum of resources. Hence, every client is a participant in this competition. So, a preliminary analysis of the problem reveals that it can be modelled as a game between clients. A game theoretic modelling of this problem provides us an ability to find an optimal resource allocation by employing game theoretic concepts. Resource allocation problems are NP-Hard, involving VM allocation and migration within and possibly, among data centres. Owing to the dynamic nature and number of requests, static methods fail to surmount race conditions. Using a Min-Max Game approach, we propose an algorithm that can overcome the problems mentioned. We propose to employ a utility maximization approach to solve the resource provisioning and allocation problem. We implement a new factor into the game called the utility factor which considers the time and budget constraints of every user. Resources are provisioned for tasks having the highest utility for the corresponding resource.

This paper investigates the spectrum sharing problem in vehicular networks, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We model the resource sharing as a multi-agent reinforcement learning (RL) problem, which is then solved using a fingerprint-based deep Q-network method. The V2V links, each acting as an agent, collectively interact with the vehicular environment, receive distinctive observations yet a common reward, and then improve policy design through updating their Q-networks with gained experiences. Simulation results demonstrate desirable performance of the proposed resource allocation scheme based on multi-agent RL in terms of both V2I capacity and V2V payload delivery probability.

We consider a two-level, two criteria, optimization problem of resource allocation in communication networks, which consists of maximizing the total network utility, i.e., the fee paid by the consumers, and minimizing the costs of implementing these resources. In this paper we present a new dual iterative method for solving this problem, which enables us to utilize its decomposable structure via sequential solution of families of one-dimensional problems. We compare our new dual method to known methods for solving this problem. In general, we give a new promising approach in the paper to solve resource allocation problems in communication networks. Moreover, we suggest a new way to adjust the basic model to networks with moving nodes. We present numerical results about the computational efficiency of the methods considered.

Resource allocation problem in high-speed railway wireless communication networks is one of the key issues to improve the efficiency of resource utilization. However, traditional resource allocation methods cannot be directly applied to this special communication system. In this paper, we propose a resource allocation approach for high-speed railway downlink orthogonal frequency-division multiplexing (OFDM) system with multiple-input multiple-output (MIMO) antennas. Sub-carriers, antennas, time slots, and power are jointly considered, which is formulated as a mixed-integer nonlinear programming problem. The effect of the moving speed on Doppler shift is analyzed to calculate the inter-carrier interference power. The objective is to maximize the throughput under the constraint of total transmission power. In order to reduce computational complexity, suboptimal solution to the optimization problem is obtained by quantum-behaved particle swarm optimization. Simulation results show that the proposed resource allocation strategy has a better performance compared with an existing one.

In this paper, we study the routing and resource allocation problem in multi-hop cognitive radio networks. The collision between primary users (PUs) and secondary users (SUs) is taken into consideration, which is unavoidable if activities of PUs and SUs are not synchronized with each other. Our objective is to maximize the average throughput of a secondary traffic flow while the number of collisions is restricted to be less than a predefined threshold. Aiming at this, a proper routing path is selected according to its longtime achievable throughput performance, and then radio resource on the selected path is allocated to get a high throughput. A routing path selection algorithm is proposed which expands the paths hop by hop and discards unnecessary paths. The resource allocation problem is formulated to an optimization problem with aforementioned objective and restriction. A low-complexity heuristic algorithm is proposed to solve the problem. Simulation results show that the proposed method is effective and can achieve the performance similar to the optimal solution.

Attracted by the advantages of Non-Orthogonal Multiple Access (NOMA) in accommodating multiple users within the same resource, this paper investigates the power allocation problem for Machine Type Devices (MTDs) in a Hybrid NOMA system. Particularly, we consider a densely deployed network in which the devices are divided into orthogonal coalitions. Firstly, the power control problem is modeled as a differential game. Then, we formulate the proposed game as a Mean Field Game (MFG) in order to handle massive Internet of Things (IoT) access scenarios. Furthermore, we design an iterative algorithm that paves the way for distributed control in which the devices can appropriately regulate their transmit power in response to brief information received from the Base Station (BS). The analysis of the proposed approach is conducted through coupled equations, namely the Hamilton-Jacobi-Bellman (HJB) and the Fokker-Planck-Kolmogorov (FPK). Our simulation results prove the convergence of the proposed power control strategy and spotlight the robustness of our formulated MFG.

In order to address the data security and communication efficiency of vehicles during high-speed mobile communication, this paper investigates the problem of secure in-vehicle communication resource allocation based on slow-variable large-scale fading channel information, to meet the quality of service requirements of vehicular communication, i.e., to ensure the reliability of V2V communication and the time delay while maximizing the transmission rate of the cellular link. And an eavesdropping model is introduced to ensure the secure delivery of link information. Considering that the high mobility of vehicles causes rapid channel changes, we model the problem as a Markov decision process and propose a resource allocation optimization framework based on the Multi-Agent Reinforcement Learning Algorithm (MARL-DDQN), in which a large-scale neural network model is built to train vehicular to learn the optimal resource allocation strategy for optimal communication performance and security performance. Simulation results show that the load successful delivery rate and confidentiality performance of the vehicular communication network are effectively improved compared to the baseline and MADDPG strategies while ensuring link security. This study provides useful references and practical value for the optimization of secure communication resource allocation in vehicular networking.

Mean Field Game-based Decentralized Optimal Charging Control for large-scale of electric Vehicles