Traditional Resource Allocation Algorithms Research Articles

Reinforcement learning-based resource allocation for dynamic aggregated WiFi/VLC HetNet

High transmission rates and low power consumption make visible light communication (VLC) a highly promising supplementary technology for the next generation of mobile communication. Taking into account the limited coverage area, VLC can be combined with WiFi as a heterogeneous network (HetNet), thanks to their non-overlapping spectrum. In order to fully utilize the advantages of the WiFi and VLC technologies, a new aggregated WiFi/VLC HetNet consisting of a single WiFi AP and multiple VLC APs, is designed, where the user equipment (UE), for the first time, can access multiple VLC APs and one WiFi AP simultaneously, and be allowed to acquire multiple resource blocks (RB) in each AP at the same time. To optimize the performance of the above designed HetNet, a multi-objective optimization problem (MOOP) is formulated, which aims to maximize system throughput while reducing the handover rate. Since the above MOOP is non-convex and nonlinear, the traditional resource allocation (RA) algorithm has a complex calculation process and poor timeliness performance to deal with this problem. To solve the above MOOP, a reinforcement learning (RL)-based RA algorithm is proposed, and considering the RB handover overhead in the aggregated WiFi/VLC HetNet, a reward function related to the system throughput and the UE handover rate is carefully designed. System throughput, system handover rate, user satisfaction, as well as user fairness performance are analyzed under three typical indoor illumination layouts. Finally, compared with the greedy algorithm and the hypergraph-based carrier aggregation algorithm, numerical results show that the proposed RL-based RA algorithm could improve the system throughput over the former two algorithms by 30.26% and 19.71%, while reducing the system handover rate by 0.15% and 0.02%, respectively.

Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum-energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

Nonconvex resource allocation for inelastic enterprise applications deployment into the cloud via particle swarm optimization

The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme.

Image Model and Algorithm of Human Resource Optimal Configuration Based on FPGA and Microsystem Analysis

Human resources are the most dynamic factor among all productivity factors. The goal of human resource management is to achieve effective and optimal allocation of resources, while the goal of optimal allocation is to promote the realization of corporate goals. At present, the overall domestic human resource management is relatively backward, and most of it is nonstandardized management, and the level of informatization and technology is not high. Therefore, this paper proposes the image model and algorithm research of human resource optimization configuration based on FPGA and microsystem analysis. First, through the data analysis method, in-depth study of traditional resource allocation algorithms: genetic algorithm and particle swarm optimization algorithm, combined with machine learning algorithms, established a human resource optimization configuration model based on FPGA and microsystem analysis, and then optimized the configuration of human resources for the impact The standard deviation and variance measurement of 18 factors of the company, as well as the analysis of the matching degree between employees and positions, and required capabilities. Finally, it is concluded that the sample average is higher and the top three are employees’ positive spirit A12, employees’ executive ability A15, and the relationship between salary and performance A11. The sample averages are 4.615, 4.522, and 4.479, respectively; standard deviation of the three factors with the smallest values are good values A8, strong employee cooperation ability A17, and perfect reward and punishment system A16. This shows that the above considerations should be considered in the optimization of enterprise human resources. Moreover, the algorithm in this paper is easier to obtain the optimal solution, and it is easier to obtain the optimal allocation result of human resources.

Resource allocation of Folk‐Art in Central Plains based on Information‐Centric cloud platform

The folk‐art in Central Plains is rich in connotation and diversified in form, which is an important part of Chinese traditional culture and an art form created by the laborers to meet their life and aesthetic needs. The special symbols in folk‐art reflect the values and aesthetics of the public. It is the crystallization of collective wisdom, the concrete reflection of cultural spirit, and the guarantee of the continuity of local culture, which has unique cultural value. So it is necessary for appreciation of folk‐art in Central Plains. In‐network caching of Information‐Centric Networking (ICN) can cache resources of folk‐art in Central Plains, and then upload to the cloud platform for resource allocation. In this paper, a resource allocation strategy to meet task deadlines in cloud platform is proposed, and a two‐layer resource allocation model is constructed. The primary scheduler allocates meta‐tasks to specific resource districts, and the local scheduler uses an improved banker algorithm to allocate resources to meta‐tasks. Experimental results show that the performance of the proposed algorithm is better than that of traditional resource allocation algorithms in terms of task deadline resource satisfaction, resource utilization rate, and Quality of Experience (QoE).

A survivable VON embedding algorithm based on resource awareness of correlation lightpaths in elastic optical networks

To address the problems of low spectrum utilization and high bandwidth blocking probability of virtual optical network embedding in elastic optical networks, we propose a Survivable virtual optical network Embedding algorithm based on resource awareness of correlation lightpaths (SE-RACL), which can further optimize network resource allocation compared to traditional single lightpath resource allocation algorithms. Firstly, according to the multi-dimensional attributes of nodes, the importance sorting formulas of virtual nodes and physical nodes are designed to increase the cooperation between virtual node embedding and link embedding and reduce the bandwidth consumption of link embedding. Secondly, in the spectrum allocation process of the working lightpath, the spectrum block matching degree formula is designed by combining the spectrum fragmentation degree changes and resource reductions of correlation lightpaths, which is used to optimize the network spectrum resource allocation. Finally, in order to improve the sharing degree of protection resources and the success probability of spectrum allocation under single link failure, a correlation lightpaths spectrum allocation method based on the minimum free frequency slot consumption is proposed. Simulation results show that the SE-RACL algorithm can effectively reduce the bandwidth blocking probability and enhance the spectrum utilization.

Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing

Resource allocation has always been a key technology in wireless sensor networks (WSN), but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm (PCOA, MCMH, and TDMA), it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes.

Broadband Power Line Carrier Communication MAC Layer Scheduling Algorithm for Multi-service Transmission Requirements in Electric Internet of Things

Since traditional power line carrier communication resource allocation algorithm does not consider the mixed services QoS requirements and the queue buffer the MAC layer, the user could not be reasonably scheduled in mixed service flow, which causes the problem of poor distribution balance among different users. This paper proposes a MAC layer user scheduling algorithm for broadband power line carrier communication in a concurrent mixed service scenario. The algorithm dynamically prioritizes the data packets in the user buffer area according to the QoS requirements of the application layer power multi-service and the queue length in the data link layer buffer area, and gives the minimum physical layer required to meet the QoS requirements of mixed services transmission rate, and then realize the MAC layer user scheduling process of broadband power line carrier communication in the concurrent mixed service scenario.

Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network.

The rationalization of human resource management is helpful for enterprises to efficiently train talents in the field, improve the management mode, and increase the overall resource utilization rate of enterprises. The current computational models applied in the field of human resources are usually based on statistical computation, which can no longer meet the processing needs of massive data and do not take into account the hidden characteristics of data, which can easily lead to the problem of information scarcity. The paper combines recurrent convolutional neural network and traditional human resource allocation algorithm and designs a double recurrent neural network job matching recommendation algorithm applicable to the human resource field, which can improve the traditional algorithm data training quality problem. In the experimental part of the algorithm, the arithmetic F1 value in the paper is 0.823, which is 20.1% and 7.4% higher than the other two algorithms, respectively, indicating that the algorithm can improve the hidden layer features of the data and then improve the training quality of the data and improve the job matching and recommendation accuracy.

“DRL + FL”: An intelligent resource allocation model based on deep reinforcement learning for Mobile Edge Computing

With the emergence of a large number of computation-intensive and time-sensitive applications, smart terminal devices with limited resources can only run the model training part of most intelligent applications in the cloud, so a large amount of training data needs to be uploaded to the cloud. This is an important cause of core network communication congestion and poor Quality-of-Experience (QoE) of user. As an important extension and supplement of cloud computing, Mobile Edge Computing (MEC) sinks computing and storage resources from the cloud to the vicinity of User Mobile Devices (UMDs), greatly reducing service latency and alleviating the burden on core networks. However, due to the high cost of edge servers deployment and maintenance, MEC also has the problems of limited network resources and computing resources, and the edge network environment is complex and mutative. Therefore, how to reasonably allocate network resources and computing resources in a changeable MEC environment has become a great aporia. To combat this issue, this paper proposes an intelligent resource allocation model “DRL + FL”. Based on this model, an intelligent resource allocation algorithm DDQN-RA based on the emerging DRL algorithm framework DDQN is designed to adaptively allocate network and computing resources. At the same time, the model integrates the FL framework with the mobile edge system to train DRL agents in a distributed way. This model can well solve the problems of uploading large amounts of training data via wireless channels, Non-IID and unbalance of training data when training DRL agents, restrictions on communication conditions, and data privacy. Experimental results show that the proposed “DRL + FL” model is superior to the traditional resource allocation algorithms SDR and LOBO and the intelligent resource allocation algorithm DRLRA in three aspects: minimizing the average energy consumption of the system, minimizing the average service delay, and balancing resource allocation.

Joint User Association and Power Allocation in Heterogeneous NOMA Networks With Imperfect CSI

Non-orthogonal multiple access (NOMA)-enabled heterogeneous networks (N-HetNets) can provide higher system capacity and spectrum efficiency by allowing multiple users cooperating in the same channel. However, traditional resource allocation algorithms are achieved under perfect channel state information (CSI) which is difficult to obtain in practical systems. In this paper, we study the total energy efficiency maximization problem in a downlink multicell N-HetNets under imperfect CSI, where the constraints of the quality of service of small-cell users, the maximum transmit power, and the cross-tier interference are considered. With the help of the ellipsoidal uncertainty sets, an iterative-based power allocation and user association algorithm is proposed based on the worst-case approach and the Lagrange dual method. Moreover, computational complexity, robust sensitivity and the impact of imperfect CSI error on user's outage probability are provided to insight the performance of the proposed algorithm. Simulation results demonstrate the robustness of the proposed algorithm.

Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN

The emerging edge computing paradigm has given rise to a new promising mobile network architecture, which can address a number of challenges that the operators are facing while trying to support growing end user’s needs by shifting the computation from the base station to the edge cloud computing facilities. With such powerfully computational power, traditional unpractical resource allocation algorithms could be feasible. However, even with near optimal algorithms, the allocation result could still be far from optimal due to the inaccurate modeling of interference among sensor nodes. Such a dilemma calls for a measurement data-driven resource allocation to improve the total capacity. Meanwhile, the measurement process of inter-nodes’ interference could be tedious, time-consuming and have low accuracy, which further compromise the benefits brought by the edge computing paradigm. To this end, we propose a measurement-based estimation solution to obtain the interference efficiently and intelligently by dynamically controlling the measurement and estimation through an accuracy-driven model. Basically, the measurement cost is reduced through the link similarity model and the channel derivation model. Compared to the exhausting measurement method, it can significantly reduce the time cost to the linear order of the network size with guaranteed accuracy through measurement scheduling and the accuracy control process, which could also balance the tradeoff between accuracy and measurement overhead. Extensive experiments based on real data traces are conducted to show the efficiency of the proposed solutions.

FEMOS: Fog-Enabled Multitier Operations Scheduling in Dynamic Wireless Networks

Fog computing has recently emerged as a promising technique in content delivery wireless networks to alleviate the heavy bursty traffic burdens on backhaul connections. In order to improve the overall system performance, in terms of network throughput, service delay and fairness, it is very crucial and challenging to jointly optimize node assignments at control tier and resource allocation at access tier under dynamic user requirements and wireless network conditions. To solve this problem, in this paper, a fog-enabled multitier network architecture is proposed to model a typical content delivery wireless network with heterogeneous node capabilities in computing, communication, and storage. Further, based on Lyapunov optimization techniques, a new online low-complexity algorithm, namely fog-enabled multitier operations scheduling (FEMOS), is developed to decompose the original complicated problem into two operations across different tiers. Rigorous performance analysis derives the tradeoff relationship between average network throughput and service delay, i.e., ${[O(1/V), O(V)] }$ with a control parameter $\boldsymbol {V}$ , under FEMOS algorithm in dynamic wireless networks. For different network sizes and traffic loads, extensive simulation results show that FEMOS is a fair and efficient algorithm for all user terminals and, more importantly, it can offer much better performance, in terms of network throughput, service delay, and queue backlog, than traditional node assignment and resource allocation algorithms.

On Ergodic Sum Capacity of Fading Channels in OFDMA-Based Cognitive Radio Networks

An orthogonal frequency-division multiple access (OFDMA)-based cognitive radio network (CRN) is considered where cognitive radio users (CRUs) can only access the frequency bands not being occupied by primary users (PUs) while keeping the cross-channel interference to each PU under a specified threshold. In this paper, we investigate the ergodic sum capacity of all CRUs in OFDMA-based networks and consider the constraints on the long-term transmit power of all CRUs, the long-term interference thresholds of PUs, and the statistical proportional fairness of all CRUs. In OFDMA-based CRNs, the number of available subchannels is smaller than that of CRUs in some OFDM symbol durations. To deal with this time-varying nature of available spectrum resource and achieve the ergodic sum capacity of all CRUs, we propose an optimal resource-allocation algorithm under the given constraints. Simulation results show that the proposed algorithm can achieve substantial capacity gains for CRUs over the traditional resource-allocation algorithm and maintain the statistical proportional rate constraints of all CRUs.

Credibility-based cloud media resource allocation algorithm

Traditional cloud media resource allocation algorithms have the problem of low efficiency during resources allocation in cloud environment, which is caused by lacking credibility between media resource nodes, a credibility-based cloud media resource allocation (CCMRA) algorithm is proposed in this paper. According to the continuous double auction mechanism, the resource applicants and resource owners submit their requests to the allocation agents. Based on the total credibility, the allocation agents allocate the media resources to get the optimal allocation sequence for higher allocation efficiency and Quality of Service (QoS). Finally, the effectiveness of the proposed algorithm is proved by the simulation.

Discrete polynary coding immune clonal selection‐based joint subcarrier and power allocation in uplink cognitive OFDM network

SummaryCognitive radio has been considered to be one of the main technologies to solve the problem of low spectrum utilization, while the adaptive allocation of network resource is one of the key technologies. A discrete polynary coding immune clonal selection (DPICS)‐based joint subcarrier and power allocation algorithm is proposed to solve the resource allocation problem in uplink cognitive OFDM networks. The novelties of DPICS include the following: A unique coding method is adopted to deal with multi‐value discrete variables. Compared with the traditional methods, the proposed method can acquire the shortest code. Meanwhile, the constraints of the subcarrier allocation are avoided. A heuristic mutation scheme is used to direct the mutation. Subcarriers are reallocated randomly to the secondary users with larger homotactic noise, which has a large probability to produce the optimal solution and improves the searching process. Subcarriers and power are allocated simultaneously, which is different with the traditional biphasic resource allocation algorithms. The biphasic resource allocation algorithms cannot acquire the subcarrier allocation result and power allocation result simultaneously, which makes the final result imprecise. The proposed algorithm avoids this situation and improves the accuracy of the final result. Compared with state‐of‐the‐art algorithms, the proposed algorithm is shown as effective by simulation results. Copyright © 2014 John Wiley & Sons, Ltd.

Cross-layer Resource Allocation on Broadband Power Line Based on Novel QoS-priority Scheduling Function in MAC Layer

Traditional resource allocation algorithms use the hierarchical system, which does not apply to the bad channel environment in broadband power line communication system. Introducing the idea of cross-layer can improve the utilization of resources and ensure the QoS of services. This paper proposes a cross-layer resource allocation on broadband power line based on QoS priority scheduling function on MAC layer. Firstly, the algorithm considers both of real-time users’ requirements for delay and non-real-time users’ requirements for queue length. And then user priority function is proposed. Then each user’s scheduled packets number is calculated according to its priority function. The scheduling sequences are based on the utility function. In physical layer, according to the scheduled packets, the algorithm allocates physical resources for packets. The simulation results show that the proposed algorithm give consideration to both latency and throughput of the system with improving users’ QoS.

Hybrid Resource Allocation Scheme with Proportional Fairness in OFDMA-Based Cognitive Radio Systems

In this paper, the resource allocation problem for proportional fairness in hybrid Cognitive Radio (CR) systems is studied. In OFDMA-based CR systems, traditional resource allocation algorithms can not guarantee proportional rates among CR users (CRU) in each OFDM symbol because the number of available subchannels might be smaller than that of CRUs in some OFDM symbols. To deal with this time-varying nature of available spectrum resource, a hybrid CR scheme in which CRUs are allowed to use subchannels in both spectrum holes and primary users (PU) bands is adopted and a resource allocation algorithm is proposed to guarantee proportional rates among CRUs with no undue interference to PUs.

A Power Allocation Algorithm Based on Cooperative Game Theory in Multi-cell OFDM Systems

A centralized resource allocation algorithm in multi-cell OFDM systems is studied, which aims at improving the performance of wireless communication systems and enhancing user’s spectral efficiency on the edge of the cell. The proposed resource allocation algorithm can be divided into two steps. The first step is sub-carrier allocation based on matrix searching in single cell and the second one is joint power allocation based on cooperative game theory in multi-cell. By comparing with traditional resource allocation algorithms in multi-cell scenario, we find that the proposed algorithm has lower computational complexity and good fairness performance.