An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

With the emergence of 5G mobile multimedia services, end users’ demand for high-speed, low-latency mobile communication network access is increasing. Among them, the device-to-device (D2D) communication is one of the considerable technology. In D2D communication, the data does not need to be relayed and forwarded by the base station, but under the control of the base station, a direct local link is allowed between two adjacent mobile devices. This flexible communication mode reduces the processing bottlenecks and coverage blind spots of the base station, and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks. One of the important factors which affects the quality-of-service (QoS) of D2D communications is co-channel interference. In order to solve this problem of co-channel interference, this paper proposes a graph coloring based algorithm. The main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user resource. The proposed algorithm also provides simpler power control. The heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is acquired. Simulation results show that the proposed algorithm effectively reduced the co-channel interference, power consumption and improved the system throughput as compared with existing algorithms.     

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device (D2D) communication which is also called terminal pass-through technology. It can directly communicate between devices under the control of a base station and does not require a base station to forward it. The advantages of applying D2D communication technology to cellular networks are: It can increase the communication system capacity, improve the system spectrum efficiency, increase the data transmission rate, and reduce the base station load. Aiming at the problem of co-channel interference between the D2D and cellular users, this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning, which creates multi-agent learners from multiple D2D users, and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users. The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted. The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users. Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.     

面向多用户环境的MapReduce集群调度算法研究

针对MapReduce集群现有调度策略在多用户环境下无法根据用户的实际资源需求实现动态资源分配的问题,提出了一种基于历史执行信息(HEI)的MapReduce集群调度算法——HEI Scheduler。该算法通过建立集群作业执行信息的收集和分析机制,得到各用户组资源需求随时间变化的规律,并以作业实际占用slot的时间作为作业占用资源量的衡量标准,进而动态地确定资源池的最小共享资源以及集群剩余资源分配的权值。实验结果表明,执行信息分析机制能够更准确地表征作业对资源的需求,采用集群调度算法HEI Scheduler能够有效地缩短作业的整体执行时间。     

Dynamic Resource Pricing and Allocation in Multilayer Satellite Network

The goal of delivering high-quality service has spurred research of 6G satellite communication networks. The limited resource-allocation problem has been addressed by next-generation satellite communication networks, especially multilayer networks with multiple low-Earth-orbit (LEO) and non-low-Earth-orbit (NLEO) satellites. In this study, the resource-allocation problem of a multilayer satellite network consisting of one NLEO and multiple LEO satellites is solved. The NLEO satellite is the authorized user of spectrum resources and the LEO satellites are unauthorized users. The resource allocation and dynamic pricing problems are combined, and a dynamic game-based resource pricing and allocation model is proposed to maximize the market advantage of LEO satellites and reduce interference between LEO and NLEO satellites. In the proposed model, the resource price is formulated as the dynamic state of the LEO satellites, using the resource allocation strategy as the control variable. Based on the proposed dynamic game model, an open-loop Nash equilibrium is analyzed, and an algorithm is proposed for the resource pricing and allocation problem. Numerical simulations validate the model and algorithm.     

全复用多小区系统下行链路用户调度与功率分配

对全复用多小区系统下行链路用户调度与功率分配问题进行了研究,提出了一种半分布式的用户调度与功率分配方案.首先,各小区根据自身所辖用户的信道与干扰信息,采用最大信干噪比准则进行用户调度,然后各基站将所调度用户的信息上报给中央资源控制器,最后中央资源控制器基于最速下降法的思想,采用贪婪功率分配(GPA)算法为各用户分配相应的发射功率.所提方案利用小区之间的相互协作有效地弱化了小区间干扰,与现有方法相比获得了更好的吞吐量性能和更高的功率效率.     

基于部分可观察马尔可夫决策过程的受控无线网络系统动态资源分配

研究了受控无线网络的动态资源分配。针对传统无线通信传输模型的局限性随着无线通信系统架构的发展日益凸显的问题,提出了一种引入反馈控制策略的受控无线网络模型。该模型结合部分可观察马尔可夫决策过程(POMDP),将用户接收功率与数据传输误码率作为反馈观测对象,对通信小区内基站天线开启数与用户接入数进行动态资源最优匹配。仿真结果表明,这种方法能够有效提升系统传输能效性与可靠性,降低传输误码率,改善系统资源动态匹配控制性能。     

Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

Non-orthogonal multiple access (NOMA) is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. NOMA allows multiple terminals to share the same resource unit at the same time. The receiver usually needs to configure successive interference cancellation (SIC). The receiver eliminates co-channel interference (CCI) between users and it can significantly improve the system throughput. In order to meet the demands of users and improve fairness among them, this paper proposes a new power allocation scheme. The objective is to maximize user fairness by deploying the least fairness in multiplexed users. However, the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker (KKT) constraints. Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.     

Adaptive opportunistic fair scheduling in power-controlled code division multiple access systems

The authors treat the multiuser scheduling problem for practical power-controlled code division multiple access (CDMA) systems under the opportunistic fair scheduling (OFS) framework. OFS is an important technique in wireless networks to achieve fair and efficient resource allocation. Power control is an effective resource management technique in CDMA systems. Given a certain user subset, the optimal power control scheme can be derived. Then the multiuser scheduling problem refers to the optimal user subset selection at each scheduling interval to maximise certain metric subject to some specific physical-layer constraints. The authors propose discrete stochastic approximation algorithms to adaptively select the user subset to maximise the instantaneous total throughput or a general utility. Both uplink and downlink scenarios are considered. They also consider the time-varying channels where the algorithm can track the time-varying optimal user subset. Simulation results to show the performance of the proposed algorithms in terms of the throughput/ utility maximisation, the fairness, the fast convergence and the tracking capability in time-varying environments are presented.     

Resource Allocation for Throughput Maximization in Cognitive Radio Network with NOMA

Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications.     

Resource Allocation and Power Control Policy for Device-toDevice Communication Using Multi-Agent Reinforcement Learning

Device-to-Device (D2D) communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity. In this paper, we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput. Firstly, we treat each D2D pair as an independent agent. Each agent makes decisions based on the local channel states information observed by itself. The multi-agent Reinforcement Learning (RL) algorithm is proposed for our multi-user system. We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected, so the problem is modeled as a stochastic non-cooperative game. Hence, each agent becomes a player and they make decisions together to achieve global optimization. Thereby, the multi-agent Q-learning algorithm based on game theory is established. Secondly, in order to accelerate the convergence rate of multi-agent Q-learning, we consider a power allocation strategy based on Fuzzy Cmeans (FCM) algorithm. The strategy firstly groups the D2D users by FCM, and treats each group as an agent, and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users. The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system. In particular, FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.     

On scheduling models for the frequency interval assignment problem with cumulative interferences

In this article, models and methods for solving a real-life frequency assignment problem based on scheduling theory are investigated. A realistic frequency assignment problem involving cumulative interference constraints in which the aim is to maximize the number of assigned users is considered. If interferences are assumed to be binary, a multiple carrier frequency assignment problem can be treated as a disjunctive scheduling problem since a user requesting a number of contiguous frequencies can be considered as a non-preemptive task with a processing time, and two interfering users can be modelled through a disjunctive constraint on the corresponding tasks. A binary interference version of the problem is constructed and a disjunctive scheduling model is derived. Based on the binary representation, two models are proposed. The first one relies on an interference matrix and the second one considers maximal cliques. A third, cumulative, model that yields a new class of scheduling problems is also proposed. Computational experiments show that the case-study frequency assignment problem can be solved efficiently with disjunctive scheduling techniques.     

Joint Spectrum Partition and Performance Analysis of Full-Duplex D2D Communications in Multi-Tier Wireless Networks

Full-duplex (FD) has been recognized as a promising technology for future 5G networks to improve the spectrum efficiency. However, the biggest practical impediments of realizing full-duplex communications are the presence of self-interference, especially in complex cellular networks. With the current development of self-interference cancellation techniques, full-duplex has been considered to be more suitable for device-to-device (D2D) and small cell communications which have small transmission range and low transmit power. In this paper, we consider the full-duplex D2D communications in multi-tier wireless networks and present an analytical model which jointly considers mode selection, resource allocation, and power control. Specifically, we consider a distance based mode selection scheme. The performance analysis of different D2D communications modes are performed based on stochastic geometry, and tractable analytical solutions are obtained. Then we investigate the optimal resource partitions between dedicated D2D mode and cellular mode. Numerical results validate the theoretical anlaysis and indicate that with appropriate proportions of users operated in different transmission modes and optimal partitioning of spectrum, the performance gain of FD-D2D communication can be achieved.     

Interference-limited resource allocation for cognitive radio in orthogonal frequency-division multiplexing networks

Efficient and fair resource allocation strategies are being extensively studied in current research in order to address the requirements of future wireless applications. A novel resource allocation scheme is developed for orthogonal frequency-division multiplexing (OFDM) networks designed to maximise performance while limiting the received interference at each user. This received interference is in essence used as a fairness metric; moreover, by defining different interference tolerances for different sets of users, the proposed allocation scheme can be exploited in various cognitive radio scenarios. As applied to the scheme, the authors investigate a scenario where two cellular OFDM-based networks operate as primary and secondary systems in the same band, and the secondary system benefits by accessing the unused resources of the primary system if additional capacity is required. The primary system benefits either by charging the secondary system for the use of its resources or by some form of reciprocal arrangement allowing it to use the secondary system's licenced bands in a similar manner, when needed. Numerical results show our interference-limited scheduling approach to achieve excellent levels of efficiency and fairness by allocating resources more intelligently than proportional fair scheduling. A further important contribution is the application of sequential quadratic programming to solve the non-convex optimisation problems which arise in such scenarios.     

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximization-based fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact of signal to noise ratio (SNR) per subcarrier and the channel gains of the paired users on the performance of NOMA in terms of the capacity and the user fairness is presented. Next, a closed-form equation for the power allocation coefficient of CMFPA as a function of SNR, and the channel gains of the paired users is provided. In addition, to jointly optimize UP and PA in NOMA systems an efficient low-complexity UP (ELCUP) method is proposed to be incorporated with the proposed CMFPA to compromise the proposed joint resource allocation (JRA). Simulation results demonstrate that the proposed CMFPA can improve the capacity and fairness performance of existing UP methods, such as conventional UP, and random UP methods. Furthermore, the simulation results show that the proposed JRA significantly outperforms the existing schemes and gives a near-optimal performance.     

Fair power control for wireless ad hoc networks using game theory with pricing scheme

Resource allocation in wireless ad hoc networks is usually modelled in a non-cooperative game theoretic framework with the objective of maximising individual utility. However, the selfishness of autonomous users under such framework may lead to throughput unfairness which only benefits certain users. To alleviate this unfairness problem, the authors propose a payment-based power control scheme using game theory where each user announces a set of price coefficients that reflects different compensations paid by other users for the interference they produce. Users who generate higher interference are required to pay more by transmitting at a lower power to give other users a fairer chance of sharing the throughput.Without any incentive to play fairly, users could misbehave by broadcasting high price coefficients to force other users to transmit at a lower power. The authors treat this problem casting it into a price game which resembles a Prisoner's Dilemma game. Users who play this game iteratively will behave cooperatively and broadcast the price coefficients truthfully. Together with analytical proof, the proposed approach is shown to converge to Nash equilibrium where at this point it is able to provide a fairer throughput share among users at the expense of a slight loss in total throughput.     

OFDM水声通信下行非正交多址接入功率分配方法研究

针对基于功率域非正交多址接入（Power Domain Non-orthogonal Multiple Accesses,PD-NOMA）的正交频分复用（Orthogonal Frequency Division Multiplexing,OFDM）水声下行通信系统的功率分配问题,提出了一种基于中断概率的功率分配方法。用户节点在系统初始化阶段根据源节点广播的组网数据包获取水声信道的统计特征,源节点根据水下用户反馈的信道特征参数建立水下用户的中断概率模型,以最小化两用户的中断概率和为目标建立目标函数,在中断概率区域边界上遍历搜索最优的功率分配系数。仿真结果表明,该方法在保证公平性的条件下,有效降低了用户节点的中断概率,提高了系统的频谱利用率和误码性能。     

WiFi6 Dynamic Channel Optimization Method for Fault Tolerance in Power Communication Network

As the scale of power networks has expanded, the demand for multi-service transmission has gradually increased. The emergence of WiFi6 has improved the transmission efficiency and resource utilization of wireless networks. However, it still cannot cope with situations such as wireless access point (AP) failure. To solve this problem, this paper combines orthogonal frequency division multiple access (OFDMA) technology and dynamic channel optimization technology to design a fault-tolerant WiFi6 dynamic resource optimization method for achieving high quality wireless services in a wirelessly covered network even when an AP fails. First, under the premise of AP layout with strong coverage over the whole area, a faulty AP determination method based on beacon frames (BF) is designed. Then, the maximum signal-to-interference ratio (SINR) is used as the principle to select AP reconnection for the affected users. Finally, this paper designs a dynamic access selection model (DASM) for service frames of power Internet of Things (IoTs) and a scheduling access optimization model (SAO-MF) based on multi-frame transmission, which enables access optimization for differentiated services. For the above mechanisms, a heuristic resource allocation algorithm is proposed in SAO-MF. Simulation results show that the method can reduce the delay by 15% and improve the throughput by 55%, ensuring high-quality communication in power wireless networks.     

一种面向卷烟混合生产线的优化调度模型研究

目的 基于多品种、小批量的生产制造模式,在规定的8 h工作之内,快速有效地生产出多品种、多批量的卷烟,并对所需物料进行准确调度,达到经济效益最大化的目的。方法 针对某烟草生产企业订单需要生产6种型号的品牌香烟,通过分析卷烟生产线的工艺流程,提出一种解决卷烟厂车间资源优化调度的CSS模型,该模型可以根据产品之间的销售需求情况来匹配生产线资源配置,按需求比采用粒子群优化算法计算出单次投料后混合生产香烟所需的最小时间与最大收益。结果 将所得方案进行综合对比后,计算得出在规定工期内,生产香烟获取利益的最优分配方案,在迭代在10次以内时已完成了优化过程,最大获利为3.65万元。结论 该优化模型通过改变相关工艺参数能够实现对不同混合生产线的资源调度优化,并对其他制造行业提供借鉴价值。     

Machine Learning Based Resource Allocation of Cloud Computing in Auction

Resource allocation in auctions is a challenging problem for cloud computing. However, the resource allocation problem is NP-hard and cannot be solved in polynomial time. The existing studies mainly use approximate algorithms such as PTAS or heuristic algorithms to determine a feasible solution; however, these algorithms have the disadvantages of low computational efficiency or low allocate accuracy. In this paper, we use the classification of machine learning to model and analyze the multi-dimensional cloud resource allocation problem and propose two resource allocation prediction algorithms based on linear and logistic regressions. By learning a small-scale training set, the prediction model can guarantee that the social welfare, allocation accuracy, and resource utilization in the feasible solution are very close to those of the optimal allocation solution. The experimental results show that the proposed scheme has good effect on resource allocation in cloud computing.     

A Heterogeneous Virtual Machines Resource Allocation Scheme in Slices Architecture of 5G Edge Datacenter

In the paper, we investigate the heterogeneous resource allocation scheme for virtual machines with slicing technology in the 5G/B5G edge computing environment. In general, the different slices for different task scenarios exist in the same edge layer synchronously. A lot of researches reveal that the virtual machines of different slices indicate strong heterogeneity with different reserved resource granularity. In the condition, the allocation process is a NP hard problem and difficult for the actual demand of the tasks in the strongly heterogeneous environment. Based on the slicing and container concept, we propose the resource allocation scheme named Two-Dimension allocation and correlation placement Scheme (TDACP). The scheme divides the resource allocation and management work into three stages in this paper: In the first stage, it designs reasonably strategy to allocate resources to different task slices according to demand. In the second stage, it establishes an equivalent relationship between the virtual machine reserved resource capacity and the Service-Level Agreement (SLA) of the virtual machine in different slices. In the third stage, it designs a placement optimization strategy to schedule the equivalent virtual machines in the physical servers. Thus, it is able to establish a virtual machine placement strategy with high resource utilization efficiency and low time cost. The simulation results indicate that the proposed scheme is able to suppress the problem of uneven resource allocation which is caused by the pure preemptive scheduling strategy. It adjusts the number of equivalent virtual machines based on the SLA range of system parameter, and reduces the SLA probability of physical servers effectively based on resource utilization time sampling series linear. The scheme is able to guarantee resource allocation and management work orderly and efficiently in the edge datacenter slices.