Resource scheduling scheme for 5G mmWave CP-OFDM based wireless networks with delay and power allocation optimizations

In this paper, to optimize the average delay and power allocation (PA) for system users, we propose a resource scheduling scheme for wireless networks based on Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) according to the first fifth-generation standards. For delay minimization, we solve a throughput maximization problem that considers CP-OFDM systems with carrier aggregation (CA). Regarding PA, we consider an approach that involves maximizing goodput using an effective signal-to-noise ratio. An algorithm for jointly solving delay minimization through computation of required user rates and optimizing the power allocated to users is proposed to compose the resource allocation approach. In wireless network simulations, we consider a scenario with the following capabilities: CA, 256-Quadrature Amplitude Modulation, millimeter waves above 6 GHz, and a radio frame structure with 120 KHz spacing between the subcarriers. The performance of the proposed resource allocation algorithm is evaluated and compared with those of other algorithms from the literature using computational simulations in terms of various Quality of Service parameters, such as the throughput, delay, fairness index, and loss rate.     

基于多门限预留机制的自适应带宽分配算法

针对异构无线网络融合环境提出了一种基于多门限预留机制的自适应带宽分配算法,从而为多业务提供QoS保证。该算法采用多宿主传输机制,通过预设各个网络中不同业务的带宽分配门限,并基于各个网络中不同业务和用户的带宽分配矩阵,根据业务k支持的传输速率等级需求和网络状态的变化,将自适应带宽分配问题转化为一个动态优化问题并采用迭代方法来求解,在得到各个网络中不同业务和用户优化的带宽分配矩阵的同时,在带宽预留门限和网络容量的约束条件下实现网络实时吞吐量的最大化,以提高整个异构网络带宽的利用效率。数值仿真结果显示,所提算法能够支持满足QoS需求的传输速率等级,减小了新用户接入异构网络的阻塞概率,提高了平均用户接入率并将网络吞吐量最大提高40%。     

End-to-end TCP-friendly streaming protocol and bit allocation for scalable video over wireless Internet

With the convergence of wired-line Internet and mobile wireless networks, as well as the tremendous demand on video applications in mobile wireless Internet, it is essential to an design effective video streaming protocol and resource allocation scheme for video delivery over wireless Internet. Taking both network conditions in the Internet and wireless networks into account, in this paper, we first propose an end-to-end transmission control protocol (TCP)-friendly multimedia streaming protocol for wireless Internet, namely WMSTFP, where only the last hop is wireless. WMSTFP can effectively differentiate erroneous packet losses from congestive losses and filter out the abnormal round-trip time values caused by the highly varying wireless environment. As a result, WMSTFP can achieve higher throughput in wireless Internet and can perform rate adjustment in a smooth and TCP-friendly manner. Based upon WMSTFP, we then propose a novel loss pattern differentiated bit allocation scheme, while applying unequal loss protection for scalable video streaming over wireless Internet. Specifically, a rate-distortion-based bit allocation scheme which considers both the wired and the wireless network status is proposed to minimize the expected end-to-end distortion. The global optimal solution for the bit allocation scheme is obtained by a local search algorithm taking the characteristics of the progressive fine granularity scalable video into account. Analytical and simulation results demonstrate the effectiveness of our proposed schemes.     

基于深度强化学习的异构云无线接入网自适应无线资源分配算法

为了满足无线数据流量大幅增长的需求,异构云无线接入网(H-CRAN)的资源优化仍然是亟待解决的重要问题。该文在H-CRAN下行链路场景下,提出一种基于深度强化学习(DRL)的无线资源分配算法。首先,该算法以队列稳定为约束,联合优化拥塞控制、用户关联、子载波分配和功率分配,并建立网络总吞吐量最大化的随机优化模型。其次,考虑到调度问题的复杂性,DRL算法利用神经网络作为非线性近似函数,高效地解决维度灾问题。最后,针对无线网络环境的复杂性和动态多变性,引入迁移学习(TL)算法,利用TL的小样本学习特性,使得DRL算法在少量样本的情况下也能获得最优的资源分配策略。此外,TL通过迁移DRL模型的权重参数,进一步地加快了DRL算法的收敛速度。仿真结果表明,该文所提算法可以有效地增加网络吞吐量,提高网络的稳定性。     

Multi-layer optimization with backpressure and genetic algorithms for multi-hop wireless networks

This paper presents an efficient scheme to optimize multiple layers in multi-hop wireless networks with throughput objectives. Considering channel sensing and power control at the physical layer, a non-convex throughput optimization problem is formulated for resource allocation and a genetic algorithm is designed to allow distributed implementation. To address link and network layers, a localized back-pressure algorithm is designed to make routing, scheduling, and frequency band assignments along with physical-layer considerations. Our multi-layer scheme is extended to cognitive radio networks with different user classes and evaluate our analytical solution via simulations. Hardware-in-the-loop emulation test results obtained with real radio transmissions over emulated channels are presented to verify the performance of our distributed multi-layer optimization solution for multi-hop wireless networks. Finally, a security system is considered, where links have their security levels and data flows require certain security levels on each of its links. This problem is addressed by formulating additional constraints to the optimization problem.     

Cross-layer resource allocation for QoS guarantee with finite queue constraint in multirate OFDMA wireless networks

Efficient radio resource allocation is essential to provide quality of service （QoS） for wireless networks. In this article, a cross-layer resource allocation scheme is presented with the objective of maximizing system throughput, while providing guaranteed QoS for users. With the assumption of a finite queue for arrival packets, the proposed scheme dynamically a/locates radio resources based on user＇s channel characteristic and QoS metrics derived from a queuing model, which considers a packet arrival process modeled by discrete Markov modulated Poisson process （dMMPP）, and a multirate transmission scheme achieved through adaptive modulation. The cross-layer resource allocation scheme operates over two steps. Specifically, the amount of bandwidth allocated to each user is first derived from a queuing analytical model, and then the algorithm finds the best subcarrier assignment for users. Simulation results show that the proposed scheme maximizes the system throughput while guaranteeing QoS for users.     

基于非正交多址接入的网络切片联合用户关联和功率分配算法

为了满足网络切片多样化需求,实现无线虚拟资源的动态分配,该文提出在C-RAN架构中基于非正交多址接入的联合用户关联和功率资源分配算法。首先,该算法考虑在不完美信道条件下,以切片和用户最小速率需求及时延QoS要求、系统中断概率、前传容量为约束,建立在C-RAN场景中最大化长时平均网络切片总吞吐量的联合用户关联和功率分配模型。其次,将概率混合优化问题转换为非概率优化问题,并利用Lyapunov优化理论设计一种基于当前时隙的联合用户调度和功率分配的算法。最后采用贪婪算法求得用户关联问题次优解;基于用户关联的策略,将功率分配的问题利用连续凸逼近方法将其转换为凸优化问题并采用拉格朗日对偶分解方法获得功率分配策略。仿真结果表明,该算法能满足各网络切片和用户需求的同时有效提升系统时间平均切片总吞吐量。     

Resource allocation and dynamic power control for D2D communication underlaying uplink multi-cell networks

Underlaying device-to-device (D2D) communication is suggested as a promising technology for the next generation cellular networks (5G), where users in close proximity can transmit directly to one another bypassing the base station. However, when D2D communications underlay cellular networks, the potential gain from resource sharing is highly determined by how the interference is managed. In order to mitigate the resource reuse interference between D2D user equipment and cellular user equipment in a multi-cell environment, we propose a resource allocation scheme and dynamic power control for D2D communication underlaying uplink cellular network. Specifically, by introducing the fractional frequency reuse (FFR) principle into the multi-cell architecture, we divide the cellular network into inner region and outer region. Combined with resource partition method, we then formulate the optimization problem so as to maximize the total throughput. However, due to the coupled relationship between resource allocation and power control scheme, the optimization problem is NP-hard and combinational. In order to minimize the interference caused by D2D spectrum reuse, we solve the overall throughput optimization problem by dividing the original problem into two sub-problems. We first propose a heuristic resource pairing algorithm based on overall interference minimization. Then with reference to uplink fractional power control (FPC), a dynamic power control method is proposed. By introducing the interference constraint, we use a lower bound of throughput as a cost function and solve the optimal power allocation problem based on dual Lagrangian decomposition method. Simulation results demonstrate that the proposed algorithm achieves efficient performance compared with existing methods.     

A prioritized resource allocation algorithm for multiple wireless body area networks

This paper presents a prioritized resource allocation algorithm to share the limited communication channel resource among multiple wireless body area networks. The proposed algorithm is designed based on an active superframe interleaving scheme, one of the coexistence mechanisms in the IEEE 802.15.6 standard. It is the first study to consider the resource allocation method among wireless body area networks within a communication range. The traffic source of each wireless body area network is parameterized using the traffic specification, and required service rate for each wireless body area networks can be derived. The prioritized resource allocation algorithm employs this information to allocate the channel resource based on the wireless body area networks’ service priority. The simulation results verified that the traffic specification and the wireless body area network service priority based resource allocation are able to increase quality of service satisfaction, particularly for health and medical services.     

Optimized BS assignment and resource allocation in cooperative OFDM networks

Cooperative transmission (CT) and orthogonal frequency division multiple (OFDM) are promising technologies for extending coverage and increasing throughput in broadband wireless access (BWA) networks. Therefore, we propose a novel BWA network architecture, that can set up inter-cell collaboration using physical layer cooperative transmissions among distributed wired access networks with a powerful coordination capability at the central office. However, conventional base station (BS) assignment and resource allocation schemes cannot be used directly because a user can be serviced by more than one BS with cooperative transmission technology. This study proposes a novel framework of BS assignment and resource allocation in a cooperative OFDM network. We provide three approaches of resource allocation for minimizing bandwidth usage, minimizing transmission power consumption, and balancing resource costs respectively. An optimized resource allocation scheme can be implemented by flexibly choosing one of these approaches based on network load. The simulation results show the efficiency of the proposed mathematical formulations and linearization approach of our scheme. The performance benefit of CT technology on the bandwidth saving is demonstrated by comparing the new BS assignment and resource allocation scheme with conventional non-cooperative transmission.     

云雾混合网络下基于多智能体架构的资源分配及卸载决策研究

针对D2D辅助的云雾混合架构下资源分配及任务卸载决策优化问题,该文提出一种基于多智能体架构深度强化学习的资源分配及卸载决策算法。首先,该算法考虑激励约束、能量约束以及网络资源约束,联合优化无线资源分配、计算资源分配以及卸载决策,建立了最大化系统总用户体验质量(QoE)的随机优化模型,并进一步将其转化为MDP问题。其次,该算法将原MDP问题进行因式分解,并建立马尔可夫博弈模型。然后,基于行动者-评判家(AC)算法提出一种集中式训练、分布式执行机制。在集中式训练过程中,多智能体通过协作获取全局信息,实现资源分配及任务卸载决策策略优化,在训练过程结束后,各智能体独立地根据当前系统状态及策略进行资源分配及任务卸载。最后,仿真结果表明,该算法可以有效提升用户QoE,并降低了时延及能耗。     

联合网络容量与认知用户满意度的资源分配优化方案

机会频谱接入技术是提高频谱利用率的一种有效方法,论文综合考虑信道的时变性、子信道空闲的检测可信度、认知网络与主网络间的互干扰等因素,建立了一种最大化认知网络有效容量和认知用户满意度的多目标优化模型,提出了一种联合资源分配和用户调度的PAUS算法.仿真结果表明,在主用户分布密度较低的环境中,该模型较最大化网络有效容量模型具有更高的用户满意度;较最大化认知用户满意度模型可获得更高的网络有效容量.     

异构网络中多网络并行传输的网络选择算法

为了充分利用异构网络中的无线资源,提出了一种基于多网络并行传输的异构网络接入选择算法,该算法根据终端接收信号功率确定能够进行通信的无线网络,以这些无线网络的任意非空子集作为候选方案,计算各个方案对应的聚合属性,再根据吞吐量和功耗阈值条件限定候选网络方案,对这些方案建立多属性决策矩阵,采用基于用户偏好的逼近理想值排序法(TOPSIS)得到各个候选网络方案的效用函数值,从中选出与理想方案最接近的多网络接入方案。仿真结果表明该算法能有效改善用户服务质量,提高用户吞吐量,降低用户的单位吞吐量对应的功耗和费用,保证网络的负载均衡。      

Distributed Resource Allocation for Femtocell Networks: Regret Learning with Proportional Self-belief

Femtocell networks promise improvement in network quality and performance for dense wireless networks, but will suffer from inter-cell interference if resource management is not properly employed. This paper presents distributed joint resource allocation (sub-channel and power) to address co- and cross-tier interference issues in two-tier heterogeneous femtocell networks. Due to uncoordinated nature of femtocell base stations (HeNB) deployment, the interactions among self-interested HeNBs are formulated using game-theoretical tools. Then, we designed individual utility function for every HeNB in order to enforce cooperative behaviour among HeNBs as well as to avoid cross-tier interference towards macrocell user equipments within HeNB coverage. Based on the designed utility function, we propose a fully distributed adaptive learning algorithm with a proportional self-belief concept that can lead to correlated equilibrium with fast and decisive convergence. Finally, performance analysis on the proposed algorithm done in simulated environment showed positive results indicating improvements in terms of co- and cross-tier interference mitigation as compared to generic regret-based learning scheme and utility functions.     

基于智能网络的租户服务质量保证技术

在多租户虚拟网络环境中,用户对于网络服务的多样性以及性能的稳定性需求并不会随着网络架构和运营模式的升级而削弱,用户需求之间的差异性和动态性对于不同切片间资源的分配和调度效率提出了新的挑战.针对多租户虚拟网络的特殊环境,首先提出了QVR(QoS-Virtual Routing)流量调度算法,同时将用户流量调度与网络虚拟资...     

Resource planning and incentive engineering for a congested wireless access point: an integrated multiple time scale control mechanism

Wireless networks are playing an increasingly important role for global communications. Many resource allocation mechanisms have been proposed to efficiently utilize the limited radio resources in wireless networks to support a large number of mobile users with a diversity of applications. Among them, pricing frameworks that provide incentives to users to maximize their individual utility while optimizing allocation of network resources have attracted a lot of attention recently. Nevertheless, most of these pricing schemes require dynamic charging rates and may be too complex for wide acceptance by users, as most users would prefer relatively simple charging schemes. Moreover, use of a pricing framework to facilitate resource planning and future expansion at the service provider’s side has not yet been widely considered. In this paper, we propose Integrated Multiple Time Scale Control (IMTSC), a novel incentive engineering mechanism to facilitate resource allocation and network planning. Over different time scales, IMTSC combines the functions of network capacity planning, admission control for resource allocation, and tracking of users’ instantaneous traffic demands. The proposed mechanism is applied for access control at a congested access point in a wireless network. By decomposing the original problem into distributed optimization problems that are solved locally by the service provider through adjusting charging rate and remotely by individual users by appropriately changing her service requests, we show that maximization of user’s utility and increase of network efficiency can be simultaneously achieved. Results from extensive simulations demonstrate the effectiveness of the proposed IMTSC mechanism.     

Index Policies for Resource Allocation in Wireless Networks

We consider the problem of resource allocation for data transfer between the base station and the users within a cell of a wireless telecommunication network with infinite data queues for each user. The aim is to study the tradeoff between the conflicting objectives of maximizing the system throughput and the quality of service (QoS) to an individual user. Using a policy improvement approach based on Markov decision processes, we develop an intuitive and easy-to-implement index policy. We also demonstrate its superior performance over the existing proportional fair metric algorithm through simulation experiments.     

Downlink user selection and resource allocation for semi-elastic flows in an OFDM cell

We are concerned with user selection and resource allocation in wireless networks for semi-elastic applications such as video conferencing. While many packet scheduling algorithms have been proposed for elastic applications, and many user selection algorithms have been proposed for inelastic applications, little is known about optimal user selection and resource allocation for semi-elastic applications in wireless networks. We consider user selection and allocation of downlink transmission power and subcarriers in an orthogonal frequency division multiplexing cellular system. We pose a utility maximization problem, but find that direct solution is computationally intractable. We first propose a method that makes joint decisions about user selection and resource allocation by transforming the utility function into a concave function so that convex optimization techniques can be used, resulting in a complexity polynomial in the number of users with a bounded duality gap. This method can be implemented if the network communicates a shadow price for power to power allocation modules, which in turn communicate shadow prices for rate to individual users. We then propose a method that makes separate decisions about user selection and resource allocation, resulting in a complexity linear in the number of users.     

Optimal resource allocation in wireless ad hoc networks: a price-based approach

The shared-medium multihop nature of wireless ad hoc networks poses fundamental challenges to the design of effective resource allocation algorithms that are optimal with respect to resource utilization and fair across different network flows. None of the existing resource allocation algorithms in wireless ad hoc networks have realistically considered end-to-end flows spanning multiple hops. Moreover, strategies proposed in wireline networks are not applicable in the context of wireless ad hoc networks, due to their unique characteristics of location-dependent contention. In this paper, we propose a new price-based resource allocation framework in wireless ad hoc networks to achieve optimal resource utilization and fairness among competing end-to-end flows. We build our pricing framework on the notion of maximal cliques in wireless ad hoc networks, as compared to individual links in traditional wide-area wireline networks. Based on such a price-based theoretical framework, we present a two-tier iterative algorithm. Distributed across wireless nodes, the algorithm converges to a global network optimum with respect to resource allocations. We further improve the algorithm toward asynchronous network settings and prove its convergence. Extensive simulations under a variety of network environments have been conducted to validate our theoretical claims.     

Sustainable Spectrum Allocation Strategy for 5G Mobile Network

These days, 5G wireless communication are being created for different modern IoT (Internet of Things) applications around the world, arising with the IoT. All things considered, it is feasible to send energy efficient innovation in a manner that advances the drawn out sustainability of networks. Next-generation heterogeneous wireless communication is composed of different base stations. In this network, sustainable spectrum allocation is required to maximize the bandwidth utilization along with a reduction in power consumption. This paper proposes an algorithm for allocating an optimized spectrum to clusters in a multi-cluster environment for sustainable 5G environment using particle swarm optimization (PSO). The proposed strategy is applicable for 3G, 4G, and 5G mobile networks. Mobile devices enter and leave the cluster randomly and stay within the cluster for an uncertain amount of time. During that period the user demands may vary. Consequently, various bandwidth allocations are required. For such cases, static allocation might result in inefficient utilization of bandwidth, wastage of power, and degrade user satisfaction. The proposed algorithm will optimize the spectrum allocated to a cluster from time to time to solve this problem and produce an optimized solution within a given deadline. PSO based proposed scalable spectrum allocation method is applicable for the different frequency range for each cluster, hence scalable from 3G telecommunication to 5G-mobile edge technology. The convergence of the strategy is analyzed. From simulation analysis, it is observed that the proposed strategy reduces power consumption by approximately 8%, 11%, and 6% in 3G, 4G, and 5G communications respectively than the existing scheme.