一种软件定义无线网络负载均衡架构及算法

软件定义网络( SDN)为实现异构无线网络中的负载均衡提供了新的思路。设计了一种软件定义的无线网络负载均衡架构,并提出对应算法。首先,根据接收信号强度构建候选网络列表;其次,根据各候选网络的可用负载比率标准差进行负载差异分级;再次,将服务质量匹配度函数和负载均衡指数线性组合成联合优化函数,并根据负载差异分级对联合优化的权重进行动态调整,合理设置门限进行接纳控制。与传统算法相比,所提算法一方面可使各类业务阻塞率明显降低大约20%,另一方面使不同网络的归一化负载更加接近。该算法在进行网络负载均衡的同时,能够有效降低业务阻塞率,从而有效提升异构无线网络的整体性能。     

异构无线网络业务接入多目标优化控制算法

提出了一种应用于异构无线网络环境的基于多目标优化的业务接入控制算法.该算法以业务占用总资源最少、业务阻塞率最低和网络间负载最均衡为目标建立了多目标优化控制模型,采用高斯和戒上型组合隶属函数将多目标问题模糊化,利用最大化满意度指标法将多目标问题转化为单目标问题,并通过遗传算法求得最优解.仿真结果表明,所提算法能在兼顾各网络资源高效利用的同时,保证业务接入的可靠性,并相对于参考算法有效降低了业务阻塞率.     

时分波分无源光网络与云无线接入网联合架构中负载平衡的用户关联与资源分配策略

在时分波分无源光网络(TWDM-PON)与云无线接入网(C-RAN)的联合架构中,由于无线域的负载不均衡问题,限制了网络整体的传输效率。为了充分利用TWDM-PON与C-RAN联合架构的网络资源,并保证用户的服务质量(QoS),该文提出一种负载平衡的用户关联与资源分配算法(LBUARA)。首先根据不同用户的服务质量需求以及分布式无线射频头端(RRH)的负载对用户的影响,构建用户收益函数。进而,在保证用户服务质量的前提下,根据网络状态建立随机博弈模型,并基于多智能体Q学习提出负载均衡的用户关联和资源分配算法,从而获得最优的用户关联与资源分配方案。仿真结果表明,所提的用户关联和资源分配策略能够实现网络的负载均衡,保证用户的服务质量,并提高网络吞吐量。     

时分波分无源光网络与云无线接入网联合架构中负载平衡的用户关联与资源分配策略

在时分波分无源光网络(TWDM-PON)与云无线接入网(C-RAN)的联合架构中,由于无线域的负载不均衡问题,限制了网络整体的传输效率.为了充分利用TWDM-PON与C-RAN联合架构的网络资源,并保证用户的服务质量(QoS),该文提出一种负载平衡的用户关联与资源分配算法(LBUARA).首先根据不同用户的服务质量需求以及分布式无线射频头端(RRH)的负载对用户的影响,构建用户收益函数.进而,在保证用户服务质量的前提下,根据网络状态建立随机博弈模型,并基于多智能体Q学习提出负载均衡的用户关联和资源分配算法,从而获得最优的用户关联与资源分配方案.仿真结果表明,所提的用户关联和资源分配策略能够实现网络的负载均衡,保证用户的服务质量,并提高网络吞吐量.     

基于混沌遗传的异构无线网络接入选择策略

为了解决异构无线网络接入选择问题,提出一种基于混沌遗传算法的解决方法.将网络接入选择转换为一个多属性优化问题,利用混沌遗传算法解决全局寻优问题.算法首先通过超混沌系统产生初始种群和混沌扰动向量,对遗传算法进行改进;然后利用混沌遗传算法对适应度函数求解得到网络选择评价指标权重.仿真结果表明,该算法能够使新到达的用户更均匀地分布在各备选网络中,有效降低网络阻塞率并减小各候选网络阻塞率差距,实现网络的负载均衡.     

LTE系统移动负载均衡研究

针对小区负载分布越来越不均衡,提出一种适用于LTE系统的负载均衡算法,通过调整小区独立偏置CIO,强制过载小区用户切换到周围轻载小区,实现用户转移。但是,轻载小区盲目接纳用户容易造成过载。针对这一问题,运用迭代法,逐步调整CIO,当过载小区负载低于过载门限或轻载小区可用资源耗尽,得到最优CIO值。仿真结果表明,提出的算法能有效提高系统均衡程度,降低用户呼叫阻塞率,提升系统性能,增强用户体验。     

LTE中基于位置预测的双层负载均衡算法

当前负载均衡算法中,超负载小区在负载均衡切换决策阶段主要考虑把负载转移到邻近轻负载小区,而忽略该轻负载小区是否适合接受来自超负载小区的负载转移。文章提出了一种基于位置预测的双层负载均衡算法,结合用户运动机制和方向改变规律预测下一时刻的接收信号强度,同时在超负载小区的负载转移时考虑两层信息。仿真结果表明,当呼叫到达率低于1时,使用基于位置预测的双层负载均衡算法可以有效地降低整个系统的呼叫阻塞率,提高全网资源利用率。     

基于最大化畅通概率优化模型的固定路由算法

针对以最小化网络阻塞率为目标的光网络路由及波长分配(RWA)问题,考虑到全网结构不均衡易导致部分链路负载过高,进而造成全网阻塞率过高问题,在基于爱尔兰损失公式的链路阻塞概率模型的基础上,建立了最大化路径畅通概率的优化模型。为了克服优化模型的非线性造成的求解困难,借鉴大系统中分解协调的思想对链路负载进行预估,将原优化问题转化成乘积最长路问题,并结合负载滚动预估更新及类Dijkstra算法进行近似求解。仿真比较实验表明,本文算法能够较好地近似求解所提出的最大化畅通概率模型,有效地均衡了全网负载,降低了全网阻塞率,提高了网络传输性能。     

基于负载均衡的非合作博弈CoMP节点选择算法

在CoMP-JP中,通过对自身负载最小化和网络负载均衡性问题进行折中考虑,建立了基于非合作博弈的CoMP节点选择数学模型,将自身效益和网络效益作为优化目标,通过优化CoMP用户分类权值矢量来改变各小区负载,达到整体效益的最优化。仿真结果表明,所提算法具有较好的收敛性,而且能够在保证负载均衡性的同时降低小区资源占用率。     

基于负载均衡的非合作博弈CoMP节点选择算法北大核心

在CoMP-JP中,通过对自身负载最小化和网络负载均衡性问题进行折中考虑,建立了基于非合作博弈的CoMP节点选择数学模型,将自身效益和网络效益作为优化目标,通过优化CoMP用户分类权值矢量来改变各小区负载,达到整体效益的最优化。仿真结果表明,所提算法具有较好的收敛性,而且能够在保证负载均衡性的同时降低小区资源占用率。     

LTE中关于冲突避免的切换参数动态优化

In order to achieve dynamical optimization of mobility load balancing, we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameters. To this end, a method of Handover Parameters Adjustment for Conflict Avoidance (HPACA) is proposed. Considering the movement of users, HPCAC can dynamically adjust handover range to optimize the mobility load balancing. The movement of users is an important factor of handover, which has a dramatic impact on system performance. The numerical evaluation results show the proposed approach outperforms the existing method in terms of throughput, call blocking ratio, load balancing index, radio link failure ratio, ping-pong handover ratio and call dropping ratio.     

Low-Complexity Load Balancing with a Self-Organized Intelligent Distributed Antenna System

A high call blocking rate is a consequence of an inefficient utilization of system resources, which is often caused by a load imbalance in the network. Load imbalances are common in wireless networks with a large number of cellular users. This paper investigates a load-balancing scheme for mobile networks that optimizes cellular performance with constraints of physical resource limits and users quality of service demands. In order to efficiently utilize the system resources, an intelligent distributed antenna system (IDAS) fed by a multi base transceiver station (BTS) has the ability to distribute the system resources over a given geographic area. To enable load balancing among distributed antenna modules we dynamically allocate the remote antenna modules to the BTSs using an intelligent algorithm. A self-optimizing network for an IDAS is formulated as an integer based linear constrained optimization problem, which tries to balance the load among the BTSs. A discrete particle swarm optimization (DPSO) algorithm as an evolutionary algorithm is proposed to solve the optimization problem. The computational results of the DPSO algorithm demonstrate optimum performance for small-scale networks and near-optimum performance for large-scale networks. The DPSO algorithm is faster with marginally less complexity than an exhaustive search algorithm.     

Proactive load balancing with admission control for heterogeneous overlay networks

The next generation wireless networks will be the coexistence of heterogeneous wireless technologies. Balancing the traffic load among different networks can effectively utilize the overall radio resources in the system. In this paper, we propose an efficient load balancing scheme for the heterogeneous overlay systems, which is applied in the call admission control process. If the available network(s) cannot provide enough resource for the request call without degrading the quality‐of‐service (QoS) obtained by the ongoing calls, the system will perform load balancing operations first by initiating vertical handoffs among networks in order to create more rooms for the request call. The load balancing algorithm is to minimize the variance between the network utilizations of the entire system, which can be formulated as a quadratic binary programming problem. Simulation results show that the proposed scheme can admit more calls into the system compared with the other three reference schemes and then improve the overall throughput. Meanwhile, the scheme can keep the networks working in effective states and provide a better QoS support for users. Copyright © 2011 John Wiley & Sons, Ltd.     

Proportionally fair load balancing with statistical quality of service provisioning for aerial base stations

Aerial base stations (ABSs) seem promising to enhance the coverage and capacity of fifth-generation and upcoming networks. With the flexible mobility of ABSs, they can be positioned in air to maximize the number of users served with a guaranteed quality of service (QoS). However, ABSs may be overloaded or underutilized given inefficient placement, and user association has not been well addressed. Hence, we propose a three-dimensional ABS placement scheme with a delay-QoS-driven user association to balance loading among ABSs. First, a load balancing utility function is designed based on proportional fairness. Then, an optimization problem for joint ABS placement and user association is formulated to maximize the utility function subject to statistical delay QoS requirements and ABS collision avoidance constraints. To solve this problem, we introduce an efficient modified gray wolf optimizer for ABS placement with a greedy user association strategy. Simulation results demonstrate that the proposed scheme outperforms baselines in terms of load balancing and delay QoS provisioning.     

Cross-Layer optimization for LDPC-coded multirate multiuser systems with QoS constraints

In this paper, we propose a new multirate multiple-access wireless system implemented by variable spreading gain and chip-level random interleaving. The receiver employs a flexible chip-level iterative multiuser detection scheme where the variable spreading gain affects only the despreading parameters. Optimization across the physical and network layers in the uplink of such a system is treated. It is assumed that each user employs an low-density parity-check (LDPC) code to protect its data. At the physical layer, the quality of service (QoS) requirement is specified in terms of the target bit error rate (BER) of each user. Optimal user transmit powers are dynamically adjusted according to the current system load and the corresponding rate requirements. At the network layer, the QoS requirements include the call blocking probabilities, call connection delays, packet congestion probabilities and packet loss rates. To maximize the average revenue of the network subject to both call-level and packet-level QoS constraints, a multicriterion reinforcement learning (MCRL)-based adaptive call admission control (CAC) method is proposed that can easily handle multiple average QoS requirements. Unlike existing model-based approaches, the MCRL-based technique does not require the explicit knowledge of the state transition probabilities to derive the optimal policy. This feature is important when the number of states is so large that model-based optimization algorithms become infeasible, which is typically the case for a large integrated service network supporting a number of different service types.     

移动性感知下基于负载均衡的任务迁移方案

针对移动边缘计算中用户移动性导致服务器间负载分布不均,用户服务质量(Quality of Service, QoS)下降的问题,提出了一种移动性感知下的分布式任务迁移方案。首先,以优化网络中性能最差的用户QoS为目标,建立了一个长期极大极小化公平性问题(Max Min Fairness, MMF),利用李雅普诺夫(Lyapunov)优化将原问题转化解耦。然后,将其建模为去中心化部分可观测马尔可夫决策过程(Decentralized Partially Observable Markov Decision Process, Dec-POMDP),提出一种基于多智能体柔性演员-评论家(Soft Actor-Critic, SAC)的分布式任务迁移算法,将奖励函数解耦为节点奖励和用户个体奖励,分别基于节点负载均衡度和用户QoS施加奖励。仿真结果表明,相比于现有任务迁移方案,所提算法能够在保证用户QoS的前提下降低任务迁移率,保证系统负载均衡。     

LTE-Advanced中继系统中一种公平的下行资源分配机制

LTE-Advanced(LTE-A)系统引入中继后,带来了如提高频谱效率、拓展网络覆盖等优点,但是也给资源分配带来了更多的挑战。针对LTE-A中继系统的下行资源分配问题,在资源足够使用的情况下,提出一种在eNB和中继功率受限且用户数据速率限制的条件下,以权衡系统吞吐量和用户公平性为目标的资源分配机制。通过仿真验证表明,该算法较好地权衡了系统吞吐量和用户公平性,并且能满足用户速率要求。