H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度策略

针对异构云无线接入网络(H-CRAN)网络下基于网络切片的在线无线资源动态优化问题,该文通过综合考虑业务接入控制、拥塞控制、资源分配和复用,建立一个以最大化网络平均和吞吐量为目标,受限于基站(BS)发射功率、系统稳定性、不同切片的服务质量(QoS)需求和资源分配等约束的随机优化模型,并进而提出了一种联合拥塞控制和资源分配的网络切片动态资源调度算法。该算法会在每个资源调度时隙内动态地为性能需求各异的网络切片中的用户分配资源。仿真结果表明,该文算法能在满足各切片用户QoS需求和维持网络稳定的基础上,提升网络整体吞吐量,并且还可通过调整控制参量的取值实现时延和吞吐量间的动态平衡。     

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。     

自组织无线网络中的移动负载均衡技术

文章认为基于人工进行的网络优化方法难以适应未来网络发展需要,为此LTE系统引入了自组织网络技术。自组织网络技术涉及网络部署、运行的各个环节,其中移动负载均衡技术是自组织网络技术的一个关键技术。移动负载均衡技术通过用户在不同小区之间的切换实现负载的转移,从而实现负载的均衡分布,提高网络性能。针对很多移动负载均衡算法没有考虑用户的服务质量(QoS)要求,文章介绍了一种保证QoS要求的移动负载均衡算法。     

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D−S证据理论的异构无线网络接入选择算法

对异构无线网络中无线资源管理和接入选择进行研究,提出一种基于D?S证据理论的异构无线网络接入选择算法。针对接入判决所需参数具有时变性和不确定性的特点,结合能够有效反映候选网络负载状况的效用函数,建立基于D?S证据理论的接入效用评估模型,得到各候选网络的综合接入效用值。仿真结果表明,该算法具有复杂度低(一般性)的特点,在保证不同业务服务质量(QoS)需求的前提下能够动态调整各候选网络的负载情况,从而有效降低整个系统的呼叫阻塞率。     

基于模糊神经网络的异构无线网络接入选择算法

针对现有异构无线网络基于模糊逻辑及神经网络的接入选择方法未能合理考虑网络负载状况的问题,提出一种基于RBF(径向基函数)模糊神经网络的接入选择方法.该方法以可接入网络的接入阻塞率相等为模糊神经网络参数强化学习的目标,对网络负载程度具有很好的动态适应性,实现了智能化的接入判决.仿真结果表明,该方法能有效均衡异构无线网络间的负载,保障实时与非实时业务的QoS,并且相对于负载均衡算法(MLB算法)降低了网络的接入阻塞率.     

CVANET中基于POMDP模型的频谱接入算法

针对认知车载Ad Hoc网络(CVANET)信道的动态特性,以部分可观测马尔科夫决策过程(POMDP)为模型对认知车辆用户的频谱感知和频谱接入过程进行研究,提出基于POMDP模型的分布式机会频谱接入算法,并通过贪心算法降低POMDP算法计算量,最后通过仿真研究影响认知车辆用户吞吐量的主要因素,并验证算法的可行性。仿真分析结果表明,认知车辆用户通过本算法接入吞吐量得到有效提高,降低了交通中广播风暴的可能,并且降低了计算量。     

基于最佳虚拟网关的认知SCMA系统

在5G的海量机器类通信(mMTC)场景下,为解决频谱资源不足和网络拥塞问题,本文提出一种基于最佳虚拟网关的认知SCMA系统模型.该模型根据QoS要求和地理位置把机器类通信设备(MTCD)分成独立的集群,在每个集群中通过认知无线电技术感知可能提供空闲频谱资源的LTE用户,定义为虚拟网关,再根据满意度最大规则选出最佳虚拟网关,将最佳虚拟网关提供的频谱资源分为多个子载波组,并设计MTCD与子载波组的最优匹配算法,MTCD以SCMA(稀疏码多址接入Sparse Code Multiple Access)方式接入网络.仿真结果表明所提方案可有效提高系统的吞吐量.     

基于用户个性化服务质量的蜂窝车联网与车载自组织网异构车联网资源分配方法

蜂窝车联网(C-V2X)与车载自组织网络(VANET)的异构融合能够有效提高网络容量。然而,不同网络在非授权频段上共存而引起的信道冲突会导致系统吞吐量降低和用户接入时延增大,无法满足车联网用户对服务质量(QoS)的需求。针对该问题,该文提出一种基于用户个性化QoS需求的时频资源分配方法。首先,分别对C-V2X 和 VANET 的吞吐量和时延进行建模分析,刻画用户数据传输时间配置与吞吐量和时延的数学关系;然后,基于上述模型构建吞吐量-时延联合优化函数,根据用户的个性化QoS需求实现异构网络中吞吐量和时延的优化;最后,提出一种基于改进多目标粒子群优化的时延-吞吐量联合优化算法(DT-JOA)进行求解。仿真结果表明,该文所提网络资源分配算法可以有效地保证用户的个性化QoS需求,提升异构网络综合性能。     

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。      

Reinforcement-Learning-Based Double Auction Design for Dynamic Spectrum Access in Cognitive Radio Networks

In cognitive radio networks, an important issue is to share the detected available spectrum among different secondary users to improve the network performance. Although some work has been done for dynamic spectrum access, the learning capability of cognitive radio networks is largely ignored in the previous work. In this paper, we propose a reinforcement-learning-based double auction algorithm aiming to improve the performance of dynamic spectrum access in cognitive radio networks. The dynamic spectrum access process is modeled as a double auction game. Based on the spectrum access history information, both primary users and secondary users can estimate the impact on their future rewards and then adapt their spectrum access or release strategies effectively to compete for channel opportunities. Simulation results show that the proposed reinforcement-learning-based double auction algorithm can significantly improve secondary users’ performance in terms of packet loss, bidding efficiency and transmission rate or opportunity access.     

Semi-blind collision resolution in random access wireless ad hocnetworks

A new signal processing based collision resolution technique for random access wireless ad hoc networks is proposed. Without assuming the knowledge of the propagation channels and signal waveforms, the proposed algorithm is capable of separating colliding packets by exploiting channel diversities and known symbols embedded in data packets. Compared with training-based methods, the proposed algorithm requires considerably fewer known symbols. This algorithm can be applied to various spread spectrum and narrowband systems along with existing medium access control protocols     

一种利用优先经验回放深度Q-Learning的频谱接入算法

针对认知无线传感器网络中频谱接入算法的频谱利用率不高、重要经验利用率不足、收敛速度慢等问题,提出了一种采用优先经验回放双深度Q-Learning的动态频谱接入算法。该算法的次用户对经验库进行抽样时,采用基于优先级抽样的方式,以打破样本相关性并充分利用重要的经验样本,并采用一种非排序批量删除方式删除经验库的无用经验样本,以降低能量开销。仿真结果表明,该算法与采用双深度Q-Learning的频谱接入算法相比提高了收敛速度;与传统随机频谱接入算法相比,其阻塞概率降低了6%~10%,吞吐量提高了18%~20%,提高了系统的性能。     

基于Stackelberg博弈的动态频谱接入控制

为了解决多主用户和多次级用户共存网络的频谱资源分配问题,提出了一种基于斯塔科尔伯格（Stackelberg）博弈的动态频谱接入控制算法。该算法通过三阶段Stackelberg博弈模拟主用户频谱竞价,博弈过程中次级用户以最大化传输速率为目的接入主用户频谱,同时设计了一种迭代过程来求解纳什均衡。实验计算与结果分析证明了纳什均衡唯一存在性的充要条件,并说明了迭代过程的收敛性以及主用户最佳效用的影响因素。     

Dynamic cooperator selection in cognitive radio networks

The primary objective of cooperation in cognitive radio (CR) networks is to increase the spectrum access efficiency and improve the network performance. However, Byzantine adversaries or unintentional erroneous conduct in cooperation can lead to destructive behavior of CR users that can decrease their own and others’ performances. This work presents a dynamic solution for cooperation reliability in conditions with constraints typical for a CR network. Specifically, in CR networks, the information on the success of cooperation can be limited only to cases with interference; when malicious, cooperators can be completely non-correlated and can alter behavior; and the set of available cooperators can dynamically change in time. In order to face these challenges, each CR user autonomously decides with whom to cooperate by learning cooperators behavior with a reinforcement learning (RL) algorithm. This RL algorithm determines the suitability of the available cooperators, and selects the most appropriate ones to cooperate with the objective to increase the efficiency of spectrum access in CR networks. The simulation results demonstrate the learning capabilities of the proposed solution and especially its reliable behavior under highly unreliable conditions.     

认知无线电网络中基于跨层设计的能效优化算法

为提高认知无线电网络中次用户节点的能量有效性,该文基于连续时间马尔科夫理论对次用户的频谱感知和接入过程进行联合建模,对影响次用户传输能效的主要因素进行了分析,提出了一种基于跨层设计的能量有效优化算法。该算法可有效减小主用户非时隙返回信道对次用户能量有效性的影响,并通过联合优化感知时间和接入概率,使次用户在感知性能和传输能效间实现了有效折衷。仿真结果表明,本文算法相对于仅考虑频谱感知或接入策略的单层优化算法,可使次用户的能量有效性得到较大提高。      

SCALE: A Low-Complexity Distributed Protocol for Spectrum Balancing in Multiuser DSL Networks

Dynamic spectrum management of digital subscriber lines (DSLs) has the potential to dramatically increase the capacity of the aging last-mile copper access network. This paper takes an important step toward fulfilling this potential through power spectrum balancing. We derive a novel algorithm called SCALE, that provides a significant performance improvement over the existing iterative water-filling (IWF) algorithm in multiuser DSL networks, doing so with comparable low complexity. The algorithm is easily distributed through measurement and limited message passing with the use of a spectrum management center. We outline how overhead can be managed, and show that in the limit of zero message-passing, performance reduces to IWF.     

Dynamic Spectrum Access is the Solution: What’s the Problem?

Dynamic access to spectrum evolved as a concept from an observation that much spectrum was under-utilized. Over time the access rules for this spectrum have changed to geo-location and it has become clear that the spectrum is often subject to interference. This has changed the thinking about the applications most likely to emerge away from classic unlicensed uses such as device-to-device and home networks and towards wide-area machine-to-machine networks and rural broadband. Such applications would prefer somewhat certain access to spectrum so further enhancements to access rules to prioritise certain users should be considered.     

Enhancing the spectral efficiency and energy efficiency of underwater channel communication by optimal cooperative spectrum sensing using hybrid optimization

Underwater wireless sensor networks (UWSNs) contain quite a lot of components such as vehicles and sensors that are deployed in a specific acoustic area to perform collaborative monitoring and data collection errands. These networks are adopted interactively between diverse nodes and ground‐based stations. Currently, UWSNs face problems and challenges that pertain to limited bandwidth, media access control, high propagation delay, 3D topology, spectrum sensing, resource utilization, routing, and power constraints. This proposal deals with the intelligent spectrum sensing in underwater cognitive sonar communication networks (CSCN). Here, the improved performance of spectrum sensing in underwater communication is attained by optimizing the cooperative spectrum sensing and data transmission. The parameters of system like subchannel allocation and transmission power is optimized by a new hybrid meta‐heuristic algorithm by integrating the concepts of deer hunting optimization algorithm (DHOA) and lion algorithm (LA) termed as lion‐enabled DHOA (L‐DHOA). The main intention of optimizing these parameters is to maximize the spectrum efficiency (SE) and energy efficiency (EE) of the underwater channel communication system. From the analysis, with respect to convergence rate, minimum detection probability, and local sensing time, it is proved that the novel hybrid optimization algorithm keeps a great role in making the trade‐off between the SE and EE in underwater channel modeling.     

基于PCN的水下认知网络动态频谱接入算法

水下网络可用频谱范围比较窄,且部分被水下生物占用,导致了水下传感器网络可用的频谱资源更为稀缺.针对上述问题,提出一种基于累积干扰预测(Predicted Cumulative Noise,PCN)的水下认知网络动态频谱接入算法.该算法把水下生物作为认知网络的主节点,水下传感器节点作为次节点;通过建立水下生物业务行为的马尔科夫模型预测累积干扰,次节点根据预测结果,采用合作的方式动态地接入授权频谱.仿真结果表明,该算法能够保护水下生物正常通信的同时,实现最优化的频谱共享,网络容量增益达到6.3dB.