Channel Estimation for Full-Duplex Relay Transmission in Cloud Radio Access Networks

Relay in full-duplex(FD) mode can achieve higher spectrum efficiency than that in half-duplex mode,while it is crucial to suppress relay self-interference to ensure transmission quality which requires instantaneous channel state information(CSI). In this paper,the channel estimation issue in FD amplify-andforward relay networks is considered,where the training-based estimation technique is adopted. Firstly,the least square(LS) estimation is implemented to obtain composite channel coefficients of source-relay-destination(SRD) channel and relay loop-interference(LI) channel in order to assist destination in performing data detection. Secondly,both LS and maximum likelihood estimation methods are utilized to perform individual channel estimation aiming at supporting successive interference cancelation at destination. Finally,simulation results demonstrate the effectiveness of both composite and individual channel estimation,and the presented ML method can achieve lower MSEs than LS solution.

Semi-Blind Pilot-Aided Channel Estimation in Uplink Cloud Radio Access Networks

In this paper,a quasi-Newton method for semi-blind estimation is derived for channel estimation in uplink cloud radio access networks(C-RANs).Different from traditional pilot-aided estimation,semi-blind estimation utilizes the unknown data symbols in addition to the known pilot symbols to estimate the channel.An initial channel state information(CSI) obtained by least-squared(LS) estimation is needed in semi-blind estimation.BFGS(Brayben,Fletcher,Goldfarb and Shanno) algorithm,which employs data as well as pilot symbols,estimates the CSI though solving the problem provided by maximum-likelihood(ML) principle.In addition,mean-square-error(MSE) used to evaluate the estimation performance can be further minimized with an optimal pilot design.Simulation results show that the semi-blind estimation achieves a significant improvement in terms of MSE performance over the conventional LS estimation by utilizing data symbols instead of increasing the number of pilot symbols,which demonstrates the estimation accuracy and spectral efficiency are both improved by semiblind estimation for C-RANs.

Performance Analysis in Cloud Radio Access Networks:User-Centralized Coordination Approach

The cloud radio access network(C-RAN) has recently been proposed as an important component of the next generation wireless networks providing opportunities for improving both spectral and energy effi ciencies. The performance of this network structure is however constrained by severe inter-cell interference due to the limited capacity of fronthaul between the radio remote heads(RRH) and the base band unit(BBU) pool. To achieve performance improvement taking full advantage of centralized processing capabilities of C-RANs,a set of RRHs can jointly transmit data to the same UE for improved spectral effi ciency. In this paper,a user centralized joint coordinated transmission(UC-JCT) scheme is put forth to investigate the downlink performance of C-RANs. The most important benefit the proposed strategy is the ability to translate what would have been the most dominant interfering sources to usable signal leading to a signifi cantly improved performance. Stochastic geometry is utilized to model the randomness of RRH location and provides a reliable performance analysis. We derive an analytical expression for the closed integral form of the coverage probability of a typical UE. Simulation results confirm the accuracy of our analysis and demonstrate that significant performance gain can be achieved from the proposed coordination schemes.

C-RAN:面向绿色的未来无线接入网演进

在高速发展的移动业务和日趋激烈的竞争环境下,移动运营商面临着多方面的挑战:高额的能耗、高涨的建设和运维成本、紧张的频谱资源、快速增长的业务流量以及日趋严峻的成本压力。为解决这些挑战并追求未来可持续的增长,根据现网条件和技术进步的趋势,提出了面向绿色演进的新型无线接入网构架C-RAN。C-RAN是基于集中化处理(Centralized),协作式无线电(Cooperative Radio)和实时云计算构架(Real-time Cloud Infrastructure)的无线接入网构架,集中式基带处理可以大大减少覆盖同样区域所需基站的数量;面向协作的无线远端模块和天线可以提高系统频谱效率;基于开放平台的实时云型基础设施和基站虚拟化技术可以降低成本,共享处理资源,减少能源消耗,提高基础设施利用率。这些特点能够很好地解决移动运营商所面临的上述挑战,并满足营收和未来移动互联网业务同步发展要求。

基于TWDM-PON与C-RAN的QoE感知视频协作缓存与传输机制

随着数据流量的爆发式增长,用户体验质量急剧恶化,为了解决密集组网与负载不均衡所引发的小区边缘用户与重负载小区用户性能较差的问题,提出了一种体验质量感知的云无线接入网视频协作缓存与传输机制。该机制通过建立协作增益感知的虚拟无源光网络,采用光域和无线域协同的方式为视频内容提供协作缓存与传输;进而,联合优化用户视频体验、带宽配置与缓存策略,分别在光域和无线域采用动态预缓存及缓冲水平感知的带宽配置方式,达到提高用户体验质量的目的。结果表明,所提机制能够有效地提高缓存命中率,增强用户体验质量。

智能反射面辅助OFDMA云接入网的资源分配方法

本文研究了智能反射面(IRS)辅助OFDMA(Orthogonal Frequency Division Multiple Access,正交频分多址接入)云无线接入网(C-RAN)的下行链路传输系统,其中基带处理单元(BBU)池通过多个远端射频头(RRH)与多个用户进行通信.RRH到用户的接入链路采用OFDMA接入技术.对于BBU池到RRH,采用无线前传链路,并且部署多个IRS以增强链路传输能力.在BBU池和每个RRH发射功率约束下,本文提出通过联合优化前传链路和接入链路资源配置使下行用户和速率最大化.由于该资源配置问题是非凸的,首先采用连续凸逼近(SCA)对目标以及约束条件进行转换.其次,将转换后的问题拆分成三个子问题来交替性求解.最后,计算机仿真结果显示了所提出的联合资源分配方法与其他基准方案相比具有显著的传输性能增益.

带有混合能量供电的TWDM-PON与C-RAN联合架构资源分配机制

针对现有虚拟化云无线接入网络资源利用率低、能耗高、用户服务质量无法得到保证等问题,提出一种带有混合能量供电的能耗感知虚拟化资源分配机制。根据不同网络设备的能量来源和能量消耗情况,建立能量到达模型和能量消耗模型;进而,在保障用户服务质量的前提下,考虑比例公平和能耗优化,利用异步更新的分布式算法对不同类型虚拟云无线接入网和用户虚拟基站分配资源和能量,从而有效提高网络能量效率。仿真结果表明,所提资源分配机制能够降低能耗,同时有效地减小了时延,提高了吞吐量。

基于C-RAN与LTE-Hi的BR-OFDMA导频图案设计

在集中式/协作式/云计算无线接入网(C-RAN)环境下,结合热点及室内覆盖的长期演进(LTE-Hi)特点,研究导频图案的设计。LTE-Hi要求插入的导频数量比传统的长期演进(LTE)少,又由于其覆盖特点导致小区边界更容易受相邻小区干扰,由此提出一种应用块重复的正交频分多址(BR-OFDMA)和压缩感知信道估计技术的导频图案设计方案。该方案能够探测各相邻小区间的干扰情况,然后根据干扰情况设置BR-OFDMA参数,最后得到导频图案。该方案能明显减少导频数量,在C-RAN的云结构下更适合LTE-Hi相邻小区间的协作配置,能根据干扰情况,在资源消耗与性能之间进行平衡。仿真表明该方案在低信干比环境下仍能表现较好性能,适合在C-RAN与LTE-Hi中应用。

一种基于SDN的C-RAN负载均衡架构及算法

在云无线接入网(C-RAN)中,基带单元(BBU)可以在一定程度上解决流量分布不均匀问题,但如何实现BBU间的负载均衡研究不多。针对上述问题,将SDN控制器加入BBU池中,设计一种新型的基于SDN技术的C-RAN负载均衡架构,并提出QoS优化算法和BBU间负载均衡算法。首先,计算各BBU剩余可用负载比率差异标准差;其次,根据BBU间负载差异等级选择不同的优化算法;当差异等级较低时,执行QoS优化算法;当差异等级较高时,执行BBU间负载均衡算法。仿真结果表明,上述算法可以有效降低分组丢失率,提高系统吞吐量。

基于物理层安全的C-RAN绿色资源管理算法

研究了基于物理层安全的云无线接入网络(C-RAN)联合频谱和功率分配算法。首先,利用单拍卖理论对C-RAN频谱分配问题进行数学建模,并且提出了最大化安全吞吐量的频谱分配算法;然后,使用Max-Min公平性原则提出了最大化最小用户的能量效率功率分配算法;最后,仿真结果表明所提算法相比其他算法能够获得更优的安全吞吐量,以及较公平的能效比。

C-RAN系统中的软件基带信号处理技术

云无线接入网(C-RAN)是融合了集中化处理、协作无线电和实时云计算的无线接入网架构,不同于传统无线通信网络架构,其基带处理单元基于统一开放的软件无线电平台。本文从灵活性、实现复杂度、运算精确度、处理时延、吞吐量、资源利用率、能耗等方面对比C-RAN系统中的软件发送接收技术与传统基带信号处理技术,并以低密度奇偶校验码(LDPC)编码载波传输系统为典型案例,展示软件技术运算精确度高、性能优良,而传统技术处理时延低的特点。

一种深度强化学习的C-RAN动态资源分配方法

移动边缘计算(MEC)技术已成为云无线接入网(C-RAN)提供近距离服务的一个很有前途的例子,从而减少了服务延迟,节约了能源消耗.本文考虑一个多用户MEC系统,解决了计算卸载策略和资源分配策略问题.我们将延迟总成本和能耗作为优化目标,在一个动态的环境中获得一个最优的策略.提出了一个基于深度强化学习的优化框架来解决资源分配问题,利用深度神经网络(DNN)对批评者的价值函数进行估计,从当前状态直接提取信息,不需要获取准确的信道状态.从而降低了优化目标的状态空间复杂度.参与者使用另一个DNN来表示参数随机策略,并在批评者的帮助下改进策略.仿真结果表明,与其它方案相比,该方案显著降低了总功耗.

基于C-RAN的BBU-RRH的动态调度方案设计

在云接入网络(Cloud Radio Access Network,C-RAN)架构中,传统的基站分为基带处理单元(Base Band Unit,BBU)和射频拉远头(Remote Radio Head,RRH),所有的RRH分布在远程站点共享运行在云中心的BBU资源池。现有的方案考虑的RRH只能将数据传输到唯一归属的BBU上,导致不同的BBU之间不能共享数据。提出了一种基于C-RAN的BBU-RRH的动态调度方案(DSSC),BBU间通过借用资源的方式动态地给RRH分配资源。仿真结果表明,本文提出的方案可以有效地提高系统的吞吐量和频谱效率,减少了资源的浪费。

C-RAN预处理单元设计与实现

利用FPGA(现场可编程门阵列)实现基于LTE(Long Term Evolution,长期演进)协议C-RAN(云无线接入网络)体系架构中的前端预处理单元来加速CRAN系统的处理速度。软件层运行在基带信号处理单元池中,并且和FPGA前端预处理单元协同完成基带信号处理的整个过程。其中FPGA前端预处理单元集成了丰富的接口资源,包括PCIE、10 Gb/s以太网口、CPRI(通用公共无线接口)接口,将传统的移动通信和高速数据处理单元有效连接起来。预处理单元通过完成基带处理中的关键算法来减轻服务器处理的压力。与此同时在预处理单元内完成循环前缀的去除和有效子载波数据的筛选,降低了系统的IO吞吐量。预处理单元已经完成了仿真与验证。

面向C-RAN的群稀疏线性预编码

云无线接入网络(cloud radio access network,C-RAN)是一种能够集中处理信号的网络架构。C-RAN能够通过算法动态选择无线电单元(remote radio head,RRH)来调整用户通信速率。而通信速率作为用户服务质量(quality of service,QoS)的关键部分,当参与服务的RRH越多时,用户的通信速率更大且体验更好,但同时所带来的能源损耗越大,因此本文研究通信速率和功率消耗二者之间的权衡关系。提出一种优化算法,将权衡问题建模成一个单目标优化模型,通过权衡系数来协调速率和RRH激活个数之间的矛盾。为了解决l0-范数的非凸问题,本文使用重复加权l1-范数去近似l0-范数,同时使用加权最小均方误差(weighted minimum mean square error,WMMSE)的方法将通信速率从非凸问题转换成一个凸问题,最后使用改进的次梯度法对预编码矩阵进行更新。仿真结果证明该算法减少了时间复杂度,同时达到了与穷举法相近的性能。

一种基于C-RAN载波迁移模型的缓冲区清空算法

在C-RAN架构下的分层载波迁移过程中,迁移管理中心的缓存单元存储着待处理的L2/L3层协议栈数据。为保证用户业务的连续性,需及时清空缓冲区。针对该问题,提出一种动态改变L2/L3层协议栈进程优先级的载波迁移缓冲区清空(ECMB)算法,并通过与传统EDF和HVF算法的仿真对比,验证了该算法在清空缓冲区操作中的有效性。

基于改进遗传算法的C-RAN网络动态无线资源分配

针对云无线网络(Cloud Radio Access Network,C-RAN)中传统静态资源分配效率低下以及动态无线资源分配中资源种类单一的问题,提出了一种基于用户服务质量(Qulity of Service,QoS)约束的动态无线资源分配方案,对无线资源从无线射频单元(Remote Radio Head,RRH)选择、子载波分配和RRH功率分配三个维度进行研究。首先,根据传统的C-RAN系统传输模型和QoS约束在时变业务环境下建立了以发射功率为变量,以吞吐量最大为优化目标的优化问题;然后,基于改进的遗传算法,将原优化方案转变为通过优化RRH选择、子载波分配和RRH功率分配来达到提高系统吞吐量的目的;最后,将改进的遗传算法与其他智能算法在种群规模变化下进行了时间复杂度对比。实验结果表明,所提算法具有较低时间复杂度,所提资源分配方案下的平均吞吐量增益为17%。

Large System Analysis of Downlink C-RAN with Phase Noise and Fronthaul Compression

This paper studies the effect of phase noise and fronthaul compression on a downlink cloud radio access network(C-RAN), where several remote radio heads(RRHs) are coordinated to communicate with users by a baseband unit(BBU) on the cloud server. In the system, the baseband signals are precoded at BBU, and then compressed before being transmitted to RRHs through capacity-limited fronthaul links which results in the compressive quantization noise. We assume the regularized zero-forcing precoding is performed with an imperfect channel state information and a compression strategy is applied at BBU. The effect of phase noise arising from nonideal local oscillators both at RRHs and users is considered. We propose an approximate expression for the downlink ergodic sum-rate of considered C-RAN utilizing large dimensional random matrix theory in the large-system regime. From simulation results, the accuracy of the approximate expression is validated, and the effect of phase noise and fronthaul compression can be analyzed theoretically based on the approximate expression.

Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks

Owing to the inherent central information processing and resource management ability,the cloud radio access network(C-RAN)is a promising network structure for an intelligent and simplified sixth-generation(6G)wireless network.Nevertheless,to further enhance the capacity and coverage,more radio remote heads(RRHs)as well as high-fidelity and low-latency fronthaul links are required,which may lead to high implementation cost.To address this issue,we propose to exploit the intelligent reflecting surface(IRS)as an alternative way to enhance the C-RAN,which is a low-cost and energy-efficient option.Specifically,we consider the uplink transmission where multi-antenna users communicate with the baseband unit(BBU)pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs.RRHs can conduct either point-to-point(P2P)compression or Wyner-Ziv coding to compress the received signals,which are then forwarded to the BBU pool through fronthaul links.We investigate the joint design and optimization of user transmit beamformers,IRS passive beamformers,and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding.By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation(SDR),we propose a successive convex approximation approach to solve non-convex problems,and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided.Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

C-RAN网络中CoMP性能分析与优化

针对无线通信中的能耗巨大和移动用户潮汐现象带来的基站利用率低下问题,C-RAN作为一种基于集中化处理、协作式无线电和实时云计算架构的无线接入网架构,是一种低成本、高性能的解决方案。针对C-RAN网络中的CoMP性能,通过计算无线链路损耗分析了JT下的CoMP的理论增益。针对C-RAN网络规划设计问题,定性分析了CoMP增益与C-RAN网络Cell数的比例关系,最后结合仿真结果给出了合理的网络规划设计建议,且理论证明其能够很好地解决移动运营商所面临的上述挑战。