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...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。展开更多
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...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。展开更多
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...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。展开更多
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(R...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%。展开更多
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...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.展开更多
文摘云无线接入网络(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。
文摘在云接入网络(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。
文摘利用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。
文摘针对云无线网络(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%。
基金Project supported by the Zhejiang Provincial Natural Science Foundation of China(Nos.LY21F010008 and LD21F010001)the National Natural Science Foundation of China(No.62171412)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2020D10)。
文摘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.
