Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interfere...Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).展开更多
Based on the analysis of the feature of cognitive radio networks, a relevant interference model was built. Cognitive users should consider especially the problem of interference with licensed users and satisfy the sig...Based on the analysis of the feature of cognitive radio networks, a relevant interference model was built. Cognitive users should consider especially the problem of interference with licensed users and satisfy the signal-to-interference noise ratio （SINR） requirement at the same time. According to different power thresholds, an approach was given to solve the problem of coexistence between licensed user and cognitive user in cognitive system. Then, an uplink distributed power control algorithm based on traditional iterative model was proposed. Convergence analysis of the algorithm in case of feasible systems was provided. Simulations show that this method can provide substantial power savings as compared with the power balancing algorithm while reducing the achieved SINR only slightly, since 6% S1NR loss can bring 23% power gain. Through further simulations, it can be concluded that the proposed solution has better effect as the noise power or system load increases.展开更多
In this paper, we propose a novel uplink power control algorithm, SMST, for multiple-input multiple-output orthogonal frequency-division multiple access （MIMQ-OFDMA）.We perform an extensive system-level simulation t...In this paper, we propose a novel uplink power control algorithm, SMST, for multiple-input multiple-output orthogonal frequency-division multiple access （MIMQ-OFDMA）.We perform an extensive system-level simulation to compare different uplink power control algorithms, including the FPC adopted in 3GPP LTE and LTE-Advanced. Simulations show that SMST adopted in IEEE 802.16m outperforms other algorithms in terms of spectral efficiency, cell-edge performance, interference control, and trade-off control between sector-accumulated throughput and cell-edge user throughput. The SMST performance gain over FPC can be more than 40%展开更多
针对工作于underlay模式的认知无线网络(CRN,Cognitive Radio Network)上行功率控制问题,本文提出一种基于多天线波束赋形,由认知基站和认知用户联合优化的分布式上行功率控制算法.联合优化的具体步骤为认知基站通过求解最大广义特征值...针对工作于underlay模式的认知无线网络(CRN,Cognitive Radio Network)上行功率控制问题,本文提出一种基于多天线波束赋形,由认知基站和认知用户联合优化的分布式上行功率控制算法.联合优化的具体步骤为认知基站通过求解最大广义特征值问题完成多天线波束赋形优化;认知用户先将非线性功率优化问题转换为几何规划凸优化问题,再使用梯度法完成分布式发送功率优化;认知基站和认知用户交替优化,实现网络效用最大化.数值仿真显示,同只优化认知用户功率的上行功率控制算法相比,认知基站和认知用户联合优化的上行功率控制算法不仅能得到更大的网络效用值,而且对主用户的干扰具有鲁棒性.展开更多
文摘Internet of things and network densification bring significant challenges to uplink management.Only depending on optimization algorithm enhancements is not enough for uplink transmission.To control intercell interference,Fractional Uplink Power Control(FUPC)should be optimized from network-wide perspective,which has to find a better traffic distribution model.Conventionally,traffic distribution is geographic-based,and ineffective due to tricky locating efforts.This paper proposes a novel uplink power management framework for Self-Organizing Networks(SON),which firstly builds up pathloss-based traffic distribution model and then makes the decision of FUPC based on the model.PathLoss-based Traffic Distribution(PLTD)aggregates traffic based on the propagation condition of traffic that is defined as the pathloss between the position generating the traffic and surrounding cells.Simulations show that the improvement in optimization efficiency of FUPC with PLTD can be up to 40%compared to conventional GeoGraphic-based Traffic Distribution(GGTD).
基金Project(61071104) supported by the National Natural Science Foundation of China
文摘Based on the analysis of the feature of cognitive radio networks, a relevant interference model was built. Cognitive users should consider especially the problem of interference with licensed users and satisfy the signal-to-interference noise ratio （SINR） requirement at the same time. According to different power thresholds, an approach was given to solve the problem of coexistence between licensed user and cognitive user in cognitive system. Then, an uplink distributed power control algorithm based on traditional iterative model was proposed. Convergence analysis of the algorithm in case of feasible systems was provided. Simulations show that this method can provide substantial power savings as compared with the power balancing algorithm while reducing the achieved SINR only slightly, since 6% S1NR loss can bring 23% power gain. Through further simulations, it can be concluded that the proposed solution has better effect as the noise power or system load increases.
文摘In this paper, we propose a novel uplink power control algorithm, SMST, for multiple-input multiple-output orthogonal frequency-division multiple access （MIMQ-OFDMA）.We perform an extensive system-level simulation to compare different uplink power control algorithms, including the FPC adopted in 3GPP LTE and LTE-Advanced. Simulations show that SMST adopted in IEEE 802.16m outperforms other algorithms in terms of spectral efficiency, cell-edge performance, interference control, and trade-off control between sector-accumulated throughput and cell-edge user throughput. The SMST performance gain over FPC can be more than 40%
文摘针对工作于underlay模式的认知无线网络(CRN,Cognitive Radio Network)上行功率控制问题,本文提出一种基于多天线波束赋形,由认知基站和认知用户联合优化的分布式上行功率控制算法.联合优化的具体步骤为认知基站通过求解最大广义特征值问题完成多天线波束赋形优化;认知用户先将非线性功率优化问题转换为几何规划凸优化问题,再使用梯度法完成分布式发送功率优化;认知基站和认知用户交替优化,实现网络效用最大化.数值仿真显示,同只优化认知用户功率的上行功率控制算法相比,认知基站和认知用户联合优化的上行功率控制算法不仅能得到更大的网络效用值,而且对主用户的干扰具有鲁棒性.
