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| 1 | A Predictive 6G Network with Environment Sensing Enhancement:From Radio Wave Propagation Perspective显示文摘In order to support the future digital society,sixth generation(6G)network faces the challenge to work efficiently and flexibly in a wider range of scenarios.The traditional way of system design is to sequentially get the electromagnetic wave propagation model of typical scenarios firstly and then do the network design by simulation offline,which obviously leads to a 6G network lacking of adaptation to dynamic environments.Recently,with the aid of sensing enhancement,more environment information can be obtained.Based on this,from radio wave propagation perspective,we propose a predictive 6G network with environment sensing enhancement,the electromagnetic wave propagation characteristics prediction enabled network(EWave Net),to further release the potential of 6G.To this end,a prediction plane is created to sense,predict and utilize the physical environment information in EWave Net to realize the electromagnetic wave propagation characteristics prediction timely.A two-level closed feedback workflow is also designed to enhance the sensing and prediction ability for EWave Net.Several promising application cases of EWave Net are analyzed and the open issues to achieve this goal are addressed finally. | Gaofeng Nie Jianhua Zhang Yuxiang Zhang Li Yu Zhen Zhang Yutong Sun Lei Tian Qixing Wang Liang Xia | 2022 | China Communications2022，19，6： | 4 |
| 2 | Discriminative least squares regression for multiclass classification and feature selection显示文摘 | Xiang Shiming Nie Feiping Meng Gaofeng Pan Chunhong Zhang Changshui | | 0，，： | 1 |
| 3 | Distributed power allocation with a novel signaling in dense OFDMA small cell networks显示文摘A distributed power allocation scheme was presented to maximize the system capacity in dense small cell networks. A new signaling called inter-cell-signal to interference plus noise ratio(ISINR) as well as its modification was defined to show the algebraic properties of the system capacity. With the help of ISINR, we have an easy way to identify the local monotonicity of the system capacity. Then on each subchannel in iteration, we divide the small cell evolved node B's(Se NBs) into different subsets. For the first subset, the sum rate is convex with respect to the power domain and the power optimally was allocated. On the other hand, for the second subset, the sum rate is monotone decreasing and the Se NBs would abandon the subchannel in this iteration. The two strategies are applied iteratively to improve the system capacity. Simulations show that the proposed scheme can achieve much larger system capacity than the conventional ones. The scheme can achieve a promising tradeoff between performance and signaling overhead. | Wang Meng Tian Hui Nie Gaofeng Wang Zhibo Liu Yang | 2015 | The Journal of China Universities of Posts and Telecommunications2015，22，3： | 0 |
| 4 | Time Efficient Joint Optimization Federated Learning over Wireless Communication Networks显示文摘Artificial intelligence(AI)has made a profound impact on our daily life.The 6 th generation mobile networks(6G)should be designed to enable AI services.The native intelligence is introduced as an important feature in 6G.6G native AI network is realized by the philosophy of federated learning(FL)to ensure data security and privacy.Federated learning over wireless communication networks is treated as a potential solution to realize native AI.However,introducing FL in the 6G will lead to expansive communication cost and unstable FL convergence with unreliable air interface.In this paper,we propose a solution for FL over wireless networks and analyze the training efficiency.To make full use of the advantages of the proposed network,we introduce a communication-FL joint optimization(CFJO)algorithm by jointly considering the effects of uplink resource,energy consumption and latency constraints.CFJO derives a transmission strategy with resource allocation and retransmissions to reduce the wireless transmission interruption probability and model upload latency.The simulation results show that CFJO significantly improves the model training efficiency and convergence performance with lower interruption probability under the latency constraint. | Junshuai Sun Yingying Wang Xin Sun Na Li Gaofeng Nie | 2022 | China Communications2022，19，6： | 0 |
| 5 | Duplicated transmission based-resource scheduling for uplink grant-free SCMA system显示文摘Sparse code multiple access-based uplink grant-free transmission(SCMA-UGFT)has been proposed to realize ultra reliable and low latency communication(URLLC)in the fifth generation(5 G)system.Without the process of resource request and grant,users may collide in the same resource.To compensate the potential user performance decline,resource scheduling becomes a tough issue in the SCMA-UGFT system.This article proposes a duplicated transmission-based resource scheduling(DTBRS)scheme for SCMA-UGFT system by considering the URLLC scenario.Different from the existing schemes,not only one shared basic transmission units(BTUs)are allocated to a user equipment(UE)in the proposed DTBRS scheme for initial transmission to realize the duplicated transmission and to guarantee the transmission reliability.Besides,according to the proposed DTBRS scheme,one or two exclusive BTUs are assigned to a UE for retransmission to avoid the re-collision.At last,each packet is given a lifetime to limit the transmission latency to meet the URLLC latency requirement.The simulation demonstrates that the DTBRS scheme can achieve a better performance than the existing state-of-the-art scheme in terms of the average packet drop rate. | Xiao Jiali Nie Gaofeng Deng Gang Tian Hui Zhang Chong | 2020 | The Journal of China Universities of Posts and Telecommunications2020，27，2： | 0 |
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