UAV-aided cellular networks,millimeter wave(mm-wave)communications and multi-antenna techniques are viewed as promising components of the solution for beyond-5G(B5G)and even 6G communications.By leveraging the power o...UAV-aided cellular networks,millimeter wave(mm-wave)communications and multi-antenna techniques are viewed as promising components of the solution for beyond-5G(B5G)and even 6G communications.By leveraging the power of stochastic geometry,this paper aims at providing an effective framework for modeling and analyzing a UAV-aided heterogeneous cellular network,where the terrestrial base stations(TBSs)and the UAV base stations(UBSs)coexist,and the UBSs are provided with mm-wave and multi-antenna techniques.By modeling the TBSs as a PPP and the UBSs as a Matern hard-core point pro-´cess of type II(MPH-II),approximated but accurate analytical results for the average rate of the typical user of both tiers are derived through an approximation method based on the mean interference-to-signal ratio(MISR)gain.The influence of some relevant parameters is discussed in detail,and some insights into the network deployment and optimization are revealed.Numerical results show that some trade-offs are worthy of being considered,such as the antenna array size,the altitude of the UAVs and the power control factor of the UBSs.展开更多
By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division ...By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division multiplexing （OFDM） systems. The researches usually assume that the channel is strictly sparse and formulate the channel estimation as a standard compressed sensing problem. However, such strictly sparse assumption does not hold true in non-sample-spaced multiple channels. The authors in this article proposed a new method of compressed sensing based channel estimation in which an over-complete dictionary with a finer delay grid is applied to construct a sparse representation of the non-sample-spaced multipath channels. With the proposed, the channel estimation was formulated as the model-based CS problem and a modified model-based compressed sampling matching pursuit （CoSaMP） algorithm was applied to reconstruct the discrete-time channel impulse response （CIR）. Simulation indicates that the new method proposed here outperforms the traditional standard CS-based methods in terms of mean square error （MSE） and bit error rate （BER）.展开更多
The rapid boosting in mobile traffic and the scarcity of available radio spectrum have hindered the improvement of capacity in cellular networks.It is necessary to discover an appropriate coexistence between cellular ...The rapid boosting in mobile traffic and the scarcity of available radio spectrum have hindered the improvement of capacity in cellular networks.It is necessary to discover an appropriate coexistence between cellular and other radio access technologies(mainly Wi-Fi)to offload the high traffic on to unlicensed bands.Dealing with joint time and power allocation to devices,a non-convex problem is modeled to maximize the throughput as well as guarantee the desired user satisfaction.A two-step traffic balancing scheme is proposed to derive the solution.We also focus on the inner competition among cellular users instead of traditional competition between cellular and Wi-Fi in the unlicensed bands.Finally,simulation results show the effectiveness of the proposed two-step traffic balancing scheme.展开更多
文摘UAV-aided cellular networks,millimeter wave(mm-wave)communications and multi-antenna techniques are viewed as promising components of the solution for beyond-5G(B5G)and even 6G communications.By leveraging the power of stochastic geometry,this paper aims at providing an effective framework for modeling and analyzing a UAV-aided heterogeneous cellular network,where the terrestrial base stations(TBSs)and the UAV base stations(UBSs)coexist,and the UBSs are provided with mm-wave and multi-antenna techniques.By modeling the TBSs as a PPP and the UBSs as a Matern hard-core point pro-´cess of type II(MPH-II),approximated but accurate analytical results for the average rate of the typical user of both tiers are derived through an approximation method based on the mean interference-to-signal ratio(MISR)gain.The influence of some relevant parameters is discussed in detail,and some insights into the network deployment and optimization are revealed.Numerical results show that some trade-offs are worthy of being considered,such as the antenna array size,the altitude of the UAVs and the power control factor of the UBSs.
文摘5G新空口(New Radio, NR)定义了侧行链路(Sidelink, SL)模式2资源分配机制,使用户能自主选择预留资源进行数据传输,以满足基站覆盖范围外终端间直接通信的需求;随着移动通信技术的快速发展,智能终端间直接通信对于速率的要求越来越高,有限的授权频谱成为限制速率的瓶颈,使用非授权频段可以缓解授权频谱资源短缺的问题,进一步提升网络的传输速率;非授权频谱中的NR(NR in the Unlicensed Spectrum, NR-U)采用先听后说(Listen Before Talk, LBT)接入非授权信道,LBT不确定性会引起NR SL用户接入预留资源失败,带来额外的传输时延。针对上述问题,提出一种基于终端自主侦听的非授权接入方法,通过配置候选预留子信道资源,提升了模式2资源分配机制下NR SL用户采用LBT机制接入非授权信道的成功率。仿真结果表明,所提机制能有效提升NR SL系统在非授权频段的性能。
基金supported by the National Science and Technology Major Project (2012ZX03001039-002)
文摘By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division multiplexing （OFDM） systems. The researches usually assume that the channel is strictly sparse and formulate the channel estimation as a standard compressed sensing problem. However, such strictly sparse assumption does not hold true in non-sample-spaced multiple channels. The authors in this article proposed a new method of compressed sensing based channel estimation in which an over-complete dictionary with a finer delay grid is applied to construct a sparse representation of the non-sample-spaced multipath channels. With the proposed, the channel estimation was formulated as the model-based CS problem and a modified model-based compressed sampling matching pursuit （CoSaMP） algorithm was applied to reconstruct the discrete-time channel impulse response （CIR）. Simulation indicates that the new method proposed here outperforms the traditional standard CS-based methods in terms of mean square error （MSE） and bit error rate （BER）.
基金supported by The National Natural Science Foundation of China(No.61471058)Beijing Nova Program(No.xx2012037).
文摘The rapid boosting in mobile traffic and the scarcity of available radio spectrum have hindered the improvement of capacity in cellular networks.It is necessary to discover an appropriate coexistence between cellular and other radio access technologies(mainly Wi-Fi)to offload the high traffic on to unlicensed bands.Dealing with joint time and power allocation to devices,a non-convex problem is modeled to maximize the throughput as well as guarantee the desired user satisfaction.A two-step traffic balancing scheme is proposed to derive the solution.We also focus on the inner competition among cellular users instead of traditional competition between cellular and Wi-Fi in the unlicensed bands.Finally,simulation results show the effectiveness of the proposed two-step traffic balancing scheme.
