传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS...传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.展开更多
In this paper, we investigate the interference coordination for downlink full-dimension multiple-input multiple-output(FD-MIMO) systems with device-to-device(D2 D) communications underlaying. With three-dimensional(3 ...In this paper, we investigate the interference coordination for downlink full-dimension multiple-input multiple-output(FD-MIMO) systems with device-to-device(D2 D) communications underlaying. With three-dimensional(3 D) beamforming transmission applied for cellular users(CUEs), an approximation of the interference to signal ratio for CUEs is derived, and a coordination strategy is proposed to mitigate the interference from D2 D pairs to CUEs. Based on the lower bound of the interference to signal ratio for D2 D pairs, we propose coordination strategies for D2 D pairs to mitigate the interference caused by base station(BS) and the interference between D2 D pairs. The proposed strategies require only some statistical channel state information(CSI) of each user and the reduced-dimensional effective CSI of a few CUEs and D2 D pairs. Simulation results show that the proposed coordination strategy performs well in terms of achieving good tradeoff between the achievable rate of CUEs and D2 D pairs.展开更多
文摘传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.
基金supported in part by the National Natural Science Foundation of China(Grants No.61831013 and No.61571112)Foundation for the Author of National Excellent Doctoral Dissertation of PR China(FANEDD)(Grant No.201446)
文摘In this paper, we investigate the interference coordination for downlink full-dimension multiple-input multiple-output(FD-MIMO) systems with device-to-device(D2 D) communications underlaying. With three-dimensional(3 D) beamforming transmission applied for cellular users(CUEs), an approximation of the interference to signal ratio for CUEs is derived, and a coordination strategy is proposed to mitigate the interference from D2 D pairs to CUEs. Based on the lower bound of the interference to signal ratio for D2 D pairs, we propose coordination strategies for D2 D pairs to mitigate the interference caused by base station(BS) and the interference between D2 D pairs. The proposed strategies require only some statistical channel state information(CSI) of each user and the reduced-dimensional effective CSI of a few CUEs and D2 D pairs. Simulation results show that the proposed coordination strategy performs well in terms of achieving good tradeoff between the achievable rate of CUEs and D2 D pairs.
