高速移动环境会导致信道的双弥散效应,给无线通信系统带来巨大挑战。正交时频空间(orthogonal time frequency space,OTFS)调制通过将时-频域的双弥散信道转换为时延-多普勒域的平坦衰落信道,能够有效缓解双弥散信道带来的频率和时间选...高速移动环境会导致信道的双弥散效应,给无线通信系统带来巨大挑战。正交时频空间(orthogonal time frequency space,OTFS)调制通过将时-频域的双弥散信道转换为时延-多普勒域的平坦衰落信道,能够有效缓解双弥散信道带来的频率和时间选择性衰落的影响。针对多用户大规模多输入多输出(multiinput multi-output,MIMO)OTFS系统中的信道参数估计问题,通过对多天线信道结构特征进行深入分析,将用户与基站间的信道建模为稀疏结构模型。将大规模MIMO信道划分为多个群组,设计了适用于多用户大规模MIMO-OTFS系统的导频图案,提出了基于群组块共稀疏阈值结构化贝叶斯学习信道估计算法。利用估计得到的信道状态信息设计了分数多普勒频移、到达角度等信道参数估计方法,从而进一步感知用户状态。仿真结果表明,提出的信道参数估计算法具有更高的估计精度和系统频谱效率。展开更多
Signal-to-noise ratio （SNR） and channel estimations are critical for 60-GHz communications to track the optimal trans- mission and reception beam pairs. However, the excessive pilot overhead for the estima- tions se...Signal-to-noise ratio （SNR） and channel estimations are critical for 60-GHz communications to track the optimal trans- mission and reception beam pairs. However, the excessive pilot overhead for the estima- tions severely reduces system throughput in fast-rotation scenarios. In order to address this problem, we firstly demonstrate the potential sparseness property of 60-GHz channel in beam tracking; subsequently, via exploiting this property, we propose a novel compressed SNR-and-channel estimation. The estimation is conducted in a three-stage fashion, includ- ing the unstructured estimation, nonzero-tap detection, and structured estimation with non- zero-tap location. Numerical simulations show that, in the case of substantial reduction of the pilot overhead, the proposed estimator still reveals a significant improvement in terms of estimation performance over the scheme in IEEE 802.1 lad. Furthermore, it is also demon- strated that the proposed SNR and channel estimators can approach the lower bounds in sparse channels so long as SNR exceeds 8 dB.展开更多
文摘高速移动环境会导致信道的双弥散效应,给无线通信系统带来巨大挑战。正交时频空间(orthogonal time frequency space,OTFS)调制通过将时-频域的双弥散信道转换为时延-多普勒域的平坦衰落信道,能够有效缓解双弥散信道带来的频率和时间选择性衰落的影响。针对多用户大规模多输入多输出(multiinput multi-output,MIMO)OTFS系统中的信道参数估计问题,通过对多天线信道结构特征进行深入分析,将用户与基站间的信道建模为稀疏结构模型。将大规模MIMO信道划分为多个群组,设计了适用于多用户大规模MIMO-OTFS系统的导频图案,提出了基于群组块共稀疏阈值结构化贝叶斯学习信道估计算法。利用估计得到的信道状态信息设计了分数多普勒频移、到达角度等信道参数估计方法,从而进一步感知用户状态。仿真结果表明,提出的信道参数估计算法具有更高的估计精度和系统频谱效率。
基金supported by the National Natural Science Foundation of China(NSFC) under Grant No.61201189 and 61132002National High Tech(863) Projects under Grant No.2011AA010202+1 种基金Research Fund of Tsinghua University under Grant No.2011Z05117 and 20121087985Shenzhen Strategic Emerging Industry Development Special Funds under Grant No. CXZZ20120616141708264
文摘Signal-to-noise ratio （SNR） and channel estimations are critical for 60-GHz communications to track the optimal trans- mission and reception beam pairs. However, the excessive pilot overhead for the estima- tions severely reduces system throughput in fast-rotation scenarios. In order to address this problem, we firstly demonstrate the potential sparseness property of 60-GHz channel in beam tracking; subsequently, via exploiting this property, we propose a novel compressed SNR-and-channel estimation. The estimation is conducted in a three-stage fashion, includ- ing the unstructured estimation, nonzero-tap detection, and structured estimation with non- zero-tap location. Numerical simulations show that, in the case of substantial reduction of the pilot overhead, the proposed estimator still reveals a significant improvement in terms of estimation performance over the scheme in IEEE 802.1 lad. Furthermore, it is also demon- strated that the proposed SNR and channel estimators can approach the lower bounds in sparse channels so long as SNR exceeds 8 dB.
