111
所属分类:其他
开发工具:matlab
文件大小:6KB
下载次数:9
上传日期:2019-06-06 14:36:38
上 传 者:
李常超
说明: MIMO仿真
一篇IEEE论文
清华大学出品
推荐下载
(MIMO IEEE Papers
Simulation Channel Modeling)
文件列表:
Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO (0, 2018-02-08)
Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO\energy_avg.m (74, 2016-05-04)
Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO\SAMP_distributed.m (4978, 2016-05-04)
Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO\system_main.m (3875, 2016-05-04)
This simulation code package is mainly used to reproduce the results of the following paper [1]:
[1] Z. Gao, L. Dai, W. Dai, B. Shim, and Z. Wang, °Structured compressive sensing based spatio-temporal joint channel estimation for FDD massive MIMO,± IEEE Trans. Commun., vol. ***, no. 2, pp. 601-617, Feb. 2016.
If you use this simulation code package in any way, please cite the original paper [1] above.
The author in charge of this simulation code pacakge is: Zhen Gao (email: gaozhen16@bit.edu.cn).
Reference: We highly respect reproducible research, so we try to provide the simulation codes for our published papers (more information can be found at:
http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.html).
Copyright reserved by the Broadband Communications and Signal Processing Laboratory (led by Dr. Linglong Dai), Tsinghua National Laboratory
for Information Science and Technology (TNList), Department of Electronic Engineering, Tsinghua University, Beijing 100084, ***.
*********************************************************************************************************************************
Abstract of the paper:
Massive MIMO is a promising technique for future
5G communications due to its high spectrum and energy efficiency.
To realize its potential performance gain, accurate channel estimation
is essential. However, due to massive number of antennas
at the base station (BS), the pilot overhead required by conventional
channel estimation schemes will be unaffordable, especially
for frequency division duplex (FDD) massive MIMO. To overcome
this problem, we propose a structured compressive sensing
(SCS)-based spatio-temporal joint channel estimation scheme to
reduce the required pilot overhead, whereby the spatio-temporal
common sparsity of delay-domain MIMO channels is leveraged.
Particularly, we first propose the nonorthogonal pilots at the
BS under the framework of CS theory to reduce the required
pilot overhead. Then, an adaptive structured subspace pursuit
(ASSP) algorithm at the user is proposed to jointly estimate channels
associated with multiple OFDM symbols from the limited
number of pilots, whereby the spatio-temporal common sparsity
of MIMO channels is exploited to improve the channel estimation
accuracy. Moreover, by exploiting the temporal channel
correlation, we propose a space-time adaptive pilot scheme to
further reduce the pilot overhead. Additionally, we discuss the proposed
channel estimation scheme in multicell scenario. Simulation
results demonstrate that the proposed scheme can accurately estimate
channels with the reduced pilot overhead, and it is capable
of approaching the optimal oracle least squares estimator.
Enjoy the reproducible research!
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