MIMO downlink spatial multiplexing algorithms applied to channel measurements

Author(s):  
Q. Spencer ◽  
T. Svantesson
2012 ◽  
Vol 457-458 ◽  
pp. 1012-1018
Author(s):  
Xian Kun Gao ◽  
Jian Hua Qu ◽  
Chuan An Yao ◽  
Yong Chang Yu

Spatial multiplexing in the multi-user MIMO downlink allows each user in the system to receive multiple data subchannels simultaneously using the same time and spectral resources. In this paper, a successive iterative optimal algorithm based on signal-to-leakage-and-noise-ratio (SLNR) maximization algorithm is proposed, which make use of the unused subspace of some known users to improve the space gain of the other users and has no strict constraint on transmit and receive antennas numbers. According to the simulation results, the proposed algorithm outperforms the original SLNR algorithm, and has a considerable improvement in the system performance.


2004 ◽  
Vol 4 (7) ◽  
pp. 739-754 ◽  
Author(s):  
Quentin H. Spencer ◽  
Thomas Svantesson ◽  
A. Lee Swindlehurst

2006 ◽  
Vol 11 (5) ◽  
pp. 597-605
Author(s):  
Bijun Zhang ◽  
Guangxi Zhu ◽  
Yingzhuang Liu

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Zhe Zheng ◽  
Jianhua Zhang ◽  
Xiaoyong Wu ◽  
Danpu Liu ◽  
Lei Tian

In order to understand how many antennas are needed in a multiuser massive MIMO system, theoretical derivation and channel measurements are conducted; the effect of a finite number of base station (BS) antennas on the performance capability of Zero-forcing (ZF) precoding in a rich scattering channel is quantified. Through the theoretical analysis, the needed number of the transmit antennas for ZF precoder to achieve a certain percentage of the broadcast channel (BC) capacity will monotonically decrease with the increase of the transmit signal-to-noise ratio (SNR), and the lower bound of the needed transmit antennas is derived with a simple expression. Then the theoretical derivation is verified by simulation results, and the transmission performance is evaluated by channel measurements in urban microcell (UMi) scenario with frequencies of 3.5 and 6 GHz. From the measurement results, the ZF capability can be enhanced by improving the SNR and enlarging the antenna array spacing when the massive MIMO channel does not under a favorable propagation condition. Furthermore, because of the lower spatial correlation, the performance of ZF precoding at 6 GHz is closer to the theoretical derivation than 3.5 GHz.


Author(s):  
Mohammed Zahid Aslam ◽  
Yoann Corre ◽  
Jakob Belschner ◽  
Gnana Soundari Arockiaraj ◽  
Monika Jager
Keyword(s):  
60 Ghz ◽  

2011 ◽  
Vol 30 (1) ◽  
pp. 81-85
Author(s):  
Er-lin Zeng ◽  
Shi-hua Zhu ◽  
Xue-wen Liao ◽  
Jun Wang

2009 ◽  
Author(s):  
Kostas Stamatiou ◽  
John G. Proakis ◽  
James R. Zeidler

2020 ◽  
Vol 14 ◽  
Author(s):  
Keerti Tiwari

: Multiple-input multiple-output (MIMO) systems have been endorsed to enable future wireless communication requirements. The efficient system designing appeals an appropriate channel model, that considers all the dominating effects of wireless environment. Therefore, some complex or less analytically acquiescent composite channel models have been proposed typically for single-input single-output (SISO) systems. These models are explicitly employed for mobile applications, though, we need a specific study of a model for MIMO system which can deal with radar clutters and different indoor/outdoor and mobile communication environments. Subsequently, the performance enhancement of MIMO system is also required in such scenario. The system performance enhancement can be examined by low error rate and high capacity using spatial diversity and spatial multiplexing respectively. Furthermore, for a more feasible and practical system modeling, we require a generalized noise model along with a composite channel model. Thus, all the patents related to MIMO channel models are revised to achieve the near optimal system performance in real world scenario. This review paper offers the methods to improve MIMO system performance in less and severe fading as well as shadowing environment and focused on a composite Weibull-gamma fading model. The development is the collective effects of selecting the appropriate channel models, spatial multiplexing/detection and spatial diversity techniques both at the transmitter and the receivers in the presence of arbitrary noise.


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