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A Novel Joint Transmitting and Receiving Antenna Selection for Spatial Multiplexing Systems

Mingjie Zhuang

Mingjie Zhuang

College of Engineering, Huaqiao UniversityQuanzhou, China
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Zhuang, Mingjie
, 
Haitao Li

Haitao Li

College of Engineering, Huaqiao UniversityQuanzhou, China
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Li, Haitao
 e 
Yisong Lin

Yisong Lin

School of Information Science and Engineering, Huaqiao UniversityXiamen, China
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Lin, Yisong
  
15 set 2020
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Pubblicato online: 15 set 2020
Pagine: 565 - 580
Ricevuto: 30 mar 2020
Accettato: 26 mag 2020
DOI: https://doi.org/10.2478/amns.2020.2.00031
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© 2020 Mingjie Zhuang et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Composition block diagram of an AS scheme based on spatial multiplexing.
Composition block diagram of an AS scheme based on spatial multiplexing.

Fig. 2

Comparison to the CC of 8 × 8 MIMO AS system
Comparison to the CC of 8 × 8 MIMO AS system

Fig. 3

Comparison to the CC of 12 × 12 MIMO AS system
Comparison to the CC of 12 × 12 MIMO AS system

Fig. 4

statistical capacity versus SNR for {5,2;5,2} system
statistical capacity versus SNR for {5,2;5,2} system

Fig. 5

statistical capacity versus SNR for {5,3;5,3} system
statistical capacity versus SNR for {5,3;5,3} system

Fig. 6

statistical capacity versus SNR for {7,2;7,2} system
statistical capacity versus SNR for {7,2;7,2} system

Fig. 7

statistical capacity versus SNR for {7,3;7,3} system
statistical capacity versus SNR for {7,3;7,3} system

Fig. 8

the curves of capacity CDF for {5,3;5,3} system, SNR=8dB
the curves of capacity CDF for {5,3;5,3} system, SNR=8dB

Fig. 9

the curves of capacity CDF for {5,3;5,3} system, SNR=20dB
the curves of capacity CDF for {5,3;5,3} system, SNR=20dB

Fig. 10

the curves of capacity CDF for {7,3;7,3} system, SNR=8dB
the curves of capacity CDF for {7,3;7,3} system, SNR=8dB

Fig. 11

the curves of capacity CDF for {7,3;7,3} system, SNR=20dB
the curves of capacity CDF for {7,3;7,3} system, SNR=20dB

The pseudo-code algorithm of Step 3

1: fori = 1 : NT − ndo
2:     Jn,i=hxHDn−1hx{J_{n,i}} = {\bf{h}}_x^H{\bf{D}}_n^{- 1}{{\bf{h}}_x}
3: end for
4: Jn = arg min Jn, j
5: ΓNT − (n+1) = ΓNT − n − hx

The pseudo-code algorithm of Step 6

1: forj = 1 : NR − ndo
2:    λn,j=gxyBn−1gxyH{\lambda _{n,j}} = {{\bf{g}}_{xy}}{\bf{B}}_n^{- 1}{\bf{g}}_{xy}^H
3: end for
4: λn = arg min λn, j
5: ΓNR − (n+1) = ΓNR − n − gxy
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