TY - GEN
T1 - Low complexity LMMSE beamforming design for uplink virtual MIMO systems
AU - Wang, Niwei
AU - Xing, Chengwen
AU - Zhou, Yuan
AU - Fei, Zesong
AU - Kuang, Jingming
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/18
Y1 - 2014/12/18
N2 - In this paper, the beamforming designs for uplink virtual MIMO systems are investigated. In such systems, the most challenging difficulty in beamforming designs comes from the fact that the nodes consisting the virtual MIMO are subjected to individual power constraints or named per-antenna power constraints instead of sum power constraints. The instinct individual power constraints for virtual MIMO prohibit the derivation for closed-form solutions. Although in existing works, it has been revealed that under per-antenna power constraints the design problems are still convex and can be efficiently solved using some famous convex optimization tools such as semidefinite programming (SDP). Unfortunately, it is far from desired for practical implementations. In our work, linear minimum mean square error (LMMSE) beamforming is designed under per-antenna power constraints. Exploiting the hidden convexity, an iterative solution is proposed, which has clear structure and well-suited for virtual MIMO communications e.g, the uplink of Machine-to-Machine (M2M)communications in cellular networks. Finally, simulation results demonstrate the advantages of the proposed design.
AB - In this paper, the beamforming designs for uplink virtual MIMO systems are investigated. In such systems, the most challenging difficulty in beamforming designs comes from the fact that the nodes consisting the virtual MIMO are subjected to individual power constraints or named per-antenna power constraints instead of sum power constraints. The instinct individual power constraints for virtual MIMO prohibit the derivation for closed-form solutions. Although in existing works, it has been revealed that under per-antenna power constraints the design problems are still convex and can be efficiently solved using some famous convex optimization tools such as semidefinite programming (SDP). Unfortunately, it is far from desired for practical implementations. In our work, linear minimum mean square error (LMMSE) beamforming is designed under per-antenna power constraints. Exploiting the hidden convexity, an iterative solution is proposed, which has clear structure and well-suited for virtual MIMO communications e.g, the uplink of Machine-to-Machine (M2M)communications in cellular networks. Finally, simulation results demonstrate the advantages of the proposed design.
UR - http://www.scopus.com/inward/record.url?scp=84921658523&partnerID=8YFLogxK
U2 - 10.1109/WCSP.2014.6992086
DO - 10.1109/WCSP.2014.6992086
M3 - Conference contribution
AN - SCOPUS:84921658523
T3 - 2014 6th International Conference on Wireless Communications and Signal Processing, WCSP 2014
BT - 2014 6th International Conference on Wireless Communications and Signal Processing, WCSP 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 6th International Conference on Wireless Communications and Signal Processing, WCSP 2014
Y2 - 23 October 2014 through 25 October 2014
ER -