Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel
An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE opti...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Dong, Anming [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
Min-Max mean square error (MSE) multiple-input multiple-output (MIMO) interfering broadcast channel (IBC) |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on vehicular technology - New York, NY : IEEE, 1967, 65(2016), 8, Seite 6024-6037 |
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| Übergeordnetes Werk: |
volume:65 ; year:2016 ; number:8 ; pages:6024-6037 |
| Links: |
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| DOI / URN: |
10.1109/TVT.2015.2472463 |
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| Katalog-ID: |
OLC1981485287 |
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| 520 | |a An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). | ||
| 650 | 4 | |a Min-Max mean square error (MSE) | |
| 650 | 4 | |a MIMO | |
| 650 | 4 | |a Algorithm design and analysis | |
| 650 | 4 | |a transceiver optimization | |
| 650 | 4 | |a robust design | |
| 650 | 4 | |a Interference alignment (IA) | |
| 650 | 4 | |a multiple-input multiple-output (MIMO) | |
| 650 | 4 | |a Signal to noise ratio | |
| 650 | 4 | |a interfering broadcast channel (IBC) | |
| 650 | 4 | |a Transceivers | |
| 650 | 4 | |a Interference | |
| 650 | 4 | |a Optimization | |
| 650 | 4 | |a Receivers | |
| 650 | 4 | |a Mean square errors | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Iterative methods (Mathematics) | |
| 650 | 4 | |a MIMO communications | |
| 650 | 4 | |a Mathematical optimization | |
| 650 | 4 | |a Usage | |
| 700 | 1 | |a Zhang, Haixia |4 oth | |
| 700 | 1 | |a Yuan, Dongfeng |4 oth | |
| 700 | 1 | |a Zhou, Xiaotian |4 oth | |
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10.1109/TVT.2015.2472463 doi PQ20161012 (DE-627)OLC1981485287 (DE-599)GBVOLC1981485287 (PRQ)c1226-8b4f214e9e6baa8894647a2959731f6dbbf46bf2de7096230189dea70eba6a1f0 (KEY)0030991520160000065000806024interferencealignmenttransceiverdesignbyminimizing DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Dong, Anming verfasserin aut Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). Min-Max mean square error (MSE) MIMO Algorithm design and analysis transceiver optimization robust design Interference alignment (IA) multiple-input multiple-output (MIMO) Signal to noise ratio interfering broadcast channel (IBC) Transceivers Interference Optimization Receivers Mean square errors Algorithms Iterative methods (Mathematics) MIMO communications Mathematical optimization Usage Zhang, Haixia oth Yuan, Dongfeng oth Zhou, Xiaotian oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 8, Seite 6024-6037 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:8 pages:6024-6037 http://dx.doi.org/10.1109/TVT.2015.2472463 Volltext http://ieeexplore.ieee.org/document/7222487 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 8 6024-6037 |
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10.1109/TVT.2015.2472463 doi PQ20161012 (DE-627)OLC1981485287 (DE-599)GBVOLC1981485287 (PRQ)c1226-8b4f214e9e6baa8894647a2959731f6dbbf46bf2de7096230189dea70eba6a1f0 (KEY)0030991520160000065000806024interferencealignmenttransceiverdesignbyminimizing DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Dong, Anming verfasserin aut Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). Min-Max mean square error (MSE) MIMO Algorithm design and analysis transceiver optimization robust design Interference alignment (IA) multiple-input multiple-output (MIMO) Signal to noise ratio interfering broadcast channel (IBC) Transceivers Interference Optimization Receivers Mean square errors Algorithms Iterative methods (Mathematics) MIMO communications Mathematical optimization Usage Zhang, Haixia oth Yuan, Dongfeng oth Zhou, Xiaotian oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 8, Seite 6024-6037 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:8 pages:6024-6037 http://dx.doi.org/10.1109/TVT.2015.2472463 Volltext http://ieeexplore.ieee.org/document/7222487 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 8 6024-6037 |
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10.1109/TVT.2015.2472463 doi PQ20161012 (DE-627)OLC1981485287 (DE-599)GBVOLC1981485287 (PRQ)c1226-8b4f214e9e6baa8894647a2959731f6dbbf46bf2de7096230189dea70eba6a1f0 (KEY)0030991520160000065000806024interferencealignmenttransceiverdesignbyminimizing DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Dong, Anming verfasserin aut Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). Min-Max mean square error (MSE) MIMO Algorithm design and analysis transceiver optimization robust design Interference alignment (IA) multiple-input multiple-output (MIMO) Signal to noise ratio interfering broadcast channel (IBC) Transceivers Interference Optimization Receivers Mean square errors Algorithms Iterative methods (Mathematics) MIMO communications Mathematical optimization Usage Zhang, Haixia oth Yuan, Dongfeng oth Zhou, Xiaotian oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 8, Seite 6024-6037 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:8 pages:6024-6037 http://dx.doi.org/10.1109/TVT.2015.2472463 Volltext http://ieeexplore.ieee.org/document/7222487 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 8 6024-6037 |
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10.1109/TVT.2015.2472463 doi PQ20161012 (DE-627)OLC1981485287 (DE-599)GBVOLC1981485287 (PRQ)c1226-8b4f214e9e6baa8894647a2959731f6dbbf46bf2de7096230189dea70eba6a1f0 (KEY)0030991520160000065000806024interferencealignmenttransceiverdesignbyminimizing DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Dong, Anming verfasserin aut Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). Min-Max mean square error (MSE) MIMO Algorithm design and analysis transceiver optimization robust design Interference alignment (IA) multiple-input multiple-output (MIMO) Signal to noise ratio interfering broadcast channel (IBC) Transceivers Interference Optimization Receivers Mean square errors Algorithms Iterative methods (Mathematics) MIMO communications Mathematical optimization Usage Zhang, Haixia oth Yuan, Dongfeng oth Zhou, Xiaotian oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 8, Seite 6024-6037 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:8 pages:6024-6037 http://dx.doi.org/10.1109/TVT.2015.2472463 Volltext http://ieeexplore.ieee.org/document/7222487 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 8 6024-6037 |
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10.1109/TVT.2015.2472463 doi PQ20161012 (DE-627)OLC1981485287 (DE-599)GBVOLC1981485287 (PRQ)c1226-8b4f214e9e6baa8894647a2959731f6dbbf46bf2de7096230189dea70eba6a1f0 (KEY)0030991520160000065000806024interferencealignmenttransceiverdesignbyminimizing DE-627 ger DE-627 rakwb eng 620 DNB 53.70 bkl 53.74 bkl Dong, Anming verfasserin aut Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). Min-Max mean square error (MSE) MIMO Algorithm design and analysis transceiver optimization robust design Interference alignment (IA) multiple-input multiple-output (MIMO) Signal to noise ratio interfering broadcast channel (IBC) Transceivers Interference Optimization Receivers Mean square errors Algorithms Iterative methods (Mathematics) MIMO communications Mathematical optimization Usage Zhang, Haixia oth Yuan, Dongfeng oth Zhou, Xiaotian oth Enthalten in IEEE transactions on vehicular technology New York, NY : IEEE, 1967 65(2016), 8, Seite 6024-6037 (DE-627)129358584 (DE-600)160444-2 (DE-576)014730871 0018-9545 nnns volume:65 year:2016 number:8 pages:6024-6037 http://dx.doi.org/10.1109/TVT.2015.2472463 Volltext http://ieeexplore.ieee.org/document/7222487 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2061 53.70 AVZ 53.74 AVZ AR 65 2016 8 6024-6037 |
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The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. 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620 DNB 53.70 bkl 53.74 bkl Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel Min-Max mean square error (MSE) MIMO Algorithm design and analysis transceiver optimization robust design Interference alignment (IA) multiple-input multiple-output (MIMO) Signal to noise ratio interfering broadcast channel (IBC) Transceivers Interference Optimization Receivers Mean square errors Algorithms Iterative methods (Mathematics) MIMO communications Mathematical optimization Usage |
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Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel |
| abstract |
An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). |
| abstractGer |
An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). |
| abstract_unstemmed |
An interference alignment (IA) transceiver design scheme for multiple-input-multiple-output (MIMO) multicell multiuser wireless communication systems is proposed by minimizing the maximum mean square error (MSE) of the received symbols to improve fairness among users. The formulated Min-Max MSE optimization problem is not jointly convex on the transmit precoders and receive filters and, thus, is very difficult to solve directly. An iterative method is proposed to solve the optimization problem to get a suboptimal solution instead. Considering that if the receive filters are fixed, the Min-Max MSE problem can be reformulated as a second-order cone programming problem, and if the transmit precoders are fixed, the closed-form receive filters minimizing the receive MSE can be easily obtained, and the formulated Min-Max MSE problem is solved by alternatively optimizing the transmit precoders and the receive filters. The convergence of the proposed algorithm is proved, which shows its feasibility. Furthermore, a robust Min-Max MSE algorithm is proposed to counter the channel uncertainty. Simulation results show that the proposed Min-Max MSE algorithm can achieve IA when the antennas are configured to be strong IA proper and when the users in the network are of the same signal-to-noise ratio (SNR). Analysis indicates that the proposed Min-Max IA transceiver design scheme can significantly improve user fairness with a possible cost of sum-rate reduction. Results also show that the proposed robust design algorithm provides better performance when lacking perfect channel state information (CSI). |
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