The conventional zero-forcing vector precoding can provide the same SNR for decentralized receiving users.In order to meet different Qos requirement of multimedia applications,we modified the conventional precoding ma...The conventional zero-forcing vector precoding can provide the same SNR for decentralized receiving users.In order to meet different Qos requirement of multimedia applications,we modified the conventional precoding matrix by using a diagonal matrix named relative SNR control matrix so that different SNR can be got for different users＇ requirements.Then,with the new precoding matrix used,we analyze the average SNR of all MTs.We find that if a proper perturbation vector is chosen under the power constraint,the average SNR can be maximized.Finally,we indicate that the choice of perturbation vector is a K-dimensional problem which has low complexity algorithm to solve.Simulation shows that our scheme is very effective for multimedia applications.展开更多
In downlink multi-user multi-input multi-output （MU-MIMO） system, not every user （user equipment （UE）） can calculate accurately signal to interference and noise ratio （SINR） without prior knowledge of the othe...In downlink multi-user multi-input multi-output （MU-MIMO） system, not every user （user equipment （UE）） can calculate accurately signal to interference and noise ratio （SINR） without prior knowledge of the other users＇ precoding vector. To solve this problem, this article proposes a channel inversion precoding scheme by using the lower bound of S1NR and zero-forcing （ZF） algorithm. However, the SINR mismatch between lower bound SINR and actual SINR causes the inaccurateness of adaptive modulation and coding （AMC）. As a result, it causes degradation in performance. Simulation results show that channel inversion precoding provides lower throughput than that of single user multi-input multi-output （SU-MIMO） at high signal-to-noise ratio （SNR） （〉14 dB）, due to the SINR mismatch, although the sum-rate of channel inversion precoding is higher than that of SU-MIMO at full SNR regime.展开更多
文摘The conventional zero-forcing vector precoding can provide the same SNR for decentralized receiving users.In order to meet different Qos requirement of multimedia applications,we modified the conventional precoding matrix by using a diagonal matrix named relative SNR control matrix so that different SNR can be got for different users＇ requirements.Then,with the new precoding matrix used,we analyze the average SNR of all MTs.We find that if a proper perturbation vector is chosen under the power constraint,the average SNR can be maximized.Finally,we indicate that the choice of perturbation vector is a K-dimensional problem which has low complexity algorithm to solve.Simulation shows that our scheme is very effective for multimedia applications.
基金supported by the National Natural Science Foundation of China (60602058)the Hi-Tech Research and Development Program of China (2006AA01Z257)
文摘In downlink multi-user multi-input multi-output （MU-MIMO） system, not every user （user equipment （UE）） can calculate accurately signal to interference and noise ratio （SINR） without prior knowledge of the other users＇ precoding vector. To solve this problem, this article proposes a channel inversion precoding scheme by using the lower bound of S1NR and zero-forcing （ZF） algorithm. However, the SINR mismatch between lower bound SINR and actual SINR causes the inaccurateness of adaptive modulation and coding （AMC）. As a result, it causes degradation in performance. Simulation results show that channel inversion precoding provides lower throughput than that of single user multi-input multi-output （SU-MIMO） at high signal-to-noise ratio （SNR） （〉14 dB）, due to the SINR mismatch, although the sum-rate of channel inversion precoding is higher than that of SU-MIMO at full SNR regime.
