TY - GEN
T1 - A novel channel predictor for interference alignment in cognitive radio network
AU - Shi, Zhenguo
AU - Wu, Zhilu
AU - Yin, Zhendong
AU - Zhuang, Shufeng
N1 - Publisher Copyright:
© 2014 National Institute of Information and Communicatio.
PY - 2015/1/19
Y1 - 2015/1/19
N2 - In a cognitive radio (CR) network, how to eliminate the interference between primary users and secondary users is a curial work. The emergence of interference alignment (IA) provides an effective way to solve this problem. However, in order to utilize the IA algorithm, the real-time and accurate channel state information (CSI) is required at both transmitters and receivers. But in practical IA system, it is hard to get the perfect CSI due to the capacity constraint, channel estimation errors and time delay, which will severely affect the system performance. In this paper, the impact of delayed CSI on average signal to interference plus noise ratio (SINR) and achievable sum rate of IA system are analyzed. To eliminate the effect of delayed CSI, a novel channel predictor based on the linear Markov chain (LMC) is proposed. Using the finite state Markov chain model, the CSI of next time instant can be predicted according to the former and current CSI. Simulation results show that the proposed IA scheme based on LMC predictor can significantly upgrade the performance of IA system with the delayed CSI, and it can achieve better results with lower complexity compared with traditional AR predictor.
AB - In a cognitive radio (CR) network, how to eliminate the interference between primary users and secondary users is a curial work. The emergence of interference alignment (IA) provides an effective way to solve this problem. However, in order to utilize the IA algorithm, the real-time and accurate channel state information (CSI) is required at both transmitters and receivers. But in practical IA system, it is hard to get the perfect CSI due to the capacity constraint, channel estimation errors and time delay, which will severely affect the system performance. In this paper, the impact of delayed CSI on average signal to interference plus noise ratio (SINR) and achievable sum rate of IA system are analyzed. To eliminate the effect of delayed CSI, a novel channel predictor based on the linear Markov chain (LMC) is proposed. Using the finite state Markov chain model, the CSI of next time instant can be predicted according to the former and current CSI. Simulation results show that the proposed IA scheme based on LMC predictor can significantly upgrade the performance of IA system with the delayed CSI, and it can achieve better results with lower complexity compared with traditional AR predictor.
KW - Channel Prediction
KW - Channel State Information
KW - Cognitive Radio
KW - Interference Alignment
KW - Markov Model
UR - https://www.scopus.com/pages/publications/84940744614
U2 - 10.1109/WPMC.2014.7014851
DO - 10.1109/WPMC.2014.7014851
M3 - 会议稿件
AN - SCOPUS:84940744614
T3 - International Symposium on Wireless Personal Multimedia Communications, WPMC
SP - 396
EP - 401
BT - 2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014
PB - IEEE Computer Society
T2 - 2014 International Symposium on Wireless Personal Multimedia Communications, WPMC 2014
Y2 - 7 September 2014 through 10 September 2014
ER -