Interference Alignment Algorithms In Non-ideal Channel State Information

As a new interference management technique,interference alignment has proved itself many advantages by aligning interference signal via pre-coding at transmitter to the specific signal dimensions and allocating the remaining signal dimensions for the transmission of the desired signals,which enables the desired signals not being interfered by other signals.Moreover,interference alignment technology can effectively deal with the multiuser interference and allow the system to obtain the maximum freedom gain and therefore significantly enhance the channel capacity.Although interference alignment technology has been widely investigated and a lot of achievements have been obtained,there still exist many problems to be solved,preventing it from being applied further in the practice.Firstly,in the design of precoding matrix for interference alignment,the transmitter needs to know the global ideal Channel state information(CSI).But in the practice,ideal CIS cannot be attained due to the channel estimation error at the receiver,limited feedback capability,and channel's rapid variation,resulting in the performance degradation of interference alignment.Secondly,to gain more freedom for multi-user interference channel in the OFDM system,two prerequisites must be met: the channels participating in interference alignment design need to be independent,and the number of subcarriers used for IA design should not be large.However,the practical wireless communication systems often include a large number of subcarriers and there exist strong correlation between adjacent subcarriers,making the direct extension of IA technique to the OFDM system inapplicable.For time-varying channels,we employ the Kalman filter for channel state prediction,and in-frame insertion for channel estimation error smoothing,theoretical analysis and simulation results show that: the improved channel estimation algorithm can significantly reducing the channel time-varying effects on the performance of the system interference alignment.To deal with the problems caused by the imperfect CSI in IA design,an improved GFS-IA algorithm is proposed by formulating the grouping into a linear programming problem,and applying the greedy search algorithm to solve it and the suboptimal solution is therefore obtained for subcarrier grouping.This algorithm ensures low correlation of each grouping of subcarriers and therefore significantly improve the performance of IA.GFS-IA algorithm has a performance close to that of the optimal grouping schemes,but has a higher efficient to obtain the grouping of subcarriers.In addition,unfairness exist due to the fact that freedom gains obtained for different varies greatly in the IA design in multiuser OFDM systems.To solve this problem,the frequency scheduling interference alignment scheme with user fairness(GFSUF-IA)is proposed by introducing fairness factor into the GFS-IA algorithm.The scheme can guarantee system to obtain better subcarrier grouping result while enabling each user a fair degree of freedom gain.