Algorithm Design And Simulation Of Wireless Communication System Based On IA

Due to the shortage of the wireless spectrum and the increasing density of wireless nodes, interference has become a bottleneck in the future wireless communication system. Thus, the urgency of interference management is particularly prominent. Traditional methods for interference management have some defects such as low spectrum efficiency and the saturation of transmission rate. Fortunately, interference alignment(IA), which can effectively improve the degree of freedom and spectrum efficiency of interference networks, can achieve better performance gain than the traditional methods, and hence has great potential in communications theory. Therefore in this paper, we focus on how to realize the IA technology from theory to practice, and the design of robust IA scheme in the practical wireless communication system.In this paper, we discuss the key performance indicators and basic principles of IA at first, so that readers can have a preliminary understanding of IA. Secondly, we briefly introduce and compare several typical IA algorithms, point out their shortcomings in practical application, and then put forward a robust WMMSE IA algorithm based on them. By using the relation between the rate and the MMSE function(1log det()k kR-? E), the weighted sum-rate maximization problem can be related to a matrix-weighted sum-MSE minimization problem, and thus a highly non-convex problem can be transformed into a convex optimization problem. At the same time, considering the influence of channel estimation error and the out of date error, we make full use of the channel estimation error statistics to make the algorithm more robust and reliable.Next, we explore the implementation of low complexity IA plan in practical wireless communication system, the main points of which include: 1) Based on the channel estimation error matrix, the robust WMMSE IA algorithm with low complexity is designed; 2) We feedback pre-coding matrix rather than the channel matrix, which greatly reduces the feedback overhead; 3) In order to reduce more overhead, the pre-coding matrix of feedback is quantified according to the minimum chordal distance criterion; 4) The receiver adaptively chooses the optimal modulation and encoding scheme based on SINR; 5) This solution takes throughput as performance metric. Based on these aforementioned key points, taking all the overhead(channel estimation and channel feedback) and channel non-ideal factors(including channel estimation errors, quantization errors and channel obsolete errors) into consideration, we build practical 3 users MIMO(2×2) interference channel simulation platform, and compare with TDMA BLAST transmission scheme based on MMSE-SIC detection. We compare these schemes in scenarios with different Doppler frequency shift and kappa coefficients, and the simulation shows that the new scheme can achieve about 1.5 times throughput enhancement as to the TDMA transmission. This result fully demonstrates the feasibility and potential of IA in the practical system. What's more, the new scheme is proved to improve the performance of the system, which provides an effective solution for IA's application in the practical communication system.