發(fā)布時間：2019-06-25 09:30

【摘要】：城市化發(fā)展帶來的整體環(huán)境的改變,使得無線信號的傳播信道愈加惡劣,與此同時,室內(nèi)數(shù)據(jù)、語音等業(yè)務需求不斷增大,這使得高質量的室內(nèi)覆蓋變得十分迫切。家庭基站作為一種室內(nèi)覆蓋技術,有效提升了整體網(wǎng)絡的容量,不僅發(fā)射功率低,而且部署方便。家庭基站和宏基站構成了雙層異構網(wǎng)絡,二者使用相同的頻譜資源,相較于傳統(tǒng)網(wǎng)絡,異構網(wǎng)絡的結構復雜,干擾也更加嚴重。異構網(wǎng)絡中得到干擾信道信息后,可以進行干擾消除。協(xié)作網(wǎng)絡中利用導頻信息通過LS、MMSE算法進行干擾信道估計,但在非協(xié)作網(wǎng)絡中,當期望信道的數(shù)據(jù)信息和干擾信道的導頻信息重疊時,利用LS、MMSE算法得到的干擾信道估計性能較差。本文基于無線寬帶信道的稀疏性質,在非協(xié)作異構網(wǎng)絡的干擾信道估計中,提出一種基于壓縮感知的迭代方法,通過迭代估計得到干擾信道信息和期望數(shù)據(jù)信息。文中建立了雙層異構網(wǎng)絡的下行鏈路系統(tǒng)仿真模型,對比采用壓縮感知和采用LS的干擾信道估計性能。仿真結果表明,利用壓縮感知技術可準確估計異構網(wǎng)絡中的干擾信道,路徑數(shù)量發(fā)生變化時,估計的性能受到部分影響,當SNR高于19dB時,基于壓縮感知的干擾信道估計性能明顯優(yōu)于LS算法。本文在異構網(wǎng)絡中基于壓縮感知對干擾信道估計性能進行優(yōu)化,提出了兩種優(yōu)化方法:(1)通過構造確定性稀疏二值觀測矩陣,獲得相應的感知矩陣,基于OMP優(yōu)化恢復算法;(2)給定稀疏基,通過求解最小Frobenius范數(shù),使觀測矩陣具有較小的互相關值,優(yōu)化觀測矩陣。仿真結果表明,利用優(yōu)化算法能夠有效降低干擾信道估計的均方誤差0.5-2dB。
[Abstract]:With the change of the overall environment brought by the development of urbanization, the propagation channel of wireless signal is becoming worse and worse. at the same time, the demand for indoor data, voice and other services is increasing, which makes high-quality indoor coverage very urgent. As an indoor coverage technology, home base station effectively improves the capacity of the whole network, which not only has low transmission power, but also is convenient to deploy. Home base station and macro base station constitute double-layer heterogeneous network, which use the same spectrum resources. Compared with the traditional network, the structure of heterogeneous network is more complex and the interference is more serious. Interference cancellation can be carried out after interference channel information is obtained in heterogeneous networks. In cooperative networks, pilot information is used to estimate interference channels by LS,MMSE algorithm, but in non-cooperative networks, when the data information of expected channels and pilot information of interference channels overlap, the performance of interference channel estimation obtained by LS,MMSE algorithm is poor. In this paper, based on the sparse nature of wireless broadband channel, an iterative method based on compressed sensing is proposed in the interference channel estimation of non-cooperative heterogeneous networks, and the interference channel information and expected data information are obtained by iterative estimation. In this paper, a downlink system simulation model for double-layer heterogeneous networks is established, and the interference channel estimation performance using compressed sensing and LS is compared. The simulation results show that the interference channel in heterogeneous networks can be accurately estimated by using compressed sensing technology. When the number of paths changes, the performance of the estimation is partly affected. When the SNR is higher than 19dB, the performance of interference channel estimation based on compression sensing is obviously better than that of LS algorithm. In this paper, the performance of interference channel estimation is optimized based on compressed sensing in heterogeneous networks, and two optimization methods are proposed: (1) the corresponding perception matrix is obtained by constructing deterministic sparse binary observation matrix, and the corresponding perceptual matrix is optimized based on OMP; (2) given sparse basis, the observation matrix has a small cross-correlation value and optimizes the observation matrix by solving the minimum Frobenius norm. The simulation results show that the mean square error of interference channel estimation can be effectively reduced by using the optimization algorithm.
【學位授予單位】：南京郵電大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN929.5

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本文編號：2505581