Sparse Channel Estimation In OFDM System

Channel estimation is one of the most important challenges in OFDM system. Asone of the major discoveries in the21th century, compressed sensing (CS) theoryleads a breakthrough in the whole information and signal processing societies. It canbe widely applied in images, audio, music, wireless communications, radar, andastronomical data etc. This thesis focuses on the research of applications ofcompressed sensing in sparse channel estimation in OFDM system. Aiming toeffectively solving the challenges of sparse channel estimation in OFDM system, thiswork constructs a sparse channel estimation framework, based on which, noveleffective sparse channel estimation methods are proposed. Specifically, the noveltiesof the thesis can be summarized as follows:1) Threshold based most significant taps detection for sparse channel estimationin OFDM system (M≥Lc p,Mis the number of pilots andLc pis the length of cyclicprefix)Numerous traditional channel estimation methods are initially based on LS in thecase of M≥Lcp,which is actually optimal when channel is rich multipath channel.However, if the channel is sparse, LS method is vulnerable to noise, which leads tothe degradations on the estimation performance. In order to overcome the drawbacksof LS method, a novel effective time domain threshold depending only on theeffective noise standard deviation estimated from the noise coefficients obtained byeliminating the channel coefficients with an initial estimated threshold is proposed todetect the most significant taps (MST). Since the proposed method requires neitherthe prior knowledge of channel statistics nor the noise standard deviation, which willsignificantly benefit the practical wireless communications. Both theoretical analysisand simulation results show that the proposed method can achieve better performancein both bit error rate (BER) and normalized mean square error (NMSE) thantraditional methods within a wide range of sparsity rate, has good spectral efficiencyand moderate computational complexity.2) A novel CS based sparse channel estimation in OFDM system (M <Lcp)a) The framework of CS based sparse channel estimation method is constructed.Based on the framework, a novel effective threshold is proposed. Specifically, channelimpulse response (CIR) is firstly estimated by OMP with the assumption of maximumiteration number ofK max,which is also the maximum possible number of significanttaps. Then, in order to improve the estimation performance by an effective threshold,partial CIR with m (m <M)coefficients is approximately estimated. With theestimated m channel taps, the noise standard deviation is estimated. Finally, aneffective threshold is obtained. With the estimated threshold, the effective channelestimation can be realized. b) In the proposed method, the m channel coefficients index is essential for theprecision of the threshold estimation. To solve this problem, the threshold ofcoherence between bases is introduced for searching the indices of the m bases.3) Efficient and effective CS based non-sample spaced sparse channel estimationin the case ofM <Lcpa) Unlike the sample spaced sparse channels, the non-sample spaced sparsechannel can cause power leakage at the receiver. We have derived the observed CIR atthe receiver with different oversampling factors R on the estimated CIR and foundthat if the oversampling R>1is considered, the leakage effect will be reducedcompared with the baseband sampling. If Râ†'∞,there will be no leakage effect.Based on this fact, measurement matrix with finer time resolutions is developed forhigh resolution CIR estimation. By employing the measurement matrix with bothsuboptimal pilot arrangement and high resolution, CIR with finer time resolution canbe effectively estimated with limited number of pilots (M <Lcp)by CS. Simulationsshow that, compared with the traditional channel estimation method, the investigatedCS based method can realize superior channel estimation performance with asignificant reduction of pilots.b) For the baseband sampling, we only getLc pbases for the measurementmatrix, however, if the oversampling is considered, we get (R1) Lcp+1(In the casewhere R (R1),which is the oversampling factor) bases for the measurementmatrix. R1times higher. When we go back to a K (K <<Lcp)sparse channel,only K bases are useful for CS reconstruction. Additionally, for sparse channel,pilots reduction is a challenging and essential task, which can effectively promote thespectral efficiency. Therefore, how to effectively reduce computational complexitymeanwhile maintain high spectral efficiency is the key issue. Firstly, the "hot zones"(location of the interested bases) in the measurement matrix with baseband samplingare roughly detected. Then, smart measurement matrix can be constructed withoversampling only in those "hot zones". With a carefully designed measurementmatrix and by adopting the effective CS based channel reconstruction algorithm,effective channel estimation performances can be obtained. The proposed method canrealize effective channel estimation in the case ofM Lcpand comparatively lowcomputational complexity.