張朝龍+邱昕+陳杰
摘 要: 提出了一種基于MMSE準(zhǔn)則的MIMO?OFDM系統(tǒng)信道估計(jì)簡化方法。該方法將信道估計(jì)問題轉(zhuǎn)化為TBT線性方程求解問題,然后利用快速求解算法避免了復(fù)雜的矩陣求逆和矩陣相乘,大大降低了實(shí)現(xiàn)復(fù)雜度。仿真結(jié)果表明,相比于傳統(tǒng)MMSE算法,該簡化算法在降低復(fù)雜度的同時(shí)僅帶來微弱的性能損失。
關(guān)鍵詞: MIMO?OFDM; 信道估計(jì); TBT; 快速求解算法
中圖分類號: TN925+.93?34 文獻(xiàn)標(biāo)識碼: A 文章編號: 1004?373X(2014)07?0001?04
A simplified method for channel estimation of MIMO?OFDM systems
ZHANG Chao?long, QIU Xin, CHEN Jie
(Communication and Multimedia SoC Lab, Microelectronics Institute of Chinese Academy of Sciences, Beijing 100029, China)
Abstract: A simplified channel of estimation method for MIMO?OFDM(multiple input multiple output ? orthogonal frequency division multiplexing) systems based on MMSE(minimum mean square error) criteria is proposed, which translated the problem of channel estimation into the finding out the solution of TBT(Toeplitz?block?Toeplitz) nonlinear equations. And then complex operation of matrix inverse and matrix multiplication can be avoided with fast solution method, which greatly reduced the computation complexity of channel estimation. Simulation results show that, compared with traditional MMSE channel estimation method, the proposed simplified method can reduce computation complexity efficiently with small performance degradation.
Keywords: MIMO?OFDM; channel estimation; TBT; fast solution method
0 引 言
現(xiàn)代通信系統(tǒng)對通信速率和帶寬效率提出了更高的要求,而多輸入多輸出正交頻分復(fù)用(MIMO?OFDM)技術(shù)能夠大幅提高通信系統(tǒng)的數(shù)據(jù)吞吐量和可靠性[1],并且與空時(shí)碼的結(jié)合可以利用空時(shí)碼較高的碼效率和良好性能進(jìn)一步的改善MIMO?OFDM系統(tǒng)的效率和性能[2],所以編碼MIMO?OFDM系統(tǒng)受到廣泛的關(guān)注。IEEE 802.16標(biāo)準(zhǔn)已經(jīng)把MIMO?OFDM 作為實(shí)現(xiàn)其物理層的核心技術(shù)[3]。對于結(jié)合空時(shí)碼編碼的MIMO?OFDM系統(tǒng)來說,無論空時(shí)碼的譯碼還是信道干擾抵消都與信道估計(jì)的準(zhǔn)確度密切相關(guān)。目前信道估計(jì)技術(shù)的主流技術(shù)為基于導(dǎo)頻的相干解調(diào),在各種估計(jì)方法中基于最小均方誤差(MMSE)準(zhǔn)則進(jìn)行的信道估計(jì)可以獲得最佳性能[4],但是復(fù)雜的矩陣求逆和矩陣乘法導(dǎo)致其復(fù)雜度難以接受。所以基于MMSE準(zhǔn)則信道估計(jì)簡化算法被廣泛研究,但無論是降階矩陣處理[5]還是奇異值分解(SVD)算法[6]都難以避免復(fù)雜的矩陣乘法和求逆運(yùn)算,雖然可以采用迭代遞歸計(jì)算的方法解決信道矩陣求逆問題[7],但是算法復(fù)雜并且收斂性難以保證。文獻(xiàn)[8]提出了可以通過優(yōu)化訓(xùn)練符號可以大大簡化信道估計(jì),但是對訓(xùn)練符號的依賴性限制了其應(yīng)用范圍,并且在信道變化較大情況下性能有限。文獻(xiàn)[9]采用了一種新型的子空間信道估計(jì)方法,這種方法對于SIMO信道具有較好的效果,但是快收斂特性并不能在MIMO信道中很好的體現(xiàn)。
本文將MMSE信道估計(jì)算法等效為一個(gè)Toeplitz塊矩陣線性系統(tǒng),進(jìn)而利用Toeplitz塊矩陣的快速變換算法,提出了一種簡化的MMSE信道估計(jì)算法,用二維FFT和簡單的矩陣對應(yīng)位置相除代替矩陣乘法和求逆,大大減少了實(shí)現(xiàn)復(fù)雜度。
1 系統(tǒng)及信道模型
1.1 空時(shí)編碼MIMO?OFDM系統(tǒng)
采用空時(shí)編碼具有兩條分支的MIMO?OFDM系統(tǒng)框圖如圖1 所示。在發(fā)射時(shí)間[n,]二進(jìn)制數(shù)據(jù)塊[b[n,k]:k=0,1,2,…]經(jīng)過空時(shí)編碼后得到兩路不同的信號[ti[n,k]:k=0,1,2,…,]其中[i=1,2]分別代表兩路信號,每一路信號分別經(jīng)過調(diào)制和IFFT變換構(gòu)成OFDM符號流通過對應(yīng)的發(fā)射天線同時(shí)送出。所以在接收端,每個(gè)接收天線上接收到的數(shù)據(jù)為兩個(gè)發(fā)射天線數(shù)據(jù)經(jīng)過信號后的疊加,可以表示為:
[rj[n,k]=i=12Hi,j[n,k]ti[n,k]+wj[n,k]] (1)
式中:[Hi,j[n,k]]為信道矩陣中的每個(gè)元素,代表第[i]個(gè)發(fā)射天線和第[j]個(gè)接收天線之間所對應(yīng)的在時(shí)間[n]第[k]個(gè)子載波上的信道頻域響應(yīng);[wj[n,k]]代表第[j]個(gè)接收天線上收到的零均值方差為[σ2n]的加性高斯白噪聲,該噪聲項(xiàng)對于不同的[n,k,j]均不相關(guān)。所以接收機(jī)的平均信噪比可以定義為:
[SNR=EH1j[n,k]2+H2j[n,k]2σ2n] (2)
即說明[H1j[n,k]2]和[H2j[n,k]2]均被視為有效信號。
圖1 空時(shí)碼MIMO?OFDM系統(tǒng)示意圖
文獻(xiàn)[2]中的空時(shí)碼譯碼算法,其函數(shù)可以表示為:
[r[n,k]-H[n,k]t[n,k]2] (3)
式中:[?]代表求范數(shù)操作;[r[n,k]]和[H[n,k]]分別為接收到的信號向量和估計(jì)得到的信道參數(shù)矩陣,具體如下:
[r[n,k]=r1[n,k]r2[n,k];H[n,k]=H11[n,k]H21[n,k]H12[n,k]H22[n,k]] (4)
而[t[n,k]]為估計(jì)得到的信號向量,定義為:
[t[n,k]=t1[n,k]t2[n,k]] (5)
因此,信道參數(shù)的準(zhǔn)確估計(jì)是空時(shí)碼譯碼和數(shù)據(jù)恢復(fù)的關(guān)鍵。
1.2 移動無線信道特征
移動無線信道脈沖響應(yīng)的復(fù)數(shù)基帶等效模型可以表示為:
[h(t,τ)=kγk(t)c(τ-τk)] (6)
式中:[τk]為第[k]徑的延遲;[γk(t)]為第[k]徑的復(fù)數(shù)振幅,[c(t)]為成形脈沖函數(shù),其頻率響應(yīng)通常為平方根升余弦奈奎斯特濾波器。因此時(shí)間[t]上的信道頻域響應(yīng)可以表示為:
[H(t,f)=-∞+∞h(t,τ)e-j2πfτdτ=C(f)kγk(t)e-j2πfτk] (7)
式中[C(f)=-∞+∞c(τ)e-j2πfτdτ。]
由于接收機(jī)的移動,[γk(t)]可以建模等效為一個(gè)廣義平穩(wěn)窄帶復(fù)數(shù)高斯過程,服從Jake功率譜且各個(gè)徑獨(dú)立分布。另外可以假設(shè)不同路徑上的幅度具有相同的歸一化時(shí)間相關(guān)函數(shù)和不同的平均功率[σ2k]。
如果OFDM系統(tǒng)中具有合適的循環(huán)前綴和采樣時(shí)間,信道頻域響應(yīng)可以表示為:
[H[n,k]=H(nTf,kΔf)=l=0K0-1h[n,l]WklK] (8)
式中:[h[n,l]=h(nTf,kts);Wk=exp(-j(2π/K)),][K]為OFDM系統(tǒng)中的子載波數(shù)目;[Tf]和[Δf]分別代表OFDM符號長度和子載波間隔;[ts]代表系統(tǒng)的采樣間隔,滿足[ts=1Δf。]可以得知[h[n,l],l=0,1,…,K0-1,]為寬穩(wěn)態(tài)窄帶復(fù)數(shù)高斯過程。[h[n,l]]的平均功率和最大多徑延遲[K0]取決于無線信道的延遲參數(shù)和散射特性。
2 信道估計(jì)算法
信道估計(jì)的難點(diǎn)在于對于不同的接收天線每個(gè)子載波對應(yīng)多個(gè)信道參數(shù)。但是每個(gè)信道下不同子載波上對應(yīng)的信道參數(shù)具有相關(guān)性,可以根據(jù)這種相關(guān)性來進(jìn)行準(zhǔn)確的信道參數(shù)估計(jì)。
2.1 基本信道估計(jì)方案
在接收端對應(yīng)于第[i]個(gè)發(fā)射天線上第[n]個(gè)OFDM符號中第[k]個(gè)子載波上的頻域信道響應(yīng)可以表示為:
[Hi[n,k]=l=0K0-1hi[n,l]WklK] (9)
可以看出為了估計(jì)得到[Hi[n,k]]只需要先估計(jì)[hi[n,l]]。則在每個(gè)接收天線上的接收信號可以表示為:
[r[n,k]=i=12Hi[n,k]ti[n,k]+w[n,k]] (10)
式中:[k=0,1,…,K-1。]如果傳輸信號為訓(xùn)練符號則[ti[n,k],i=1,2]已知,對[hi[n,l]]的時(shí)域估計(jì)可以通過MMSE代價(jià)函數(shù)的最小化優(yōu)化來得到,其MMSE代價(jià)函數(shù)為:
[Chi[n,l];i=1,2=k=0K-1r[n,k]-i=12l=0K0-1hi[n,l]WklKti[n,k]2] (11)
因此[hi[n,l]]的優(yōu)化可由下式?jīng)Q定:
[?Chi[n,l]?hi[n,l0]=12?Chi[n,l]??hi[n,l0]-j?Chi[n,l]??hi[n,l0]=0] (12)
其中[?(?)]和[?(?)]分別代表復(fù)數(shù)的取實(shí)部和取虛部計(jì)算,經(jīng)過計(jì)算后可以變化為:
[k=0K-1r[n,k]-i=12l=0K0-1hi[n,l]WklKti[n,k]?Wkl0Kt?j[n,k]=0] (13)
其中[j=1,2],[l0=0,1,…,K0-1],符號[?]為取共扼符號。然后定義:
[pj[n,l]=k=0K-1r[n,k]t?j[n,k]W-klK] (14)
[pij[n,l]=k=0K-1ti[n,k]t?j[n,k]W-klK] (15)
所以式(13)可以表示為:
[i=12l=0K0-1hi[n,l]qij[n,l0-l]=pj[n,l0]] (16)
如果表示成矩陣形式則有:
[Q[n]h[n]=p[n]] (17)
其中:
[h[n]=h1[n]h2[n] p[n]=p1[n]p2[n]Q[n]=Q11[n]Q21[n]Q12[n]Q22[n]] (18)
并且有:
[hi[n]=hi[n,0],hi[n,1],…,hi[n,K0-1]Tpi[n]=pi[n,0],pi[n,1],…,pi[n,K0-1]TQij[n]=qij[n,0]qij[n,-1]…qij[n,-K0+1]qij[n,1]qij[n,0]…qij[n,-K0+2]????qij[n,K0-1]qij[n,K0-2]…qij[n,0]] (19)
所以信道參數(shù)矩陣[h[n]]可以通過下式求得:
[h[n]=Q-1[n]p[n]] (20)
根據(jù)所得到的時(shí)域信道參數(shù)經(jīng)過FFT即可獲得信道頻域響應(yīng)。
系統(tǒng)首先利用信號中的已知訓(xùn)練序列計(jì)算得到初始的信道估計(jì),隨后的數(shù)據(jù)符號利用前一個(gè)符號估計(jì)得到的信道參數(shù)進(jìn)行譯碼和解調(diào),同時(shí)根據(jù)解調(diào)后的數(shù)據(jù)重新編碼作為信道估計(jì)的輸入?yún)⒖夹盘栠M(jìn)行信道估計(jì)以更新信道參數(shù)。這樣接收機(jī)可以對每個(gè)符號進(jìn)行信道估計(jì),可以保證快速衰落信道下的系統(tǒng)性能。
2.2 簡化方案
由式(20)可以發(fā)現(xiàn)為了獲得時(shí)域信道估計(jì)必須計(jì)算[Q-1。]如果按照傳統(tǒng)算法需要對[K0×K0]大小的矩陣進(jìn)行求逆計(jì)算。由于信道延遲長度未知,常利用循環(huán)前綴的長度來作為最大的信道延遲長度來進(jìn)行計(jì)算,所以計(jì)算量仍然很大。
從式(17)可以看出信道相關(guān)矩陣[Q]為一個(gè)Toeplitz塊矩陣,所以信道估計(jì)求解問題是可以看成一個(gè)TBT線性系統(tǒng)。文獻(xiàn)[9]給出了一種針對TBT(Toeplitz?block?Toeplitz)線性系統(tǒng)的快速算法,該算法將TBT系統(tǒng)求解問題轉(zhuǎn)化為二維的解卷積問題,利用二維FFT將解卷積轉(zhuǎn)化成二維變換域內(nèi)的矩陣向量除法,簡化了矩陣的求解過程。
所以可以將式(17)寫成二維卷積形式如下:
[Q′ij[n]??h[n]=p[n]] (21)
式中:[??]為二維卷積符號;[Q′ij[n],][h[n],][p[n]]均為[3×(2K0-1)]的矩陣,并滿足:
[Q′ij[n]=q21[n,-K0+1]…q21[n,0]…q21[n,K0-1]q11[n,-K0+1]…q11[n,0]…q11[n,K0-1]q12[n,-K0+1]…q12[n,0]…q12[n,K0-1];]
[h[n]=h[n]000;][h[n]=h1[n,0]h1[n,1]…h(huán)1[n,K0-1]h2[n,0]h2[n,1]…h(huán)2[n,K0-1];]
[p[n]=p[n]z12z21z22;][p[n]=p1[n,0]p1[n,1]…p1[n,K0-1]p2[n,0]p2[n,1]…p2[n,K0-1]] (22)
其中[z12,z21,z22]代表在二維線性卷積結(jié)果中的擴(kuò)展部分,該部分與[p[n]]有很強(qiáng)的相關(guān)性,可以利用帶限信號的數(shù)據(jù)擴(kuò)展性質(zhì)計(jì)算得到內(nèi)插濾波器系數(shù),擴(kuò)展部分?jǐn)?shù)據(jù)通過對前[2K0+1]個(gè)數(shù)據(jù)通過外插推算得到。內(nèi)插濾波器系數(shù)不需要數(shù)據(jù)統(tǒng)計(jì)信息即可確定,并且二維內(nèi)插可以利用一維內(nèi)插函數(shù)通過兩次一維內(nèi)插實(shí)現(xiàn)二維數(shù)據(jù)擴(kuò)展,僅需要計(jì)算[3(K0-1)]個(gè)數(shù)據(jù),所以數(shù)據(jù)外插的計(jì)算量非常有限。當(dāng)這些擴(kuò)展數(shù)據(jù)得到之后,整個(gè)方程求解即可按照二維解卷積問題來解決。而整個(gè)信道估計(jì)過程可以簡化,如圖2所示。
圖2 信道估計(jì)簡化框圖
具體的方程求解過程可以劃分為以下步驟:
(1) 根據(jù)[p[n]]經(jīng)過兩次一維外插得到[z12,z21,z22,]構(gòu)成[p[n];]
(2) 對[p[n]]和[Q′ij[n]]分別進(jìn)行二維DFT計(jì)算,在兩個(gè)所得到的矩陣均為非零值的位置上的數(shù)據(jù)進(jìn)行除法計(jì)算;
(3) 由于零值位置數(shù)據(jù)不完整,所以需要在上一過程結(jié)果的基礎(chǔ)上加以外插得到[h[n]]完整的二維DFT值,再對其進(jìn)行二維IDFT計(jì)算,并在對應(yīng)位置上得到[h[n]],重組后即可得到信道參數(shù)矩陣[h[n]]。
整個(gè)方程求解過程不需要復(fù)雜的矩陣求逆和矩陣乘法,需要用到的計(jì)算只有一維外插和二維DFT以及二維IDFT計(jì)算。整個(gè)過程的計(jì)算量需要[54K0log2(2K0)+][6K20-3K0+3]個(gè)實(shí)數(shù)乘加運(yùn)算。并且由于插值后的矩陣含有大量零值,所以二維DFT的計(jì)算量還可以減小。對比于文獻(xiàn)[5]中[O(K50)]和文獻(xiàn)[6]中[O(K40)]的計(jì)算量具有很大的優(yōu)勢。
3 仿真結(jié)果
在對MIMO?OFDM仿真系統(tǒng)搭建的過程中,參考了IEEE 802.16e中的系統(tǒng)參數(shù),信道帶寬為10 MHz,信道被劃分為1 024個(gè)子信道。OFDM符號長度為102.4 μs,循環(huán)前綴長度為25.6 μs。仿真信道采用TU6信道,最大信道擴(kuò)展延遲為5 μs,并且添加100 Hz多普勒頻率模擬移動信道。
仿真系統(tǒng)采用兩個(gè)發(fā)射天線和兩個(gè)接收天線實(shí)現(xiàn)空間分集,不同發(fā)射機(jī)與接收機(jī)之間的信道互相獨(dú)立。系統(tǒng)中空時(shí)分組碼(STBC)采用文獻(xiàn)[3]中定義的編碼方式,以數(shù)據(jù)簇為單位進(jìn)行編碼,編碼后的數(shù)據(jù)經(jīng)過4QAM映射分成兩個(gè)OFDM符號分別進(jìn)行發(fā)射。
圖3給出了TU6信道下多普勒頻率為100 Hz,采用4QAM調(diào)制時(shí)估計(jì)器的MSE性能和BER性能。
圖3 信道估計(jì)性能曲線
其中,理想的信道估計(jì)表示采用精確的信道參數(shù)進(jìn)行仿真,而basic MMSE表示基本的MMSE算法,simplify MMSE表示簡化后的MMSE算法。從圖3(a)中可以發(fā)現(xiàn)簡化算法的MSE性能相對于基本MMSE算法有大約0.5 dB的損失,這是由于簡化算法在進(jìn)行內(nèi)插計(jì)算引入的誤差所造成,從圖3(b)中可以看出簡化后的MMSE算法的BER性能相對于基本的MMSE算法有大約1 dB的下降,與理想信道相比有接近3 dB的性能差距。但是考慮到復(fù)雜度明顯的降低,該簡化算法的優(yōu)勢仍非常明顯。
4 結(jié) 語
本文研究了空時(shí)編碼MIMO?OFDM系統(tǒng)的信道估計(jì)算法,提出了針對MMSE信道估計(jì)算法的簡化方案,根據(jù)數(shù)據(jù)相關(guān)矩陣為Toeplitz塊矩陣的特性,將信道估計(jì)問題轉(zhuǎn)化為Toeplitz塊矩陣線性方程求解問題,并且利用Toeplitz塊矩陣快速算法快速求解方程,方程解算過程僅需要數(shù)據(jù)內(nèi)插和二維FFT/IFFT計(jì)算,避免了復(fù)雜的矩陣求逆和矩陣相乘,大大降低了信道估計(jì)的復(fù)雜度。仿真結(jié)果表明在復(fù)雜度大大降低的前提下系統(tǒng)誤碼率與MMSE算法相比僅有大約1 dB的差距,具有很強(qiáng)的實(shí)用性。
參考文獻(xiàn)
[1] STUBER G L, BARRY J R, MCLAUGHLIN S W, et al. Broadband MIMO?OFDM wireless communications [J]. Proceedings of the IEEE, 2004, 92(2): 271?294.
[2] SAMPATH H, TALWAR S, TELLADO J, et al. A fourthgeneration MIMO?OFDM broadband wireless system design, performance, and field trial results [J]. IEEE Communication Magazine, 2005, 40(9): 143?149.
[3] IEEE Std 802. 16e?2007 IEEE standard for IocaI and metropolitan area networks.Part 1 6: Air interface for fixed and mobile broadband wireless access systems amendment 2: Physical and medium access controllayers for combined fixed and mobile operation In Iicensed bands and corrigendum 1[S]. the USA: IEEE, 2007.
[4] ZHANG H, LI Y G, REID A, et al. Channel estimation for MIMO OFDM in correlated fading channels [C]// Proceedings of 2005 IEEE International Conference on Communications. Seoul, Korea: ICC, 2005: 2626?2630.
[5] LI Y G, SESHADRI N, ARIYAVISITAKUL S. Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels [J]. IEEE Journal on Selected Areas in Communications, 1999, 17(3): 461?471.
[6] HAMMARBERG P, EDFORS O. A comparison of DFT and SVD based channel estimation in MIMO OFDM systems [C]// 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications. Helsinki, Finland: IEEE, 2006: 1?5.
[7] KARIMI H R,ANDERSON N W. A novel and efficient solution to block?based joint?detection using approximate Cholesky factorization [C]// The 1998 9th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. Boston, MA: IEEE, 1998: 1340?1345.
[8] KOTECHA J H, SAYEED A M. Transmit signal design for optimal estimation of correlated MIMO channels [J]. IEEE Transactions on Signal Processing, 2004, 52(2): 546?557.
[9] YU J L, HONG D Y. A novel subspace channel estimation with fast convergence for ZP?OFDM systems [J]. IEEE Transactions on Wireless Communications, 2011, 10(10): 3168?3173.
[10] YAGLE A E. A fast algorithm for Toeplitz?block?Toeplitz linear systems [C]// Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Salt Lake City, UT, the USA: ICASSP, 2001:1929?1932.
參考文獻(xiàn)
[1] STUBER G L, BARRY J R, MCLAUGHLIN S W, et al. Broadband MIMO?OFDM wireless communications [J]. Proceedings of the IEEE, 2004, 92(2): 271?294.
[2] SAMPATH H, TALWAR S, TELLADO J, et al. A fourthgeneration MIMO?OFDM broadband wireless system design, performance, and field trial results [J]. IEEE Communication Magazine, 2005, 40(9): 143?149.
[3] IEEE Std 802. 16e?2007 IEEE standard for IocaI and metropolitan area networks.Part 1 6: Air interface for fixed and mobile broadband wireless access systems amendment 2: Physical and medium access controllayers for combined fixed and mobile operation In Iicensed bands and corrigendum 1[S]. the USA: IEEE, 2007.
[4] ZHANG H, LI Y G, REID A, et al. Channel estimation for MIMO OFDM in correlated fading channels [C]// Proceedings of 2005 IEEE International Conference on Communications. Seoul, Korea: ICC, 2005: 2626?2630.
[5] LI Y G, SESHADRI N, ARIYAVISITAKUL S. Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels [J]. IEEE Journal on Selected Areas in Communications, 1999, 17(3): 461?471.
[6] HAMMARBERG P, EDFORS O. A comparison of DFT and SVD based channel estimation in MIMO OFDM systems [C]// 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications. Helsinki, Finland: IEEE, 2006: 1?5.
[7] KARIMI H R,ANDERSON N W. A novel and efficient solution to block?based joint?detection using approximate Cholesky factorization [C]// The 1998 9th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. Boston, MA: IEEE, 1998: 1340?1345.
[8] KOTECHA J H, SAYEED A M. Transmit signal design for optimal estimation of correlated MIMO channels [J]. IEEE Transactions on Signal Processing, 2004, 52(2): 546?557.
[9] YU J L, HONG D Y. A novel subspace channel estimation with fast convergence for ZP?OFDM systems [J]. IEEE Transactions on Wireless Communications, 2011, 10(10): 3168?3173.
[10] YAGLE A E. A fast algorithm for Toeplitz?block?Toeplitz linear systems [C]// Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Salt Lake City, UT, the USA: ICASSP, 2001:1929?1932.
參考文獻(xiàn)
[1] STUBER G L, BARRY J R, MCLAUGHLIN S W, et al. Broadband MIMO?OFDM wireless communications [J]. Proceedings of the IEEE, 2004, 92(2): 271?294.
[2] SAMPATH H, TALWAR S, TELLADO J, et al. A fourthgeneration MIMO?OFDM broadband wireless system design, performance, and field trial results [J]. IEEE Communication Magazine, 2005, 40(9): 143?149.
[3] IEEE Std 802. 16e?2007 IEEE standard for IocaI and metropolitan area networks.Part 1 6: Air interface for fixed and mobile broadband wireless access systems amendment 2: Physical and medium access controllayers for combined fixed and mobile operation In Iicensed bands and corrigendum 1[S]. the USA: IEEE, 2007.
[4] ZHANG H, LI Y G, REID A, et al. Channel estimation for MIMO OFDM in correlated fading channels [C]// Proceedings of 2005 IEEE International Conference on Communications. Seoul, Korea: ICC, 2005: 2626?2630.
[5] LI Y G, SESHADRI N, ARIYAVISITAKUL S. Channel estimation for OFDM systems with transmitter diversity in mobile wireless channels [J]. IEEE Journal on Selected Areas in Communications, 1999, 17(3): 461?471.
[6] HAMMARBERG P, EDFORS O. A comparison of DFT and SVD based channel estimation in MIMO OFDM systems [C]// 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications. Helsinki, Finland: IEEE, 2006: 1?5.
[7] KARIMI H R,ANDERSON N W. A novel and efficient solution to block?based joint?detection using approximate Cholesky factorization [C]// The 1998 9th IEEE International Symposium on Personal Indoor and Mobile Radio Communications. Boston, MA: IEEE, 1998: 1340?1345.
[8] KOTECHA J H, SAYEED A M. Transmit signal design for optimal estimation of correlated MIMO channels [J]. IEEE Transactions on Signal Processing, 2004, 52(2): 546?557.
[9] YU J L, HONG D Y. A novel subspace channel estimation with fast convergence for ZP?OFDM systems [J]. IEEE Transactions on Wireless Communications, 2011, 10(10): 3168?3173.
[10] YAGLE A E. A fast algorithm for Toeplitz?block?Toeplitz linear systems [C]// Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Salt Lake City, UT, the USA: ICASSP, 2001:1929?1932.