CONSTANT FALSE ALARM RATE (CFAR) DETECTION BASED ESTIMATORS WITH APPLICATIONS TO SPARSE WIRELESS CHANNELS


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1 CONSTANT FALSE ALARM RATE (CFAR) DETECTION BASED ESTIMATORS WITH APPLICATIONS TO SPARSE WIRELESS CHANNELS A Thesis Submitted to the Graduate School of Engineering and Sciences of İzmir Institute of Technology in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in Electrical and Electronics Engineering by Ümit KARACA October 26 İZMİR
2 We approve the thesis of Ümit KARACA Date of Signature October 26 Assist. Prof. Serdar ÖZEN Supervisor Department of Electrical and Electronics Engineering İzmir Institute of Technology October 26 Assist. Prof. Mustafa Aziz ALTINKAYA Department of Electrical and Electronics Engineering İzmir Institute of Technology October 26 Assoc. Prof. Olcay AKAY Department of Electrical and Electronics Engineering Dokuz Eylül University October 26 Prof. F.Acar SAVACI Head of Department Department of Electrical and Electronics Engineering İzmir Institute of Technology Assoc. Prof. Dr. Semahat ÖZDEMİR Head of the Graduate School
3 ABSTRACT CONSTANT FALSE ALARM RATE (CFAR) DETECTION BASED ESTIMATORS WITH APPLICATIONS TO SPARSE WIRELESS CHANNELS We provide Constant False Alarm Rate (CFAR) based thresholding methods for training based channel impulse response (CIR) estimation algorithms for communication systems which utilize a periodically transmitted training sequence within a continuous stream of information symbols. After obtaining the CIR estimation by using known methods in the literature, there are estimation errors which causes performance loss at equalizers. The channel estimation error can be seen as noise on CIR estimations and CFAR based thresholding methods, which are used in radar systems to decide the presence of a target, can effectively overcome this problem. CFAR based methods are based on determining threshold values which are computed by distribution of channel noise. We provide exact and approximate distribution of channel noise appear at CIR estimate schemes. We applied Cell AveragingCFAR (CACFAR) and Order StatisticCFAR (OS CFAR) methods on the CIR estimations. The performance of the CFAR estimators are then compared by their Least Square error in the channel estimates. The Signal to Interference plus Noise Ratio (SINR) performance of the decision feedback equalizers (DFE), of which the tap values are calculated based on the CFAR estimators, are also provided. iii
4 ÖZET SABİT YANLIŞ ALARM ORANI SEZİMLEME TABANLI KANAL KESTİRİMİ VE YO GUN OLMAYAN TEKİL KANALLARA UYGULAMALARI Bu çalışmada, haberleşme sistemlerinde kullanılan Kanal Dürtü Yanıtı (CIR) kestirimlerinin eşiklemesinde kullanılmak üzere, Sabit Yanlış Alarm Oranı (CFAR) sezimleme tabanlı metotlar ele alınmıştır. Haberleşme literatüründe bilinen yöntemlerle elde edilen kanal dürtü yanıtları, kestirim hatası taşımakta, bu durum denkleştiricilerde performans kaybına neden olmaktadır. Bu kestirim hatası, kanal dürtü yanıtındaki gürültü olarak de gerlendirilebilir. Radar sistemlerinde hedef tespit edilmesinde kullanılan Sabit Yanlış Alarm Oranı (CFAR) sezimleme tabanlı eşikleme metotları, bahsedilen bu gürültünün temizlenmesinde kullanılabilir. Sabit Yanlış Alarm Oranı (CFAR) sezimleme tabanlı metotlar, kanal gürültüsünün istatistiksel da gılımı yardımıyla hesaplanan eşik de gerlerine dayanmaktadır. Proje kapsamında, Hücre Ortalamalı (CACFAR) ve İstatistiksel Sıralamalı (OS CFAR) Sabit Yanlış Alarm Oranı sezimleme tabanlı metotlar kullanılarak elde edilen eşik de gerleri, çeşitli kanal kestirimlerine uygulanmıştır. Bahsedilen metotların performansları, eşikleme işleminden sonra elde edilen kanal kestirim sinyallerinin En Küçük Kareler Hataları (NLSE) karşılaştırılarak gösterilmiştir. Ayrıca, Sinyallerin Girişim ve Gürültüye Oranları (SINR), Karar Geridönüşümlü Denkleştiriciler (DFE) kullanılarak gösterilmiştir. iv
5 TABLE OF CONTENTS LIST OF FIGURES vii LIST OF TABLES ix LIST OF ABBREVIATIONS x CHAPTER. INTRODUCTION Motivation Organization and Contributions of the Thesis CHAPTER 2. SIGNAL AND CHANNEL MODEL Introduction Overview of the Data Transmission Model CHAPTER 3. GENERALIZED LEAST SQUARES BASED CHANNEL ESTIMA TION Method of Least Squares Existing Channel Estimation Methods LeastSquares Channel Estimation Correlation Based Channel Estimation Covariance Matrix Update Based Iterative Channel Estimation Further Improvements to the Initial Channel Estimate Statistical Analysis of Baseline Noise Approximations of Distribution Iterative Algorithm to Calculate the Channel Estimate Other Approaches Background (Matching Pursuit) CHAPTER 4. CONSTANT FALSE ALARM RATE (CFAR) BASED THRESH OLDING Introduction Using CFAR Techniques for Channel Estimation v
6 4.2.. Approximations and Further Simplifications Cell Averaging (CA) CFAR Based Detection Order Statistic (OS) CFAR Based Detection Simulations CHAPTER 5. CHANNEL ESTIMATEBASED DECISION FEEDBACK EQUAL IZERS Simulations REFERENCES APPENDICES APPENDIX A. 8VSB PULSE SHAPE Complex RootRaised Cosine Pulse Complex Raised Cosine Pulse APPENDIX B. TEST CHANNELS vi
7 LIST OF FIGURES Figure Page Figure 2. System block diagram Figure 2.2 Data frame Figure 3. Correlation properties of finite PN sequences Figure 3.2 Figure 3.3 The histogram of the real and imaginary parts of the 48th tap value of the term ( A H A ) ( ) A H Hd + Qη [ Na Lq:N+N c +L q] The normality plot of the real and imaginary parts of the 48th tap value of the term ( A H A ) ( ) A H Hd + Qη [ Na L q :N+N c +L q ] Figure 4. CellAveraging CFAR Figure 4.2 NLSE of CACFAR and CAGOCFAR for different window size. 45 Figure 4.3 K versus alpha Figure 4.4 CIR estimations for Channel Figure 4.5 CIR estimations for Channel Figure 4.6 CIR estimations for Channel Figure 4.7 CIR estimations for Channel 3plus Figure 4.8 CIR estimations for Channel Figure 4.9 CIR estimations for Channel Figure 4. CIR estimations for Channel Figure 4. CIR estimations for Channel Figure 4.2 CIR estimations for Channel Figure 4.3 NLSE values of CIR estimates versus SNR(dB) for Channel... 6 Figure 4.4 NLSE values of CIR estimates versus SNR(dB) for Channel Figure 4.5 NLSE values of CIR estimates versus SNR(dB)for Channel Figure 4.6 NLSE values of CIR estimates versus SNR(dB) for Channel 3plus 64 Figure 4.7 NLSE values of CIR estimates versus SNR(dB) for Channel Figure 4.8 NLSE values of CIR estimates versus SNR(dB) for Channel Figure 4.9 NLSE values of CIR estimates versus SNR(dB) for Channel vii
8 Figure 4.2 NLSE values of CIR estimates versus SNR(dB) for Channel Figure 4.2 NLSE values of CIR estimates versus SNR(dB) for Channel Figure 5. DFE Equalizer Figure 5.2 Residual Channel after FeedForward Filter Figure 5.3 Residual Channel after FeedBack Filter Figure 5.4 SINR DFE versus SNR for Channel for various channel estimates. 78 Figure 5.5 SINR DFE versus SNR for Channel 2 for various channel estimates. 79 Figure 5.6 SINR DFE versus SNR for Channel 3 for various channel estimates. 8 Figure 5.7 SINR DFE versus SNR for Channel 3plus for various channel estimates Figure 5.8 SINR DFE versus SNR for Channel 4 for various channel estimates. 82 Figure 5.9 SINR DFE versus SNR for Channel 5 for various channel estimates. 83 Figure 5. SINR DFE versus SNR for Channel 6 for various channel estimates. 84 Figure 5. SINR DFE versus SNR for Channel 7 for various channel estimates. 85 Figure 5.2 SINR DFE versus SNR for Channel 8 for various channel estimates. 86 viii
9 LIST OF TABLES Table Page Table 4. K values computed from Equation (4.49) Table B. Simulated 9 Channel Impulse Responses ix
10 LIST OF ABBREVIATIONS AR CFAR CACFAR CAGOCFAR CIR DFE DTV ISI LE LMS LS ML MLSE MMSE MP MSE OSCFAR RLS SINR SNR VSB Autoregressive Constant False Alarm Rate Cell Averaging Constant False Alarm Rate Cell Averaging GreatestOf Constant False Alarm Rate Channel Impulse Response Decision Feedback Equalizer Digital Television InterSymbol Interference Linear Equalizer Least Mean Square Least Squares Maximum Likelihood Maximum Likelihood Sequence Estimation (Estimator) Minimum Meansquared Error Matching Pursuit Meansquared Error Order Statistic Constant False Alarm Rate Recursive Least Square SignaltoInterferencePlusNoise Ratio SignaltoNoise Ratio Vestigial Sideband x
11 CHAPTER INTRODUCTION.. Motivation In mobile wireless and digital television (DTV) channels multipath phenomena is generally attributed to the randomly changing propagation characteristics as well as the reflection, diffraction and scattering of the transmitted waves from the buildings, large moving vehicles/airplanes, mountains, ionosphere, sea surface. Many types of impairments are observed on these channels such as Doppler spread, fading, multipath spread (or delay spread), nonlinear distortion, frequency offset, phase jitter, thermal and/or impulsive noise, co and adjacent channel interference. This research focuses mainly on the effects of multipath spread and thermal noise. Large delay spread induces significant intersymbol interference (ISI) where the received symbols are a function of several adjacent symbols. A large Doppler spread causes rapid variations in the channel impulse response characteristics, and necessitates a fast converging adaptive algorithm. When the channel exhibits a deep fade, it results in a very low received signal power. Among various remedies to these problems which have been proposed thus far, the diversity reception and the multicarrier transmission combined with advanced adaptive signal processing algorithms to estimate and to track the channel variations are considered to be among the most prominent alternatives. However multicarrier transmission is out of the scope of this research. Multipath channels are generally modelled in the form of frequency selective fading as well as Rayleigh or Rician fading. As the receivers employ diversity to combat the combined effects of multipath and fading in the form of antenna arrays, as well as polarization diversity, more advanced signal processing is required at the receiver frontend to estimate and track the channel parameter variations. In order to be able to come up with better signal processing algorithms first the underlying wireless channel characteristics must be well understood and proper analytical models must be developed. As the demand increases for higher data rates and more bandwidth, the effects
12 of all aforementioned channel impairments become more severe. Better channel models, and more advanced algorithms for channel estimation, tracking and equalization are still on the current agenda of communication system researchers and designers..2. Organization and Contributions of the Thesis This research has been completed under the following two major constraints: We considered that the transmission scheme is fixed and has been standardized as in (ATSC Standard A/53 995) ; thus we were constrained to work with and implement algorithms which can only be implemented at the receivers. In other words we assumed no changes will take place to the existing North American DTV transmission standard. This eliminates the possibility of transmitter/receiver joint optimization, or any proposals of improvements to the transmission format. Due to having very high data rate, the digital TV system requires algorithms with relatively low complexity. Highly recursive algorithms or blind algorithms that require long averaging to get useful estimates is outside the focus of this research. By the time a blind algorithm converge to a reasonably reliable estimate of the channel, the channel may change significantly thus making the CIR estimate useless. For these reasons we mainly concentrated on training sequence based channel estimation algorithms. Total of five chapters follow the Introduction chapter: Chapter 2 presents the channel and signal model where the signal model obeys the American DTV transmission standard (ATSC Standard A/53 995). Notation and main important sets of equations are introduced which will be primarily used in the sequel of the thesis. Chapter 3 first overviews the method of (generalized) least squares, and applies it to the signal/channel model that has been introduced in Chapter 2. Then the correlation based initial channel estimation is also introduced. Correlation based channel estimation is considered, since it is readily available in digital receivers for frame synchronization purpose. Then the problems associated with both of these methods 2
13 will be presented. The most important contribution of Chapter 3 is the development of a detailed statistical model of the baseline noise which is a byproduct of any type of correlation processing in the receiver. However, as will be shown, this statistical model is actually a function of the channel impulse response which we are trying to estimate. For this very reason we then provide an iterative algorithm to find the generalized least squares channel estimate where the covariance matrix of the baseline noise is incorporated into the channel estimation scheme. Each iteration also involves a thresholding, which is accomplished by two different methods: (i) constant false alarm rate (CACFAR) based thresholding, and (ii) order statistic (OSCFAR) based thresholding. Chapter 4 overviews Constant False Alarm Rate (CFAR) based thresholding methods. Cell Averaging (CA) and Order Statistic (OS) based CFAR methods are introduced by the help of statistic distribution given in Chapter 2. In Chapter 5, we provide the derivation for the Decision Feedback Equalizers. The CIR estimates thresholded by CFAR methods provided in previous chapters are used for calculating the DFE filter tap values. We then provide the signaltointerferenceplusnoise (SINR) performance at the DFE output. 3
14 CHAPTER 2 SIGNAL AND CHANNEL MODEL 2.. Introduction For the communications systems utilizing periodically transmitted training sequence, leastsquares (LS) based channel estimation algorithms or the correlation based channel estimation algorithms have been the most widely used two alternatives. Both methods use a stored copy of the known transmitted training sequence at the receiver. The properties and the length of the training sequence are generally different depending on the particular communication system s standard specifications. In addition, a training sequence could also be utilized in a communication system not only for channel estimation purpose but also for synchronization purpose, to indicate the beginning and/or end of a transmitted data frame or packet. In the sequel, although the examples following the derivations of the blended channel estimator will be drawn from the ATSC digital TV 8VSB system (Hillery 2), to the best of our knowledge it could be applied with minor modifications to any digital communication system with linear modulation which employs a training sequence. Overview of the data transmission model in Section 2.2 provides the necessary background and introduces the notation which will be entirely used in the rest of the thesis Overview of the Data Transmission Model We will briefly go over the data transmission model. The baseband transmitted signal waveform of data rate /T symbols/sec depicted in Figure 2. is represented by s(t) = k I k q(t kt ) (2.) where {I k A {α,..., α M } C } is the transmitted data sequence, which is a discrete Mary sequence taking values on the generally complex Mary alphabet A, 4
15 I n δ(t nt ) n q(t) s(t) c(t) η(t) q ( t) y(t) t = nt y[n] Equalizer ĥ[n] Channel Estimator Î n Figure 2.: System block diagram. h(t) is the composite channel including transmit end receive filters as well as the physical channel. which also constitutes the two dimensional employed modulation constellation. q(t) is the transmitter pulse shaping filter of finite support [ T q /2, T q /2]. The overall complex pulse shape will be denoted by p(t) and is given by p(t) = q(t) q ( t) (2.2) where q ( t) is the receiver matched filter impulse response. Although it is not required, for the sake of simplifying the notation, we assume that the span of the transmit and receive filters, T q, is integer multiple of the symbol period, T ; that is T q = N q T = 2L q T, L q Z +. We also note that for 8VSB system (Hillery 2) the transmitter pulse shape is the Hermitian symmetric rootraised cosine pulse, which implies q(t) = q ( t). In the sequel q[n] q(t) t=nt will be used to denote both the transmit and receive filters. The physical channel between the transmitter and the receiver is denoted by c(t), and throughout this paper the concatenation of p(t) and the channel will be denoted by h(t, τ), and is defined as h(t, τ) = q(t) c(t, τ) q ( t) = p(t) c(t, τ). (2.3) The physical channel c(t, τ) is generally described by the impulse response c(t, τ) = L k= K c k (τ)δ(t τ k ) (2.4) which describes a timevarying channel, and {c k (τ)} C, where K k L, and t, τ R, {τ k } denote the multipath delays, or the TimeOfArrivals (TOA). We will 5
16 assume that the timevariations of the channel is slow enough that c(t, τ) = c(t) can be assumed to be a fixed (static) intersymbol interference (ISI) channel throughout the training period; that is we will assume that c k (τ) = c k, which in turn implies c(t) = L k= K c k δ(t τ k ) (2.5) for t NT, where N is the number of training symbols. In general c k = c k e j2πf cτ k with ck being the amplitude of the k th multipath, and f c is the carrier frequency. It is also inherently assumed that τ k < for K k, τ =, and τ k > for k L. The particular choice of the summation indices K and L, the number of maximum anticausal and causal multipath delays respectively, will be clarified in the discussion of correlation based channel estimation and onwards. It is important to clarify that the multipath delays τ k are not assumed to be at integer multiples of the sampling period T. Indeed it is one of the main contributions of this paper that we show an accurate and robust way to recover the pulse shape back into the concatenated channel estimate when the multipath delays are not exactly at the sampling instants. By combining Equations (2.3) and (2.5) (and by dropping the τ index) we get L h(t) = p(t) c(t) = c k p(t τ k ). (2.6) k= K Since both p(t) and c(t) are complex valued functions, the overall channel impulse response h(t) is also complex valued. We can write them in terms of their real and imaginary parts: p(t) = p I (t) + jp Q (t), (2.7) c(t) = c I (t) + jc Q (t), (2.8) h(t) = h I (t) + jh Q (t). (2.9) Then h(t) = p(t) c(t) = (p I (t) + jp Q (t)) (c I (t) + jc Q (t)) 6
17 = (p I (t) c I (t) p Q (t) c Q (t)) +j (p } {{ } I (t) c Q (t) + p Q (t) c I (t)). (2.) } {{ } h I (t) h Q (t) By using the notation introduced here the matched filter output y(t) is given by y(t) = ( ) I k δ(t kt ) h(t) + ν(t) (2.) k where ν(t) = η(t) q ( t) denotes the complex (colored) noise process after the pulse matched filter, with η(t) being a zeromean white Gaussian noise process with spectral density ση 2 per real and imaginary part. Similar to Equations ( ) y(t) can be also written in terms of its real and imaginary parts: y(t) = y I (t) + jy Q (t). Sampling the matched filter output at the symbol rate we obtain the discretetime representation of the overall communication system as y[n] y(t) t=nt = k I k h[n k] + ν[n]. (2.2)... a a... a N d N d N+... d N... Training Sequence Data Sequence Figure 2.2: Data frame: N symbols of known training sequence followed by N N information symbols. Referring to Figure 2.2, the transmitted symbols are composed of frames (or packets) of length N, where the first N symbols are the training symbols. Within a frame of length N, the symbols are denoted by I k = a k, for k N d k, for N n N, (2.3) 7
18 where the distinction of first N symbols is made to indicate that they are the known training symbols, and it is possible that the a k s belong to a certain subset of the Mary constellation alphabet A; that is {a k Ã A {α,..., α M }}. In fact for the 8VSB system the signal alphabet is A {±, ±3, ±5, ±7}, while the training sequence can only take binary values within the set Ã { 5, +5}. In the sequel the sampled matched filter output signal y[n] will be used extensively in vector form, and to help minimize introducing new variables, the notation of y [n :n 2 ] with n 2 n, will be adopted to indicate the column vector y [n :n 2 ] = [y[n ], y[n + ],, y[n 2 ]] T. Same notation will also be applied to the noise variables η[n] and ν[n]. Without loss of generality symbol rate sampled, complex valued, composite CIR h[n] can be written as a finite dimensional vector h = [h[ N a ], h[ N a + ],, h[ ], h[], h[],, h[n c ], h[n c ]] T (2.4) where N a and N c denote the number of anticausal and the causal taps of the channel, respectively, and are given by { } τ K T N q N a = round, and N c = round T { τl + T N q and N a +N c + is the total memory of the channel. Based on Equation (2.2) and assuming that N N a + N c +, we can write the pulse matched filter output corresponding only to the known training symbols: T }, y[n c ] = h[ N a ]a Nc+N a + + h[]a Nc + + h[n c ]a + ν[n c ] y[n c + ] = h[ N a ]a Nc+N a+ + + h[]a Nc+ + + h[n c ]a + ν[n c + ].. y[n N a ] = h[ N a ]a N + +h[]a N Na + +h[n c ]a N Na N c +ν[n N a ] which can be written compactly as y [Nc :N N a ] = Ãh + ν [Nc:N N a ] = Ãh + Qη [Nc L q :N N a +L q ], (2.5) 8
19 where Ã = T { [a Nc+N a,, a N ] T, [a Nc+N a,, a ] } (2.6) a Nc +N a a Nc +N a a a = Nc +N a + a Nc +N a a....., (2.7). a N a N 2 a N Na N c where Ã is (N N a N c ) (N a + N c + ) Toeplitz convolution matrix with first column [a Nc +N a,, a N ] T and first row [a Nc +N a,, a ], and ν [Nc :N N a ] = Qη [Nc L q:n N a+l q] (2.8) is the colored noise vector at the receiver matched filter output, with Q = q T q T q T (N N a N c ) (N N a N c +N q ) (2.9) and q is the vector containing timereversed samples of the receiver matched filter sampled at the symbol rate and is q = [q[+l q ],, q[],, q[ L q ]] T. (2.2) Note that q has N q + = 2L q + samples. Similarly the pulse matched filter output which includes all the contributions from the known training symbols (which includes the adjacent random data as well) can be written as y [ Na :N+N c ] = (A + D) h + ν [ Na:N+N c ] (2.2) = Ah + Dh + Qη [ Na L q :N+N c +L q ], (2.22) 9
20 where A = T [a,, a N,,, } {{ } ] T, [a,,, ] } {{ } N a+n c N a+n c (2.23) is a Toeplitz matrix of dimension (N +N a +N c ) (N a +N c +) with first column [a, a,, a N,,, ] T, and first row [a,,, ], and D = T [, } {{,, d } N,, d Nc +N a +N ] T, [, d,, d Nc N a ], (2.24) N is a Toeplitz matrix which includes the adjacent unknown symbols only, prior to and after the training sequence. The data sequence [d,, d Nc N a ] is the unknown information symbols transmitted at the end of the frame prior to the current frame being transmitted. Q is of dimension (N + N a + N c ) (N + N a + N c + N q ) and has the same convolution matrix structure with Q as displayed in Equation (2.9), and is given by. Q = q T q T q T (N+N a+n c) (N+N a+n c+n q) (2.25) and q = [q[+l q ],, q[],, q[ L q ]] T. Equation (2.22) which includes all the contributions from the known training symbols (which includes the adjacent random data as well) can be written explicitly as y[ N y[ N a ] a ].. y[n c ] y[n c ] y[n c ] y [ Na :N+N c ] =.. = y [Nc :N N a ] = Qη [ Na L q :N+N c +L q ] y[n N a ] y[n N a ] y[n N a ].. y[n + N c ] y[n + N c ]
21 a d d 2 d Na N c a a d d Na N c a Nc+N a a Nc+N a 2 a d a Nc +N a a Nc +N a a a Nc +N a + a Nc +N a a h (2.26).... a N a N 2 a N Na N c a N a N 2 a N 2 Na N c d N a N a N 2 Na N c d N+ d N a N d Na +N c +N d N } {{ } } {{ } A D where the entries of the vector y [ Na:N+Nc ] between the dotted lines denote the matched filter output corresponding only to the known training symbols which is provided in Equation (2.5). Note that the corresponding entries of the matrix A between the dotted lines are exactly the same as the entries of the matrix Ã which is provided in Equation (2.7), and the corresponding entries of the matrix D between the dotted lines are all zeros. We now write the contributions of the unknown symbols Dh in Equation (2.22) in a different format which will prove to be more useful in the subsequent derivations. We first define d = S d, or equivalently d = S T d, where d = [d Nc N a,, d, N, d N,, d N+Nc+N a ] T (2.27) d = [d N Na,, d, d N,, d N+Nc+Na ] T (2.28) S = I Na +N c (Na +N c ) N (Na +N c ) (N c +N a ) (2.29) (Na +N c ) (N a +N c ) (Na +N c ) N I Na+Nc (2(N c +N a )) (N+2(N a +N c )) where S is a selection matrix which retains the random data, eliminates the N zeros in the middle of the vector d. We also introduce
22 h T h T H = ht (N+N c+n a) (N+2(N a+n c)) (2.3) h = [h[n c ],, h[], h[], h[ ],, h[ N a ]] T = Jh (2.3) J = (2.32)... (N a +N c +) (N a +N c +) H = HS T (2.33) where h is the time reversed version of h (reordering is accomplished by the permutation matrix J), and H is of dimension (N + N a + N c ) (2(N c +N a )) with a hole inside which is created by the selection matrix S as defined in Equation (2.29). Then it is trivial to show that Dh = H d = HS T d = Hd. (2.34) Based on the Equations ( ) we can rewrite Equation (2.22) as y [ Na :N+N c ] = Ah + Dh + Qη [ Na L q :N+N c +L q ] = Ah + Hd + Qη [ Na L q :N+N c +L q ], (2.35) 2
23 CHAPTER 3 GENERALIZED LEAST SQUARES BASED CHANNEL ESTIMATION 3.. Method of Least Squares First we briefly overview the method of least squares in general terms that is very widely used in the statistical inference and estimation theory applications without considering any specific signal processing or communication system framework. Consider the linear model y = Ax + ν (3.) where y is the observation (or response) vector, A is the regression (or design) matrix, x is the vector of unknown parameters to be estimated, and ν is the observation noise (or measurement error) vector, and are given by y = [y,, y n ] T, (3.2) a, a,2 a,p a A = 2, a 2,2 a 2,p...., (3.3).. a n,3 a n,2 a n,p x = [x,, x p ] T, (3.4) ν = [ν,, ν n ] T. (3.5) It is assumed that all the variables in Equation (3.) are generally complex valued, that is y, ν C n, x C p and A C n p. Then the ordinary least squares solution ˆx ols can be obtained by minimizing the objective function 3
24 J OLS (x) = ν H ν = y Ax 2 (3.6) is given by ˆx ols = (A H A) A H y, (3.7) whenever the matrix A has rank p. The estimator of (3.7) is called the best linear unbiased estimate (BLUE) among all linear unbiased estimators if the noise covariance matrix is known to be (Seber 977, Casella and Berger 99) Cov{ν} = K ν 2 E{ννH } = σ 2 νi. (3.8) The estimator of (3.7) is called the minimum variance unbiased estimator (MVUE) among all unbiased estimators (not only linear) if the noise is known to be Gaussian with zero mean and with covariance matrix K ν of (3.8), that is ˆx ols is called MVUE if it is known that ν N (, σνi). 2 However if it is known that the vector ν is correlated, that is K ν σνi, 2 then in order to achieve the BLUE property we must use a modified objective function. Since K ν is positive definite, there exists an n n nonsingular matrix V such that K ν = V V H (Seber 977). Therefore setting z = V y, B = V X, and β = V ν, we have the model z = Bx + β (3.9) where B is n p of rank p, and Cov{β} = K β (generalized) objective function for the model of (3.9) by = I. Then we define the J GLS (x) = β H β = z Bx 2 = ν H V H V ν = ν H K ν ν = (y Ax) H K ν (y Ax). (3.) 4
25 The least squares estimate that minimizes Equation (3.) is ˆx gls = (B H B) B H z = (A H K ν A) A H K ν y, (3.) The estimator of (3.) is called the best linear unbiased estimate (BLUE) among all linear unbiased estimators if the noise covariance matrix is known to be Cov{ν} = K ν 2 E{ννH } = σ 2 νi (Seber 977, Casella and Berger 99). The estimator of (3.) is called the minimum variance unbiased estimator (MVUE) among all unbiased estimators (not only linear) if the noise is known to be Gaussian with zero mean and with covariance matrix K ν, that is ˆx ols is called MVUE if it is known that ν N (, K ν ) Existing Channel Estimation Methods Based on the model of Equation (2.2) we will briefly review the LS based channel estimation and correlation based channel estimation algorithms LeastSquares Channel Estimation In order to fully estimate the channel of Equation (2.6) the LS based channel estimation algorithm assumes that the starting and the ending points of the channel taps are either known or can be bounded. This assumption plays a critical role in the overall quality and robustness of the LS estimation procedure, which will be investigated in the following section. Recall that symbol rate sampled, complex valued, composite CIR h[n] can be written as a finite dimensional vector h = [h[ N a ], h[ N a + ],, h[ ], h[], h[],, h[n c ], h[n c ]] T (3.2) where N a and N c denote the number of anticausal and the causal taps of the channel, respectively, and N a + N c + is the total memory of the channel. Recall also that the pulse matched filter output corresponding only to the known training symbols is given by 5
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