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Low-Cost Channel Estimation Algorithm for DRM Receiver

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Low-Cost Channel Estimation Algorithm for DRM Receiver Journal of Communications Vol. 10, No. 6, June 2015 Low-Cost Channel Estimation Algorithm for DRM Receiver Ming Yan1 and Cheng Yan2 1GxSOC Research Institute, Communication University of China, Beijing 100024, China 2Beijing University of Posts and Telecommunications, Beijing 100876, China Email: [email protected]; [email protected] Abstract—In this paper, we consider a digital radio mondiale estimation are separated in each dimension, and (DRM) system using orthogonal frequency division techniques including linear interpolation, windowed multiplexing (OFDM) with coherent detection at the receiver. Discrete Fourier Transform (DFT) and Wiener filter The DRM pilots are two-dimensional distribution, linear interpolation are used for estimating each dimension [5]. interpolation and Wiener filter interpolation respectively used in Work in [6] gives an OFDM transmission scheme that is the time and frequency domain as the channel estimation algorithm. We propose a low-complexity improved Wiener suitable for medium frequency and high frequency filter coefficient algorithm to estimate the OFDM channels. Our channels based DRM digital AM broadcasting. proposed algorithm differs from traditional algorithms in two Other previous work includes [7] which provides a ways. Firstly, we divide the DRM pilots into n groups to pilot design to minimize the coherence of the dictionary estimate the channel responses of corresponding OFDM sub- matrix used for sparse recovery in OFDM radio system. carriers (instead of exploiting the whole pilot at the same time Reference [8] introduces a comparative performance in traditional algorithms). Secondly, we utilize the uniform evaluation of the sampling frequency synchronization distribution characteristic of the DRM pilots to reduce the method that eliminates the initial sampling frequency number of Wiener filter coefficients, and this effectively leads offset to reduce the overall synchronization time in DRM to a reduction in computation complexity. In addition, receivers, and gives a new DRM synchronization method simulation results are also presented to gain further insights. to satisfy the advanced synchronization performance Index Terms—Channel estimation, digital radio mondiale, requirements of DRM receivers. wiener filter, OFDM Although the problem of channel estimation for DRM has been widely explored, there is still possibility for a better estimator which can further reduce the I. INTRODUCTION computations and hardware resources. In this paper, we Digital radio mondiale (DRM) is a digital audio propose a low-complexity improved Wiener filter broadcasting technology that can deliver sound quality coefficient algorithm. The proposed algorithm differs comparable to Frequency Modulation (FM) systems, from traditional algorithms in two ways. Firstly, we while working over the traditional Amplitude Modulation divide the DRM pilots into n groups to estimate the (AM) frequency bands, i.e., below 30MHz [1]. DRM channel responses of the corresponding OFDM sub- utilizes orthogonal frequency division multiplexing carriers, whereas the traditional algorithm exploits the (OFDM) transmission technique with coherent whole pilots at the same time. Secondly, since DRM demodulation at the receiver. It is well known that pilots follow a uniform distribution, the number of the channel estimation is critical for coherent detection. Wiener filter coefficients can be greatly reduced. This Known channel estimation algorithms in the literature can effectively leads to a reduction in computation be categorized into two frameworks. The first framework complexity. is decision-directed estimation (DDE) [2] and the second This paper is organized as follows. The transmission framework is pilot-based estimation (PE) [3]. characteristics of DRM is introduced in Section II. In As the DRM transmitted symbol is comprised of pilots, Section III, we describe the interpolation algorithms for PE is adopted in the DRM system. Due to the two- channel estimation. In Section IV, we propose the Wiener dimensional (2-D) pilot model, the optimal channel filter coefficient algorithm for the purpose of channel estimator is the 2-D Wiener filter based on the minimum estimation. Simulation results can be found in Section V mean squared error (MMSE) criterion [4]. However, the and Section VI concludes the paper. computational complexity is too high for practical implementation. To reduce complexity, the 2-D channel II. TRANSMISSION CHARACTERISTICS OF DRM A. Transmission Frame Structure of DRM Manuscript received January 22, 2015; revised June 23, 2015. This work was supported by the Science Research Fund of the State DRM system includes three different types of data Administration of Radio Film and Television in China under Grant No. channels, namely, the Main Service Channel (MSC), the GDT1221. Corresponding author email: [email protected]. Fast Access Channel (FAC) and the Service Description doi:10.12720/jcm.10.6.423-428 Channel (SDC). The transmitted signal is organized in ©2015 Journal of Communications 423 Journal of Communications Vol. 10, No. 6, June 2015 transmission superframes, where each transmission interpolation algorithms, namely, linear interpolation, superframe consists of three transmission frames. As wiener filter and windowed DFT. In the next subsections, shown in Fig. 1, the pilot cells, which are located in the we will describe the operation of each interpolation transmission signal, consist of frequency pilots, timing algorithm in detail. pilots and gain pilots. Channel estimation mainly utilizes A. Linear Interpolation the gain pilots. The simplest implementation is the linear interpolation, which can be implemented in both time and frequency domain. The channel response for lth symbol at the kp th subcarrier can be calculated by (5). Hˆ (,)(,) k l Hˆ k l ˆ ˆ p p1 p p H(,)(,)() kp l H k p l p l l p (5) llpp1 B. Wiener Filter Based on mean square error criterion, the 2-D wiener Fig. 1. Time-frequency location of FAC and SDC signals filter interpolation is wildly used for 2-D channel estimation. The Wiener-Hopf equation can be derived by B. The Channel Models of DRM orthogonality principle in [10]-[12]. We consider a typical multi-path fading channel where E{ H ( k , l ) Hˆ * ( k , l )} the channel can be realized by a wide-sense stationary pp , uncorrelated scattering (WSSUS) model according to the E{ H ( k , l ) Hˆ * ( k , l )} kp,, l p k, l p p p p DRM standard as follows, {,}klp p k, l l {,}klp p k, l (6) h()()() ti c i t t i (1) i 1 where k l donates pilot index in frequency and time p , p where i is the attenuation of the i-th path, i is the delay domain respectively, kl,, is tap coefficients of wiener of the i-th path, and cti () can be described by a complex- p p k, l valued stationary Gaussian random process characterized filter, (.)* denotes the complex conjugation, is the set kl, by its variance and power density spectrum (PDS) as of utilized pilots. 2 1 (())f fsh i As H(,) kpp l is zero mean, no correlation h (fN )0 exp{ } (2) i 2 2 2 (i ) 2 d (i ) withT(,) k l , the cross-correlation function can d pp k kpp, l l be expressed as where fish () is the Doppler shift of the i-th path. The ˆ * Doppler Spread is given by f( i ) 2 ( i ) . E{ H ( k , l ) H ( k , l )} (7) sp d k kpp, l l p p The received symbol can be written as The auto-correlation function k k, l l can be R()()()() k T k H k N k (3) p p p p expressed as: where R(k) is the discrete received symbol in frequency ˆ * E{ H ( kp , l p ) H ( k p , l p )} (8) domain, T(k) is the transmitted symbol, H(k) is the kp k p, l p l p channel response and N(k) is the additive white Gaussian According to (4), the autocorrelation function can be noise. decomposed as 2 III. INTERPOLATION ALGORITHMS FOR CHANNEL (9) kkllpppp,,, kkll pppp kkll pppp ESTIMATION ES{}kl, Based on the known pilots, the channel where kp and lp donates the pilot index in the frequency ˆ and time domain, respectively, ES{| |} is the average response H(,) kpp l of the pilots can be calculated by (4) kl, [9]. As channel response of the pilots is known, channel energy of the transmitted symbols, and 2 is the variance response of the rest sub-carriers can be derived from of the noise. The tap coefficients of Wiener filter is given 1 interpolation algorithm. by k,, l k l , where kl, is the cross-correlation R(,)(,) k l N k l 1 ˆ p p p p matrix of the sub-carriers and pilots and is auto- H(,)(,) kp l p H k p l p (4) T(,)(,) kp l p T k p l p correlation inverse matrix of pilots. As large amount of computation is required for the two-dimensional Wiener where k p and l p donate the pilot index in the frequency filter, two cascade one-dimensional Wiener filters can be and time domain, respectively. There are three used to achieve the compromise of performance and ©2015 Journal of Communications 424 Journal of Communications Vol. 10, No. 6, June 2015 complexity. The 1-D Wiener filter interpolation formula where Dl and Dk are the distance of samples in time and at frequency direction is given by frequency direction, respectively. fTsp s and f are the Hˆ ()() k k Hˆ (10) l l p normalized channel bandwidths. As shown in Fig. 2, the l l pilot distance in time direction equals to 3 samples which 1 l()()()k l k l k (11) allows a maximum Doppler spread of around 6 Hz on the robust B according to the DRM standard. In frequency where Hˆ is the channel response of the pilots at the p direction, the spacing is 6 samples, which allows a frequency direction for lth symbol and wkl ()is the maximum delay of 1.8ms.
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