Filtered decision feedback channel estimation for OFDM-based DTV terrestrial broadcasting system

The use of orthogonal frequency division multiplexing (OFDM) in frequency selective fading environments has been well explored. However, OFDM is more prone to time selective fading as compared to single carrier systems. Rapid time variations destroy the subcarrier orthogonality and introduce inter-carrier interference (ICI). Besides this, obtaining reliable channel estimates for receiver equalization is a non-trivial task in rapidly fading systems. In our work, we have addressed the problem of channel estimation and ICI suppression by viewing the system as a state space model. Kalman filter is employed to estimate the channel; this is followed by a time domain ICI mitigation filter which maximizes the SINR (signal to interference plus noise ratio). This method is seen to provide significant SINR gain with low training overhead.

This paper describes a novel channel estimation scheme identified as coded decision directed demodulation (CD3) for coherent demodulation of orthogonal frequency division multiplex (OFDM) signals making use of any constellation format [e.g., quaternary phase shift keying (QPSK), 16-quadrature amplitude modulation (QAM), 64-QAM]. The structure of the CD3-OFDM demodulator is described, based on a new channel estimation loop exploiting the error correction capability of a forward error correction (FEC) decoder and frequency and time domain filtering to mitigate the effects of noise and residual errors. In contrast to the conventional coherent OFDM demodulation schemes, CD3-OFDM does not require the transmission of a comb of pilot tones for channel estimation and equalization, therefore yielding a significant improvement in spectrum efficiency (typically between 5-15%). The performance of the system with QPSK modulation is analyzed by computer simulations, on additive white Gaussian noise (AWGN) and frequency selective channels, under static and mobile reception conditions. For convolutional coding rate 1/2, the results indicate that CD3-OFDM allows one to achieve a very fast adaptation to the channel characteristics in a mobile environment (maximum tolerable Doppler shift of about 80 Hz for an OFDM symbol duration of 1 ms, as differential demodulation) and an E/sub b//N/sub 0/ performance similar to coherent demodulation (e.g., E/sub b//N/sub 0/=4.3 dB at bit-error rate (BER)=2/spl middot/10/sup -4/ on the AWGN channel). Therefore, CD3-OFDM can be suitable for digital sound and television broadcasting services over selective radio channels, addressed to fixed and vehicular receivers.

For digital terrestrial television broadcasting (DT-TB) systems, orthogonal frequency division multiplexing (OFDM) technique is considered to be an effective method to combat the frequency selective channel cause by multi-path effect. In the single frequency network (SFN) scenario, Alamouti space frequency block coding (SFBC) is introduced to provide spatial diversity gain and improve the link stability. However, both OFDM and Alamouti SFBC are sensitive to time-variant during mobile reception. In this paper, we propose a decision feedback equalization algorithm for inter-carrier interference (ICI) can-celation of the dual (PN) padded time domain synchronization OFDM (TDS-OFDM) DTTB system over doubly selective channel. Our algorithm is proven by numerical simulations which show bit error rate (BER) gain over mobile SFN channel and can be adopted in mobile high density television (HDTV) transmission systems.

For digital terrestrial broadcasting, OFDM (Orthogonal Frequency Division Multiplexing) is used as the transmission system. While OFDM is robust to frequency‐selective fading due to delayed waves in a multipath environment, it is not resistant to Doppler spread for reception during movement. When the digital terrestrial broadcast is received while a vehicle is in motion, this weakness is a serious problem. The present authors have proposed a method for correcting Doppler spread by effectively canceling the motion of the moving body based on an array antenna to estimate the received signal at a point stationary to the ground. In the present paper, an algorithm is developed for estimating, from the received signal, the speed of movement of the vehicle, which was considered known in the previous method. A digital terrestrial broadcasting receiver is proposed for compensating the Doppler spread by estimating the speed of movement of the vehicle. By computer simulation, it is shown that the proposed algorithm can accurately estimate the speed of movement and that the bit error rate of reception of digital terrestrial broadcasts while in motion can be significantly improved. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 89(11): 46–55, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20260

Digital broadcasting has been an area of active\nresearch, development, innovation and business models development\nin recent years. This paper presents a survey on the characteristics of\nthe digital terrestrial television broadcasting (DTTB) standards, and\nimplementation status of DTTB worldwide showing the standards\nadopted. It is clear that only the developed countries and some in the\ndeveloping ones shall be able to beat the ITU set analogue to digital\nbroadcasting migration deadline because of the challenges that these\ncountries faces in digitizing their terrestrial broadcasting. The\nchallenges to keep on track the DTTB migration plan are also\ndiscussed in this paper. They include financial, technology gap,\npolicies alignment with DTTB technology, etc. The reported\nperformance comparisons for the different standards are also\npresented. The interesting part is that the results for many\ncomparative studies depends to a large extent on the objective behind\nsuch studies, hence counter claims are common.

The ability to receive broadcast content in highly mobile scenarios plays an important role in determining the success of broadcast systems in general. The second generation of Terrestrial Digital Video Broadcasting (DVB-T2) has made several technical enhancements to increase the data carrying capacity that enabled the reception of High Definition (HD) content. Due to its properties, Orthogonal Frequency-Division Multiplexing (OFDM) is chosen as the transmission scheme in the physical layer. However, in highly mobile environments, a doubly-selective or a time-varying multipath channel that exhibits selectivity in both the time as well as the frequency domain, deteriorates the performance of the receiver. Furthermore, DVB-T2 allows up to 32k carriers on the same channel raster resulting in a smaller carrier spacing that further amplifies the effects of mobility at the receiver. Thus, channel estimation and equalization techniques play a vital role in overcoming the effects of a doubly-selective channel and enabling high mobility in a DVB-T2 receiver. Channel estimation schemes must not only be able to model the wireless channel accurately but also be computationally efficient in order to enable implementation on resource constrained consumer hardware. Compressed Sensing (CS) schemes like the Rake-Matching Pursuit (RMP) algorithm are proven to perform well in vehicular communication systems and the associated computational complexity is significantly smaller than competing state-of-art estimation schemes. In this paper, we show that the RMP algorithm for channel estimation is suitable to be used with DVB-T2 under varying channel conditions. The results show that the proposed schemes produce significantly better results when compared to the conventional channel estimation and equalization schemes. Furthermore, a detailed analysis on the computational complexity will further strengthen the need for the proposed scheme to estimate doubly-selective channels.

Orthogonal frequency division multiplexing (OFDM) systems suffer performance degradation in fast fading channels due to intercarrier interference (ICI). Combining frequency domain equalization and bit-interleaved coded modulation (BICM), the iterative receiver is able to harvest both temporal and frequency diversity. In order to perform coherent detection and estimation, channel state information (CSI) is indispensable. Being able to accurately and effectively acquire CSI in the fast time-varying channel is critical. Conventional frequency domain channel estimation (CE) methods have an irreducible error floor at high normalized Doppler frequency fdTs, since ICI corrupts the orthogonality among subcarriers. Considering that the fast time-varying channel is also a source of temporal diversity, CE ought to take place in the pre-FFT time domain. Realizing channel variations in time are often smooth, this paper proposes a time domain channel estimator using pilot symbol assisted modulation (PSAM) with complexity O(N), where N is the FFT size. Simulation results demonstrate that a PSAM system with channel estimation provides excellent trade-off between performance, complexity and spectral efficiency.

Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key transmission waveform technology in 5G, traditional channel estimation algorithms are no longer sufficient for the high-speed multipath time-varying channels faced by both existing 5G and future 6G. In addition, the existing Deep Learning (DL) based OFDM channel estimators are only applicable to Signal-to-Noise Ratios (SNRs) in a small range, and the estimation performance of the existing algorithms is greatly limited when the channel model or the mobile speed at the receiver does not match. To solve this problem, this paper proposes a novel network model NDR-Net that can be used for channel estimation under unknown noise levels. NDR-Net consists of a Noise Level Estimate subnet (NLE), a Denoising Convolutional Neural Network subnet (DnCNN), and a Residual Learning cascade. Firstly, a rough channel estimation matrix value is obtained using the conventional channel estimation algorithm. Then it is modeled as an image and input to the NLE subnet for noise level estimation to obtain the noise interval. Then it is input to the DnCNN subnet together with the initial noisy channel image for noise reduction to obtain the pure noisy image. Finally, the residual learning is added to obtain the noiseless channel image. The simulation results show that NDR-Net can obtain better estimation results than traditional channel estimation, and it can be well adapted when the SNR, channel model, and movement speed do not match, which indicates its superior engineering practicability.

Orthogonal frequency division multiplexing (OFDM) modulation, adopted by the digital video broadcasting (DVB-T) standard, has been recognized for its good performance for high data rate wireless communications. Therefore, the study of the robust transmission of multimedia data over OFDM systems has attracted extensive research interests. In the past, channel estimation, which is an important aspect in OFDM systems, has not been exploited for multimedia transmission. When using the block training based channel estimation, OFDM data blocks experience unequal decoding error rate due to the imprecision of channel estimation. We use this property to provide unequal error protection (UEP) for transmission of SPIHT coded images. Compared with the systems using pilot training channel estimation schemes, which are recommended in the DVB-T standard, the proposed scheme improves the PSNR of reconstructed images by up to 2 dB.

Millimeter wave (mmWave) channels can provide new frequency bands for communications together with improved accuracy and resolution for sensing and target localization. Those require parametric channel estimation methods capable of operating with very short coherence times in case of large Doppler spread. In this paper, we address this problem in orthogonal frequency division multiplexing (OFDM) uplink channel estimation. We first observe that it is a variation of the multidimensional harmonic retrieval (MHR) problem in which one has no direct access to the channel tensor due to the strongly time-varying channel. We then derive an improvement of the OFDM space-alternating generalized expectation-maximization (SAGE) algorithm for channel estimation. It is able to solve the problem under intense Doppler spreads and carrier frequency offset (CFO). The proposed method exploits the shift-invariance properties of the OFDM channel tensor to perform efficient coordinate descent steps. We also derive a threshold with probability of false alarm (PFA) guarantees to probe the quality of path initializations. The algorithm estimates the number of significant multipath components by progressively adding paths until the likelihood function reaches a threshold value derived from the noise distribution. We show via numerical examples that the proposed method outperforms the state-of-the-art methods both in channel tensor estimation accuracy as well as in the model order estimation performance.

Channel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less number of pilot to mitigate ISI effect. Channel estimation is simulated for different pilot number with 16QAM modulation in Ped-A Rayleigh channel. The STO effect assumed less than the cyclic prefix length to minimize ISI and maintain the orthogonality between subcarriers. The simulation results show that the high number of pilots are needed to achieve good channel estimation in case no synchronization used while similar results are achieved with smaller number of pilots if the propose modification on the synchronization method are used aligned with the channel estimation. The proposed system is evaluated in terms of BER and SNR.

The performance of time-domain channel estimation deteriorates due to the presence of Gaussian mixture model (GMM) noise, which results in high mean squared error (MSE) as a challenging issue. The performance of the estimator further decreases when the complexity of the estimator is high due to the high convergence rate. In this paper, an optimized channel estimation method is proposed with low complexity and high accuracy in the GMM environment. In this channel estimation, an improved Gauss-Seidel iterative method is utilized with a minimum number of iterations. The convergence rate of the Gauss-Seidel method is improved by estimating an appropriate initial guess value when no guard bands are used in the orthogonal frequency-division multiplexing (OFDM) symbol. Simulation results provide an acceptable MSE for GMM environments, up to the probability of 5% impulsive noise component. This paper also presents the design and implementation of the proposed estimator in the NEXYS-2 FPGA platform that provides resources allocation, reconfigurability, schematic, and the timing diagram for detailed insight.

Background: To resist the time-variant underwater acoustic (UWA) channel, large amounts of channel estimation algorithms for the UWA orthogonal frequency division multiplexing (OFDM) are presented. An updated review of the recent UWA OFDM channel estimators is suggested in this article. Objective: The goal of this work is to review and conclude the development of different types of channel estimators. The possible perspectives about the future UWA channel estimator design are also discussed. Methodology: The principles and performances of the linear channel estimators, the compressed sensing (CS)-based channel estimators, and the neural network (NN)-based channel estimators are reviewed and discussed. Simulations are conducted to compare the typical implementations of the different methods. Conclusion: To take more channel state characteristics into account, the data-driven methods have been applied in the channel estimator design. Compared with the linear and CS-based methods, the NN-based channel estimator shows the higher performance, robustness and lower complexity, which is promising to be applied with the proper structure and training sets.

In this paper, exponentially-weighted recursive least squared (EW-RLS) channel estimation for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is investigated. Channel estimation in time-domain (TD) as well as frequency-domain (FD) is investigated through exploiting preambles and pilot symbols inserted in TD and FD, respectively. The channel is assumed to be slowly time-varying frequency-selective, constant during one OFDM symbol but changing from symbol to symbol. Simulation results show that the TD EW-RLS estimator has better estimation performance in terms of the mean-squares error (MSE) and bit-error rate (BER), compared to FD EW-RLS estimation approach. The computational complexity is significantly reduced by recursively updating the channel estimates and by applying the matrix inversion lemma.

Orthogonal frequency division multiplexing (OFDM) is adopted in most wireless communication systems, and the performance of OFDM is affected by impulse noise (IN). Better channel estimation (CE) performance is required to detect the channel information on the receiving side. The OFDM system is considered with the number of subcarriers carrying the data and pilot symbols. In the OFDM modulator, the symbols of the frequency domain are transformed into the signals of the time domain using inverse discrete Fourier transformation (IDFT). Effective impulsive noise mitigation is crucial for improving the effectiveness of OFDM communication systems, and it improves the signal-to-noise ratio (SNR) at the receiver. A cyclic prefix is then appended before transmission through the channel. In the receiver noise, the effect of IN is mitigated jointly with CE using the Bayesian matching pursuit (BMP) and Moth-flame algorithm (MFA). In this approach, the IN and channel information is considered as a sparse vector. Here, the data symbols used are considered an unknown parameter. The approach incorporating all subcarriers has a lower mean square error (MSE) of IN estimate for impulsive noise reduction. The MFA algorithm is used to optimize the sensor matrix in BMP. The sparsity of the channel and the impulse response are observed in the time domain to represent the channel and IN together. It exactly recovers the sparse signal for finding the convex objectives of the sparse minimizer, and the sparse solution is obtained with fixed point updates. The proposed BMP algorithm improves the CE by explicitly considering the existence of IN. The BMP is a greedy algorithm that selects the most correlated residuals at each column. Under mutual incoherence, it recovers the signal with a higher probability. The simulated outcomes proved that the proposed CE and noise mitigation model achieved better performance based on the bit error rate (BER), throughput and MSE.