A Novel Data-Aided Channel Estimation with Reduced Complexity for TDS-OFDM Systems

In contrast to the classical cyclic prefix (CP)-OFDM, the time domain synchronous (TDS)-OFDM employs a known pseudo noise (PN) sequence as guard interval (GI). Conventional channel estimation methods for TDS-OFDM are based on the exploitation of the PN sequence and consequently suffer from intersymbol interference (ISI). This paper proposes a novel dataaided channel estimation method which combines the channel estimates obtained from the PN sequence and, most importantly, additional channel estimates extracted from OFDM data symbols. Data-aided channel estimation is carried out using the rebuilt OFDM data symbols as virtual training sequences. In contrast to the classical turbo channel estimation, interleaving and decoding functions are not included in the feedback loop when rebuilding OFDM data symbols thereby reducing the complexity. Several improved techniques are proposed to refine the data-aided channel estimates, namely one-dimensional (1-D)/two-dimensional (2-D) moving average and Wiener filtering. Finally, the MMSE criteria is used to obtain the best combination results and an iterative process is proposed to progressively refine the estimation. Both MSE and BER simulations using specifications of the DTMB system are carried out to prove the effectiveness of the proposed algorithm even in very harsh channel conditions such as in the single frequency network (SFN) case

Diversity gain for DVB-H by using transmitter/receiver cyclic delay diversity

The objective of this paper is to investigate different diversity techniques for broadcast networks that will minimize the complexity and improve received SNR of broadcast systems. Resultant digital broadcast networks would require fewer transmitter sites and thus be more cost-effective and have less environmental impact. The techniques can be applied to DVB-T, DVB-H and DAB systems that use Orthogonal Frequency Division Multplexing (OFDM). These are key radio broadcast network technologies, which are expected to complement emerging technologies such as WiMAX and future 4G networks for delivery of broadband content. Transmitter and receiver diversity technologies can increase the frequency and time selectivity of the resulting channel transfer function at the receiver. Diversity exploits the statistical nature of fading due to multipath and reduces the likelihood of deep fading by providing a diversity of transmission signals. Multiple signals are transmitted in such a way as to ensure that several signals reach the receiver each with uncorrelated fading. Transmit diversity is more practical than receive diversity due to the difficulty of locating two receive antennas far enough apart in a small mobile device. The schemes examined here comply with existing DVB standards and can be incorporated into existing systems without change. The diversity techniques introduced in this paper are applied to the DVB-H system. Bit error performance investigations were conducted by simulation for different DVB-H and diversity parameters

Performance evaluation of channel estimation techniques for a mobile fourth generation wide area OFDM system

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Blind Receiver Design for OFDM Systems Over Doubly Selective Channels

We develop blind data detectors for orthogonal frequency-division multiplexing (OFDM) systems over doubly selective channels by exploiting both frequency-domain and time-domain correlations of the received signal. We thus derive two blind data detectors: a time-domain data detector and a frequency-domain data detector. We also contribute a reduced complexity, suboptimal version of a time-domain data detector that performs robustly when the normalized Doppler rate is less than 3%. Our frequency-domain data detector and suboptimal time-domain data detector both result in integer least-squares (LS) problems. We propose the use of the V-BLAST detector and the sphere decoder. The time-domain data detector is not limited to the Doppler rates less than 3%, but cannot be posed as an integer LS problem. Our solution is to develop an iterative algorithm that starts from the suboptimal time-domain data detector output. We also propose channel estimation and prediction algorithms using a polynomial expansion model, and these estimators work with data detectors (decision-directed mode) to reduce the complexity. The estimators for the channel statistics and the noise variance are derived using the likelihood function for the data. Our blind data detectors are fairly robust against the parameter mismatch