In ENGINES work package two (WP2), individual system architecture components were studied, and the results from these studies have been forwarded to standardization work (DVB-T2 Lite, DVB-NGH). The outcome of WP2 work is summarized in five deliverables. Deliverable 2.1 focuses on system architectural work performed by ENGINES partners. Deliverable 2.2 deals with DVB-NGH receiver implementation related issues. Devised advanced component techniques for DVB-NGH are presented in deliverable 2.3. Additionally there is work on overall architectures, especially issues not covered by direct standardization that are novel access technologies (deliverable 2.4) and end-to-end system integration (deliverable 2.5).
This deliverable focuses on algorithms applied at the receiver. These algorithms cover channel estimation, time/frequency synchronization, MIMO detection. Further, receiver complexity regarding selected algorithms is estimated and algorithms for reducing receiver complexity are presented. The performance of the algorithms is presented via simulations. The topics considered are:
Generic channel equalization techniques for OFDM based systems in time-variant channels
A Shuffled Iterative Receiver for the DVB-T2 Bit-Interleaved Coded Modulation: Architecture Design, Implementation and FPGA Prototyping
Expected DVB-T2 Performance Over Time Varying Environments
This deliverable is dedicated to issues related to DVB-NGH receiver algorithms and implementation issues. As DVB-NGH is destined for mobile users, complexity of the receiver plays an important role in the design. The rest of the deliverable is structured as follows.
In Chapter 2, a generic channel equalization technique for OFDM based systems in time variant channels is presented. It is proven that the most known equalization algorithms for OFDM signals in time variant channels with mobile reception scenarios are part of this generic theoretical model. This model is developed mathematically, and based on it, a general classification for channels in terms of their time variability is presented. Besides, the equalization methodology reliability and the channel classification validity have been proved in both the TU-6 and MR channels. This generic methodology could be considered for the equalization stages in the DVB-T2/NGH receivers working in mobile scenarios.
Chapter 3 introduces an efficient shuffled iterative receiver for the second generation of the terrestrial digital video broadcasting standard DVB-T2. A simplified detection algorithm is presented, which has the merit of being suitable for hardware implementation of a Space-Time Code (STC). Architecture complexity and measured performance validate the high potential of iterative receiver as both a practical and competitive solution for the DVB-T2 standard.
Chapter 4 focuses on the performance of DVB-T2 in time varying environments. In order to model the channel impulse response, a TU6 channel is considered. The latter constitutes the most common channel model of DTT standards for mobile environments. The performance of the standard is simulated for both single and diversity 2 reception. Since DVB-T2 contains a huge number of possible configurations, focus is mainly given to two configurations: UK mode, and Germany-like candidate mode.
Chapter 5 presents two implementations of LDPC decoders optimized for decoding the long codewords specified by the second generation of digital television broadcasting standards: i.e. DVB-T2, DVB-S2, and DVB-C2. These implementations are highly parallel and especially optimized for modern GPUs (graphics processing units) and general purpose CPUs (central processing units). High-end GPUs and CPUs are quite affordable compared to capable FPGAs, and this hardware can be found in the majority of recent personal home computers.
Finally, Chapter 6 studies MIMO detection in the receiver. Considered issues are the complexity needed to perform maximum likelihood (ML) decoding for MIMO systems and iterative MIMO receiver processing. The DVB-NGH standard is the first to include a full rate MIMO scheme. Even though the number of antennas is relatively small, the complexity to implement an ML decoder can be prohibitive. This chapter proposes and studies ways to reduce complexity of the DVB-NGH MIMO reception.