Pan-European project B21C (broadcasting for the 21st century) aims to develop technology for DVB-T2 (digital video broadcasting, terrestrial), which is a spectrum efficient broadcasting system for future. The evaluated technologies include MIMO, which has been widely studied in perspective of B3G (beyond 3rd generation) systems, but never for any UHF band systems. In addition, the latest decision of WRC (World Radiocommunication Conference) highlights that the B3G systems can be deployed in UHF bands. This paper introduces a measurement campaign performed in UHF frequency and presents the most important results with regard to channel modeling and MIMO.

This paper describes different techniques for mitigation of high power and long duration interference in terrestrial digital video broadcasting (DVB-T/H) systems. The interference is assumed to distort the DVB-T/H signal severely so that on average one fourth of the DVB-T/H symbol is lost. We investigate the use of long enough time interleaver in combination with simple soft bits scaling. Additional mitigation of the interference effects is also achieved using the pilots carried by the DVB-T/H signal. The mitigation schemes are evaluated in the case where the interference occurs in additive white Gaussian noise (AWGN) and static frequency selective channels.

This paper gives a brief review of the new Digital Video Broadcasting-Handheld(DVB-H) standard. This is based on the earlier standard DVB-T, which is used for terrestrial digital TV broadcasting. The new extension brings features that make it possible to receive digital video broadcast type services in handheld, mobile terminals. The paper discusses the key technology elements-4K mode and in-depth interleavers, time slicing and additional forward error correction-in some detail. It also gives extensive range of performance results based on laboratory measurements and real field tests. Finally it presents viewpoints relevant for DVB-H network design and system use in general.

The turbo equalization can improve the receiver performance significantly. The problem is that the complexity of the optimal algorithms increases exponentially with respect to used code and channel taps. We propose a suboptimal turbo equalizer which is based on iterative equalizer for PSK modulated signals and recurrent neural network convolutional decoder. These algorithms were earlier proposed separately to decrease the complexity of the receiver and it seems possible to make them as an analog chip. Here we combine those methods to form a turbo equalizer. The results show that this can be done and that the idea works well.

A neural convolutional decoder which exploits the channel information is introduced. The method uses a recurrent neural network, tailored to the used convolutional code and the channel model. No supervision-besides possible channel estimation-is required. Also, no distinct equalizer is needed. As an example, we show the structure of the neural decoder for 1/2 rate code with constraint length 3 in a two-path channel environment. For testing, the 1/2 rate code with constraint length 5 is used in two-path fading channels. The simulation results show that the proposed decoder works well compared to the traditional way of using some equalizer and the Viterbi decoder. The hardware implementation of the neural decoder seems feasible and its complexity increases only polynomially while in Viterbi algorithm the complexity increases exponentially as a function of the constraint length.

A new neural approach to decode convolutional codes is presented. The method uses a recurrent neural network tailored to the used convolutional code. No supervision is required. As an example, decoders for 1/2 and 1/3 rate convolutional codes with constraint length 9 are studied. Such codes have been proposed, e.g., for the third generation WCDMA cellular system. The new decoders have been tested in a Gaussian channel and it is shown that the performance of the Viterbi algorithm can be approached very closely. The decoder lends itself to pleasing implementations in hardware. Its complexity increases only polynomially with increasing constraint length, which is slower than the exponential increase of the Viterbi algorithm. However, the speed of the current circuits may set limits to the codes used. With increasing speeds of the circuits in the future, the proposed technique may become a tempting choice for decoding convolutional coding with long constraint lengths.

We show how recurrent neutral network (RNN) convolutional decoders can be derived. As an example, we derive the RNN decoder for 1/2 rate code with constraint length 3. The derived RNN decoder is tested in Gaussian channel and the results are compared to results of optimal Viterbi decoder. Some simulation results for other constraint length codes are also given. The RNN decoder is tested also with the punctured code. It is seen that RNN decoder can achieve the performance of the Viterbi decoder. The complexity of the RNN decoder seems to increase only polynomially, while in Viterbi algorithm the increase is exponential. Also, the hardware implementation of the proposed RNN decoder is feasible.

An efficient technique for sampling rate conversion for arbitrary (incommensurate) ratios is proposed. The technique is based on fractional delay filters that are efficient to implement and that can be controlled with a small number of arithmetic operations per output sample. The authors consider an application in digital television (DTV) transmission where, according to present standard proposals, conversions between several incommensurate sampling rates must be possible. Rather than trying to design separate rate fixed filters for each possible conversion, the authors outline a system which may be tuned for any possible downsampling ratio. A sampling rate conversion system based on the straightforward and simple Lagrange interpolation technique is illustrated with a level and highly efficient implementation structure. Various error sources involved are analyzed and a mean-square-error (MSE) type cost function is defined to aid in the system design.

An adaptive algorithm combining traditional linear equalization techniques and a self-organizing neural learning algorithm is presented. The results show that the performance of the neural equalizer is insensitive to nonlinear learning distortions in dynamic discrete-signal detection. Stabilization of the self-organizing map during undistorted transmission has to be further considered to decrease the absolute mean-square error (MSE) rate of the neural equalizer. The error is due to oscillations in the self-organizing map, mainly caused by the neighborhood learning. The oscillations can be decreased by taking more samples to the map before adapting themivalues and by decreasing the neighborhood learning parameter β

Diversity combining methods for medium- and high-capacity digital microwave radio links employing QAM signaling are studied theoretically and by computer simulations. The selective fading diversity channels are represented by a two-path model with complex envelopes. Emphasis is put to find simple receiver structures where combining is controlled by detected symbols to minimize a given error criterion. Two criteria are discussed-minimum mean-square error (MMSE) and minimum projection (MP). The latter criterion is believed to be new and its behavior is analyzed in two extreme cases. It turns out that the MP combiners obey the maximal ratio rule in the absence of dispersion and the minimum distortion rule in the absence of noise. This versatility is achieved with minor circuit complexity. The results have been verified by computer simulations for a 4 PSK 70 Mbit/s system. The resulting diversity signatures are very narrow on the frequency axis, indicating good performance. Moreover, the MP combining tolerates extremely well nonminimum phase fading in one diversity branch, situations where most cophasing schemes have difficulties.