Jukka Henriksson

DVB-H: Digital Broadcast Services to Handheld Devices

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.

Combining linear equalization and self-organizing adaptation in dynamic discrete-signal detection

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 the mi values and by decreasing the neighborhood learning parameter β

Convolutional decoding using recurrent neural networks

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.

Experimental Investigations on MIMO Radio Channel Characteristics on UHF Band

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.

Novel use of channel information in a neural convolutional decoder

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.

Neural detection of QAM signal with strongly nonlinear receiver

Abstract Neural receiver structures have been developed for adaptive discrete-signal detection in telecommunication applications. Neural networks combined with conventional equalizers improve the performance especially in compensating for nonlinear distortions. These distortions may result, for instance, from nonlinear amplification implemented for reducing the power consumption. In this paper, the behavior of the neural receiver in multipath channel with additive white Gaussian noise has been investigated. The transmitted signal is quadrature amplitude modulated (QAM). A receiver structure based on self-organizing map (SOM) is compared with a conventional decision feedback equalizer (DFE).

Tunable downsampling using fractional delay filters with applications to digital TV transmission

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.

Mitigation Techniques for High Power and Long Duration Interference in DVB-T/H Systems

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.

Decision-Directed Diversity Combiners--Principles and Simulation Results

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.

PERFORMANCE EVALUATION OF SELF-ORGANIZING MAP BASED NEURAL EQUALIZERS IN DYNAMIC DISCRETE-SIGNAL DETECTION

Novel equalizer structures utilizing neural computation have recently been developed for adaptive discrete-signal detection. The equalizer structures combine the traditional transversal equalizer and the Self-Organizing Map algorithm in parallel or cascade. Extensive simulations have been run to investigate different parameter effects using a two-path channel model and 16-QAM modulation. The results have shown that the neural equalizer adapts very well to changing channel conditions, including both linear multipath and nonlinear distortions. Especially in difficult channels, the new structures are superior when compared with the traditional equalizers. The computational complexities of the combined structures are not significantly higher when compared to the practical linear equalizers.