Browsing by Author "Garcia-Frias, Javier"
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Item Erratum: Serially-concatenated LDGM codes for correlated sources over Gaussian broadcast channels (IEEE Communications Letters (2009) 13:10 (788-790))(2010-03) Hernaez, Mikel; Crespo, Pedro; Ser, Javier Del; Garcia-Frias, Javier; IAItem Iterative concatenated zigzag decoding and blind data fusion of correlated sensors(2009) Del Ser, Javier; Garcia-Frias, Javier; Crespo, Pedro M.; IAThis paper addresses the sensor network scenario where several nodes sense a common information source S. When such sensors forward their correlated observations to a common shared receiver, it is necessary to combine the received information in order to obtain an estimation of S with high reliability. In this manuscript we propose the use of low-complexity concatenated Zigzag codes for the transmission of correlated sensors through orthogonal AWGN channels. In reception, a novel albeit simple correlation estimation procedure is integrated into the iterative decoding and data fusion algorithm, which is based on the Sum-Product Algorithm applied over the factor graph describing the system. Fundamental limits are also derived for the end-to-end probability of error. Simulation results verify that the Bit Error Rate (BER) performance of the proposed receiver is very close to the aforementioned fundamental limits, while requiring less decoding complexity than other capacity-approaching codes.Item Iterative fusion of distributed decisions over the gaussian multiple-access channel using concatenated BCH-LDGM codes(2011) Del Ser, Javier; Manjarres, Diana; Crespo, Pedro M.; Gil-Lopez, Sergio; Garcia-Frias, Javier; IAThis paper focuses on the data fusion scenario where N nodes sense and transmit the data generated by a source S to a common destination, which estimates the original information from S more accurately than in the case of a single sensor. This work joins the upsurge of research interest in this topic by addressing the setup where the sensed information is transmitted over a Gaussian Multiple-Access Channel (MAC). We use Low Density Generator Matrix (LDGM) codes in order to keep the correlation between the transmitted codewords, which leads to an improved received Signal-to-Noise Ratio (SNR) thanks to the constructive signal addition at the receiver front-end. At reception, we propose a joint decoder and estimator that exchanges soft information between the N LDGM decoders and a data fusion stage. An error-correcting Bose, Ray-Chaudhuri, Hocquenghem (BCH) code is further applied suppress the error floor derived from the ambiguity of the MAC channel when dealing with correlated sources. Simulation results are presented for several values of N and diverse LDGM and BCH codes, based on which we conclude that the proposed scheme outperforms significantly (by up to 6.3dB) the suboptimum limit assuming separation between Slepian-Wolf source coding and capacity-achieving channel coding.Item Joint source-channel coding of sources with memory using turbo codes and the burrows-wheeler transform(2010-07) Del Ser, Javier; Crespo, Pedro M.; Esnaola, Inaki; Garcia-Frias, Javier; IAThe Burrows-Wheeler Transform (BWT) [1] is a block sorting algorithm which has been proven to be useful in compressing text data [2]. More recently, schemes based on the BWT have been proposed for lossless data compression using LDPC [3]-[5] and Fountain [6] codes, as well as for joint source-channel coding of sources with memory [7],[8]. In this paper we propose a source-controlled Turbo coding scheme for the transmission of sources with memory over AWGN channels also based on the Burrows-Wheeler Transform. Our approach combines the BWT with a Turbo code and employs different energy allocation techniques for the encoded symbols before their transmission. Simulation results show that the performance of the designed scheme is close (within 1.5 dB) to the theoretical Shannon limit.Item On the performance of single LDGM codes for iterative data fusion over the multiple access channel(2010) Del Ser, Javier; Garcia-Frias, Javier; Crespo, Pedro M.; Manjarres, Diana; Olabarrieta, Ignacio; IAOne of the applications of wireless sensor networks currently undergoing active research focuses on the scenario where the information generated by a data source S is simultaneously sensed by N nodes and therefrom transmitted to a common receiver. Based on the received information from such N nodes, such receiver infers the original information from S potentially more accurately than in the case of a single sensor. Often referred to as the CEO (Central Estimating Officer) problem [1], in this scenario we propose the use of single Low Density Generator Matrix (LDGM) codes for the transmission of the information registered by the nodes over the Multiple Access Channel (MAC). The corresponding receiver iterates between a soft demodulator, the set of N LDGM decoders and a soft-information fusion stage. Simulation results for the AWGN MAC channel show that 1) the proposed coding scheme outperforms the suboptimum limit assuming separated Slepian-Wolf distributed coding and capacity-approaching codes; and 2) the end-to-end Bit Error Rate (BER) performance is lower bounded, for increasing N, by the error floor due to the inherent ambiguity of the MAC channel when dealing with correlated sources. This paves the way for future research aimed at applying concatenated coding schemes to this setup.Item Serially-concatenated LDGM codes for correlated sources over gaussian broadcast channels(2009) Hernaez, Mikel; Crespo, Pedro; Del Ser, Javier; Garcia-Frias, Javier; IAWe propose a superposition scheme, based on the use of serially-concatenated LDGM codes, for the transmission of spatially correlated sources over Gaussian broadcast channels. The messages intended for each receiver are independently encoded using the same code. In this manner, a strong degree of correlation is kept between the encoded sequences,which are then modulated with different energies and symbolwise added. By properly designing the encoding process, simulation results show that our proposed scheme easily outperforms the suboptimal theoretical limit assuming separation between source and channel coding.