This 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 6.3 dB) the suboptimum limit assuming separation between Slepian-Wolf source coding and capacity-achieving channel coding. of the estimation will be with respect to the case of a single sensor. Therefore, the challenging paradigm in this specific scenario lies on how to optimally fuse the information from all sources while taking into account the aforementioned probability of sensing error, specially when dealing with practical communication channels. One of the first contributions in this area was done by Lauer et al. in [13] , who extended classical results from decision theory to the case of distributed correlated signals. Subsequently, Ekchian and Tenney [14] formulated the distributed detection problem for several network topologies. Later, in [15] Chair and Varshney derived an optimum data fusion rule which combines individually performed decisions on the data sensed at every sensor. This data fusion rule was shown to minimize the end-to-end probability of error of the overall system. More recently, several contributions have tackled the data fusion problem in diverse uncoded communication scenarios, for example, multihop networks subject to fading [16] [17] [18] and delays [19] , parallel channels subject to fading [20] [21] [22] , and asynchronous multiple-access channels [23, 24] , among others. On the other hand, when dealing with coded scenarios over noisy channels, it is important to point out that the data fusion problem can be regarded as a particular case of the so-called distributed joint source-channel coding of correlated sources, since the nonzero probability of sensing error imposes a spatial correlation among the data registered by the sensors. In the last decade, intense research effort has been conducted towards the design of practical iterativelydecodable (i.e., Turbo-like) joint source-channel coding schemes for the transmission of spatially and temporally correlated sources over diverse communication channels, for example, see [25] [26] [27] [28] [29] [30] [31] and references therein. However, these contributions address the reliable transmission of the information generated by a set of correlated sensors, whereas the encoded data fusion paradigm focuses on the reliable communication of an information source S read by a set of N sensors subject to a nonzero probability of sensing error; based on this, a certain error tolerance can be permitted when detecting the data registered by a given sensor. In
In REF , correlated quantities gathered by the sensor nodes are transmitted to a common destination via AWGN multiple access channels using a concatenation of a BCH with a low density generator matrix (LDGM) code, with the BCH acting to reduce the error floor produced by the LDGM.
Iterative Fusion of Distributed Decisions over the Gaussian Multiple-Access Channel Using Concatenated BCH-LDGM Codes
EURASIP Journal on Wireless Communications and Networking
Computer Science