We propose the use of punctured turbo codes for compression of correlated binary sources. Compression is achieved because of puncturing. The resulting performance is close to the theoretical limit provided by the Slepian-Wolf (1973) theorem. No information about the correlation between sources is required in the encoding process. The proposed source decoder utilizes iterative schemes, and performs well even when the correlation between the sources is not known in the decoder, since it can be estimated jointly with the iterative decoding process.

We propose the use of linear codes with low density generator matrix to achieve a performance similar to that of turbo and standard low-density parity check codes. The use of iterative decoding techniques - message passing -over the corresponding graph achieves a performance close to the Shannon theoretical limit. As an advantage with respect to turbo and standard low-density parity check codes, the complexity of the decoding and encoding procedures is very low.

We investigate the performance of a discrete-time all-analog-processing joint source-channel coding system for the transmission of memoryless sources over average power constrained AWGN channels. First, N:1 bandwidth compression systems are analyzed and optimized. At the encoder, N samples of an i.i.d. source are directly mapped into one channel symbol using a non-linear curve. Different from previous work in the literature, we introduce an additional degree of freedom at the encoder, MMSE decoding instead of ML decoding is considered, and we focus on both high and low channel signal-to-noise ratio (CSNR) regions. By using MMSE decoding, the proposed system presents a performance very close to the theoretical limits, even at low CSNR, as long as the system parameters are properly optimized. Then, N:K bandwidth compression systems are constructed by parallel combination of an M:1 system and a 1:1 uncoded system, and the optimal power allocation between the two constituent systems is derived in order to maximize the overall output signal-to-distortion ratio (SDR). Finally, 1:2 bandwidth expansion systems using mapping functions similar to those used in 2:1 system are investigated. Different from digital systems, the proposed scheme does not require long block lengths to achieve good performance, and shows graceful degradation when the CSNR is lower than the one used for the design.

This letter presents a new method for the digital predistortion (DPD) of power amplifiers (PAs) based on sparse behavioral models. The Gram-Schmidt orthogonalization is synergistically integrated into the orthogonal matching pursuit algorithm to decorrelate the selected model regressors against the components still to be selected. Experiments on a test bench based on a GaN PA driven by a 15-MHz orthogonal frequency division multiplexing signal were conducted in order to validate the algorithm. Experimental results in a DPD application and a comparison with other state-of-the-art algorithms highlight the enhancement of its pruning capabilities, reducing the number of coefficients while maintaining the performance.

We propose a coding scheme based on the use of systematic linear codes with low-density generator matrix (LDGM codes) for channel coding and joint source-channel coding of multiterminal correlated binary sources. In both cases, the structures of the LDGM encoder and decoder are shown, and a concatenated scheme aimed at reducing the error floor is proposed. Several decoding possibilities are investigated, compared, and evaluated. For different types of noisy channels and correlation models, the resulting performance is very close to the theoretical limits.

In this letter, we propose the use of punctured turbo codes to perform near-lossless compression and joint source-channel coding of binary memoryless sources. Compression is achieved by puncturing the turbo code to the desired rate. No information about the source distribution is required in the encoding process. Moreover, the source parameters do not need to be known in the decoder, since they can be estimated jointly with the iterative decoding process.