Receiver implementations with very low quantization resolution will play an important role in 5G, as high precision quantization and signal processing are costly in terms of computational resources and chip area. Therefore, low resolution receivers with quasi optimum performance will be required to meet complexity and latency constraints. The Information Bottleneck method allows for a novel, information centric approach to design such receivers. The method was originally introduced by Naftali Tishby et al. and mostly used in the machine learning field so far. Interestingly, it can also be applied to build surprisingly good digital communication receivers which work fundamentally different than state-of-the-art receivers. Instead of minimizing the quantization error, receiver components with maximum preservation of relevant information for a given bit width can be designed. All signal processing in the resulting receivers is performed using only simple lookup operations. In this paper, we first provide a brief introduction to the design of receiver components with the Information Bottleneck method. We keep referring to decoding of low-density parity-check codes as a practical example. The focus of the paper lies on practical decoder implementations on a digital signal processor which illustrate the potential of the proposed technique. An Information Bottleneck decoder with 4bit message passing decoding is found to outperform 8bit implementations of the well-known min-sum decoder in terms of bit error rate and to perform extremely close to an 8bit belief propagation decoder, while offering considerably higher net decoding throughput than both conventional decoders.

Bag-of-Visual-Words representation has recently become popular for scene classification. However, learning the visual words in an unsupervised manner suffers from the problem when faced these patches with similar appearances corresponding to distinct semantic concepts. This paper proposes a novel supervised learning framework, which aims at taking full advantage of label information to address the problem. Specifically, the Gaussian Mixture Modeling (GMM) is firstly applied to obtain "semantic interpretation" of patches using scene labels. Each scene induces a probability density on the low-level visual features space, and patches are represented as vectors of posterior scene semantic concepts probabilities. And then the Information Bottleneck (IB) algorithm is introduce to cluster the patches into "visual words" via a supervised manner, from the perspective of semantic interpretations. Such operation can maximize the semantic information of the visual words. Once obtained the visual words, the appearing frequency of the corresponding visual words in a given image forms a histogram, which can be subsequently used in the scene categorization task via the Support Vector Machine (SVM) classifier. Experiments on a challenging dataset show that the proposed visual words better perform scene classification task than most existing methods.