Distributed probabilistic-data-association-based soft reception employing base station cooperation in MIMO-aided multiuser multicell systems

Intercell cochannel interference (CCI) mitigation is investigated in the context of cellular systems relying on dense frequency reuse (FR). A distributed base-station (BS)-cooperation-aided soft reception scheme using the probabilistic data association (PDA) algorithm and soft combining (SC) is proposed for the uplink of multiuser multicell MIMO systems. The realistic 19-cell hexagonal cellular model relying on unity FR is considered, where both the BSs and the mobile stations (MSs) are equipped with multiple antennas. Local-cooperation-based message passing is used, instead of a global message passing chain for the sake of reducing the backhaul traffic. The PDA algorithm is employed as a low-complexity solution for producing soft information, which facilitates the employment of SC at the individual BSs to generate the final soft decision metric. Our simulations and analysis demonstrate that, despite its low additional complexity and backhaul traffic, the proposed distributed PDA-aided SC (DPDA-SC) reception scheme significantly outperforms the conventional noncooperative benchmarkers. Furthermore, since only the index of the possible discrete value of the quantized converged soft information has to be exchanged for SC in practice, the proposed DPDA-SC scheme is relatively robust to the quantization errors of the soft information exchanged. As a beneficial result, the backhaul traffic is dramatically reduced at negligible performance degradation

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Iterative Equalization and Source Decoding for Vector Quantized Sources

In this contribution an iterative (turbo) channel equalization and source decoding scheme is considered. In our investigations the source is modelled as a Gaussian-Markov source, which is compressed with the aid of vector quantization. The communications channel is modelled as a time-invariant channel contaminated by intersymbol interference (ISI). Since the ISI channel can be viewed as a rate-1 encoder and since the redundancy of the source cannot be perfectly removed by source encoding, a joint channel equalization and source decoding scheme may be employed for enhancing the achievable performance. In our study the channel equalization and the source decoding are operated iteratively on a bit-by-bit basis under the maximum aposteriori (MAP) criterion. The channel equalizer accepts the a priori information provided by the source decoding and also extracts extrinsic information, which in turn acts as a priori information for improving the source decoding performance. Simulation results are presented for characterizing the achievable performance of the iterative channel equalization and source decoding scheme. Our results show that iterative channel equalization and source decoding is capable of achieving an improved performance by efficiently exploiting the residual redundancy of the vector quantization assisted source coding

Distributed Estimation of a Parametric Field Using Sparse Noisy Data

The problem of distributed estimation of a parametric physical field is stated as a maximum likelihood estimation problem. Sensor observations are distorted by additive white Gaussian noise. Prior to data transmission, each sensor quantizes its observation to $M$ levels. The quantized data are then communicated over parallel additive white Gaussian channels to a fusion center for a joint estimation. An iterative expectation-maximization (EM) algorithm to estimate the unknown parameter is formulated, and its linearized version is adopted for numerical analysis. The numerical examples are provided for the case of the field modeled as a Gaussian bell. The dependence of the integrated mean-square error on the number of quantization levels, the number of sensors in the network and the SNR in observation and transmission channels is analyzed.Comment: to appear at Milcom-201