Abstract-In this paper, we consider multiple access schemes with correlated sources. Distributed source coding is not used; rather, the correlation is exploited at the access point (AP). In particular, we assume that each source uses a channel code to transmit, through an additive white Gaussian noise (AWGN) channel, its information to the AP, where component decoders, associated with the sources, iteratively exchange soft information by taking into account the correlation. The key goal of this paper is to investigate whether there exist optimized channel codes for this scenario, i.e., channel codes which guarantee a desired performance level (in terms of average bit error rate, BER) at the lowest possible signal-to-noise ratio (SNR). A twodimensional extrinsic information transfer (EXIT) chart-inspired optimization approach is proposed. Our results suggest that by properly designing serially concatenated convolutional codes (SCCCs), the theoretical performance limits can be approached better than by using parallel concatenated convolutional codes (PCCCs) or low-density parity-check (LDPC) codes. It is also shown that irregular LDPC codes tend to perform better than regular LDPC codes, so that the design of appropriate LDPC codes remains an open issue.

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Abstract-In this paper, we consider multiple access schemes with correlated sources, where a priori information, in terms of source correlation, is available at the access point (AP). In particular, we assume that each source uses a proper low-density parity-check (LDPC) code to transmit, through an additive white Gaussian noise (AWGN) channel, its information sequence to the AP. At the AP, the information sequences are recovered by an iterative decoder, with component decoders associated with the sources, which exploit the available a priori information. In order to analyze the behaviour of the considered multiple access coded system, we propose a density evolution-based approach, which allows to determine a signal-to-noise ratio (SNR) transfer chart and compute the system multi-dimensional SNR feasible region. The proposed technique, besides characterizing the performance of LDPC-coded multiple access scheme, is expedient to design optimized LDPC codes for this application.

Abstract-We discuss the accuracy of the time-discrete phase noise model described by a random walk with symbol-period spaced Gaussian increments. While this model is widely used for its simplicity, it is strictly valid in the slow phase regime only. It is customary to consider this model as a worst case, but a clear understanding of the phase dynamics which can be reliably represented may not be readily available.We address this problem by comparing the symbol-period spaced model with fractional-period spaced ones, which more reliably describe the physical system, in terms of the achievable information rate of the resulting phase noise channels. We show that indeed the symbol-period spaced model is a worst case that may offer a good modeling accuracy under a wide range of phase dynamics.

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The goal of this paper is to investigate Ultra Wide-Band (UWB) localization with Time Difference of Arrival (TDoA) processing at the anchors. We consider scenarios where the anchors are placed very close to each other and the target to be localized is around the group of anchors. All target-anchor communications are assumed to be in Line-Of-Sight (LOS). Since our analysis shows that symmetries in anchors' placement, with respect to the target position, degrade the positioning accuracy of standard algorithms, we propose to use a Subset Selection (SS) strategy, where position estimates obtained with properly selected subsets of asymmetric anchors are fused together to get the final localization output. Our results show improved localization accuracy with respect to the use of all anchors, especially in estimating the angle of arrival. Finally, we analyze the impact of an inaccurate time synchronization among the anchors, deriving guidelines for hardware implementation.

In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve a decision-making process at each step, including bitflipping (BF) decoding, residual belief propagation, and anchor decoding. We then illustrate how such algorithms can be mapped to Markov decision processes allowing for data-driven learning of optimal decision strategies, rather than basing decisions on heuristics or intuition. As a case study, we consider BF decoding for both the binary symmetric and additive white Gaussian noise channel. Our results show that learned BF decoders can offer a range of performance-complexity trade-offs for the considered Reed-Muller and BCH codes, and achieve near-optimal performance in some cases. We also demonstrate learning convergence speed-ups when biasing the learning process towards correct decoding decisions, as opposed to relying only on random explorations and past knowledge.