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We consider a problem of localizing a temporal path signal that evolves over time on a graph. A path signal represents the trajectory of a moving agent on a graph in a series of consecutive time stamps. Through combining dynamic programing and graph partitioning, we propose a path-localization algorithm with significantly reduced computational complexity. To analyze the localization performance, we use two evaluation metrics to quantify the localization error: the Hamming distance and the destination's distance between the ground-truth path and the estimated path. In random geometric graphs, we provide a closed-form expression for the localization error bound, and a tradeoff between localization error and the computational complexity. Finally, we compare the proposed technique with the maximum likelihood estimate in terms of computational complexity and localization error, and show significant speedup (100×) with comparable localization error (4×) on a graph from real data. [ABSTRACT FROM AUTHOR]

The successive cancellation list (SCL) decoding is the most powerful decoding algorithm for polar codes. However, the long latency makes it unsuitable for practical applications, and thus many algorithms have been presented to resolve the problem. While most of the previous algorithms have reduced the latency by pruning unnecessary operations in the middle of the process, this paper proposes an early stopping criterion. The proposed method can determine the minimum metric path without having to traverse the tree to the end if a special condition is satisfied at a certain point. The remaining bits located after the stopped bit position are decoded directly from the soft information. The simulation results show that the criterion reduces the decoding latency effectively for various code rates and list sizes without any performance loss. We also propose a hardware architecture that is efficient in realizing the proposed method. Compared to previous SCL decoding algorithms, the proposed decoder achieves 16% higher throughput and 12% improved area efficiency at an SNR of 5.0 dB when the list size is 4. [ABSTRACT FROM AUTHOR]

This contribution considers the non-Bayesian Cramér-Rao bound (CRB) related to parameter estimation from a linearly modulated signal observed in additive white Gaussian noise. We compare the exact CRB expression for coded modulation with two ad hoc CRB (ACRB) expressions; the first ACRB is obtained by substituting in the exact CRB expression for uncoded modulation the a priori symbol probabilities by the extrinsic symbol probabilities; the second ACRB, which has received some attention in recent scientific publications, additionally assumes that the real and imaginary parts of the symbols are independent. Our exposition focuses on the particular case of phase shift estimation. By means of examples we show that although for some coded modulation schemes the exact and ACRBs yield virtually the same numerical result, for other coded modulation schemes the ACRBs differ considerably among themselves and from the exact CRB. We provide some explanations for this behavior. We also argue that both ACRBs are expected to virtually coincide with the exact CRB in the case of bit-interleaved coded modulation combined with a rectangular constellations with independent in-phase and quadrature mapping and a binary code for which the factor graph does not contain short cycles. [ABSTRACT FROM AUTHOR]