Search

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]

For Gaussian multiple-input multiple-output (MIMO) relay channels with partial decode-and-forward, the optimal type of input distribution is still an open question in general. Recent research has revealed that in some other scenarios with unknown optimal input distributions (e.g., interference channels), improper (i.e., noncircular) Gaussian distributions can outperform proper (circular) Gaussian distributions. In this paper, we show that this is not the case for partial decode-and-forward in the Gaussian MIMO relay channel with Gaussian transmit signals, i.e., we show that a proper Gaussian input distribution is the optimal one among all Gaussian distributions. In order to prove this property, an innovation covariance matrix is introduced, and a decomposition is performed by considering the optimization over this matrix as an outer problem. A key point for showing optimality of proper signals then is a reformulation that reveals that one of the subproblems is equivalent to a sum rate maximization in a two-user MIMO broadcast channel under a sum covariance constraint, for which the optimality of proper signals can be shown. [ABSTRACT FROM PUBLISHER]

Polar codes have recently received a lot of attention because of their capacity-achieving performance and low encoding and decoding complexity. The performance of the successive cancellation decoder (SCD) of the polar codes highly depends on that of the partial-sum network (PSN) implementation. Hence, in this work, an efficient PSN architecture is proposed, based on the properties of polar codes. First, a new partial-sum updating algorithm and the corresponding PSN architecture are introduced which achieve a delay performance independent of the code length. Moreover, the area complexity is also reduced. Second, for a high-performance and area-efficient semi-parallel SCD implementation, a folded PSN architecture is presented to integrate seamlessly with the folded processing element architecture. This is achieved by using a novel folded decoding schedule. As a result, both the critical path delay and the area (excluding the memory for folding) of the semi-parallel SCD are approximately constant for a large range of code lengths. The proposed designs are implemented in both FPGA and ASIC and compared with the existing designs. Experimental result shows that for polar codes with large code length, the decoding throughput is improved by more than 1.05 times and the area is reduced by as much as 50.4%, compared with the state-of-the-art designs. [ABSTRACT FROM PUBLISHER]