Evolution of Distributed Detection Performance over Balanced Binary Relay Tree Networks with Bit Errors

Target detection based on wireless sensor networks can be considered as a distributed binary hypothesis testing problem. In this paper, the evolution of detection error probability with the increase of network scale is studied for the balanced binary relay tree network with channel noise. Firstly, the iterative expressions of false-alarm probability and missed-detection probability depending on the number of tree network layers are given. Then, the iterative process of two types of error probabilities in the network space is described as a discrete nonlinear switched dynamic system, and the dynamic properties of two types of error probabilities are analyzed in a plane rectangular coordinate system. A globally attractive invariant set of the state of the dynamic system, which is not related to the channel noise, is derived. The switching mode of the system and the total error probability in the invariant set are further analyzed, and a necessary and sufficient convergence condition of the total error probability is provided. Based on this condition the following detection properties of the network are revealed: (1) as long as the channel bit error probability is not zero, the total error probability does not tend to zero with the increasing network size; (2) when the channel bit error probability is greater than 2-/3/ 2 the total error probability will continue to increase with the increase of network size.

Ratio-based multi-level resistive memory cells

Ratio-based encoding has recently been proposed for single-level resistive memory cells, in which the resistance ratio of a pair of resistance-switching devices, rather than the resistance of a single device (i.e. resistance-based encoding), is used for encoding single-bit information, which significantly reduces the bit error probability. Generalizing this concept for multi-level cells, we propose a ratio-based information encoding mechanism and demonstrate its advantages over the resistance-based encoding for designing multi-level memory systems. We derive a closed-form expression for the bit error probability of ratio-based and resistance-based encodings as a function of the number of levels of the memory cell, the variance of the distribution of the resistive states, and the ON/OFF ratio of the resistive device, from which we prove that for a multi-level memory system using resistance-based encoding with bit error probability x, its corresponding bit error probability using ratio-based encoding will be reduced to $$x^2$$ x 2 at the best case and $$x^{\sqrt{2}}$$ x 2 at the worst case. We experimentally validated these findings on multiple resistance-switching devices and show that, compared to the resistance-based encoding on the same resistive devices, our approach achieves up to 3 orders of magnitude lower bit error probability, or alternatively it could reduce the cell’s programming time and programming energy by up 5–10$$\times$$ × , while achieving the same bit error probability.

Additive boundary of error probability in a discrete data transmission channel with noise-immune coding and grouping of errors

Introduction: Data transmission reliability analysis when using noise-immune coding in channels with grouping of errors (in particular, in radio channels with interference and fading of the received signals) is complicated by the need to use discrete data transmission channel models which take into account the error grouping, differing from the traditional binomial model. The complexity of the analytical description of such models leads to the fact that the quality indicators of data transmission over channels with error grouping are usually analyzed by simulation methods, and the development of analytical models of data transmission discrete channels with grouping of errors is one of the modern direction in the noise-immune coding theory development. Purpose: Finding the additive boundary of a bit error probability for data transmission discrete channel with grouping of symbol errors, described by Elliot — Hilbert model. Results: For the case of data transmission using a group noise-immune code, analytical expressions are obtained for calculating the additive boundary of a bit error probability in a discrete data transmission channel with grouping of symbol errors. The obtained expressions take into account the features of data transmission over a channel with error grouping, in particular, the fact that the probabilities of various combinations of the same number of errors are not equal to each other. Examples are presented of calculating a bit error probability for the case of using noise-immune codes which correct errors. It is shown that for any code length, the use of the Elliot — Hilbert model allows you to substantially refine the results of calculating the probabilistic indicators of the reliability of data transmission in channels with error grouping, as compared to the original binomial model. The obtained results are compared to the results of the simulation. Practical relevance: The results can be used in the design and analysis of the characteristics of data transmission systems for various purposes, operating under conditions of error grouping. Using analytical expressions to calculate the probability indicators of the reliability of data transfer allows you to abandon complex simulation modeling of transmitting data in channels with error grouping at the stage of choosing a noise-immune code and its parameters.