Novel hysteretic noisy chaotic neural network for broadcast scheduling problems in packet radio networks

Enhancing the performance of coherent Ising machines in the large-noise regime with a fifth-order nonlinearity

Coherent Ising machines (CIMs), leveraging the bistable physical properties of coherent light to emulate Ising spins, exhibit great potential as hardware accelerators for tackling complex combinatorial optimization problems. Recent advances have demonstrated that the performance of CIMs can be enhanced either by incorporating large random noise or higher-order nonlinearities, yet their combined effects on CIM performance remain mainly unexplored. In this work, we develop a numerical CIM model that utilizes a tunable fifth-order polynomial nonlinear dynamic function under large noise levels, which has the potential to be implemented in all-optical platforms. We propose a normal form of a CIM model that allows for both supercritical and subcritical pitchfork bifurcation operational regimes, with fifth-order nonlinearity and tunable hyperparameters to control the Ising spin dynamics. In the benchmark studies, we simulate various sets of MaxCut problems using our fifth-order polynomial CIM model. The results show a significant performance improvement, achieving an average of 59.5% improvement in median time-to-solution (TTS) and an average of 6 times improvement in median success rate (SR) for dense Maxcut problems in the BiqMac library, compared to the commonly used third-order polynomial CIM model with low noise. The fifth-order polynomial CIM model in the large-noise regime also shows better performance trends as the problem size scales up. These findings reveal the enhancements on the computational performance of Ising machines in the large-nose regime from fifth-order nonlinearity, showing important implications for both simulation and hardware perspectives.

Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap

Recently, many researchers have been interested in the application of chaos in cryptography. Specifically, numerous research works have been focusing on chaotic image encryption. A comprehensive survey can highlight existing trends and shed light on less-studied topics in the area of chaotic image encryption. In addition to such a survey, this paper studies the main challenges in this field, establishes an ecosystem for chaotic image encryption, and develops a future roadmap for further research in this area.

A GCM Neural Network with Piecewise Logistic Chaotic Map

In order to explore dynamic mechanisms and chaos control of globally coupled map (GCM) chaotic neural networks, a new GCM model, called the PL-GCM model is proposed, of which a piecewise logistic chaotic map is used instead of a logistic map. As a result of the strong chaotic features of the map, the neurons’ period and chaotic characteristics over a wide range of parameters are discussed, the dynamic mechanism is demonstrated in detail, and the numerical simulations such as state evolution, the largest Lyapunov exponent (LLE), contour map, and so on are exhibited. Furthermore, chaos control of the proposed PL-GCM model is investigated by adopting two chaos control methods. It is shown that the network with conventional coupling or delay coupling can be precisely controlled to any specified periodic orbit by feedback control, and its dynamic associative memory is realized by the variable threshold parameter control method with external information. The results of simulations and experiments suggest that the network is controlled successfully and can output period patterns with a specified period that contains the stored pattern closest to the initial pattern. All features suggest that the network is fit for pattern recognition and information processing.

Hysteretic Noisy Chaotic Neural Networks for Resource Allocation in OFDMA System

This paper addresses two-stage resource allocation in the orthogonal frequency division multiplexing access system. In the subcarrier allocation stage, hysteretic noisy chaotic neural network (HNCNN) with a newly established energy function is proposed for subcarrier allocation to improve the optimization performance and reduce the computational complexity. Activation functions with both anticlockwise and clockwise hysteretic loops are applied to the HNCNN. A new energy function is established for an objective function, which can be calculated offline, resulting in a lower computational complexity in solving subcarrier allocation than the previous energy function. In the power allocation stage, the water-filling algorithm is employed to attain optimal power allocation. Simulation results show that the energy function established in this paper can decrease the runtimes of the neural networks, and that the HNCNN with both anticlockwise and clockwise hysteretic-loop activation functions can improve probabilities of feasible and optimal solutions at higher noises. The two-stage algorithm in this paper outperforms the previous algorithms in fairness, system throughput, and resource utilization.

Noise-tuning-based hysteretic noisy chaotic neural network for broadcast scheduling problem in wireless multihop networks

Compared with noisy chaotic neural networks (NCNNs), hysteretic noisy chaotic neural networks (HNCNNs) are more likely to exhibit better optimization performance at higher noise levels, but behave worse at lower noise levels. In order to improve the optimization performance of HNCNNs, this paper presents a novel noise-tuning-based hysteretic noisy chaotic neural network (NHNCNN). Using a noise tuning factor to modulate the level of stochastic noises, the proposed NHNCNN not only balances stochastic wandering and chaotic searching, but also exhibits stronger hysteretic dynamics, thereby improving the optimization performance at both lower and higher noise levels. The aim of the broadcast scheduling problem (BSP) in wireless multihop networks (WMNs) is to design an optimal time-division multiple-access frame structure with minimal frame length and maximal channel utilization. A gradual NHNCNN (G-NHNCNN), which combines the NHNCNN with the gradual expansion scheme, is applied to solve BSP in WMNs to demonstrate the performance of the NHNCNN. Simulation results show that the proposed NHNCNN has a larger probability of finding better solutions compared to both the NCNN and the HNCNN regardless of whether noise amplitudes are lower or higher.