Non-orthogonal multiple access (NOMA) is one of the key technology of 5G system to enhance the capacity and spectral efficiency. However, the fast changing wireless channel makes the ideal power allocation be a challenging task in practice. A chaotic shape-forming filter (CSF) based NOMA system is proposed and a deep learning (DL) based method is used to estimate the user channel gains for power allocation in this paper. The contributions of the work lie in: 1) The CSF and the corresponding matched filter (MF) enhance the noise resistance performance of the NOMA system. 2) The autocorrelation function (ACF) of the superposition signal composed by the chaotic signals generated by the CSF of different users is proved to be the same as the ACF of the base function of the CSF, which is an interesting and important base point to use the previous theoretical result for blind channel identification. 3) For the flat fading channel assumed in the NOMA system, an analytical formula of the channel gain, sole parameter in this type of the channel, with respect to the ACFs of the received signal and the base function of the CSF is derived for the first time, which provides the underlying mechanism to use deep neural network (DNN) for channel gain prediction. Simulation results show that 1) the chaos-based NOMA using the CSF and corresponding MF achieves better performance as compared to the conventional binary phase shift keying NOMA (BPSK-NOMA) with root-raised cosine (RRC) filter; 2) the DNN with simplified structure and the less input neuron as compared to that for the frequency selective fading channel is capable of achieving superior performance for both blind channel identification and NOMA system in the sense of low bit error rate (BER).

In this work, a Chaos Division Multiple Access (Chaos-DMA) communication system based on chaotic filter groups are proposed. A Chaotic Quasi-orthogonal Shape-forming Filter (CQSF) group is designed at the transmitter to generate quasi-orthogonal signals for multiuser, achieving a good balance between spectrum efficiency and high bit transmission rate. At the receiver, two groups of filters, namely Chaotic Correlation Filter (CCF) group and Chaotic Matched Filter (CMF) group, are designed to decode the information. The transmitted information bits are recovered by averaging the sampled sequence from the CMF group outputs to derive the numbers of different bit polarity and sorting the sampling sequence from the CCF group outputs. Compared with traditional Code Division Multiple Access (CDMA) and Frequency Division Multiple Access (FDMA) communication systems, the proposed Chaos-DMA communication system not only provides high reliability and high data transmission rate, but also achieves higher spectrum efficiency and throughput. The Bit Error Rate (BER) expressions are derived for both additive white Gaussian noise (AWGN) channel and wireless multipath fading channel. The effectiveness and superiority of the proposed Chaos-DMA are demonstrated through numerical simulations and experimental tests based on software-defined radio platform.

Abstract Single sample per person (SSPP) face recognition uses only a single face image of each subject in the gallery set to recognize the probe sample. Since single sample cannot provide intra‐class variation information, the matching accuracy of the gallery faces with the faces captured in unconstrained video is usually low. Recently, in order to improve the accuracy, the sparse representation‐based classification (SRC) technology has been extended to generic learning method, which uses prototype and variation dictionary (P+V) model for face recognition. Because the inter‐class scatter between atoms in prototype dictionary is not big enough, and the intra‐class scatter in constructed variation dictionary (such as posture and expression) is not rich enough, the robustness of P+V model for SSPP face recognition is poor. To solve this problem, a robust prototype dictionary and robust variation dictionary construction (RPRV) method is proposed. First, a set of atoms is obtained by dictionary learning method using gallery images and generic images. Second, some effective atoms are selected by the proposed function index method. Finally, the robust prototype dictionary (RP) and the robust variation dictionary (RV) are represented linearly using these effective atoms, respectively. The face recognition is performed according to the proposed RP+RV model. Experiment results using public datasets show that the proposed RPRV has strong robustness for the face captured under the unconstrained environment. Comparison results show that the proposed RPRV method outperform state‐of‐the‐art SRC‐based methods for SSPP face recognition.

A new Differential Chaos Shift Keying modulation scheme exploiting Chaotic Shape-forming Filter and Sequence Mapping (CSF-SM-DCSK) is being proposed. The new CSF-SM-DCSK system employs a novel sequence mapping rule and includes a data correction module to achieve a good trade-off between the low Bit Error Rate (BER) performance and high transmission rate. It transmits two data streams simultaneously and preserves the simplicity and robustness of DCSK method. Channel one, transmitting a Low Priority Stream (LPS), generates the chaotic carrier by a Chaotic Shape-forming Filter (CSF) at the transmitter and applies a coherent Matched Filter (MF) at the receiver to recover the information. Channel two, transmitting a High Priority Stream (HPS), relies on conventional DCSK modulation, while the reference and information-bearing parts are transmitted simultaneously with orthogonal sine and cosine carriers. This double-stream solution eliminates the need for analog RF delay lines and doubles the data transmission rate. Before feeding the LPS data stream into the modulator, each LPS bit is encoded into a symbol sequence using sequence mapping. This approach, together with the coherent MF reception, equips the LPS channel with an extremely high robustness against channel noise and multipath propagation. To handle every possible redundancy in the received signal and to minimize the possibility of making wrong decisions, a data correction block is also introduced. Initially, a rough estimation of the received HPS DCSK bit is done at the receiver, then this estimation is used to remove the DCSK modulation from the received information-bearing signal. The three inputs of data correction blocks are: (i) the reference and (ii) the information-bearing parts of the received signal in their original form, and (iii) the received information-bearing signal where the DCSK modulation is removed. The data correction block improves the BER performance while the increased channel capacity, enabled by the double-stream approach, improves the spectral efficiency. Analytical expressions are derived to predict the BER performances in additive white Gaussian noise channel for both the LPS and HPS channels. Computer simulations are used to show that the system performance of the CSF-SM-DCSK modulation scheme proposed in this work is superior to that of the already published solutions. In addition to the computer simulations, the new chaos-based wireless communications system has been implemented on a wireless open-access research platform to experimentally demonstrate the feasibility and the superiority of CSF-SM-DCSK.

Abstract It was reported recently that chaos properties could be used to relieve inter‐symbol interference caused by multipath propagation in chaos‐based wireless communication system. Although there exists the optimal decoding threshold to theoretically eliminate the inter‐symbol interference, its practical implementation is still a challenge due to the strong requirement to know the future symbols to be transmitted. To tackle this almost ‘impossible’ task, convolutional neural network with deep learning structure is proposed to predict future symbols based on the received signal, to further reduce inter‐symbol interference and to obtain a better bit error rate performance. Due to the short time predictability of chaotic signal, the proposed method is able to predict short‐term future symbols and get a better threshold suitable for the time‐variant channel. The analytical bit error rate of the proposed method is derived. The contributions of the paper are as follows: firstly, a convolutional neural network with deep learning structure is proposed for the first time to predict the future symbols in the chaos baseband wireless communication system, which does not require much training in this important application; secondly, the future bits predicted by the trained convolutional neural network are used together with the past decoded bits to calculate more accurate decoding threshold compared with the existing methods, yielding a better bit error rate performance. Numerical simulations and experimental results validate the effectiveness of our theory and the superiority of the proposed method.

Chaos has been successfully applied in many fields to improve the performance of engineering systems, such as communication, vibration compact, and mixing. Generating chaos from originally non-chaotic systems is a relevant topic because of potential applications. In this work, the impulse control is shown to generate chaos from non-chaotic system. Using non-chaotic Chen system as an example, we prove by analytical and numerical methods that chaos is indeed generated. The features of the chaos generated by impulse control are analysed using Lyapunov exponents, bifurcation diagram, power spectrum, Poincaré mapping and Kaplan-Yorke dimension. Furthermore, we demonstrate the chaotic attractor generation by impulse control using a circuit experiment. The last but not minor point is that the existence of topological horseshoe is given by rigorous computer-aided proof.

The inter-symbol interference (ISI) caused by multipath propagation in wireless channel is one of the main reasons for high bit error rate (BER) in wireless communication system. Chaos was proved to be an ideal communication baseband signal because of its special properties, including the corresponding simple matched filter and multipath resistance ability. Although the ISI caused by the multipath is promised to be completely eliminated by using the optimal symbol decoding threshold, the future symbols are needed to be known for calculating the optimal threshold, which is hard to be practically implemented. To deal with such problem, an echo state network (ESN), because of its short-term memory ability, is proposed to predict the future chaotic baseband signal based on the signal received after the matched filter. Thus, the first future symbol is estimated, which is used to improve the accuracy of the decoding threshold in the chaotic baseband wireless communication system (CBWCS) in order to further relieve the effect of ISI. Compared to the sub-optimal decoding threshold considering only past symbols, the improved threshold proposed here considers not only the ISI from past symbols, but also the ISI from one future symbol. The simulation in both the static and time-varying wireless channels are performed, the results show that the BER performance of CBWCS is improved more significantly in multipath channels as compared to single path channel. The experiments based on wireless open-access research platform verify the ISI resistance performance under the practical scenarios. Both simulation and experimental results show the effectiveness and the superiority of the proposed method.

Robust adaptation plays a key role in gene regulatory networks, and it is thought to be an important attribute for the organic or cells to survive in fluctuating conditions. In this paper, a simplified three-node enzyme network is modeled by the Michaelis-Menten rate equations for all possible topologies, and a family of topologies and the corresponding parameter sets of the network with satisfactory adaptation are obtained using the multi-objective genetic algorithm. The proposed approach improves the computation efficiency significantly as compared to the time consuming exhaustive searching method. This approach provides a systemic way for searching the feasible topologies and the corresponding parameter sets to make the gene regulatory networks have robust adaptation. The proposed methodology, owing to its universality and simplicity, can be used to address more complex issues in biological networks.

The compound active clamp soft switching three-phase power factor correction converter, as an improved three-phase PWM converter topology, has the advantages of high efficiency, high power factor, soft switching of all switches and so on. Its traditional control system configuration is the double closed loop control scheme with the current loop as inner loop and the voltage loop as outer loop. As a multivariable and strong coupling nonlinear system, the controller design of the three phase PWM converter, either the classical PID control method or the nonlinear control method, involves the time consuming controller parameters tuning problem. It is even worse that the improperly adjusted controller parameters might damage the system. In this paper, the particle swarm optimization is proposed to optimize the control parameters of the double closed loop PI controllers. To deal with this multi-parameter multi-objective optimization problem, we combine the PSO algorithm with the Pareto optimal solution theory to optimize the PI control parameters. The concrete steps are explained in this paper, and the effectiveness of proposed optimization method is verified by both the simulation and experiment results.

Pneumatic system has many advantages, such as simple, reliable, low-cost, long life, etc. These make it to get rapid development and widespread application. But the complexity of the gas through the valve port and the friction between the cylinder and piston make it difficult to establish exact mathematical model, and to control the pneumatic system with high precision. We proposed an adaptive backstepping controller for a pneumatic position servo system based on the proportional valve. The controller was designed using backstepping method and assuming a third order linear model of the pneumatic system with unknown parameters. A parameter adaption law was developed to adjust the parameters and to track the reference output with high precision. The experiments were conducted to show the effectiveness of the proposed method. The comparison was made between the proposed method and two existing variable structure control methods to show the proposed method possessing better tracking accuracy.