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.

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.

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.

This letter puts forward a novel algorithm for the inversion of biophysical and biochemical parameter of vegetation leaves (BBPV) by means of the multiscale two-dimension discrete wavelet decomposition (DWD) of remote sensing (RS) image. With the aid of a multiangle imaging spectroradiometer (MISR) RS image, the multiscale BBPV inversion is implemented with the algorithm on each scale of wavelet decomposition based on DWD of MISR RS image. The retrieved results of the rough-scale wavelet decomposition are regarded as a priori knowledge of the fine-scale inversion, and then, the retrieved results of the rough-scale wavelet decomposition are also regarded as the initial values of the fine-scale inversion. In this way, the wavelet decomposition of the MISR RS image is gradually refined, and the retrieved result is likewise gradually refined until optimal BBPV is achieved in the end. Furthermore, this letter also probes the algorithm principle of the model inversion of BBPV, by employing the radiative transfer model of vegetation leaves (RTMV). Taking the model inversion of leaf area index as an example, a satisfactory retrieval result of higher precision is finally obtained. The experiment demonstrates that the proposed algorithm is very effectual and reliable in the model inversion of BBPV. The proposed algorithm opens up a novel algorithmic pathway for the model inversion of BBPV by means of DWD of MISR RS image.