Non-cooperative Communication Research Articles

A Novel Multi-user Detection Approach on Fluctuations of Autocorrelation Estimators in Non-Cooperative Communication

Recently, blind multi-user detection has become an important topic in code division multiple access (CDMA) systems. Direct-Sequence Spread Spectrum (DSSS) signals are well-known due to their low probability of detection, and secure communication. In this article, the problem of blind multi-user detection is studied in variable processing gain direct-sequence code division multiple access (VPG DS-CDMA). The method based on the fluctuations of autocorrelation estimators,which previously described in a single and multi-user context, is extended to an asynchronous variable processing gain (VPG) DS/CDMA systems without restrictions on the processing gains in multi-path channel condition. According to the proposed algorithm, not only the presence of the common peaks in the second-order statistic of the received signal is proved, but the relationship between these peaks and the spreading code length is also derived. The simulation results show that the proposed algorithm even in the presence a very low signal-to-noise ratio (SNR) is completely efficient in signal detection. It is remarkable that the minimum description length (MDL) method is used to determine the number of users.

Deep Learning-Based Cooperative Automatic Modulation Classification Method for MIMO Systems

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver, equipped with multiple antennas, cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types, based on these sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

Carrier frequency and symbol rate estimation based on cyclic spectrum

Carrier frequency and symbol rate estimation are the main contents of parameter estimation, which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication. With the development of wireless communication, the signal transmission environment has become increasingly bad, causing more difficulties in parameter estimation. It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related. In practice, it is impossible to calculate the cyclic spectrum of infinite length data signals. When using finite length data to obtain a cycle spectrum, the truncation noise is induced, resulting in interference. It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines. An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.

LightAMC: Lightweight Automatic Modulation Classification via Deep Learning and Compressive Sensing

Automatic modulation classification (AMC) is an promising technology for non-cooperative communication systems in both military and civilian scenarios. Recently, deep learning (DL) based AMC methods have been proposed with outstanding performances. However, both high computing cost and large model sizes are the biggest hinders for deployment of the conventional DL based methods, particularly in the application of internet-of-things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. In this correspondence, a novel DL based lightweight AMC (LightAMC) method is proposed with smaller model sizes and faster computational speed. We first introduce a scaling factor for each neuron in convolutional neural network (CNN) and enforce scaling factors sparsity via compressive sensing. It can give an assist to screen out redundant neurons and then these neurons are pruned. Experimental results show that the proposed LightAMC method can effectively reduce model sizes and accelerate computation with the slight performance loss.

Automatic Modulation Classification for MIMO Systems via Deep Learning and Zero-Forcing Equalization

Automatic modulation classification (AMC) is one of the most critical technologies for non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have attracted significant attention due to their preferable performance. However, the study of most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are only a few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Therefore, we propose in this work a convolutional neural network (CNN) based zero-forcing (ZF) equalization AMC (CNN/ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN/ZF-AMC method achieves better performance than the artificial neural network (ANN) with high order cumulants (HOC)-based AMC method under the condition of the perfect channel state information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the CNN/ZF-AMC method. Simulation results demonstrated that the classification performance is not only influenced by the imperfect CSI, but also associated with the number of the transmit and receive antennas.

Modulation Recognition With Graph Convolutional Network

In most non-cooperative communication systems, modulation recognition is a fundamental and critical technique. Traditional methods of modulation recognition can be categorized as maximum likelihood hypothesis algorithms and pattern recognition algorithms. However, these methods have high complexities or need additional data preprocessing. Recently, neural network algorithms have shown great potential in modulation recognition. In this letter, we propose a method of modulation recognition by exploiting the graph convolutional network (GCN). However, GCNs cannot be directly used to perform modulation recognition since modulated signals are not graphs. To convert signals to graphs, the modulation dataset is divided into multiple subsets. We design a feature extraction convolutional neural network (CNN) and a graph mapping CNN to extract signal features and map subsets into graphs, respectively. Then we input the graphs into the GCN to predict modulation modes of unlabeled signals. The experimental results show that the proposed GCN algorithm achieves higher recognition accuracy than CNN algorithm and K-nearest neighbor (KNN) algorithm, especially when SNR is low.

Automatic Modulation Classification Scheme Based on LSTM With Random Erasing and Attention Mechanism

Automatic modulation classification (AMC) is a key technology of cognitive radio used in non-cooperative communication. Recently, deep learning has been applied to AMC tasks. In this paper, an AMC scheme based on deep learning is proposed, which combines random erasing and attention mechanism to achieve high classification accuracy. Firstly, we propose two data augmentation methods, random erasing at sample level and random erasing at amplitude/phase (AP) channel level. The former replaces training samples with noise information, while the latter replaces AP channel information of training samples with noise information. Erased data segments are randomly stitched to enable training data expansion. Training data of different qualities enables deep learning model to have stronger generalization capability and higher robustness. Then, we propose a single-layer Long Short-Term Memory (LSTM) model based on attention mechanism. In the first part of this model, we propose the signal embedding, which enables the input to contain modulation information more comprehensively and accurately. Then hidden state output by LSTM is input into the attention module, and weighting is applied to the hidden state to help the LSTM model capture the temporal features of modulated signals. Compared to a model without attention mechanism, this model has faster convergence speed and better classification performance. Lastly, we propose a random erasing-based test time augmentation (RE-TTA) method. Test data is randomly erased for multiple times and classification results are comprehensively evaluated, in order to further improve classification accuracy. Experimental results on dataset RML2016.10a show that classification accuracy of the proposed scheme is competitive compared with the state-of-the-art methods.

Improved Gardner Suppression Timing Jitter Synchronization Algorithm

In non-cooperative digital communication systems, the symbol synchronization of the received signal is of great significance for the correct demodulation of the signal. Synchronization, as a key factor in the accurate transmission of information at the sender in communication, is also a vital technology in blind demodulation structures. Strictly speaking, the synchronization technology in non-cooperative communication systems includes not only carrier synchronization and bit synchronization, but also group synchronization and network synchronization. Although the existing synchronization algorithms have good reference value, they also have their limitations. For the traditional symbol synchronization loop based on the Gardner algorithm, when the roll-off coefficient is small and the symbol rate is high, the loop self-noise is serious. This paper uses a symbol synchronization method based on interpolation adjustment sequences, and proposes an improved algorithm that sets a pre-filter that meets the condition of zero timing jitter before the traditional timing error detector to compensate for the effect of self-noise on synchronization. The simulation results of the two algorithms are compared and analyzed. Through the comparison and analysis of the simulation results of the two algorithms, it is found that the improved algorithm can effectively suppress the timing jitter caused by the self-noise of the loop, and at the same time, can accelerate the symbol synchronization speed, effectively improve the limitations of the traditional algorithm and the slow convergence speed. Engineering significance.

Analysis of Blind Frame Recognition and Synchronization Based on Sync Word Periodicity

Blind recognition of communication parameters has been studied for various applications including cognitive radios, non-cooperative communications, electronic warfare, etc. In this article, the identification of frame information, such as frame length and synchronization word (SW), and frame synchronization, is addressed over the wiretap channel, where no prior information of the frame structure is available, but only the existence of a repeated SW is known. Modifying the previous study, we propose two blind frame recognition and synchronization algorithms based on the correlation between two distinct windows of the received signal as well as the mean of periodic samples. The algorithms run in two steps. A multiple of frame length is estimated via the correlation and then other frame parameters are acquired using the mean of periodic samples with the estimate. Asymptotic analysis of the first algorithm shows that the error probability of estimation vanishes as the received data increases both in noiseless and noisy transmissions. The second algorithm improves the performance in the limited received data scenario. Simulation results as well as complexity analysis of the proposed algorithm are also shown.

Automatic Modulation Classification Using a Deep Multi-Stream Neural Network

In wireless communication, modulation classification is an important part of the non-cooperative communication, and it is difficult to classify the various modulation schemes using conventional methods. The deep learning network has been used to handle the problem and acquire good results. In the deep convolutional neural network (CNN), the data length in the input is fixed. However, the signal length varies in communication, and it causes that the network cannot take advantage of the input signal data to improve the classification accuracy. In this paper, a novel deep network method using a multi-stream structure is proposed. The multi-stream network form increases the network width, and enriches the types of signal features extracted. The superposition convolutional unit in each stream can further improve the classification performance, while the shallower network form is easier to train for avoiding the over-fitting problem. Further, we show that the proposed method can learn more features of the signal data, and it is also shown to be superior to common deep networks.

Blind Estimation of Spreading Sequence and Data Bits in Direct-Sequence Spread Spectrum Communication Systems

At the transmitter of a Direct Sequence Spread Spectrum (DSSS) communication system, data bits are spread using a spreading sequence. To demodulate the transmitted data bits, the received signal should be de-spread using the same spreading sequence. However, there are many non-cooperative communication systems, where a receiver does not have the information on the spreading sequence. In such systems, to demodulate the transmitted data bits across a wide area, it is necessary to blindly estimate the spreading sequence at low Signal-to-Noise Ratio (SNR). Furthermore, for real-time processing, the computational complexity for the blind estimation should be minimized. In this paper, a novel algorithm is proposed to blindly estimate a spreading sequence and data bits based on turbo processing for both synchronous and asynchronous cases. The proposed algorithm can significantly improve the error rates of estimated data bits and the chips in the spreading sequence compared to an Eigen-Value Decomposition (EVD) based algorithm. Furthermore, compared to the EVD-based algorithm, the proposed algorithm drastically reduces the number of required multiplications.

An Improved Blind Recognition Method for Synchronization Position and Coding Parameters of k/n Rate Convolutional Codes in a Noisy Environment

In digital communication systems, error correction codes are used to recover data that has been disturbed in a noisy environment. Convolutional code is a commonly used error correction code. In non-cooperative communication systems, it is very meaningful to blindly identify the synchronization position and coding parameters of convolutional codes. Existing reference had proposed a blind recognition algorithm for coding parameters of k/n rate convolutional codes in a noisy environment, but it didn't take into account the case where the received code words were not synchronized. In this paper, this problem is improved by using the quasi-correlation between the receive sequence and the check matrix to estimate the synchronization position, while obtaining the check matrix. It implements blind recognition of the synchronization position and coding parameters of k/n rate convolutional codes in a noisy environment. Simulation experimental results show that the improved algorithm in this paper is effective.

A Modulation Classification Algorithm for Multipath Signals Based on Cepstrum

Automatic modulation classification (AMC) is an important intermediate step between signal detection and demodulation by the receiving instrument in the noncooperative communication system. Multipath fading causes serious degradation in classification performance of AMC. For the purpose of improving the performance of AMC for multipath modulation signals, we proposed a novel cepstrum-based preprocessing algorithm to eliminate the effect of multipath channel coefficient, and a logarithmic functional fitting method to classify modulated signals. First, we mathematically proved the feasibility of the proposed approach. Then, we experimentally tested our algorithm by the practically measured modulation data of quadrature phase-shift keying (QPSK), 16-ary quadrature amplitude modulation (16QAM), and 64-ary quadrature amplitude modulation (64QAM). The results confirmed that our algorithm performed well for multipath signals and was robust to slight frequency and timing offsets. Compared with the existing methods, our algorithm also performed better than the techniques based on high-order cumulants or cyclic statistics and suffered less computational complexity than the cumulant-based method with channel estimation procedure.

Blind parameter estimation of turbo convolutional codes: Noisy and non-synchronized scenario

Blind estimation of forward error correction code parameters at the receiver plays a significant role in non-cooperative communication, adaptive modulation and coding systems, and reconfigurable receiver systems. Turbo convolutional codes, a parallel concatenation of multiple convolutional codes, are used in digital communication and storage systems to achieve low bit error rate. The present paper proposes innovative algorithms for the blind estimation of code parameters and reconstruction of turbo convolutional encoder over noisy scenario. The turbo convolutional code is designed using two component codes along with an interleaver. Recursive systematic convolutional codes are used as component codes. Any imperfection in synchronization of received data for the proposed code parameter estimation algorithm is compensated through a bit position adjustment parameter. The performance of the proposed algorithms in terms of parameter estimation accuracy is investigated for different modulation order, code rate, and constraint length values. It is observed that the performance improves with decrease in modulation order and constraint length values.

Introducing an Efficient Statistical Model for Automatic Modulation Classification

Nowadays, Automatic Modulation Classification (AMC) plays an important role in many applications of cooperative and non-cooperative communication such as spectrum management, cognitive radio, intelligent modems, and interference identification. In this paper, a new robust AMC algorithm based on Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model is proposed. Primarily, multi-level wavelet transform is applied on the received data samples. To select the efficient statistical model for wavelet coefficient description, the statistical characteristics of these coefficients are surveyed. The proposed analysis precisely illustrates that these coefficients have heteroscedasticity property which has not been mentioned before. Subsequently, to describe the wavelet coefficients, the heteroscedastic GARCH model is employed and its parameters are extracted as the features. Finally, the obtained features are applied to support vector machine (SVM) classifier to simultaneously determine the modulation type and constellation size. Eleven different types and sizes of the digital modulation schemes in various channels such as AWGN, flat fading and multipath fading in presence of common channel impairments are examined. The experimental results reveal the superior performance of the proposed method in comparison with the previously introduced approaches.

Hierarchical multistage Gaussian signaling games in noncooperative communication and control systems

We analyze in this paper finite horizon hierarchical signaling games between (information provider) senders and (decision maker) receivers in a dynamic environment. The underlying information evolves in time while sender and receiver interact repeatedly. Different from the classical communication (control) models, however, the sender (sensor) and the receiver (controller) have different objectives and there is a hierarchy between the players such that the sender leads the game by announcing his policies beforehand. He needs to anticipate the reaction of the receiver and the impact of the actions on the horizon while controlling the transparency of the disclosed information at each interaction. With quadratic cost functions and multivariate Gaussian processes, evolving according to first order auto-regressive models, we show that memoryless “linear” sender signaling rules are optimal (in the sense of game-theoretic hierarchical equilibrium) within the general class of measurable policies in the noncooperative communication context. In the noncooperative control context, we also analyze the hierarchical equilibrium for linear signaling rules and provide an algorithm to compute the optimal linear signaling rules numerically with global optimality guarantees.

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized.

An Automatic Modulation Recognition Method with Low Parameter Estimation Dependence Based on Spatial Transformer Networks

Recently, automatic modulation recognition has been an important research topic in wireless communication. Due to the application of deep learning, it is prospective of using convolution neural networks on raw in-phase and quadrature signals in developing automatic modulation recognition methods. However, the errors introduced during signal reception and processing will greatly deteriorate the classification performance, which affects the practical application of such methods. Therefore, we first analyze and quantify the errors introduced by signal detection and isolation in noncooperative communication through a baseline convolution neural network. In response to these errors, we then design a signal spatial transformer module based on the attention model to eliminate errors by a priori learning of signal structure. By cascading a signal spatial transformer module in front of the baseline classification network, we propose a method that can adaptively resample the signal capture to adjust time drift, symbol rate, and clock recovery. Besides, it can also automatically add a perturbation on the signal carrier to correct frequency offset. By applying this improved model to automatic modulation recognition, we obtain a significant improvement in classification performance compared with several existing methods. Our method significantly improves the prospect of the application of automatic modulation recognition based on deep learning under nonideal synchronization.

An Improved Blind Recognition Algorithm of Frame Parameters Based on Self-Correlation

The blind recognition of the frame parameter plays a crucial role in frame synchronization in the background of a non-cooperation communication system. This paper proposes an algorithm based on self-correlation on the foundation of existed cumulative filtering algorithm. To overcome high BER, the peak-to-average ratio (PAR) is calculated to improve the algorithm. The simulation results proved that the performance of the algorithm has been improved.

A path to computational efficiency through membrane computing

The search for new mechanisms and tools allowing us to tackle the famous P versus NP problem from new perspectives is an important task, due to the relevance of that problem. The concept of efficiency of computing models is associated with the ability to solve intractable (in a classical sense) problems in polynomial time. Assuming that P≠NP, that concept is equivalent to the capability to solve NP-complete problems in an efficient way. Different frontiers of the efficiency have been given in Membrane Computing in terms of syntactical or semantic ingredients of the models. In particular, in the framework of tissue P systems with cell division using symport/antiport rules, the length of communication rules (passing from length 1 to length 2) provides an optimal borderline of the efficiency.Cell-like P systems with symport/antiport rules and membrane division is a restricted variant of such tissue P systems in both its structure (rooted tree versus undirected graph) and in the way membranes communicate with each other and with the environment. The limitations of efficient computations in such kind of P systems which use non-cooperative communication rules have been previously established. In this paper, a uniform polynomial time solution for the Hamiltonian cycle problem, a well known NP-complete problem, by means of cell-like P systems with membrane division using minimal cooperation in communication rules (at most two objects are involved), is provided. Hence, a new optimal boundary between tractability and NP-hardness, is provided: passing from non-cooperative rules to cooperative rules in cell-like P systems with symport/antiport rules and membrane division amounts to passing from non-efficiency to efficiency.