Non-cooperative Communication Systems Research Articles

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.

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.

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.

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.

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.

Robust Automatic Modulation Classification Under Varying Noise Conditions

Automatic modulation classification (AMC) plays a key role in non-cooperative communication systems. Feature-based (FB) methods have been widely studied in particular. Most existing FB methods are deployed at a fixed SNR level, and the pre-trained classifiers may no longer be effective when the SNR level changes. The classifiers may also need to be re-trained to be suitable for the varying channel environment. To address these problems, a robust AMC method under varying noise conditions is proposed in this paper. The method attempts to select noise-insensitive features from a large feature set to ensure that the trained classifiers will be robust to SNR variations. First, a feature set consisting of 25 types of features is extracted, and 4 features that are insensitive to noise are chosen through a feature selection method based on rough set theory. The generalizability of an SVM classifier trained on the 4 chosen features is evaluated based on numerical results. The classification accuracy remains reasonable when the SNR varies between 5 and 20 dB, indicating that the proposed method can be deployed under varying noise conditions.

Optimal Detection Thresholds in Spectrum Sensing with Receiver Diversity

We investigate the performance of receiver diversity techniques by considering energy detector based optimal spectrum sensing in cognitive radio networks over Rayleigh fading channel for both non-cooperative and cooperative communication systems. The analysis is first focused on the non-cooperative spectrum sensing part, including the analyses of selection combining (SC), equal gain combining (EGC), and maximal ratio combining (MRC) receiver diversity systems. We derive exact closed-form expressions for the optimal detection thresholds for each diversity scheme. By using these expressions, we present their detection performance compared to the no-diversity case. Then in the second part of this paper, the performance analysis of the proposed system is investigated for amplify-and-forward relaying spectrum sensing with SC, EGC, and MRC diversity techniques in Rayleigh fading channels. Particularly, average detection probability of end-to-end signal-to-noise ratio for SC and EGC schemes and the exact closed-form expressions of the optimal detection threshold for SC, EGC, and MRC schemes are derived. We also present detection performance for the cooperative system compared to the non-cooperative one. The analyses are validated by simulated receiver operating characteristic curves.

A Cooperation and Access Spectrum Sharing Protocol with Cooperative Interference Management

In cognitive radio network, the spectrum sharing is considered where secondary (unlicensed) users' communication co-exists with primary (licensed) users' communication in the presence of an interferer, and the interferer has a significant impact on the primary performance. Hybrid ARQ (automatic-repeat-request) retransmission mechanisms are employed at primary and interfering links. Based on the mechanisms, a cooperate-and-access spectrum sharing protocol is proposed where the secondary system switches between cooperation mode with forwarding interference and access mode with decoding interfering packet. In the cooperation mode, the secondary transmitter forwards information about the interference to the primary receiver for interference mitigation. Thus, the primary performance is improved compared to the traditional non-cooperative communication system and credits are collected by the secondary system. Adequate credits allow the secondary system running on the access mode, i.e. the secondary packet is delivered to the secondary receiver by using the primary spectrum. Then, compared to the traditional system the primary performance is degraded in the access mode. The condition that this degradation can be fully offset by the advantage accrued from interference mitigation is investigated. The primary throughput in cooperation and access modes and the secondary throughput in access mode are derived. Numerical results show that the proposed protocol has the equal or higher average primary throughput than the traditional system in the low primary SNR region. In addition, when the average transmits SNR of the primary is less than that of the interfering user, the proposed protocol is more efficient than the protocol in Li et al. (IEEE Transactions on Communication 60(10):2861---2870, 2012).

Parameter estimation of DSSS signals in non-cooperative communication system

Abstract A new adaptive estimator for direct sequence spread spectrum (DSSS) signals using fourth-order cumulant based adaptive method is considered. The general higher-order statistics may not be easily applied in signal processing with too complex computation. Based on the fourth-order cumulant with 1-D slices and adaptive filters, an efficient algorithm is proposed to solve the problem and is extended for nonstationary stochastic processes. In order to achieve the accurate parameter estimation of direct sequence spread spectrum (DSSS) signals, the first step uses the modified fourth-order cumulant to reduce the computing complexity. While the second step employs an adaptive recursive system to estimate the power spectrum in the frequency domain. In the case of intercepted signals without large enough data samples, the estimator provides good performance in parameter estimation and white Gaussian noise suppression. Computer simulations are included to corroborate the theoretical development with different signal-to-noise ratio conditions and recursive coefficients.

Incentive Mechanism Design for Selfish Hybrid Wireless Relay Networks

Non-cooperative behaviors in communication networks can significantly adversely affect the entire network. Recently, researchers have begun to study such non-cooperative communication systems within a game theory framework and strive to engineer the system to prevent performance degradation under non-cooperative behaviors. The WWAN/WLAN two-hop-relay system described in [1] integrates two types of wireless technologies to improve wireless access throughput and coverage. The relay nodes in the two-hop-relay system can be wireless relay routers deployed by wireless service providers, or dual-mode users who voluntarily relay traffic for other users. However, it is reasonable to assume that all dual-mode terminals are selfish and are not willing to relay for other users without an incentive. In this paper, we will use the basic concepts of game theory, especially the concept of the Nash Equilibrium, to design our scheduling algorithms. Several scheduling algorithms, including the maximum rate C/I scheduler, the proportional fair scheduler, and the round robin scheduler, are examined to understand performance while operating under the assumption that all users are selfish. Under the C/I scheduler or the proportional fair scheduler, Nash Equilibriums exist at the operating points where no user will relay for other users--an undesirable situation. Under the round robin scheduler, selfish users are indifferent on relaying voluntarily or not relaying. Therefore, we are inspired to design a novel incentive scheduler. By applying the proposed incentive scheduler, all selfish users relay cooperatively at the Nash Equilibrium.

Spread-spectrum sequence estimation for DSSS signal in non-cooperative communication systems

An innovative and effective algorithm is proposed to estimate the spread-spectrum sequence of a direct spread-spectrum sequence (DSSS) signal in non-cooperative communication systems. In particular, a measurement function, which is used for sequence synchronisation, and a detection statistic, which is for sequence estimation chip by chip, are developed based on the idea of correlation. Only linear complex addition and multiplication are required for the implementation of the algorithm. Theoretical analysis and computer simulations verify the effectiveness of the algorithm.