Non-cooperative Context Research Articles

Joint Blind Reconstruction of the Cyclic Codes and Self-Synchronous Scramblers in a Noisy Environment

Reverse analysis of the intercepted signal to reconstruct the communication scheme used by the transmitter is a key problem in the non-cooperative context. In this paper, we propose a new algorithm for the joint blind reconstruction of the cyclic codes and self-synchronous scramblers directly using soft-decision sequences. First, we derive the conclusion that if the parity polynomial of the factor of the generator polynomial of the cyclic code and the feedback polynomial of the self-synchronous scrambler are multiplied, then the vector corresponding to the inverse polynomial of this product forms a parity-check relation with the row vector of the self-synchronously scrambled cyclic code bit matrix, which is constructed by the sequence of self-synchronously scrambled cyclic codes. To detect this parity-check relation, the concept of average parity-check probability (APCP) is defined, which uses the intercepted soft-decision sequence to measure the reliability of the parity-check relation. An optimal threshold based on the minimum error criterion is then derived. When the APCP is greater than this optimal threshold, the parity-check relation is considered to be detected, i.e., the corresponding irreducible polynomial is a factor of the generator polynomial of the cyclic code and the corresponding candidate feedback polynomial is the feedback polynomial of the self-synchronous scrambler. Therefore, the blind reconstruction problem in this paper is exactly equivalent to the hypothesis-testing problem. Simulation results show that the algorithm is more fault-tolerant than existing algorithms in noisy environments.

Reconstruction of a Synchronous Scrambler Based on Average Check Conformity

Channel coding technology is indispensable in digital communication systems. In noncooperative contexts, the identification of the channel codes of the synchronous scrambler is essential. In this paper, a new algorithm that directly uses a soft decision sequence for blind reconstruction of the synchronous scrambler is proposed. First, considering imbalanced signal sources and the principle of scrambling and descrambling the synchronous scrambler, the error-containing equation of the synchronous scrambler is established. Second, the average check conformity is introduced to complete the check relationship detection. Then, based on the statistical characteristics of the average check conformity, the corresponding discrimination threshold is established, and the reconstruction of the feedback polynomial of the synchronous scrambler is completed by traversing the possible primitive polynomials. Finally, the verification equation is determined by the method of subsection optimization, which greatly reduces the number of initial states that need to be traversed; this is critical for scramblers with high-order feedback polynomials (i.e., when the order of the feedback polynomial is greater than 15). Simulations show that the algorithm can effectively reconstruct the synchronous scrambler under imbalanced signal sources. Moreover, the proposed algorithm offers improved performance with about 1–2 dB gain at low signal-to-noise ratios compared with existing methods, and its computational complexity is reasonable.

Radar Emitter Classification Based on a Multiperspective Collaborative Clustering Method and Radar Characteristic Spectrum

Modern cognitive electronic reconnaissance methods for radar systems must contend with the complex electromagnetic environments arising from the deployment of multiple signal sources and radar countermeasures, which greatly limit access to the degree of prior information required to enable effective target recognition. The present work addresses this issue by proposing a multiperspective collaborative clustering method for sorting radiation sources based on the multiperspective information of radar signals. In contrast to conventional collaborative training approaches, which are suitable only for semisupervised learning, the proposed multiperspective collaborative clustering method performs unsupervised clustering, cluster label transfer, and dimensionality reduction by linear discriminant analysis iteratively based on the differences between the clustering results obtained from two signal perspectives radiation signal sorting can be conducted in a noncooperative context. The results of comparative experiments demonstrate that the proposed multiperspective sorting method can make full use of the difference information between basic signal characteristics and intrapulse features and thereby improve the accuracy of clustering-based radiation source sorting. Accordingly, the sorting ability of the proposed method is superior to those of other state-of-the-art clustering methods and that of the single-perspective clustering-based sorting method.

Blind Estimation of Self-Synchronous Scrambler Using Orthogonal Complement Space in DSSS Systems

In a non-cooperative context, a receiver has to estimate the communication parameters without any prior knowledge of the transmitter, which is highly demanding. Estimating a self-synchronous scrambler is even more challenging because the scrambling sequence of the self-synchronous scrambler is affected by the input sequence. This paper proposes an improved algorithm for blind estimation of a self-synchronous scrambler using the orthogonal property without any bias condition of the received signals in direct sequence spread spectrum (DSSS) systems. We first examine the linear relation of a scrambling sequence using the repetitive property of the spreading code used in a DSSS system. Using the obtained linear relation and the basis of the orthogonal complement space, we then acquire the feedback polynomial candidates of the scrambler. Finally, by calculating the greatest common divisor polynomial of the feedback polynomial candidates, we estimate the correct feedback polynomial. Through computer simulations, we verify that the proposed method achieves superior estimation performance compared to the existing method. Furthermore, we show that the proposed method has practically acceptable computational complexity. For these reasons, it is expected that the proposed method can be applied to blind estimation of a self-synchronous scrambler in a practical non-cooperative system.

Novel blind interleaver parameters estimation based on Hamming weight distribution of linear codes

Interleaving techniques are used to improve the probability of error correction in communication systems. In a non-cooperative context, interleaver parameters must be determined first so that the received data can be decoded into relevant information. This paper proposes blind interleaver parameter estimation method based on the Hamming weight distribution. Conventional methods based on rank distributions suffer from the high computational complexity of Gaussian elimination. In this study, we exploit the fact that the Hamming weight distributions of linear codes differ from those of random sequences owing to the linear dependence of linear codes. By exploiting this property, the proposed algorithm can estimate the interleaver period without a rank calculation. The values of the χ2 test are used to estimate the interleaver period by constructing the Hamming weight distribution that differs the most from the binomial distribution. The experimental results indicate that the proposed algorithm outperforms conventional methods.

Blind Estimation of Self-Synchronous Scrambler in DSSS Systems

Self-synchronous scramblers are more difficult to blindly estimate than synchronous scramblers because their input sequence affects the state of the scrambler's linear feedback shift register. There has been much research on the estimation of synchronous scramblers and relatively little on the estimation of self-synchronous scramblers. For non-cooperative contexts, this paper proposes a method for the estimation of a self-synchronous scrambler in direct sequence spread spectrum systems. We blindly estimate the self-synchronous scrambling parameter, the feedback polynomial, using the repeated patterns by a spreading code, the linearity between the bits in a scrambling sequence, and the coidentity of the self-synchronous scrambled sequence and the linear feedback shift register states. To validate the proposed method, we show the estimation performance in terms of the computational complexity, required minimum scrambled sequence length, execution time, and detection probability.

Classification of audio codecs with variable bit-rates using deep-learning methods

A large portion of the Internet bandwidth is used for transmission of multimedia such as audio data. As the file sizes are usually much bigger than the maximum network packet size, the audio data are segmented into fragments. For eavesdropping or network surveillance purposes, the first step of a sniffer may be to determine the codec by which a fragment is generated. This problem is usually modeled as a multi-class classification problem. The basic methods for determining the codec type of each fragment rely on the metadata in the corresponding file header. However, in a non-cooperative context, the whole file is not available. Statistical features extracted from the fragments are generally used for solving this problem by employing machine learning methods. In this paper, we present an end to end scalable deep learning approach for the classification of audio codecs with variable bit-rates. This method is based on an efficient variant of convolutional neural networks, which learns hidden layer representations to encode input vectors. Moreover, by integrating the statistical features and Cascade Deep Forest method, we jointly optimize classification labels assignment and learn the features that are suitable for classification. In several experiments on a dataset of speech codecs, we demonstrate that our approach outperforms the state-of-the-art methods by a significant margin.

Metalinguistic negotiations in moral disagreement

ABSTRACT The problem of moral disagreement has been presented as an objection to contextualist semantics for ‘ought’, since it is not clear that contextualism can accommodate or give a convincing gloss of such disagreement. I argue that independently of our semantics, disagreements over ‘ought’ in non-cooperative contexts are best understood as indirect metalinguistic disputes, which is easily accommodated by contextualism. If this is correct, then rather than posing a problem for contextualism, the data from moral disagreements provides some reason to adopt a semantics that allows contextual variance in the meanings of ‘ought’.

Digital blind despreading method for intermediate frequency short‐code DSSS signal

Direct sequence spread spectrum (DSSS) signals are widely used in various military and civilian communication systems owing to its useful properties, such as lower working signal-to-noise ratio, strong anti-jamming capability, and robustness to multi-path fading. In past few years, the estimation for spreading sequence in non-cooperative context has attracted lots of attention. Several methods are developed to realise a recovery of the sequence. Eigenvalue decomposition (EVD) is a classical method to obtain an effective estimation by extracting the main eigenvectors. The only obstacle to apply EVD method into practical systems is high complexity of computation when spreading sequence is long. Alternative algorithms such as neural network and clustering method have lower complexity, but an evident loss in performance. In this work, the authors propose to generalise the EVD algorithm to intermediate frequency (IF) short-code DSSS with an unknown carrier offset. The blind despreading process is designed to complete correlation demodulation. As a result, the computational afford is largely decreased due to the operation on IF real signal. To evaluate the performance, they derive the Crammer-Rao bound for spreading code estimation and compare the performance of several methods. Simulation demonstrates the superiority of the proposed method in estimation accuracy and runtime.

Blind Recognition of Cyclic Codes Based on Average Cosine Conformity

Channel coding technology is indispensable in digital communication systems. In noncooperative contexts, the blind identification of channel codes is very important. In this paper, a new algorithm that directly uses a soft-decision sequence for blind reconstruction of cyclic codes is proposed. From the algebraic structure of cyclic codes, we deduce that a cyclic code of length n must be able to divide the polynomial xn + 1, and the dual spaces corresponding to the factors of the generator polynomial can form a parity-check relation with the code words. In order to detect this relation, the concept of the average cosine conformity is defined, which can fully utilize the intercepted soft-decision sequence to measure the reliability of the parity-check relation. Further, the statistical characteristics of the average cosine conformity with respect to a candidate factor of the generator polynomial are analyzed in detail. When the traversal code length n is equal to the actual length, some factors in xn + 1 allow this constraint relation to be detected, and the generator polynomial of the cyclic codes consists of these factors. Hence, the blind reconstruction of the cyclic code is exactly equivalent to the hypothesis testing problem. Simulations show that the derived theoretical distribution of the average cosine conformity is consistent with the simulation results. Moreover, the proposed algorithm has preferable performance at low signal-to-noise ratios compared with existing methods, and its computational complexity is reasonable.

Blind Estimation of Interleaver Parameter With a Limited Number of Data

In non-cooperative contexts, one needs to estimate communication parameters by using collected data without any prior information. Particularly, when collecting only a limited number of data, estimation becomes a more challenging task. This paper presents a method for estimation of interleaver parameter when only a limited number of collected data are available. We first create additional data by combining a limited number of collected data. We then investigate the rank deficiency of the matrices composed of the collected and additionally-created data. Finally, we estimate the interleaver parameter by using the difference of average rank deficiencies. Through computer simulations, we validate the proposed method in terms of detection probability and the number of false alarms.

Novel Blind Interleaver Parameter Estimation in a Noncooperative Context

It is well known that the distribution of the ranks of random matrices follows a specific distribution. By partitioning ranks into two possible outcomes, we can transform the estimating interleaver parameters into the counting of success events. Using this probabilistic framework, we can efficiently determine interleaver parameters in a noncooperative context.

Blind Identification of Block Interleaved Convolution Code Parameters

Most of the digital communication system uses forward error correction (FEC) in addition with interleaver to achieve reliable communication over a noisy channel. To get useful information from intercepted data, in non-cooperative context, it is necessary to have algoritihms for blind identification of FEC code and interleaver parameters. In this paper, a matrix rank-based algebraic algorithm for the joint and blind identification of block interleaved convolution code parameters for cases, where interleaving length is not necessarily an integer multiple of codeword length is presented. From simulations, it is observed that the code rate and block interleaver length are identified correctly with probability of detection equal to 1 for bit error rate values of less than or equal to 10-4.

Game theory for resource sharing in large distributed systems

<span lang="EN-US">In game theory, cooperative and non-cooperative approaches are distinguished in terms of two elements. The first refers to the player's ability to engage: in a non-cooperative context, they are entirely free to make decisions when they make their choices; However, in a cooperative context, they have the opportunity to engage contractually the strategies that should be adopted during the game, that during a phase of discussions held before the game and during combinations which may be formed.In this context, the problem is not so much to predict the outcome of the game between players to leave the benefit of cooperation. To achieve this, and this is the second major difference with the non-cooperative approach, it adopts an axiomatic approach (or normative) by which we set upstream properties a priori reasonable (or desirable) on the outcome of the game. The purpose of this paper is to present briefly the main types of non-cooperative games and the tools that allow them to be analyzed in a complete information context where all aspects of the game are well known to decision makers.</span>

Improved Method for Interleaving Parameter Estimation in a Non-Cooperative Context

In a non-cooperative context, a sequence acquired from remote sensing through satellites and aircraft can be recognized as an unknown sequence to a receiver who lacks the information of the transmission parameters. Therefore, the transmission parameters have to be estimated to reconstruct the unknown sequence. This paper focuses on the estimation of an interleaving period among transmission parameters and proposes an improved method to blindly estimate the interleaving period. Through computer simulations, we validate the method by analyzing the estimation performance in terms of the detection probability and the false alarm probability in a fading channel.

Rethinking Media Synchronicity Theory

Information systems (IS) research often assumes team members have aligned goals. In practice, people interpret and pursue goals to suit personal agendas, even when they are collaborating. This motivates our examination of the cooperative assumption in Media Synchronicity Theory (MST)--a leading IS theory of communication performance. We assess the boundaries of MST by relaxing the assumption of cooperation. Our results support MST for explaining communication and task performance in a cooperative context. However, MST was insufficient to capture how media capabilities influence performance in a non-cooperative context. Our study shows that relaxing the assumption of cooperation changes MST in profound ways--altering which media capabilities are central to the model and the very processes that underlie communication.

Classification Based on Euclidean Distance Distribution for Blind Identification of Error Correcting Codes in Noncooperative Contexts

The use of channel code is mandatory in current digital communication systems. It allows us to access the information on the receiver side despite the presence of noise. In this paper, we are interested in the blind identification of the parameters of an error correcting code from a received noisy data stream. The literature provides a large amount of contributions for this problem in the hard-decision case but few in the soft-decision case. It is well known that soft-decision methods allow significant gain in decoding techniques. Thence, we propose an algorithm which is able to identify the length of a code through a classification process from the bits likelihood values. It highlights a difference of behavior between an independent identically distributed sequence and an encoded one. This method does not rely on any a priori knowledge about the encoder involved. Indeed, the distribution of n-length code words in an n-dimensional space depends on the encoder characteristics. Some areas of this n-dimensional space are left vacant because of the redundancy added by the encoder. Despite the presence of noise, it is still possible to detect this phenomenon. Furthermore, an adaptation of a collisions method based on the birthday paradox gives us access to an estimation of the code dimension. Finally, we investigate the performance of this estimation methods to show their efficiency.

PERIODIC STRATEGIES: A NEW SOLUTION CONCEPT AND AN ALGORITHM FOR NONTRIVIAL STRATEGIC FORM GAMES

We introduce a new solution concept, called periodicity, for selecting optimal strategies in strategic form games. This periodicity solution concept yields new insight into nontrivial games. In mixed strategy strategic form games, periodic solutions yield values for the utility function of each player that are equal to the Nash equilibrium ones. In contrast to the Nash strategies, here the payoffs of each player are robust against what the opponent plays. Sometimes, periodicity strategies yield higher utilities, and sometimes the Nash strategies do, but often the utilities of these two strategies coincide. We formally define and study periodic strategies in two player perfect information strategic form games with pure strategies and we prove that every nontrivial finite game has at least one periodic strategy, with nontrivial meaning nondegenerate payoffs. In some classes of games where mixed strategies are used, we identify quantitative features. Particularly interesting are the implications for collective action games, since there the collective action strategy can be incorporated in a purely noncooperative context. Moreover, we address the periodicity issue when the players have a continuum set of strategies available.

Estimating the number of users in TDMA networks based on redundancy of adaptive channel coding

Blind identification of protocols in wireless communication networks is a critical subject in non-cooperative context. In this paper, a novel method is proposed to estimate the number of active users (NoAU) in wireless time division multiple access (TDMA) networks based on the redundancy of adaptive channel coding. The proposed method, which is based on decision trees and machine learning, can also be used in adaptive coding systems to identify the number of distinct code rates employed by a single transmitter. Simulation results show that the proposed method can estimate the number of codes with accuracy around 99% in noiseless situation. Moreover, for noisy environments, reasonable accuracies compared to naïve methods are obtained.

The interplay between sharing behavior and beliefs about others in children during dictator games

Previous studies in adults demonstrated that beliefs and sharing decisions in social scenarios are closely related. However, to date, little is known about the development of this relationship in children. By using a modified dictator game, we assessed sharing behavior and beliefs about others in children between 3 and 12 years old. We performed four studies (N = 376) aimed to assess whether decisions were related to beliefs (Studies 1 and 2) and whether information about the recipient’s forced sharing behavior would shape decisions and beliefs (Studies 3 and 4). Results of Studies 1 and 2 showed that beliefs about others’ generosity were related to children’s sharing behavior. In Studies 3 and 4, we found that only children older than 9 years shared more pieces of candy when they knew that the recipient would be forced to share (cooperative context) than when they knew that the recipient would be forced not to share (noncooperative context). Besides, children older than 6 years did not modify their beliefs about others’ generosity according to these social contexts. These results suggest that normative or preconceived beliefs about the functioning of the social world may guide social behavior in children.