Non-cooperative Context Research Articles

Executive Mechanisms for Thinking about Negative Situations in Both Cooperative and Non-Cooperative Contexts.

Mentalizing is a fundamental aspect of social cognition that includes understanding the mental states of others. This process involves the participation of a well-defined set of brain regions. However, it is still unknown how different contextual situations, such as previous cooperative or non-cooperative interactions, can modulate the brain activity related to the inference of others’ mental states. Hence, this study investigated whether a previous social interaction can modulate the neural mechanisms involved in a way to response to inferred mental states of cooperators and non-cooperators in positive vs. negative emotional situations. Participants first engaged in a Dictator game with cooperator and non-cooperator confederates. Then, in an fMRI setup, participants had to infer the mental states of the cooperator and non-cooperator confederates under positive and negative situations. Results showed that in addition to the mentalizing network, inferring mental states recruited occipital and cerebellar areas in the cooperative context. A differential pattern of activity that depended on the emotional valence of the situation was also detected, i.e., negative situations recruited prefrontal cortex (PFC) in both contexts, while temporal regions were recruited only for the non-cooperative context. Overall, these results suggest that our previous experiences with others modulate the brain activity related to the inferences we make about their mental states in specific emotional situations.

Blind Classification of Line-Coding Schemes Based on Characteristic Features

In modern defense technology, signal intelligence plays an important role in military operations as an intelligence source. Among its target signals, telemetry and telecommand signals can use line-coding schemes for their advantageous properties in the presentation of binary data of transmission systems. Therefore, in a non-cooperative context, blind classification of the line-coding scheme is the final crucial step in recovering target information from an unknown received signal. In this paper, we examine the characteristic features of line-coding schemes and then propose a simple blind classification algorithm for the schemes. We also analyze correct classification probabilities of the proposed algorithm in a noiseless and noisy environment through computer simulations. The proposed method can discern line-coding schemes, allowing reconstruction of the original information data.

Blind recognition of k/n rate convolutional encoders from noisy observation

Blind recognition of convolutional codes is not only essential for cognitive radio, but also for non-cooperative context. This paper is dedicated to the blind identiscation of rate k/n convolutional encoders in a noisy context based on Walsh-Hadamard transformation and block matrix (WHT-BM). The proposed algorithm constructs a system of noisy linear equations and utilizes all its coefscients to recover parity check matrix. It is able to make use of fault-tolerant feature of WHT, thus providing more accurate results and achieving better error performance in high raw bit error rate (BER) regions. Moreover, it is more computationally efscient with the use of the block matrix (BM) method.

Parameter estimation of block and helical scan interleavers in the presence of bit errors

In data communication and digital storage applications, interleaver, which rearranges the encoded data stream, follows the channel encoder to prevent burst errors. In general, forward error correction (FEC) codes provide protection against randomly distributed errors, whereas interleaver provides protection against error bursts. To de-interleave the interleaved data, accurate information about the interleaver parameters is required at the receiver. However, in non-cooperative context, the parameters are either not known or only partially known at the receiver. Therefore, blind/semi-blind estimation of interleaver parameters is mandatory at the receiver in order to successfully de-interleave the interleaved data. In this context, this paper proposes innovative algorithms for the estimation of interleaver parameters such as interleaver period (or size of the interleaver matrix), number of rows and columns of the interleaver matrix, and helical array step size in the presence of bit errors provided the knowledge of type of interleaver is known at the receiver. In addition, it has been assumed that the block and helical scan interleavers follow a convolutional encoder.

Probabilistic pragmatics, or why Bayes’ rule is probably important for pragmatics

Pragmatics is about language use in context. This involves theorizing about speakers’ choices of words and listeners’ ways of interpreting. More often than not, this also involves a certain amount of noise and uncertainty: speakers and listeners may not know exactly what the relevant contextual parameters are, they may make mistakes, believe that their interlocutor is uncertain and possibly prone to err, etc. We believe that taking this picture seriously can, despite its apparent messiness, inspire a stringent formal approach to pragmatics that lends itself to precise empirical testing. We call it probabilistic pragmatics here, to emphasize the role that probabilities play in it. But it contains much more. In the following, we try to sketch its main characterizing features in relation to other approaches and give some example applications. We argue that probability models are the natural and most practicable tool for modeling the richness of pragmatic phenomena which are affected by many unknown contextual factors. Sections 2 and 3 characterize probabilistic pragmatics. Section 2 discusses different levels of analysis in pragmatic theory, so as to contrast probabilistic pragmatics with alternative approaches. Section 3 discusses key properties of probabilistic pragmatics. Sections 4, 5 and 6 sketch examples of applications. Section 4 introduces a baseline model for reasoning about referential expressions to demonstrate how the probabilistic modeling, inspired by classical pragmatic theory, can be fit to experimental data. Section 5 exemplifies further ways in which probabilistic pragmatics can shed light on gradient patterns in empirical data. The leading example for illustration is that of scalar implicature. Section 6 argues that considering (multiple levels of) gradient subjective contextual uncertainty, as captured by a probability distribution, is essential to understanding indirect speech acts. This section demonstrates how explicit representations, inspired from game theory, of interlocutors’ preferences and likely dialogue moves help tackle indirectness of speech in non-cooperative contexts.

On the Analytical Solution of Rank Problem in the Convolutional Code Identification Context

The rank-based method is very effective to identify the convolutional code parameters in a non-cooperative context. This method cuts the received sequence up into vectors of length $l$ to form the rows of matrix $C^{(l)}$ . It is well known that there is a relationship between the code parameters and the rank behavior of $C^{(l)}$ . However, finding the general form of this relation is a challenging open problem. In this letter, we formulate and solve this problem for rate $\frac{k}{n}$ convolutional codes. Our analytical solution show that the rank behavior is highly dependent on the dual code constraint lengths. Moreover, it is proved that the applied experimental rank relation in prior works is true only for rate $\frac{n-1}{n}$ codes.

Blind Recognition of (n−1)/n Rate Punctured Convolutional Encoders in a Noisy Environment

Blind recognition of error-correcting code is an essential problem to decode intercepted data. In this paper, a method dedicated to the blind recognition of punctured convolutional encoders is presented. The blind recognition of such encoders is of great significance, because convolutional encoders are embedded in most digital transmission systems where the puncturing principle is used to increase the code rate. After a brief review of the principle of puncturing codes, a method mainly based on the Walsh-Hadamard transform is presented for blind recognition of both the mother code and the puncturing pattern when the received bits are erroneous. Compared to existing techniques, our algorithm has advantages of robustness and efficiency. Experiments are conducted to illustrate the performances of this new blind recognition method. Index Terms—Blind Recognition, convolutional code, punctured pattern, walsh-hadamard transform. I. INTRODUCTION Most digital transmission systems are encoded to enhance the communication quality. Redundancy bits are appended in the informative binary data stream to better withstand channel noise. In a non-cooperative context, in order to perform information analysis, it is necessary to decode intercepted data with no knowledge of the parameters of the code. In this case, the blind recognition problem needs to be addressed. Convolutional codes are a class of important codes due to their flexibility in code length, soft decodability, short decoding delay and their role as component codes in parallelly serially concatenated codes. Puncturing allows convolutional codes to flexibly change rates and is widely used in applications where high code rates are required and where rate adaptivity is desired. In this paper, we only focus on communications encoded with punctured convolutional codes. This article is not the first to deal with blind recognition of convolutional codes in a noisy environment. A systematic algebraic approach for the reconstruction of linear and convolutional error

A low complexity algorithm of blind estimation of convolutional interleaver parameters

Interleaving plays an important role in many wireless communication systems, which is used to cope with burst errors and improve the reliability of data transmission. In this paper, we propose a new method to blindly estimate the parameters of convolutional interleaver in a non-cooperative context. The proposed method exploits both the property of interleaved stream and the deinterleaving technique. The product of the depth and width of the interleaver is firstly determined by analyzing interleaved data stream. Then the interleaved data stream is let to pass through a series of reconstructed deinterleavers. Thus the delay, the depth and width of the convolutional interleaver can be estimated by analyzing the deinterleaved data streams. Compared with other algorithms, the proposed method significantly reduces the searching scope and improves computational efficiency. Simulation results show that the algorithm has good performance even if errors exist.

Blind Channel Estimation for STBC Systems Using Higher-Order Statistics

This paper describes a new blind channel estimation algorithm for Space-Time Block Coded (STBC) systems. The proposed method exploits the statistical independence of sources before space-time encoding. The channel matrix is estimated by minimizing a kurtosis-based cost function after Zero-Forcing equalization. In contrast to subspace or Second-Order Statistics (SOS) approaches, the proposed method is more general since it can be employed for the general class of linear STBCs including Spatial Multiplexing, Orthogonal, quasi-Orthogonal and Non-Orthogonal STBCs. Furthermore, unlike other approaches, the method does not require any modification of the transmitter and, consequently, is well-suited for non-cooperative context. Numerical examples corroborate the performance of the proposed algorithm.

Two-stage cooperation model with input freely distributed among the stages

Shared flow has been widely used in production scenarios where inputs and outputs are shared among various activities. In DEA literature, shared flow represents situations that DMUs are divided into different components that require common resources or produce goods or services obtained through collaboration among them. The objective of this paper is to offer an approach for studying shared flow in a two-stage production process in series, where shared inputs can be freely allocated among different stages. A product-form cooperative efficiency model is proposed to illustrate the overall efficiency of the DMU, and the relationship between the stages. First, we use a game-theory framework to decide the upper and lower bounds of the efficiencies of the stages in a non-cooperative context. A heuristic is suggested to transform the non-linear model into a parametric linear one, which is then used to solve the cooperative model. The model is justified by a numerical evaluation of bank performances.

Teaching Competition in Professional Sports Leagues

In recent years, there has been some dispute over the appropriate way to model decision making in professional sports leagues. In particular, Szymanski and Késenne (2004) argue that formulating the decision-making problem in a noncooperative game leads to radically different conclusions about the nature of competition in sports leagues. The author describes a simulation model that can be used in a classroom to demonstrate how competition works in a noncooperative context. More generally, in simulation exercises, students typically gravitate quickly toward the Nash equilibrium, making this a simple and powerful way to introduce them to the concept. The supporting Excel spreadsheet used to conduct the game can be downloaded from the author's personal Web page, http://www.cass.city.ac.uk/faculty/s.szymanski/sports-league-simulation-blank.xls.

Blind multiuser spreading sequences estimation algorithm for the direct-sequence code division multiple access signals

By combining the multiple signal classification (MUSIC)-based algorithm, proposed by Haghighat and Soleymani, and the segmentation-based idea, a blind multiuser spreading sequences estimation algorithm for the direct-sequence code division multiple access (DS-CDMA) signals is presented in this study. The main idea of the proposed algorithm is to divide the spreading sequences into short-time segments so as to recover them in a sequential procedure. First, the received signal samples are divided into K collections of temporal windows. Each collection is corresponding to a group of short-time segments of the spreading sequences. Secondly, by performing a sub-segmentation-based estimation scheme on the first collection of temporal windows, the first short-time segments of all the users' spreading sequences are extracted. Finally, with the help of the segments acquired in advance, the remaining segments of the spreading sequences can be recovered chip-by-chip through K−1 subspace projection loops. This algorithm can be applied not only to the synchronous short-code (SC) DS-CDMA signals, but also to the long-code (LC) DS-CDMA signals in non-cooperative contexts, even with minus SNR. Simulations are presented to illustrate the performance of the proposed method. In addition, for the synchronous SC-DS-CDMA signals, the computational cost of the proposed algorithm is much lower than that of the MUSIC-based algorithm. Furthermore, a derivation for blind synchronisation of the synchronous SC-DS-CDMA signals is also presented.

Blind classification of the short-code and the long-code direct sequence spread spectrum signals

Direct sequence spread spectrum (DSSS) signals are now widely used for communications. According to the relationship between spreading factor and code length, the DSSS signals can be divided into two types: the short-code (SC) and the long-code (LC) DSSS. For the cooperative receivers, the above type information is known in advance. However, under the non-cooperative contexts, such information becomes one of the unknown parameters of interest. To extract this information, a blind algorithm that is based on the second-order statistics of the matrix norms of the signal correlation matrices is proposed in this study. A set of correlation matrices is constructed from the received signal samples following which the matrix norm of each correlation matrix is computed and normalised by the maximum. Then, by comparing the variance of the normalised matrix norms with a preset threshold, the type of the received DSSS signal can be identified. Simulation results verify the capability of the proposed method in various scenarios, such as multipath fading channel and multiple access interference.

Blind detection of interleaver parameters

Interleaving is a key component of many digital communication systems involving error correction schemes. It provides a kind of time diversity to protect the transmitted data against bursts of errors. Recently, interleavers have become an even more integral part of the code design itself, if we consider for example turbo and turbo-like codes. In a non-cooperative context, a passive adversary has to solve the problem of estimating the interleaver parameters. In this paper, we propose an algorithm that is able to estimate the size, the starting position (frame synchronization) of the interleaver, and some information about the interleaver function. This is accomplished blindly at the output of a binary symmetric channel (BSC). Moreover, an improvement of the proposed method is introduced when a soft information on the decided bits is available. A theoretical analysis of the proposed technique is given. This allows us to express the optimal detection threshold and the theoretical probability of detection. This analysis gives us insight on the behavior of our method and allows us to improve our algorithm to get better performance. Some experimental results are run to validate the probability of success of our algorithm.

Improved blind-spreading sequence estimation algorithm for direct sequence spread spectrum signals

Direct sequence spread spectrum (DSSS) signals are now widely used for communications. DSSS transmitters use a spreading sequence to modulate the baseband signal before transmission. A receiver which does not know the spreading sequence cannot demodulate the signal. Burel and Bouder introduced an eigenanalysis-based blind-spreading sequence estimation algorithm, which performs well even when the received signal is far below the noise level. However, this algorithm does not applied to the long-code DSSS signals. An improved blind-spreading sequence estimation algorithm is presented. This algorithm is based on segmentation. The received signal is divided into K collections of temporal windows, from which K covariance matrices can be computed. The authors prove that K short-time segments of the spreading waveform can be recovered from these matrices using the eigenanalysis technique. Then, the spreading sequence can be reconstructed by concatenating these short-time segments. Simulations show that the proposed algorithm can provide a good estimation for long- or short-code DSSS signals in non-cooperative context, even with low signal-to-noise ratio. Furthermore, for short-code DSSS signals, the computational cost of the proposed algorithm is much lower than that of the original algorithm.

Multi-channel blind deconvolution algorithm for multiple-input multiple-output DS/CDMA system

Direct sequence spread spectrum transmission can be realized at low SNR, and has low probability of detection. It is aly problem how to obtain the original users' signal in a non-cooperative context. In practicality, the DS/CDMA sources received are linear convolute mixing. A more complex multichannel blind deconvolution MBD algorithm is required to achieve better source separation. An improved MBD algorithm for separating linear convolved mixtures of signals in CDMA system is proposed. This algorithm is based on minimizing the average squared cross-output-channel-correlation. The mixture coefficients are totally unknown, while some knowledge about temporal model exists. Results show that the proposed algorithm can bring about the exactness and low computational complexity.

Multichannel Convolution Blind Separation Algorithm for MIMO DSSS/CDMA System

Direct-sequence spread-spectrum (DSSS)/code division multiple access (CDMA) transmissions are now widely used for secure communications and multiple access. They can be transmitted at a low signal-to-noise ratio, and have a low probability of interception and capture. How to obtain the original users' signal in a noncooperative context or estimate the spreading sequence in blind conditions is a very difficult problem. Most of the signal sources are assumed to be instantaneous mixtures. In fact, the received CDMA signals are linearly convoluted. A more complicated blind source separation (BSS) algorithm is required to achieve better source separation. In this paper, a new BSS algorithm is proposed for separating linearly convolved signals in CDMA systems when the mixture coefficients of the signal and channel response are totally unknown, but some knowledge about the temporal model does exist. This algorithm is based on minimizing the squared cross-output-channel-correlation criterion. The simulation results show the effectiveness of the algorithm in the blind detection of DS-CDMA signals.

FMCW radar signal analysis by time-frequency representations and correlation techniques

Two LPI signal detection and parameter estimation methods are analyzed in a non-cooperative context. The first method is based on the time-frequency signal analysis along with pattern recognition techniques. The second one is based on the estimation of a periodic autocorrelation function. Frequency modulation continuous wave (FMCW) signals are analyzed as a class of waveforms often used in LPI radars. Experimentally obtained signals are used in tests. An experiment with a real FMCW signal transceiver and a receiver is carried out and the obtained signal analysis results are presented.

Detection and parameter estimation of spread spectrum radar signals in non-cooperative context

In this paper the specialties in interception of spread spectrum radar signals are exposed in regard interception of conventional pulsed radar signals. According to [1-3] and exposed examples it follow that future intercept receivers need 10-30 db selectivity enhancement to cope with modern LPI radar systems. One way is to use narrow beam, high gain antennas. Another way is trough signal processing. There is a review of existing algorithms in detection and parameter estimation of spread spectrum radar signals in non-cooperative context. Coherent methods and cross-correlation methods are put in the focus.

Non-linear game models for large-scale network bandwidth management

The design of dynamic Label-Switched Paths (LSP’s) in MultiProtocol Label Switched (MPLS) networks is an NP-hard optimization problem. An LSP is a logical path between two nodes in the network. This path has a pre-reserved amount of bandwidth that defines its size. The LSP design problem consists of determining the number of these logical links and configuring the physical path and the size of each LSP. This paper presents an optimization model based on game theory. In this approach, connection requests are modeled as competitive players in a noncooperative game context. The transport network bandwidth constitutes the resource for which optimization is sought. The outcome of this optimization is a set of LSPs upon which the competing connections are routed.