nils-herrmann/clean_scirepeval huggingface dataset:
Outage analysis of multi-node amplify-and-forward relay networks

In this paper, we consider the outage probability analysis of multi-node amplify-and-forward relay network with N relay nodes helping the source. We consider a system in which each relay node amplifies the source signal only. We obtain an approximation for the outage probability which is tight at high signal-to-noise ratio (SNR). This tight outage approximation shows that the system can achieve a maximum diversity of order N+1. For the case of N = 1, our approach gives the same result obtained previously by Laneman et al. for the single relay scenario
Radio shot: Through-the-wall human recognition

In this work, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity WiFi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from WiFi signals using time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has significant number of multipaths, which can be altered by human body interferences, the proposed system can recognize individuals in the TR domain without line-of-sight path. We built a prototype of the TR human identification system using standard WiFi chipsets with 3 × 3 MIMO transmission. The performance of the proposed system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen of individuals using a single transmitter and receiver pair.
Joint source and channel matching with channel estimation for robust image transmission over wireless channel

and channel coding (JSCC), multicarrier modulation (MCM) and diversity are some key techniques for multimedia transmission over wireless channels. We first investigate the channel estimation problem for the MCM system with multiple transmitter antennas. A model based channel estimation approach is proposed to identify the multiple channels simultaneously. We then apply the model based channel estimation in a joint source and channel matching (JSCM) scheme for the robust progressive image transmission. It is shown that the channel estimation affects not only the image decoding at the receiver but also the rate allocation at the transmitter. It is important to jointly consider the channel estimation and JSCM scheme to improve the quality of image transmission.
Convergence analysis of an adaptive channel estimation algorithm using cyclic prefix in multicarrier modulation systems

Multicarrier modulation (MCM) gains growing interests in high data rate communications for both wire and wireless environment. The channel estimation is a crucial aspect in MCM systems. A novel channel estimation algorithm (Wang and Liu, 1999) exploiting the channel information contained in the cyclic prefix outperforms the existing adaptive equalization scheme (Chow et al., 1991). In this paper theoretical analysis is carried on the convergence of the adaptive channel estimation algorithm considering both channel noise and decision error. We prove that the algorithm is guaranteed to converge with proper loading. We also analyze several factors that affect the convergence rate such as data structure and loading function. Computer simulation shows that our analytical results are quite close to the simulation.
Optimizing Rekeying Cost for Contributory Group Key Agreement Schemes

Although a contributory group key agreement is a promising solution to achieve access control in collaborative and dynamic group applications, the existing schemes have not achieved the performance lower bound in terms of time, communication, and computation costs. In this paper, we propose a contributory group key agreement that achieves the performance lower bound by utilizing a novel logical key tree structure, called PFMH, and the concept of phantom user position. In particular, the proposed scheme only needs O(1) rounds of the two-party Diffie-Hellman (DH) upon any single-user join event and O(log n) rounds of the two-party DH upon any single-user leave event. Both the theoretical bound analysis and simulation show that the proposed scheme achieves a lower rekeying cost than the existing tree-based contributory group key agreement schemes.
Modulation forensics for wireless digital communications

Modulation forensics is to detect the modulation type in wireless communications without any prior information. It finds both military and civilian applications such as surveillance and cognitive radio. It is a challenging task, especially in a non-cooperative environment, as no prior information on the incoming signal is available at the receiver. In this paper, we investigate the modulation forensics of linear digital modulations and space-time orthogonal code in slowly varying frequency-selective fading channels. With unknown channel vector, and phase distortion at the receive-side, we derive a composite test consisting second-moment nonlinearity and maximum likelihood test, and discuss the performance and forensic system confidence measure. It is shown that the proposed algorithm achieves almost perfect identification of the space-time coding, and high accuracy rate of modulation type detection.
Computerized radiographic mass detection. I. Lesion site selection by morphological enhancement and contextual segmentation

This paper presents a statistical model supported approach for enhanced segmentation and extraction of suspicious mass areas from mammographic images. With an appropriate statistical description of various discriminate characteristics of both true and false candidates from the localized areas, an improved mass detection may be achieved in computer-assisted diagnosis (CAD). In this study, one type of morphological operation is derived to enhance disease patterns of suspected masses by cleaning up unrelated background clutters, and a model-based image segmentation is performed to localize the suspected mass areas using a stochastic relaxation labeling scheme. We discuss the importance of model selection when a finite generalized Gaussian mixture is employed, and use the information theoretic criteria to determine the optimal model structure and parameters. Examples are presented to show the effectiveness of the proposed methods on mass lesion enhancement and segmentation when applied to mammographical images. Experimental results demonstrate that the proposed method achieves a very satisfactory performance as a preprocessing procedure for mass detection in CAD.
Multiband uwb system performance with random-clustering multipath-rich fading channels

This paper provides a novel performance analysis for UWB systems that successfully captures the unique multipath-rich property and multipath-clustering phenomenon of UWB channels. Using the Saleh-Valenzuela model, we characterize the pairwise error probability and the outage probability for UWB systems employing multiband OFDM based on the cluster arrival rate, the ray arrival rate within a cluster, and the cluster and ray decay factors. Furthermore, the effect of random-clustering phenomenon on the performance of UWB-MIMO systems is analyzed. The theoretical results reveal that regardless of the clustering behavior of UWB channels, the diversity gain can be improved by increasing the number of jointly encoded subcarriers, the number of jointly encoded OFDM symbols, or the number of antennas. The coding gain on the other hand, depends heavily on the cluster-arriving channels. Extensive simulation results are provided to support the theoretical analysis
Dynamic Chinese Restaurant Game in cognitive radio networks

In a cognitive radio network with mobility, secondary users can arrive at and leave the primary users' licensed networks at any time. After arrival, secondary users are confronted with channel access under the uncertain primary channel state. On one hand, they have to estimate the channel state, i.e., the primary users' activities, through performing spectrum sensing and learning from other secondary users' sensing results. On the other hand, they need to predict subsequent secondary users' access decisions to avoid competition when accessing the ”spectrum hole”. In this paper, we propose a Dynamic Chinese Restaurant Game to study such a learning and decision making problem in cognitive radio networks. We introduce a Bayesian learning based method for secondary users to learn the channel state and propose a Multi-dimensional Markov Decision Process based approach for secondary users to make optimal channel access decisions. Finally, we conduct simulations to verify the effectiveness and efficiency of the proposed scheme.
User participation game in collaborative filtering

Collaborative filtering (CF) is widely used in recommendation systems. A user can get good recommendations only when both the user himself/herself and other users actively participate, i.e. providing sufficient rating data. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a trade-off between the rating cost and recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated rating data are used for recommendation, we design a behavior rule which allows users to achieve a SE via iteratively rating items. Experimental results based on real data demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing too many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.
Wireless Network Association Game With Data-Driven Statistical Modeling

The explosion in demand for wireless data services in recent years has triggered pervasive deployment of wireless networks. How to associate to one of the wireless networks in the best interest of a user is an essential problem to mobile computing. In this paper, we analyze a data set of wireless LAN traces collected from campus networks, from which we observe that the user arrival distribution is approximately Poisson distributed; the session time and the waiting time to switch network can be approximated by exponential distributions. Based on the data analysis, we formulate a wireless access network association game as a multidimensional Markov decision process with negative network externality, where the best response strategy is an approximate Nash equilibrium. A modified value iteration algorithm is proposed to search the best response strategy profile. Applying the proposed algorithm to the data-driven stochastic model, the best response strategy is shown to achieve a better individual expected utility while satisfying the individual rationality, and attain a near-optimal social welfare performance compared to other strategies such as the centralized method and the greedy algorithm.
A novel systolic array processor for MVDR beamforming

A fully pipelined systolic array for computing the minimum variance distortionless response (MVDR) was first proposed by J.G. McWhirter and T.J. Shepherd (1989). The fundamental concept is to fit the MVDR beamforming to the nonconstrained recursive least-squares (RLS) minimization. A different approach is proposed for MVDR beamforming which does not require the nonconstrained RLS minimization; then a fully parallel and pipelined systolic array is presented for the newly proposed algorithm, and finally the similarity and differences between both MVDR beamforming designs are described.<<ETX>>
A new loading algorithm for image transmitting over spectrally shaped channels: combined source coding and multicarrier modulation approach

A combined source coding and multicarrier modulation scheme for transmitting image over noisy channels is proposed. The source image is divided into several layers which are transmitted in parallel, each occupying a number of subchannels. For spectrally shaped channels, the existing loading algorithms try to remove the property that channel gain and noisy variance are quite different across the subchannels, and use the power and rate allocation to provide an uniform error performance at the usable subchannels. In contrast, we deem the above property as an advantage and develop a new loading algorithm to provide unequal error protection as well as achieve better power efficiency. For subband coded images transmission, simulations show that the proposed algorithm achieves significant performance improvement, especially, 4-10 dB improvement over the existing algorithms for typical spectrally shaped channels.
Wireless Access Network Selection Game with Negative Network Externality

Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is to study the rational strategy considering the negative network externality, i.e, the influence of subsequent users' decisions on an individual's throughput due to the limited available resources. In this work, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modelled as a multi-dimensional Markov Decision Process (MDP). A modified value iteration algorithm is proposed to efficiently obtain the optimal decision rule with a simple threshold structure, which enables us to reduce the storage space of the strategy profile. We further investigate the mechanism design problem with incentive compatibility constraints, which enforce the networks to reveal the truthful state information. The formulated problem is a mixed integer programming problem which in general lacks an efficient solution. Exploiting the optimality of substructures, we propose a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multi-network scenario, the proposed algorithm can outperform the heuristic greedy approach in a polynomial-time complexity. Finally, simulation results are shown to validate the analysis and demonstrate the effectiveness of the proposed algorithms..
Impact of Social Network Structure on Multimedia Fingerprinting Misbehavior Detection and Identification

Users in video-sharing social networks actively interact with each other, and it is of critical importance to model user behavior and analyze the impact of human factors on video sharing systems. In video-sharing social networks, users have access to extra resources from their peers, and they also contribute their own resources to help others. Each user wants to maximize his/her own payoff, and they negotiate with each other to achieve fairness and address this conflict. However, some selfish users may cheat to their peers and manipulate the system to maximize their own payoffs, and cheat prevention is a critical requirement in many social networks to stimulate user cooperation. It is of ample importance to design monitoring mechanisms to detect and identify misbehaving users, and to design cheat-proof cooperation stimulation strategies. Using video fingerprinting as an example, this paper analyzes the complex dynamics among colluders during multiuser collusion, and explores possible monitoring mechanisms to detect and identify misbehaving colluders in multiuser collusion. We consider two types of colluder networks: one has a centralized structure with a trusted ringleader, and the other is a distributed peer-structured network. We investigate the impact of network structures on misbehavior detection and identification, propose different selfish colluder identification schemes for different colluder networks, and analyze their performance. We show that the proposed schemes can accurately identify selfish colluders without falsely accusing others even under attacks. We also evaluate their robustness against framing attacks and quantify the maximum number of framing colluders that they can resist.
Joint beamforming and handoff for traffic improvements in wireless networks

A cellular wireless network with beamforming capabilities at the receiver is considered which allows two or more transmitters to share the same channel for communicating with the base station. A novel approach is introduced which combines the effects of the digital signal processing at the physical layer along with the network layer protocol on the overall queuing model of a cell. From this effective cell model, we find the closed form solutions for blocking probabilities of the calls and total carried traffic in the system. Our analytical as well as numerical results show that adaptive beamforming at the receiver reduces the blocking probability of the calls and increases the total carried traffic in the system.
Source-Channel-Cooperation Tradeoffs for Adaptive Coded Communications [Transactions Papers]

Transmit power limitations and quality-impairing channels present key challenges to wireless communications. A possible solution to these problems is the application of user-cooperation techniques so as to improve link quality. This paper studies the tradeoffs involved in combining user cooperation with practical source and channel coding in systems featuring rate-adaptive source and channel coding of conversational traffic. Performance is measured through the D-SNR curve, which measures the relation between end-to-end distortion and channel signal-to-noise ratio (SNR). The D-SNR curve is accurately characterized as a linear function in log-log scales. Also, it is shown that the tradeoffs involved in combining amplify-and-forward or decode-and-forward cooperation with source and channel coding translates into cooperative schemes showing a decrease in distortion at approximately half the rate as non-cooperative schemes but with larger coding gain. Because of this, the studied non-cooperative schemes show better performance only at high SNR. In addition, the D-SNR characterization is used to compare amplify-and-forward and decode-and-forward cooperation for channel codes of different strength and to study the effects of source codec efficiency, where it is shown that diversity gain is reduced proportionally to the source codec loss of efficiency.
Game-Theoretic Pricing for Video Streaming in Mobile Networks

Mobile phones are among the most popular consumer devices, and the recent developments of 3G networks and smart phones enable users to watch video programs by subscribing data plans from service providers. Due to the ubiquity of mobile phones and phone-to-phone communication technologies, data-plan subscribers can redistribute the video content to nonsubscribers. Such a redistribution mechanism is a potential competitor for the mobile service provider and is very difficult to trace given users' high mobility. The service provider has to set a reasonable price for the data plan to prevent such unauthorized redistribution behavior to protect or maximize his/her own profit. In this paper, we analyze the optimal price setting for the service provider by investigating the equilibrium between the subscribers and the secondary buyers in the content-redistribution network. We model the behavior between the subscribers and the secondary buyers as a noncooperative game and find the optimal price and quantity for both groups of users. Based on the behavior of users in the redistribution network, we investigate the evolutionarily stable ratio of mobile users who decide to subscribe to the data plan. Such an analysis can help the service provider preserve his/her profit under the threat of the redistribution networks and can improve the quality of service for end users.
Joint optimal power control and beamforming in wireless networks using antenna arrays

The interference reduction capability of antenna arrays and the power control algorithms have been considered separately as means to increase the capacity in wireless communication networks. The minimum variance distortionless response beamformer maximizes the signal-to-interference-and-noise ratio (SINR) when it is employed in the receiver of a wireless link. In a system with omnidirectional antennas, power control algorithms are used to maximize the SINR as well. We consider a system with beamforming capabilities in the receiver, and power control. An iterative algorithm is proposed to jointly update the transmission powers and the beamformer weights so that it converges to the jointly optimal beamforming and transmission power vector. The algorithm is distributed and uses only local interference measurements. In an uplink transmission scenario, it is shown how base assignment can be incorporated in addition to beamforming and power control, such that a globally optimum solution is obtained. The network capacity and the saving in mobile power are evaluated through numerical study.
Network economics in cognitive networks

Cellular networks are confronted with unprecedented pressure to accommodate the dramatically increasing demands of mobile Internet. This phenomenon is principally due to the restricted spectrum resource and low-efficiency spectrum management. Recently, cognitive radio technology has been corroborated to significantly improve spectrum utilization efficiency. In order to enhance the spectrum management efficiency in cellular networks, the concept of “cognitive cellular networks” was introduced. It has been realized that the network architecture, algorithms, and protocols cannot be simply designed without considering the socio-economic aspects involved. In this article, we consider the economic issues in cognitive cellular networks from the perspectives of game theoretic modeling and mechanism design. Specifically, both the simultaneous and sequential dynamic spectrum access games are investigated, respectively. The mechanism design related issues (i.e., spectrum auction, pricing, and contract) to ensure desired outcomes are introduced with detailed applications in cognitive cellular networks. The goal of this article is to provide an overview to understand the motivation, problem formulation, methodology, and solutions of the economic issues in cognitive cellular networks.
An ADMM approach to dynamic sharing problems

In this paper, we study a dynamic version of the sharing problem, in which a dynamic system cost function composed of time-variant local costs of subsystems and a shared time-variant cost of the whole system is minimized. A dynamic alternating direction method of multipliers (ADMM) is proposed to track the varying optimal points of the dynamic optimization problem in an online manner. We analyze the convergence properties of the dynamic ADMM and show that, under several standard technical assumptions, the iterations of the dynamic ADMM converge linearly to some neighborhoods of the time-varying optimal points. The sizes of these neighborhoods depend on the drifts of the dynamic objective functions: the more drastically the dynamic objective function evolves across time, the larger the sizes of these neighborhoods. We also investigate the impact of the drifts on the steady state convergence behaviors of the dynamic ADMM. Finally, numerical results are presented to corroborate the effectiveness of the proposed dynamic ADMM.