nils-herrmann/clean_scirepeval huggingface dataset:
A cheat-proof game theoretic demand response scheme for smart grids

While demand response has achieved promising results on making the power grid more efficient and reliable, the additional dynamics and flexibility brought by demand response also increase the uncertainty and complexity of the centralized load forecast. In this paper, we propose a game theoretic demand response scheme that can transform the traditional centralized load prediction structure into a distributed load prediction system by the participation of customers. Moreover, since customers are generally rational and thus naturally selfish, they may cheat if cheating can improve their payoff. Therefore, enforcing truth-telling is crucial. We prove analytically and demonstrate with simulations that the proposed game theoretic scheme is cheat-proof, i.e., all customers are motivated to report and consume their true optimal demands and any deviation will lead to a utility loss. We also prove theoretically that the proposed demand response scheme can lead to the solution that maximizes social welfare and is proportionally fair in terms of utility function. Moreover, we propose a simple dynamic pricing algorithm for the power substation to control the total demand of all customers to meet the target demand curve. Finally, simulations are shown to demonstrate the efficiency and effectiveness of the proposed game theoretic algorithm.
Game-theoretic strategies and equilibriums in multimedia fingerprinting social networks

Multimedia social network is a network infrastructure in which the social network users share multimedia contents with all different purposes. Analyzing user behavior in multimedia social networks helps design more secured and efficient multimedia and networking systems. Multimedia fingerprinting protects multimedia from illegal alterations and multiuser collusion is a cost-effective attack. The colluder social network is naturally formed during multiuser collusion with which colluders gain reward by redistributing the colluded multimedia contents. Since the colluders have conflicting interest, the maximal-payoff collusion for one colluder may not be the maximal-payoff collusion for others. Hence, before a collusion being successful, the colluders must bargain with each other to reach agreements. We first model the bargaining behavior among colluders as a noncooperative game and study four different bargaining solutions of this game. Moreover, the market value of the redistributed multimedia content is often time-sensitive. The earlier the colluded copy being released, the more the people are willing to pay for it. Thus, the colluders have to reach agreements on how to distribute reward and risk among themselves as soon as possible. This paper further incorporates this time-sensitiveness of the colluders' reward and studies the time-sensitive bargaining equilibrium. The study in this paper reveals the strategies that are optimal for the colluders; thus, all the colluders have no inventive to disagree. Such understanding reduces the possible types of collusion into a small finite set.
Power controlled channel allocation for multiuser multiband UWB systems

Emerging ultra-wideband (UWB) technology offers a great potential for the design of high-speed short-range communications. However, in order for a UWB device to coexist with other devices, the transmitted power level of UWB is strictly limited by the FCC spectral mask. Such limitation poses a significant design challenge to any UWB system. An efficient management of the limited power is thus a key feature to fully exploit the advantages of UWB. In this paper, a cross layer multiuser multiband UWB scheme is proposed to obtain the optimal subband and power allocation strategy. Optimization criteria involve minimization of power consumption under the constraints on the packet error rate, the data rate, and the FCC limit. To ensure the system feasibility in variable channel conditions, an algorithm to jointly manage the rate assignment of UWB devices, subband allocation, and power control is proposed. A computationally inexpensive suboptimal approach is also developed to reduce the complexity of the problem, which is found to be NP hard. Simulation results under UWB channel model specified in the IEEE 802.15.3a standard show that the proposed algorithm achieves comparable performances to those of the complex optimal full search approach, and it can save up to 61% of transmit power compared to the current multiband scheme in the standard proposal. Moreover, the proposed algorithm can obtain the feasible solutions adaptively when the initial system is not feasible for the rate requirements of the users.
Throughput maximization using adaptive modulation in wireless networks with fairness constraint

In multi-access wireless communication networks, cochannel interference and timing varying nature of the channel are two important detrimental effects to reduce the network throughput. Adaptive modulation techniques have the potential to substantially increase the spectrum efficiency by providing the users with different levels of throughput according to the channel conditions. In this paper, we study how to adaptively allocate each user's throughput in the multi-access networks. We optimize the network throughput, while each user's time average throughput is kept as a constant to ensure fairness. We solve the problem in two steps. First, we find the throughputs range that each user can select at different time to ensure the fairness. Second, we find the best throughput allocation to different users at each time to generate the maximum overall throughput. From the simulation results, it shows that our scheme increases overall network throughput about 10%, compared with the previous works.
Optimal power allocation strategy against jamming attacks using the Colonel Blotto game

Cognitive radio technologies have become a promising approach to increase the efficiency of spectrum utilization. Although cognitive radio has been intensively studied in recent years, only a few works have discussed security aspects. In this paper, we focus on the jamming attack, one of major threats to cognitive radio networks, where a malicious user wants to jam the communications of secondary users by injecting interference. Aware of the absence of several primary users and the presence of a malicious user, a secondary user can allocate power to those fallow bands with a randomized strategy, in hope of alleviating the damage caused by the malicious user. We model this scenario into a two-player zero-sum game, and derive its unique Nash Equilibrium under certain conditions using the Colonel Blotto game approach, which provides a minimax strategy that the secondary user should adopt in order to minimize the worst-case damage caused by the malicious user. Simulation results are presented to verify the performance.
Static and dynamic convergence behavior of adaptive blind equalizers

This paper presents a theoretical analysis of the static and dynamic convergence behavior for a general class of adaptive blind equalizers. We first study the properties of prediction error functions of blind equalization algorithms, and then, we use these properties to analyze the static and dynamic convergence behavior based on the independence assumption. We prove in this paper that with a small step size, the ensemble average of equalizer coefficients will converge to the minimum of the cost function near the channel inverse. However, the convergence is not consistent. The correlation matrix or equalizer coefficients at equilibrium are determined by a Lyapunov equation. According to our analysis results, for a given channel and stepsize, there is an optimal length for an equalizer to minimize the intersymbol interference. This result implies that a longer-length blind equalizer does not necessarily outperform a shorter one, which is contrary to what is conventionally conjectured. The theoretical analysis results are confirmed by computer simulations.
The subband modulation: a joint power and rate allocation framework for subband image and video transmission

A new approach of reliable image and video transmission over noisy channels is proposed. For subband decomposed image and video, combined source coding and channel modulation design can achieve high compression efficiency and preferable quality. Further performance gain is obtained by multiresolution modulation as well as a bit remapping scheme that assigns efficient mapping from each source codeword to channel modulation points. We show that the combined source coding and modulation design outperforms conventional approaches, which design source coding and modulation separately. A simple channel distortion approximation is derived by applying a bit-remapping scheme, which allows the power allocation to be employed to further enhance the performance. Compared to the joint source and channel coding with a binary phase shift keying modulation system and fixed modulation with the one-to-one intelligent mapping system, the proposed system performs better in a middle-range signal-to-noise ratio and low channel bandwidth. The simulation is carried out on additive white Gaussian noise channels.
A MIMO-OFDM channel estimation approach using time of arrivals

Channel estimation is critical in designing multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for coherent detection and decoding. We propose a channel estimation scheme based on time of arrivals (TOAs) estimation. The TOAs are first determined using a variant of probabilistic data association (PDA) and employing the minimum description length principle, then the PDA is augmented by group decision feedbacks to refine the TOA estimates. With the channels of typical urban and hilly terrain delay profiles, simulation results, compared with the alternating projection (AP) and the Fourier transform based methods, show that the proposed scheme provides a promising accuracy-complexity trade-off for MIMO OFDM channel estimation.
An anti-jamming stochastic game for cognitive radio networks

Various spectrum management schemes have been proposed in recent years to improve the spectrum utilization in cognitive radio networks. However, few of them have considered the existence of cognitive attackers who can adapt their attacking strategy to the time-varying spectrum environment and the secondary users' strategy. In this paper, we investigate the security mechanism when secondary users are facing the jamming attack, and propose a stochastic game framework for anti-jamming defense. At each stage of the game, secondary users observe the spectrum availability, the channel quality, and the attackers' strategy from the status of jammed channels. According to this observation, they will decide how many channels they should reserve for transmitting control and data messages and how to switch between the different channels. Using the minimax-Q learning, secondary users can gradually learn the optimal policy, which maximizes the expected sum of discounted payoffs defined as the spectrum-efficient throughput. The proposed stationary policy in the anti-jamming game is shown to achieve much better performance than the policy obtained from myopic learning, which only maximizes each stage's payoff, and a random defense strategy, since it successfully accommodates the environment dynamics and the strategic behavior of the cognitive attackers.
Trans-Modulation in Wireless Relay Networks

In this letter, we consider the trans-modulation design for the decode-and-forward relay networks. We propose to reassign the constellation points at the relay nodes to minimize the symbol error rate (SER) at the destination node. The proposed trans-modulation scheme can significantly improve the system SER performance without increasing the complexity of the system, especially when the relays are close to the source. For this case, improvements of about 2 dB for 16-QAM constellation and about 3 dB for 64-QAM constellation are achieved for the single-relay case.
Chinese Restaurant Game

In this letter, by introducing the strategic decision making into the Chinese restaurant process, we propose a new game, called Chinese Restaurant Game, as a new general framework for analyzing the individual decision problem in a network with negative network externality. Our analysis shows that a balance in utilities among the customers in the game will eventually be achieved under the strategic decision making process. The equilibrium grouping is defined to describe the predicted outcome of the proposed game, which can be found by a simple algorithm. The simulation results confirm that the rational customers in Chinese restaurant game automatically achieve a balance in loading in order to reduce the impact from the negative network externality.
Algorithm-based low-power and high-performance multimedia signal processing

Low power and high performance are the two most important criteria for many signal-processing system designs, particularly in real-time multimedia applications. There have been many approaches to achieve these two design goals at many different implementation levels ranging from very-large-scale-integration fabrication technology to system design. We review the works that have been done at various levels and focus on the algorithm-based approaches for low-power and high-performance design of signal processing systems. We present the concept of multirate computing that originates from filterbank design, then show how to employ it along with the other algorithmic methods to develop low-power and high-performance signal processing systems. The proposed multirate design methodology is systematic and applicable to many problems. We demonstrate that multirate computing is a powerful tool at the algorithmic level that enables designers to achieve either significant power reduction or high throughput depending on their choice. Design examples on basic multimedia processing blocks such as filtering, source coding, and channel coding are given. A digital signal-processing engine that is an adaptive reconfigurable architecture is also derived from the common features of our approach. Such an architecture forms a new generation of high-performance embedded signal processor based on the adaptive computing model. The goal of this paper is to demonstrate the flexibility and effectiveness of algorithm-based approaches and to show that the multirate approach is an effective and systematic design methodology to achieve low-power and high throughput signal processing at the algorithmic and architectural level.
Distributed Relay-Assignment Protocols for Coverage Expansion in Cooperative Wireless Networks

One important application of cooperative communications is to extend coverage area in wireless networks without increasing infrastructure. However, a crucial challenge in implementing cooperation protocols is how to select relay-source pairs. In this paper, we address this problem based on the knowledge of the users' spatial distribution which determines the channel statistics. We consider two scenarios at the destination node, when the receiver uses MRC and when no-MRC is used. First, we characterize the optimal relay location to minimize the outage probability. Then, we propose and analyze the performance of two schemes: a distributed nearest neighbor relay assignment in which users can act as relays, and an infrastructure-based relay-assignment protocol in which fixed relay nodes are deployed in the network to help the users forward their data. The outage probabilities of these two schemes are derived. We also derive universal lower bounds on the performance of relay-assignment protocols to serve as a benchmark for our proposed protocols. Numerical results reveal significant gains when applying the proposed simple distributed algorithms over direct transmission in terms of coverage area, transmit power, and spectral efficiency. At 1 percent outage probability, more than 200 percent increase in coverage area can be achieved, 7 dBW savings in the transmitted power, and the system can operate at 2 b/s/Hz higher spectral efficiency.
Harmonic variable-size dictionary learning for music source separation

Dictionary learning through matrix factorization has become widely popular for performing music transcription and source separation. These methods learn a concise set of dictionary atoms which represent spectrograms of musical objects. However, there is no guarantee that the atoms learned will be perceptually meaningful, particularly when there exists significant spectral and temporal overlap among the musical sources. In this paper, we propose a novel dictionary learning method that imposes additional harmonic constraints upon the atoms of the learned dictionary while allowing the dictionary size to grow appropriately during the learning procedure. When there is significant spectral-temporal overlap among the musical sources, our method outperforms popular existing matrix factorization methods as measured by the recall and precision of learned dictionary atoms.
An indirect reciprocity game theoretic framework for dynamic spectrum access

In this paper, we propose a spectrum access framework to address the efficient allocation of channels of a base station (BS) by stimulating cooperation between primary users (PUs) and secondary users (SUs). We model the cooperation stimulation problem as an indirect reciprocity game, where SUs help PUs relay information and gain reputations to access the vacant channels in the future. We design a reputation updating policy under time-varying channels and prove the existence of stationary reputation distribution. We further formulate the decision making of an SU as a Markov Decision Process (MDP) and use a modified value iteration algorithm to find the optimal action rule. Moreover, we theoretically derive the condition under which the optimal action rule is an evolutionarily stable strategy (ESS). Finally, simulation results are shown to verify the effectiveness of the proposed scheme.
Data-Driven Stochastic Scheduling and Dynamic Auction in IaaS

With the emergence of large scale data processing systems and big data analysis, cloud computing has become more and more popular. In this paper, we focus on the mechanism design in the infrastructure as a service (IaaS) cloud computing service market. Most of existing works on mechanism design assume static and independent individual utility, while in practice the cloud service is provided in a dynamic environment. To solve such problems, we propose a stochastic matching algorithm based on Markov Decision Process (MDP), which aims at optimizing the long-term system efficiency by considering the opportunity cost in the future. Based on the MDP formulation, we further design an efficient (EF), incentive compatible (IC), individual rational (IR) auction mechanism. Finally, we conduct experiment using Google cluster-usage traces dataset and show that the proposed MDP-based VCG auction mechanism can achieve EF, IC and IR properties simultaneously.
Multimedia services over digital subscriber lines

We study the problem of reliable and yet efficient multimedia communications over asymmetric digital subscriber line (ADSL) through joint consideration of compression/coding and channel transmission techniques. The transmission of digital multimedia data requires the existing systems to be augmented with functions that can handle not only ordinary (nonmultimedia) data. In addition, the high volume of multimedia data can be handled efficiently only if all available system resources are carefully optimized. We consider the special characteristics of ADSL channels to formulate optimization criteria. We present a system where the encoder consists of a layer coder that divides and compresses the source data into coded layers of multimedia data with different performance and quality-of-service requirements. The encoded bit streams are then transmitted over a noisy channel, where channel noise may distort the data. The decoder removes all the coding and compression applied in the encoder to obtain as close as possible the original data. It is conceivable that for such a system the loss of information occurs not only during the source compression but also in the channel transmission. The source coding scheme and bit stream arrangement have a direct impact on the channel distortion. Therefore, the joint consideration of source/channel coding design is ideal to reduce the overall information loss.
Image Tampering Identification using Blind Deconvolution

Digital images have been used in growing number of applications from law enforcement and surveillance, to medical diagnosis and consumer photography. With such widespread popularity and the presence of low-cost image editing softwares, the integrity of image content can no longer be taken for granted. In this paper, we propose a novel technique based on blind deconvolution to verify image authenticity. We consider the direct output images of a camera as authentic, and introduce algorithms to detect further processing such as tampering applied to the image. Our proposed method is based on the observation that many tampering operations can be approximated as a combination of linear and non-linear components. We model the linear part of the tampering process as a filter, and obtain its coefficients using blind deconvolution. These estimated coefficients are then used to identify possible manipulations. We demonstrate the effectiveness of the proposed image authentication technique and compare our results with existing works.
Performance Analysis for Two-Way Network-Coded Dual-Relay Networks With Stochastic Energy Harvesting

In this paper, we consider an energy harvesting (EH) two-way (TW) dual-relay network, including one non-EH relay and one EH relay equipped with a finite-sized battery. In the network, a space-time transmission protocol with space-time network coding is designed, and an optimal transmission policy for the EH relay is proposed by using a stochastic solar EH model. In this optimal policy, the long-term paired-wise error probability (PEP) of the system is minimized by adapting the EH relay’s transmission power to the knowledge of its current battery energy, channel fading status, and causal solar EH information. The designed problem is formulated as a Markov decision process framework, and the conditional capability of the contribution to PEP by the EH relay is adopted as the reward function. We uncover a monotonic and limited difference structure for the expected total discounted reward. Furthermore, a non-conservative property and a monotonic structure of the optimal policy are revealed. Based on the optimal policy and its special structures, the expectation, lower and upper bounds, and asymptotic approximation of the PEP are computed and an interesting result on the system diversity performance is revealed, i.e., the full diversity order can be achieved only if the EH capability index, a metric to quantify the EH node’s capability of harvesting and storing energy, approaches to infinity; otherwise, the EH diversity order is only equal to one, and the coding gain of the network is increasing with the EH capability index at this time. Furthermore, a full diversity criterion for the EH TW dual-relay network is proposed. Finally, computer simulations confirm our theoretical analysis and show that our proposed optimal policy outperforms other compared policies.