nils-herrmann/clean_scirepeval huggingface dataset:
Clustered cooperative communications in wireless networks

In this paper, a cluster-based cooperative communication scheme is introduced in which each cluster acts as a virtual node with multiple antennas. The presented model considers the effects of random deployment of nodes and hence their random distribution across the network. Taking these effects into account, a pairwise-error probability analysis for a generic space-time code structure is provided. The analysis reveals that the diversity and coding gain of the system depends on both the code structure as well as the nodes' distribution, and hence new code design criteria are developed. Simulation results are presented to confirm the theoretical analysis.
A near-optimal reinforcement learning scheme for energy efficient point-to-point wireless communications

We consider the problem of average throughput maximization per total consumed energy in packetized point-to-point wireless sensor communications. Our study results in an optimal transmission strategy that chooses the optimal modulation level and transmit power while adapting to the incoming traffic rate, buffer condition, and channel condition. We formulate the optimization problem as a Markov decision process (MDP). When the state transition probability of MDP is available, the optimal policy of MDP can be obtained using dynamic programming (DP). Since in practical situations, the state transition probability may not be available when the optimization is done, we propose to learn the near-optimal policy through the reinforcement learning (RL) algorithm. We show that the RL algorithm learns a policy that achieves almost the same throughput as the optimal one, and the learned policy obtains more than twice average throughput compared to the simple constant signal to noise ratio (CSNR) policy, particularly in high packet arrival rate. Moreover, the learning algorithm is robust in tracking the variation of the governing probability.
Pricing Game for Time Mute in Femto–Macro Coexistent Networks

In heterogenous networks, intercell interference coordination (ICIC) is a big challenge. This paper presents an analytical framework to evaluate power control and time mute schemes in closed access femto and macro coexistent networks. We use stochastic geometry to model the downlink scenario and derive the coverage probability of indoor macro users and femto users. The optimal operating parameters for altruistic power control and time mute schemes are achieved. Considering the selfishness of the owners of femtos, we formulate the two-tier interference coordination as pricing games and obtain the closed-form of Nash equilibria. Simulation results demonstrate the influence of different parameters on the performance of ICIC schemes and show that when target SINR ≥ 3 dB, the time mute scheme outperforms the power control scheme in handling the indoor macro user coverage problem.
An interference cancellation scheme for the multiuser TRDMA uplink system

The concept of Time Reversal Division Multiple Access (TRDMA) has recently been proposed as a promising medium access technology for the multi-user wireless broadband communications. Compared with the existing multi-carrier technology like OFDM/OFDMA, the TRDMA provides a cost-effective single-carrier alternative technology to combat the inter-symbol interference (ISI) for broadband communications, and at the same time leverages the degrees of freedom in a large number of multi-paths to form a unique high-resolution spatial focusing effect. Previous work on TRDMA mainly focus on the multi-user downlink system. In this paper, we first introduce a TRDMA-based multi-user uplink architecture and then propose a 2-dimensional (2D) parallel interference cancellation scheme to further enhance the system performance. The TRDMA uplink architecture keeps the cost of end-users at a minimum level, and reuses the processing power at the base station (BS) that has already been made available for the downlink. The proposed 2D parallel interference cancellation scheme utilizes the tentative decisions of detected symbols to effectively cancel the interference in both the time domain (i.e. ISI) and the user domain (i.e. inter-user interference (IUI)), which significantly improve the bit-error-rate performance in the high signal-to-noise-ratio (SNR) regime. Simulation results are provided and compared with the basic TRDMA system without interference cancellation.
Securing dynamic membership information in multicast communications

In secure multicast communications, key management schemes are employed to prevent unauthorized access to multicast content. Key management, however, can disclose the information about the dynamics of the group membership, such as the group size and the number of join and departure users, to both inside and outside attackers. This is a threat to applications with confidential group membership information. This paper investigates attack/anti-attack strategies for stealing/protecting group dynamic information in the existing key management schemes. We show that attackers can successfully acquire the membership information by exploiting the key updating procedure in popular centralized key management schemes. Particularly, we develop two attack strategies and demonstrate their effectiveness through simulations. Further, we propose an anti-attack technique utilizing hatch rekeying and phantom users, and derive performance criteria that describe the security level of the proposed scheme using mutual information. The proposed anti-attack scheme is evaluated based on the data obtained from real MBone sessions.
Diversity analysis for two-way multi-relay networks with stochastic energy harvesting

The pairwise error probability (PEP) performance is investigated for the energy harvesting (EH) two-way (TW) multi-relay network with stochastic EH models. In this network, two relay nodes exploit the amply-and-forward (AF) protocol and space-time network coding (STNC) to help two source nodes exchange information simultaneously. We assume only one relay node is solar-powered and equipped with a finite-capacity battery. The optimal transmission policy for the EH relay is proposed to minimize the long-term PEP by considering the causal EH information, battery energy and stochastic channel status. Thus, the design framework is formulated as a Markov decision process (MDP). We reveal that the full diversity order is achievable only if the EH relay's battery empty probability is equal to zero. Further, the PEP performance can be improved when the battery empty probability becomes smaller. Simulations validate the theoretical analysis and show that the proposed optimal policy outperforms other myopic policies.
Joint Power Waveforming and Beamforming for Wireless Power Transfer

In the Internet of Things, wireless devices need more easily accessible energy resources, which motivates the development of wireless power transfer (WPT) using radio frequency signals. Beamforming technique has been widely adopted by using multiple transmit antennas to form a sharp energy beam toward an intended receiver. However, few of them have considered the potential gain of multipath propagation. In this paper, we propose a joint power waveforming and beamforming in the time domain for WPT, in which the waveforms on multiple transmit antennas driven by a common reference signal are designed to maximize the gain of energy delivery efficiency. We consider both nonperiodic and periodic reference signals and propose low-complexity waveforms that can achieve near-optimal performance. It is found that the energy delivery efficiency gain of the proposed approach increases with the waveform length until saturation. We theoretically analyze the outage probability of the proposed approach under a uniform power delay channel profile, which quantifies the impact of the number of antennas and multipaths. Simulations are performed to validate the theoretic analysis and the effectiveness of the proposed joint power waveforming and beamforming approach.
Energy-Efficient Base-Station Cooperative Operation with Guaranteed QoS

With the explosive deployment of the information and communications technology (ICT) infrastructures, the rising cost of energy and increased environmental awareness has sparked a keen interest in the development and deployment of energy-efficient communication technologies. As a major player in the ICT sector, the energy efficiency of the mobile cellular networks can be significantly improved by switching off some base stations during off-peak periods. In this paper, we propose an energy-efficient BS switching strategy, and use cooperative communication techniques among the base stations to effectively extend network coverage. We incorporate both the path-loss and fading effects in our system model, and derive closed-form expressions for two important quality of service metrics, the call-blocking probability and the channel outage probability. The proposed scheme guarantees the quality of service of the user equipments by identifying the user equipments situated at the worst-case locations. The energy-saving performance is evaluated and compared with the conventional uni-pattern operation. Both analytical and numerical results show that the proposed energy-efficient switching strategy, facilitated by BS cooperation, can provide significant energy-saving potential for the cellular networks with guaranteed quality of service.
Extrinsic Channel-Like Fingerprinting Overlays Using Subspace Embedding

We present a physical-layer fingerprint-embedding scheme for orthogonal frequency-division-multiplexing (OFDM) transmissions, where the fingerprint signal conveys a low capacity communication suitable for authenticating the transmission and further facilitating secure communications. Our system strives to embed the fingerprint message into the noise subspace of the channel estimates obtained by the receiver, using a number of signal spreading techniques. When side information of the channel state is known and leveraged by the transmitter, the performance of the fingerprint embedding can be improved. When channel state information is not known, blind spreading techniques are applied. The fingerprint message is only visible to aware receivers who explicitly preform detection of the signal, but is invisible to receivers employing typical channel equalization. A taxonomy of overlay designs is discussed and these designs are explored through experiment using time-varying channel-state information (CSI) recorded from IEEE802.16e Mobile WiMax base stations. The performance of the fingerprint signal as received by a WiMax subscriber is demonstrated using CSI measurements derived from the downlink signal. Detection performance for the digital fingerprint message in time-varying channel conditions is also presented via simulation.
Evolutionary Information Diffusion Over Heterogeneous Social Networks

A huge amount of information, created and forwarded by millions of people with various characteristics, is propagating through the online social networks every day. Understanding the mechanisms of the information diffusion over the social networks is critical to various applications including online advertisement and website management. Different from most of the existing works, we investigate the information diffusion from an evolutionary game-theoretic perspective and try to reveal the underlying principles dominating the complex information diffusion process over the heterogeneous social networks. Modeling the interactions among the heterogeneous users as a graphical evolutionary game, we derive the evolutionary dynamics and the evolutionarily stable states (ESSs) of the diffusion. The different payoffs of the heterogeneous users lead to different diffusion dynamics and ESSs among them, in accordance with the heterogeneity observed in real-world datasets. The theoretical results are confirmed by simulations. We also test the theory on Twitter hashtag dataset. We observe that the derived evolutionary dynamics fit the data well and can predict the future diffusion data.
Belief-based packet forwarding in self-organized mobile ad hoc networks with noise and imperfect observation

In self-organized mobile ad hoc networks (MANET) where each user is its own authority, fully cooperative behaviors, such as unconditionally forwarding packets for each other, cannot be directly assumed. In this paper, we focus on cooperation enforcement in the self-organized mobile ad hoc networks with noise and imperfect observation and study the basic packet-forwarding function using the repeated game models with imperfect information. A belief-based packet forwarding framework is proposed to obtain cooperation-enforcement strategies solely based on each node's own past actions and its private imperfect observation of other nodes' information. The simulation results illustrate that the proposed belief-based packet forwarding approach can enforce the cooperation with only a small performance degradation compared to the unconditionally cooperative outcomes in the ad hoc networks with noise and imperfect observation
Performance analysis of space-time codes over correlated Rayleigh fading channels

The potential for capacity increase in multiantenna wireless communication systems has drawn considerable attention to space-time codes. However, most of the existing space-time code construction methods have assumed ideal channel models: either quasi-static fading of fast fading. In this work, we derive the performance criteria for space-time coded wireless communications systems taking into account both spatial and temporal channel correlation. We show that if the space-time correlation matrix is of full rank, the space-time code design problem for fast fading channels can be reduced to the code design problem for fast fading channels. Some simulation results are also presented to support the theory.
Time-sensitive behavior dynamics in multimedia fingerprinting social networks

Multimedia social network is a network infrastructure in which the social network members share multimedia contents with all different purposes. Analyzing user behavior in multimedia social networks help design more secured and efficient multimedia and networking systems. In this paper, we focus on the colluder social network in multimedia fingerprinting systems in which colluders gain reward by redistributing the colluded multimedia signal. However, the market value of the redistributed multimedia content is time sensitive. The earlier the colluded copy being released, the more the people who are willing to pay for it. Thus the colluders have to reach agreement on how to distribute reward and the probability being detected among themselves as soon as possible. This paper incorporates the time-sensitiveness of the colluders' reward, and models the dynamics among colluders as a noncooperative game, and studies the time-restricted bargaining equilibrium. We provide the solution to the equilibrium that all the colluders have no inventive to disagree in order to maximize their own payoff.
Data Trading With Multiple Owners, Collectors, and Users: An Iterative Auction Mechanism

In the big data era, it is vital to allocate the vast amount of data to heterogeneous users with different interests. To clinch this goal, various agents including data owners, collectors, and users should cooperate to trade data efficiently. However, the data agents (data owners, collectors, and users) are selfish and seek to maximize their own utilities instead of the overall system efficiency. As such, a sophisticated mechanism is imperative to guide the agents to distribute data efficiently. In this paper, the data trading problem of a data market with multiple data owners, collectors, and users is formulated and an iterative auction mechanism is proposed to coordinate the trading. The proposed mechanism guides the selfish data agents to trade data efficiently in terms of social welfare and avoids direct access of the agents’ private information. We theoretically prove that the proposed mechanism can achieve the socially optimal operation point. Moreover, we demonstrate that the mechanism satisfies appealing economic properties such as individual rationality and weakly balanced budget. Then, we expand the mechanism to nonexclusive data trading, in which the same data can be dispensed to multiple collectors and users. Simulations as well as real data experiments validate the theoretical properties of the mechanism.
Community detection game

Real-world networks are often cluttered and hard to organize. Recent studies show that most networks have the community structure, i.e., nodes with similar attributes form a certain community, which enables people to better understand the constitution of the networks. Hitherto, various community detection methods have been proposed in the literature yet none of them takes the strategic interactions among nodes into consideration. Additionally, many real-world observations of networks are noisy and incomplete, i.e., with some missing links or fake links, due to either technology constraints or privacy regulations. In this work, a game-theoretic framework of community detection is established, where nodes interact and produce links with each other in a rational way based on mutual benefits. Given the proposed game-theoretic generative models for communities, we use expectation maximization (EM) algorithm to detect communities. Simulations on synthetic networks and experiments on real-world networks demonstrate that the proposed detection method outperforms the state-of-the-art.
Joint power control and beamforming for capacity improvement in wireless networks with 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 beamformer maximizes the carrier to interference ratio (CIR) 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 CIR as well. We consider a system with power control capabilities which uses antenna arrays at the receivers. An iterative algorithm is proposed to jointly update the transmission powers and the beamformer weights so that it converge 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 increase and the saving in mobile power achieved by beamforming are evaluated through a numerical study.
A Time-Reversal Paradigm for Indoor Positioning System

In an indoor environment, there commonly exist a large number of multipaths due to rich scatterers. These multipaths make the indoor positioning problem very challenging. The main reason is that most of the transmitted signals are significantly distorted by the multipaths before arriving at the receiver, which causes inaccuracies in the estimation of the positioning features such as the time of arrival (TOA) and the angle of arrival (AOA). On the other hand, the multipath effect can be very constructive when employed in the time-reversal (TR) radio transmission. By utilizing the uniqueness of the multipath profile at each location, TR can create a resonating effect of focusing the energy of the transmitted signal only onto the intended location, which is known as the spatial focusing effect. In this paper, we propose exploiting such a high-resolution focusing effect in the indoor positioning problem. Specifically, we propose a TR indoor positioning system (TRIPS) by utilizing the location-specific characteristic of multipaths. By doing so, we decompose the ill-posed single-access-point (AP) indoor positioning problem into two well-defined subproblems. The first subproblem is to create a database by mapping the physical geographical location with the logical location in the channel impulse response (CIR) space, whereas the second subproblem is to determine the real physical location by matching the estimated CIR with those in the database. To evaluate the performance of our proposed TRIPS, we build a prototype to conduct real experiments. The experimental results show that, with a single AP working in the 5.4-GHz band under the non-line-of-sight (NLOS) condition, our proposed TRIPS can achieve perfect 10-cm localization accuracy with zero-error rate within a 0.9 m by 1 m area of interest.
Cooperation Stimulation in Cognitive Networks Using Indirect Reciprocity Game Modelling

In cognitive networks, since nodes generally belong to different authorities and pursue different goals, they will not cooperate with others unless cooperation can improve their own performance. Thus, how to stimulate cooperation among nodes in cognitive networks is very important. However, most of existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, according to the well-known Prisoner's Dilemma and the backward induction principle, the unique Nash equilibrium (NE) is to always play non-cooperatively. In this paper, we propose a cooperation stimulation scheme for the scenario where the number of interactions between any pair of players are finite. The proposed algorithm is based on indirect reciprocity game modelling where the key concept is ``I help you not because you have helped me but because you have helped others''. We formulate the problem of finding the optimal action rule as a Markov Decision Process (MDP). Using the packet forwarding game as an example, we show that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy (ESS). Finally, simulations are shown to verify the efficiency and effectiveness of the proposed algorithm.
Distributed Relay Selection and Power Control for Multiuser Cooperative Communication Networks Using Stackelberg Game

The performance in cooperative communication depends on careful resource allocation such as relay selection and power control, but the traditional centralized resource allocation requires precise measurements of channel state information (CSI). In this paper, we propose a distributed game-theoretical framework over multiuser cooperative communication networks to achieve optimal relay selection and power allocation without knowledge of CSI. A two-level Stackelberg game is employed to jointly consider the benefits of the source node and the relay nodes in which the source node is modeled as a buyer and the relay nodes are modeled as sellers, respectively. The proposed approach not only helps the source find the relays at relatively better locations and "buyrdquo an optimal amount of power from the relays, but also helps the competing relays maximize their own utilities by asking the optimal prices. The game is proved to converge to a unique optimal equilibrium. Moreover, the proposed resource allocation scheme with the distributed game can achieve comparable performance to that employing centralized schemes.
Game theoretical mechanism design methods

Dynamic spectrum access with cognitive radios has become a promising approach to improve spectrum efficiency by adaptively coordinating different users' access according to spectrum dynamics. However, selfish users competing with each other for spectrum may exchange false private information or collude with others to get more access to the spectrum and achieve higher profits. In this article, we investigate two game-theoretical mechanism design methods to suppress cheating and collusion behavior of selfish users: a self-enforcing truth-telling mechanism for unlicensed spectrum sharing and a collusion-resistant multistage dynamic spectrum pricing game for licensed spectrum sharing.