nils-herrmann/clean_scirepeval huggingface dataset:
Joint power control and blind beamforming in wireless networks

Traditional power control with beamforming achieves a targeted signal-to-inference-noise-ratio (SINR) in wireless networks assuming measurement of SINR and direction of arrival (DOA) at the receivers. Blind beamforming is an emerging technique for DOA measurement without consuming extra bandwidth. Here we propose a joint power control and blind beamforming algorithm that reformulates the power control problem and, importantly, does not need additional measurements or sending training sequences. In contrast to traditional power control that achieves a targeted SINR threshold, the scheme achieves a threshold on a quantity available from blind beamforming, which is directly related to bit error rate. We have shown in both analysis and simulation results that the algorithm converges to the desired solution.
Power Waveforming: Wireless Power Transfer Beyond Time Reversal

This paper explores the idea of time-reversal (TR) technology in wireless power transfer to propose a new wireless power transfer paradigm termed power waveforming (PW), where a transmitter engages in delivering wireless power to an intended receiver by fully utilizing all the available multipaths that serve as virtual antennas. Two power transfer-oriented waveforms, energy waveform and single-tone waveform, are proposed for PW power transfer systems, both of which are no longer TR in essence. The former is designed to maximize the received power, while the latter is a low-complexity alternative with small or even no performance degradation. We theoretically analyze the power transfer gain of the proposed power transfer system over the direct transmission scheme, which can achieve about 6 dB gain, under various channel power delay profiles and show that the PW is an ideal paradigm for wireless power transfer because of its inherent ability to recollect all the power that is possible to harvest from the surrounding environment. In addition, the outage performances in harvesting power of the PW system and the conventional multiple-input multiple-output (MIMO) system are derived. It reveals that the PW system can achieve the same outage performance of the MIMO system as long as the number of resolvable multipaths is sufficiently large. Simulation results validate the analytical findings and experimental results demonstrate the effectiveness of the proposed PW technique.
Robust H.263 video transmission over spectrally shaped channels using multicarrier modulation

This paper proposes an efficient and robust technique for transmission of entropy coded video over noisy channels, especially for spectrally shaped channels which are commonly used in ADSL environments. Combining multicarrier modulation and the error resilient entropy coding (EREC) technique together, we develop a new loading algorithm which achieves unequal error protection (UEP) by optimally allocating the available channel resources among the subchannels. Without adding any redundancy, the proposed system offers significant improvement over the EREC system. Simulation results on the H.263 video coder over typical spectrally shaped channels which are typical in ADSL are provided.
Sequential Chinese Restaurant Game

In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents' experiences through learning, or make decisions earlier to avoid competitions from huge crowds. Both these two effects, social learning and negative network externality, play important roles in the decision process of an agent. While there are existing works on either social learning or negative network externality, a general study on considering both effects is still limited. We find that Chinese restaurant process, a popular random process, provides a well-defined structure to model the decision process of an agent under these two effects. By introducing the strategic behavior into the non-strategic Chinese restaurant process, we propose a new game, called the Chinese restaurant game, to formulate the social learning problem with negative network externality. Through analyzing the proposed Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is studied through simulations. We also illustrate the spectrum access problem in cognitive radio networks as one of the application of Chinese restaurant game. We find that the proposed Chinese restaurant game theoretic approach indeed helps users make better decisions and improves the overall system performance.
Performance Analysis of MB-OFDM UWB System with Imperfect Synchronization and Intersymbol Interference

This paper provides performance analysis of multiband OFDM UWB system that takes into consideration the effect of intersymbol interference and imperfect frequency and timing synchronization. The system performance is analyzed under UWB multipath fading channels, as specified in the IEEE 802.15.3a channel standard. Average signal-to-noise ratio is first derived for the system. Then an approximation technique is employed to obtain a closed-form average bit error probability that provides a profound understanding of the performance of the multiband OFDM UWB system. Simulation results validate the theoretical analysis.
Multi-User Collusion Behavior Forensics: Game Theoretic Formulation of Fairness Dynamics

Multi-user collusion is an cost-effective attack against digital fingerprinting, in which a group of attackers collectively undermine the traitor tracing capability of digital fingerprints. However, during multi-user collusion, each colluder wishes to minimize his/her own risk and maximize his/her own profit, and different colluders have different objectives. Thus, an important issue during collusion is to agree on how to distribute the risk/profit among colluders and ensure fairness of the attack. To have a better understanding of the attackers' behavior during collusion to achieve fairness, this paper models the dynamics among colluders as a non-cooperative game. We then study the Pareto-optimal set, where no colluder can further increase his/her own payoff without decreasing others', and analyze the Nash bargaining solution of this game.
Key management and distribution for secure multimedia multicast

The problem of controlling access to multimedia multicasts requires the distribution and maintenance of keying information. Typically, the problem of key management is considered separately from the problem of distributing the rekeying messages. Multimedia sources provide two approaches to distributing the rekeying messages associated with securing group communication. The first, and more conventional approach employs the use of a media-independent channel to convey rekeying messages. We propose, however, a second approach that involves the use of a media-dependent channel, and is achieved for multimedia by using data embedding techniques. Compared to a media-independent channel, the use of data embedding to convey rekeying messages provides enhanced security by masking the presence of rekeying operations. This covert communication makes it difficult for an adversary to gather information regarding the group membership and its dynamics. In addition to proposing a new mode of conveyance for the rekeying messages, we introduce a new message format that is suitable for multicast key management schemes. This new message format uses one-way functions to securely distribute new key material to subgroups of users. An advantage of this approach over the traditional message format is that no additional messages must be sent to flag the users which portion of the message is intended for them, thereby reducing communication overhead. We then show how to map the message to a tree structure in order to achieve desirable scalability in communication and computational overhead. Next, as an example of the interplay between the key management scheme and the mode of conveyance, we study the feasibility of embedding rekeying messages using a data embedding method that has been recently proposed for fractional-pel video coding standards such as H.263 and MPEG-2. Finally, since multimedia services will involve multiple layers or objects, we extend the tree-based key management schemes to include new operations needed to handle multilayer multimedia applications where group members may subscribe or cancel membership to some layers while maintaining membership to other layers.
Robust progressive image transmission over OFDM systems using space-time block code

A joint source-channel coding (JSCC) scheme for robust progressive image transmission over broadband wireless channels using orthogonal frequency division multiplexing (OFDM) systems with spatial diversity is proposed for the application environments where no feedback channel is available such as broadcasting services. Most of current research about JSCC focuses on either binary symmetric channels (BSC) or additive white Gaussian noise (AWGN) channels. To deal with fading channels in most previous methods, the fading channel is modeled as two state Gilbert-Elliott channel model and the JSCC is normally aimed at the BER of bad channel status, which is not optimal when the channel is at good status. By using diversity techniques and OFDM, the frequency selective fading effects in broadband wireless channels can be significantly decreased and we show that subchannels in OFDM systems approach Gaussian noisy channels when the diversity gain gets large; as a result, the system performance can be improved in terms of throughput and channel coding efficiency. After analyzing the channel property of OFDM systems with spatial diversity, a practical JSCC scheme for OFDM systems is proposed. Simulation results are presented for transmit diversity with different numbers of antennas and different multipath delay and Doppler spread. It is observed from simulations that the performance can be improved more than 4 dB in terms of peak signal-to-noise ratio (PSNR) of the received image Lena and the performance is not very sensitive to different multipath spread and Doppler frequency.
Single-block differential transmit scheme for frequency selective MIMO-OFDM systems

In this paper, we propose a differential encoding and decoding scheme for MIMO-OFDM systems under frequency-selective fading channels. We differentially encode the signal within each OFDM symbol period. This scheme not only reduces encoding and decoding delay, but also relaxes the restriction on channel assumptions. The successful differential decoding of the proposed scheme depends on the assumption that fading channels keep constant over two OFDM symbol periods rather than multiple periods as required in previous schemes. We provide the pairwise error probability formulation, and quantify the performance criteria in terms of diversity and coding advantages. Our design criteria reveal that the existing diagonal cyclic codes can be applied to achieve full diversity with high coding gain. Performance simulations in various channel conditions show that our proposed scheme yields superior performance to the previously proposed differential schemes.
Dual-state systolic architectures for up/downdating RLS adaptive filtering

A dual-state systolic structure to perform joint up/down-dating operations encountered in windowed recursive least-squares, (RLS) estimation problems is proposed. It is based on successively performing Givens rotations for updating and hyperbolic rotations for downdating. Due to data independence, a series of Givens and hyperbolic rotations can be interleaved and parallel processing can be achieved by alternatively performing updating and downdating both in time and space. This flip-flop nature of up/down-dating characterizes the feature of the dual-state systolic triarray. Efficient implementation on the evaluation of optimal residuals is also considered. This systolic architecture is promising for the VLSI implementation of fixed size sliding-window recursive least-squares estimations.
Model-based receptor quantization analysis for PET parametric imaging

Dynamic PET (positron emission tomography) imaging technique allows image-wide quantification of physiologic and biochemical parameters. Compartment modeling is the most popular approach for receptor binding studies. However, current compartment-model based methods often either require the accurate arterial blood measurements as the input function or assume the existence of a reference region. To obviate the need for the input function or a reference region, in this paper, we propose to estimate the input function and the kinetic parameters simultaneously. The initial estimate of the input functions is obtained by the analysis of space intersections. Then both the input function and the receptor parameters, thus the underlying distribution volume (DV) parametric image, are estimated iteratively. The performance of the proposed scheme is examined by both simulations and real brain PET data in obtaining the underlying parametric images.
Spatial focusing inspired 5G spectrum sharing

Next-generation wireless networks are expected to support exponentially increasing number of users and demands of data, which all rely on the essential media: spectrum. Notice that the 5G networks are featured either by wide bandwidth like mmWave systems, or by large-scale antennas like massive MIMO. We find those two trends, together with waveforming or beamforming, can lead to a common phenomenon: the spatial focusing effect. Based on this focusing effect, we propose a general spatial spectrum sharing framework that enables concurrent multi-users spectrum sharing without the requirement of orthogonal resource allocation. Simulation results show that both TR wideband and massive MIMO system can achieve equivalently high throughput performance with the spatial spectrum sharing scheme.
Tracking the Herd: Resynchronization Analysis of Cell-Cycle Gene Expression Data in Saccharomyces Cerevisiae

Identification of genes expressed in a cell-cycle-specific periodical manner is of great interest to understand cyclic systems which play a critical role in many biological processes. However, identification of cell-cycle regulated genes by microarray gene expression data is complicated by the factor of synchronization loss, thus remains a challenging problem. Decomposing the expression measurements will allow to better represent the single-cell behavior and improve the accuracy in identifying periodically expressed genes. In this paper, we propose a resynchronization-based algorithm for identifying cell-cycle-related genes, where we present a simple synchronization loss model by modeling the gene expression measurements as a superposition of different cell populations growing at different rates. The underlying expression profile is reconstructed through resynchronization and is further fitted to the measurements in order to identify periodically expressed genes. Results from both simulations and real microarray data show that the proposed scheme is promising for identifying cycling genes and revealing underlying gene expression profiles
Joint Uplink and Downlink Optimization for Real-Time Multiuser Video Streaming Over WLANs

In this paper, a network-aware and source-aware video streaming system is proposed to support interactive multiuser communications within single-cell and multicell IEEE 802.11 networks. Unlike the traditional streaming video services, the strict delay constraints of an interactive video streaming system pose more challenges. These challenges include the heterogeneity of uplink and downlink channel conditions experienced by different users, the multiuser resource allocation of limited radio resources, the incorporation of the cross-layer design, and the diversity of content complexities exhibited by different video sequences. With the awareness of video content and network resources, the proposed system integrates cross-layer error protection mechanism and performs dynamic resource allocation across multiple users. We formulate the proposed system as to minimize the maximal end-to-end expected distortion received by all users, subject to maximal transmission power and delay constraints. To reduce the high dimensionality of the search space, fast multiuser algorithms are proposed to find the near-optimal solutions. Compared to the strategy without dynamically and jointly allocating bandwidth resource for uplinks and downlinks, the proposed framework outperforms by 2.18~7.95 dB in terms of the average received PSNR of all users and by 3.82~11.50 dB in terms of the lowest received PSNR among all users. Furthermore, the proposed scheme can provide more uniform video quality for all users and lower quality fluctuation for each received video sequence.
Decision Learning : Data analytic learning with strategic decision making

With the increasing ubiquity and power of mobile devices as well as the prevalence of social systems, more activities in our daily life are being recorded, tracked, and shared, creating the notion of social media. Such abundant and still growing real-life data, known as big data, provide a tremendous research opportunity in many fields. To analyze, learn, and understand such user-generated data, machine learning has been an important tool, and various machine-learning algorithms have been developed. However, since the user-generated data are the outcome of users' decisions, actions, and socioeconomic interactions, which are highly dynamic, without considering users' local behaviors and interests, existing learning approaches tend to focus on optimizing a global objective function at the macroeconomic level, while totally ignoring users' local interactions at the microeconomic level. As such, there is a growing need to combine learning with strategic decision making, which are two traditionally distinct research disciplines, to be able to jointly consider both global phenomena and local effects to better understand, model, and analyze the newly arising issues in the emerging social media with user-generated data. In this article, we present an overview of the emerging notion of decision learning, i.e., learning with strategic decision making, which involves users' behaviors and interactions by combining learning with strategic decision making. We will discuss some examples from social media with real data to show how decision learning can be used to better analyze users' optimal decision from a user's perspective, as well as design a mechanism from the system designer's perspective to achieve a desirable outcome.
Blocking probability of handoff calls and carried traffic in wireless networks with antenna arrays

In this paper we consider a wireless network with beamforming capabilities at the receiver. We derive the blocking probabilities of the calls in the system under different traffic policies for such a network. For a set of co-channel transmitters, their success probabilities for being captured by separate antenna beams are computed. These success probabilities are taken into account in the queueing model of the system. Our analytical and also numerical results show that adaptive beamforming at the receiver indeed reduces the blocking probability of the calls and increases the total carried traffic in the system.
Mixture principal component analysis for distribution volume parametric imaging in brain PET studies

In this paper, we present a mixture principal component analysis (mPCA)-based approach for voxel level quantification of dynamic positron emission tomography (PET) data in brain studies. The parameters of the probabilistic mixture model are determined using an EM algorithm. The problem of interest here requires neither the accurate arterial blood measurements as the input function nor the existence of a reference region. The effects of mPCA are examined in two different ways on the basis of whether the compartmental model for tracer dynamics is considered. First, the mPCA approach itself is used to classify all voxels into the specific binding and non-specific binding groups, and the resulting power is used for revealing the underlying distribution volume (DV) image. Second, the proposed mPCA-based classification approach is incorporated as the clustering preprocessing into our earlier work to simultaneously estimate the DV parametric image and the input function. The efficiency and superiority of the proposed scheme is demonstrated by real brain PET data
Performance Analysis of Two-Way Relaying with Non-Coherent Differential Modulation

This work focuses on a two-way denoise-and-forward relaying system using non-coherent Differential Binary Phase-Shift Keying (DBPSK) modulation, which has the well-defined relay denoising function when channel state information is unknown. We first design the relay denoising function and source decoders using Maximum Likelihood (ML) principles for the general case with K parallel relays. As the ML denoising function is hard to manipulate, we approximate it as a multi-user detector followed by a physical layer network coding encoder and obtain the closed-form relay decoding error. For the single-relay case, we show that the ML source decoder is actually equivalent to the typical DBPSK decoder for the relay-source channel and thus derive the exact end-to-end Bit Error Rate (BER). To minimize the average BER, we also investigate the power allocation problem by use of asymptotic analysis at high Signal-to-Noise Ratio (SNR). We show that the optimal source power is inversely proportional to the square root of the channel gain of the source-relay channel, and the optimal relay power decreases with SNR. For the multi-relay case, though the exact analysis is intractable, we develop upper bound and lower bound on BER and show that the diversity order is exactly ⌈κ/2⌉.
Qrd-based Square Root Free And Division Free Algorithms And Architectures

We introduce a family of square root free and division free rotation based algorithms. Our approach suggests a new perspective of the Q R decomposition (QRD) algorithms and leads to a considerable reduction of the circuitry complexity and time delay in the associated architectures. The optimal residual and the optimal weight ext:raction for the recursive least squares (RLS) problem are considered in this paper. The systolic structures that are described are very promising, since they involve less computational complexity from the structures known to date and they make the VLSI implementation more tractable.
Authenticating MIMO Transmissions Using Channel-Like Fingerprinting

A framework for introducing an extrinsic fingerprint signal to space-time coded transmissions at the physical-layer is presented, whereby the fingerprint signal conveys a low capacity digital communication suitable for authenticating the transmission and further facilitating secure communications. A novel approach is discussed where the fingerprint signal mimics distortions similar to time-varying channel effects. Specifically, the fingerprint signal is only visible to aware receivers considering previous channel state information (CSI) and is otherwise invisible to a receiver equalizing according to current CSI. An augmented signal is created consisting of the original transmission, or primary message, and the fingerprint message. An example fingerprint signal and detection rule are presented based on a phase-shift keying approach. Simulation results including bit error rate (BER) are presented for both the primary and fingerprint signals using the 2x2 Alamouti code, and authentication signal BERs lower than the primary signal are demonstrated.