nils-herrmann/clean_scirepeval huggingface dataset:
A Multiuser TRDMA Uplink System with 2D Parallel Interference Cancellation

The concept of Time-Reversal Division Multiple Access (TRDMA) has recently been proposed as a promising medium access technology for wireless broadband communications. Previous work on TRDMA mainly focuses on the multi-user downlink system. In this paper, we first introduce a TRDMA-based multi-user uplink architecture and then propose a 2D parallel interference cancellation scheme to enhance system performance. The TRDMA uplink architecture keeps the cost of endusers at a minimum level, and reuses the processing power at the base station that has already been made available for the downlink. The proposed 2D parallel interference cancellation scheme utilizes tentative decisions of detected symbols to effectively cancel both inter-symbol interference and inter-user interference, which significantly improve the bit-error-rate (BER) performance in high SNR regime. To further improve BER performance, a multi-stage processing can be implemented by cascading multiple stages of the proposed 2D interference cancellation, with a total delay that increases linearly with the number of stages, but independent of the number of users. The BER performance of the single-stage cancellation is analyzed, and the approximated theoretical result well matches the simulation results. Simulations are provided for up-to 3 stages of interference cancellation and compared with the basic TRDMA system without interference cancellation.
Fast algorithms for 2-D circular convolutions and number theoretic transforms based on polynomial transforms over finite rings

We develop new fast algorithms for 2-D integer circular convolutions and 2-D number theoretic transforms (NTT). These new algorithms, which offer improved computational complexity, are constructed based on polynomial transforms over Z/sub p/; these transforms are Fourier-like transforms over Z/sub p/, which is the integral domain of polynomial forms over Z/sub p/[x]. Having defined such polynomial transforms over Z/sub p/ we prove several necessary and sufficient conditions for their existence. We then apply the existence conditions to recognize two applicable polynomial transforms over Z/sub p/. One is for p equal to Mersenne numbers and the other for Fermat numbers. Based on these two transforms, referred to as Mersenne number polynomial transforms (MNPT) and Fermat number polynomial transforms (FNPT), we develop fast algorithms for 2-D integer circular convolutions, 2-D Mersenne number transforms, and 2-D Fermat number transforms. As compared to the conventional row-column computation of 2-D NTT for 2-D integer circular convolutions and 2-D NTT, the new algorithms give rise to reduced computational complexities by saving more than 25 or 42% in numbers of operations for multiplying 2/sup i/, i/spl ges/1; these percentages of savings also grow with the size of the 2-D integer circular convolutions or the 2-D NTT.
Time-recursive architectures and wavelet transform

The time-recursive computation has been proved as a particularly useful tool in real-time data compression and in transform domain adaptive filtering, with applications in the areas of audio, radio, sonar, and video. An architectural framework for parallel time-recursive computation is proposed. The authors consider a class of linear operators that consists of the discrete time, time invariant, compactly supported, but otherwise arbitrary kernel functions. They define a shift property of the linear operators and reveal its relation with the time-recursive implementation. The potential of the proposed framework is demonstrated by designing a time-recursive architecture for the discrete wavelet transform.<<ETX>>
A Game Theoretic Framework for Colluder-Detector Behavior Forensics

Digital fingerprinting is an emerging technology in media security to identify the source of illicit copies and trace traitors. Collusion is a powerful attack, in which a group of attacker collectively mount attacks against digital fingerprinting. In multimedia fingerprinting, there exists complex dynamics between the colluders and the fingerprint detector, who have conflicting objectives and influence each other's performance and decisions. This paper proposes a game-theoretic framework to formulate and analyze the colluder-detector dynamics, in an effort to understand its impact on the traitor tracing performance of multimedia fingerprints. We investigate how colluders adjusts the collusion attacks to minimize their risk under the fairness constraint; and study how the fingerprint detector adapts his/her detection strategy accordingly to improve the collusion resistance, which is shown to be the min-max solution.
Collusion-Resistant Multi-Winner Spectrum Auction for Cognitive Radio Networks

In order to fully utilize spectrum, auction-based dynamic spectrum allocation has become a promising approach which allows unlicensed wireless users to lease unused bands from spectrum license holders. Because spectrum resources are reusable by users far apart, in some scenarios, spectrum is more efficiently utilized by awarding one band to multiple secondary users simultaneously, which distinguishes it from traditional auctions where only one user can be the winner. However, the multi-winner auction is a new concept posing new challenges in the traditional auction mechanisms, because such mechanisms may yield low revenue and are not robust to some newly-emerging collusion. Therefore, in this paper, we propose an efficient mechanism for the multi-winner spectrum auction with collusion- resistant pricing strategies, in which the optimal spectrum allocation can be solved by binary linear programming and the pricing is formulated as a convex optimization problem. Furthermore, a greedy algorithm is proposed to reduce complexity for multi- band auctions. Simulation results are presented to evaluate our proposed auction mechanisms.
Transmit and receive diversity and equalization in wireless networks with fading channels

A joint power control and space-time diversity scheme is proposed for the uplink and downlink. In the uplink, mobile power and equalization/diversity combining vectors at the base stations are calculated jointly. The mobile transmitted power is minimized while the signal to noise ratio (SNR) at each link is maintained above a threshold. A multitap transmit diversity scheme for the downlink is also proposed where the transmit weight vectors are adjusted such that the SNR at each mobile is greater than a threshold. The proposed transmit and receive diversity combining schemes can be applied to networks with fading channels and in cases where the number of cochannels and multipaths are larger than the number of antenna elements. The proposed algorithm achieves the optimal solution for the uplink that minimizes the mobile power, and achieves a feasible solution for the downlink if any exists.
A unified SQRT-free rank-1 up/down-dating approach for recursive least-squares problems

Planar (Givens) and hyperbolic rotations are the most commonly used methods in performing QRD up/downdating. Since the square-root operation takes up much area and its computational time is slow (due to many iterations), the associated area/time efficiency is poor. This is the first effort to establish the basic understanding toward all known square-root-free QRD algorithms, from which the basic criterion is seen to be simple. This unified approach provides a fundamental framework for the square-root-free RLS algorithms essential for practical VLSI implementations.<<ETX>>
Genomic processing for cancer classification and prediction - Abroad review of the recent advances in model-based genomoric and proteomic signal processing for cancer detection

This article discusses signal processing and modelling of genomic and proteomic data from two cutting edge technologies, namely microarray technology and mass spectrometry (MS) technologies, as they are clearly among the leading frontiers that can reshape cancer study. The paper is organised as follows: first, a review of the few major design methodologies for cancer classification and prediction using genomic pr proteomic data. We then present an ensemble dependence model (EDM)-based framework and discuss the concept of dependence network. The EDM network is applied to both microarray gene expression and MS data sets in cancer study. We also present the performance-based idea and dependence network-based idea for biomarker identification. Our goal is to provide a broad review of the recent advances on model-based genomic and proteomic signal processing for cancer detection and prediction
Distributed Energy-Efficient Cooperative Routing in Wireless Networks

Recently, cooperative routing in wireless networks has gained much interest due to its ability to exploit the broadcast nature of the wireless medium in designing power-efficient routing algorithms. Most of the existing cooperation-based routing algorithms are implemented by finding a shortest-path route first. As such, these routing algorithms do not fully exploit the merits of cooperative communications at the physical layer. In this paper, we propose a cooperation-based routing algorithm, namely, minimum power cooperative routing (MPCR) algorithm, which makes full use of the cooperative communications while constructing the minimum-power route. The MPCR algorithm constructs the minimum-power route as a cascade of the minimum-power single-relay building blocks from the source to the destination. Hence, any distributed shortest-path algorithm can be utilized to find the optimal route with polynomial complexity, while guaranteeing certain throughput. We show that the MPCR algorithm can achieve power saving of 57.36% compared to the conventional shortest-path routing algorithms. Furthermore, the MPCR algorithm can achieve power saving of 37.64% compared to the existing cooperative routing algorithms, in which the selected routes are constructed based on the noncooperative routes.
Power minimization for multi-cell OFDM networks using distributed non-cooperative game approach

In this paper, we use a noncooperative game approach to have distributed subchannel assignment, adaptive modulation, and power control for multi-cell OFDM networks. The goal is to minimize the overall transmitted power under each user's maximal power and minimal rate constraints. Our contribution is to model and solve this complicated problem by a distributed noncooperative game approach: Each user water-fills its power to different subchannels regarding other users' powers as interference. A noncooperative game is constructed for each user to compete with others. A method is constructed as a mediator (judge) for the game. From the simulation results, the proposed scheme reduces the overall transmitted power greatly compared with the fixed channel assignment algorithm and pure water-filling algorithm.
Energy-efficient cellular network operation via base station cooperation

The rising cost of energy and increased environmental awareness have sparked a keen interest in the development and deployment of energy-efficient communication technologies. The energy efficiency of cellular networks can be increased significantly by selectively switching off some of the base stations (BSs) during periods of low traffic load. In this paper, we propose a scalable BS switching strategy and use cooperative communications and power control to extend network coverage to the service areas of the switched-off BSs. The outage probability and the achievable power savings of the proposed scheme are analyzed, both analytically and numerically, and a potential of up to 50% power saving is observed in the numerical results.
Time reversal based wireless events detection

In this work, we propose a novel wireless time-reversal indoor events detection system (TRIEDS). By leveraging the time-reversal (TR) technique to capture the changes of channel state information (CSI) in the indoor environment, TRIEDS enables low-complexity single-antenna devices that operate in the ISM band to perform through-the-wall multiple events detection. In TRIEDS, each indoor event is detected by matching the instantaneous CSI to a multipath profile in a training database. To validate the feasibility of TRIEDS and to evaluate the performance, we build a prototype that works on ISM band with carrier frequency being 5.4 GHz and a 125 MHZ bandwidth. Experiments are conducted to monitor the states of the indoor wooden doors. Experimental results show that with a single receiver (AP) and transmitter (client), TRIEDS can achieve a detection rate higher than 96:92% and a false alarm rate smaller than 3:08% under either line-of-sight (LOS) or non-LOS transmission.
Length- and cost-dependent local minima of unconstrained blind channel equalizers

Baud-rate linear blind equalizers may converge to undesirable stable equilibria due to different mechanisms. One such mechanism is the use of linear FIR filters as equalizers. It is shown that this type of local minima exist for all unconstrained blind equalizers whose cost functions satisfy two general conditions. The local minima generated by this mechanism are thus called length-dependent local minima. Another mechanism is generated by the cost function adopted by the blind algorithm itself. This type of local minima are called cost-dependent local minima. It is shown that several well-designed algorithms do not have cost-dependent local minimum, whereas other algorithms, such as the decision-directed equalizer and the stop-and-go algorithm (SGA), do. Unlike many existing convergence analysis, the convergence of the Godard (1980) algorithms (GAs) and standard cumulant algorithms (SCAs) under Gaussian noise is also presented. Computer simulations are used to verify the analytical results.
Adaptive beamforming and power allocation for OFDM over wireless networks

The performance of a multiuser wireless network using OFDM, combined with power control and adaptive beamforming for uplink transmission is presented. An adaptive power control algorithm is exploited to achieve the desired signal to noise and interference ratio (SINR) at each subchannel and increase the power efficiency of the mobile transmitter. At the receiver side, the base station uses an antenna array to optimize the SNR-power efficiency, and attenuate the interference from other users dramatically. Therefore, we can achieve a better overall error probability with a fixed total power. A distributed iterative algorithm is used to jointly update the transmission power and the beamformer weights at each subchannel so that it can converge to the optimal solution for both power and beamforming vectors at each subchannel. The algorithm uses only the interference measured locally by the transmitter. Unlike most of the loading algorithms which optimize the bit distribution and subchannel power allocation for a single transmitter this approach tries to optimize the power allocation and decrease the interference for the whole network.
Advances in Energy Harvesting Communications: Past, Present, and Future Challenges

Recent emphasis on green communications has generated great interest in the investigations of energy harvesting communications and networking. Energy harvesting from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many attractive benefits to the environment and deployment. However, unlike the conventional stable energy, the intermittent and random nature of the renewable energy makes it challenging in the realization of energy harvesting transmission schemes. Extensive research studies have been carried out in recent years to address this inherent challenge from several aspects: energy sources and models, energy harvesting and usage protocols, energy scheduling and optimization, implementation of energy harvesting in cooperative, cognitive radio, multiuser and cellular networks, etc. However, there has not been a comprehensive survey to lay out the complete picture of recent advances and future directions. To fill such a gap, in this paper, we present an overview of the past and recent developments in these areas and highlight a number of possible future research avenues.
Outage probability optimization with equal and unequal transmission rates under energy harvesting constraints

In this paper, we study the power scheduling problem to optimize the outage probability with both equal and unequal transmission rates under energy harvesting constraints. In particular, we first model the average outage probability minimization problem over finite horizon, and then reformulate the problem by minimizing its upper bound, which converts the non-convex problem to a more efficient convex optimization problem. Next, we show the optimal power scheduling solution is related to the global minimum of an energy-and-time function which is defined as arithmetic average of harvested energy from current time slot to the arbitrary time slot in the future before the end of transmission. Based on such properties, we propose a Min-Average strategy which presents the optimal performance in upper bound of outage probability for equal and non-equal transmission rates. Simulation results show that, our power scheduling scheme achieves better outage performance compared to other alternative strategies, e.g, best-effort, fixed-saving and random saving strategies.
VLSI implementation considerations for turbo decoding using a low latency log-MAP

The soft-output Viterbi  p.260-4, 1998) algorithm (SOVA) and the log-maximum a posterior probability (log-MAP) algorithm are commonly used in turbo decoding. We propose to modify the sliding window MAP-algorithm of Viterbi to reduce the computational delay even further. We compare the simulation performance of this low latency log-MAP algorithm with the sliding window log-MAP. We also estimate the VLSI implementation complexities of the SOVA, the log-MAP and the proposed low latency log-MAP.
Anti-forensics of JPEG compression

The widespread availability of photo editing software has made it easy to create visually convincing digital image forgeries. To address this problem, there has been much recent work in the field of digital image forensics. There has been little work, however, in the field of anti-forensics, which seeks to develop a set of techniques designed to fool current forensic methodologies. In this work, we present a technique for disguising an image's JPEG compression history. An image's JPEG compression history can be used to provide evidence of image manipulation, supply information about the camera used to generate an image, and identify forged regions within an image. We show how the proper addition of noise to an image's discrete cosine transform coefficients can sufficiently remove quantization artifacts which act as indicators of JPEG compression while introducing an acceptable level of distortion. Simulation results are provided to verify the efficacy of this anti-forensic technique.
Space-Time Network Codes Utilizing Transform-Based Coding

Cooperative communications can be used to improve communication reliability. However, the transmissions from different relaying nodes result in a great challenge in practice. The use of TDMA for relaying transmissions causes large transmission delay. In contrast, FDMA and CDMA associate with the issue of imperfect frequency and timing synchronization due to the simultaneous transmissions from the relaying nodes. In this work, we propose a space-time network coding (STNC) scheme utilizing transform-based coding to improve communication reliability while maintaining a stable network throughput and overcoming the issue of imperfect frequency and timing synchronization. Based on TDMA, a node in the network forms a linearly-coded signal from the overheard symbols and transmits it to the destination in its dedicated time slot. The pairwise error probability (PEP) is analyzed and design criteria of the STNC are derived to ensure achieving full diversity. Simulations are conducted to verify the performance of the proposed scheme and to reveal its advantage over a distributed space-time block coding scheme under timing synchronization errors.
Graphical Evolutionary Game for Information Diffusion Over Social Networks

Social networks have become ubiquitous in our daily life, as such they have attracted great research interests recently. A key challenge is that it is of extremely large-scale with tremendous information flow, creating the phenomenon of “Big Data.” Under such a circumstance, understanding information diffusion over social networks has become an important research issue. Most of the existing works on information diffusion analysis are based on either network structure modeling or empirical approach with dataset mining. However, the information diffusion is also heavily influenced by network users' decisions, actions and their socio-economic connections, which is generally ignored in existing works. In this paper, we propose an evolutionary game theoretic framework to model the dynamic information diffusion process in social networks. Specifically, we analyze the framework in uniform degree and non-uniform degree networks and derive the closed-form expressions of the evolutionary stable network states. Moreover, the information diffusion over two special networks, Erdos-Renyi random network and the Barabasi-Albert scale-free network, are also highlighted. To verify our theoretical analysis, we conduct experiments by using both synthetic networks and real-world Facebook network, as well as real-world information spreading dataset of Memetracker. Experiments shows that the proposed game theoretic framework is effective and practical in modeling the social network users' information forwarding behaviors.
Traitor-Within-Traitor Behavior Forensics: Strategy and Risk Minimization

Multimedia security systems have many users with different objectives and they influence each other's performance and decisions. Behavior forensics analyzes how users with conflicting interests interact with and respond to each other. Such investigation enables a thorough understanding of multimedia security systems and helps the digital rights enforcer offer stronger protection of multimedia. This paper analyzes the dynamics among attackers during multiuser collusion. The colluders share not only the profit from the redistribution of multimedia but also the risk of being detected by the content owner, and an important issue in collusion is fairness of the attack (i.e., whether all attackers share the same risk) (e.g., whether they have the same probability of being detected). While they might agree so, some selfish colluders may break their fair-play agreement in order to further lower their risk. This paper investigates the problem of "traitors within traitors" in multimedia forensics, in an effort to formulate the dynamics among attackers and understand their behavior to minimize their own risk and protect their own interests. As the first work on the analysis of this colluder dynamics, this paper explores some possible strategies that a selfish colluder can use to minimize his or her probability of being caught. We show that processing his or her fingerprinted copy before multiuser collusion helps a selfish colluder further lower his or her risk, especially when the colluded copy has high resolution and good quality. This paper also investigates the optimal precollusion processing strategies for selfish colluders to minimize their risk under the quality constraints