This paper derives highly accurate and effective closed-form formulas for the average upper bound on the pairwise error probability (PEP) of the multi-carrier index keying orthogonal frequency division multiplexing (MCIK-OFDM) system with low-complexity detection (i.e., greedy detection) in two-wave with diffuse power (TWDP) fading channels. To be specific, we utilize an exact moment generating function (MGF) of the signal-to-noise ratio (SNR) under TWDP fading to guarantee highly precise investigations of error probability performance; existing formulas for average PEP employ the approximate probability density function (PDF) of the SNR for TWDP fading, thereby inducing inherent approximation error. Moreover, some special cases of TWDP fading are also considered. To quantitatively reveal the achievable modulation gain and diversity order, we further derive asymptotic formulas for the upper bound on the average PEP. The obtained asymptotic expressions can be used to rapidly estimate the achievable error performance of MCIK-OFDM with the greedy detection over TWDP fading in high SNR regimes.

This letter considers a legitimate proactive eavesdropping scenario, where a half-duplex legitimate monitor hires a third-party jammer for jamming the suspicious communication to improve the eavesdropping performance. The interaction between the third-party jammer and the monitor is modeled as a Stackelberg game, where the jammer moves first and sets the price for jamming the suspicious communication, and then the legitimate monitor moves subsequently and determines the requested transmit power of the jamming signals. We derive the optimal jamming price and the optimal jamming transmit power. It is shown that the proposed price-based proactive eavesdropping scheme is effective in improving the successful eavesdropping probability compared to the case without jamming. It is also shown that the proposed scheme outperforms the existing full-duplex scheme when the residual self-interference cannot be neglected.

The recent switch from analog to digital TV broadcasting around the world has led to the development of communications standards that consider the use of TV White Spaces (TVWS). One such standard is the IEEE 802.22 wireless regional area network (WRAN), which considers the use of TVWS to provide broadband wireless services over long transmission links, and therefore presents an opportunity to bring connectivity and data-based services from urban to rural areas. Services that could greatly benefit from the deployment of wireless broadband data links between urban and rural areas are those related to telemedicine and m-health. To enable proper telemedicine service delivery from urban (e.g. an urban hospital) to rural locations (e.g. a rural clinic) it is of paramount importance to provide a certain quality of service (QoS) level. In this context, QoS provisioning for telemedicine applications over wireless networks presents a major challenge that must be addressed to fulfill the potential that rural wireless telemedicine has to offer. In this paper, a cross-layer approach combining medium access control (MAC) and application (APP) layers is proposed with the aim of reducing blocking probability in teleconsulting services operating over IEEE802.22/WRANs. At the APP layer, a teleconsulting traffic profile based on utilization rates is defined. On the other hand, at the MAC layer, an Adaptive Bandwidth Management (ABM) mechanism is used to perform a QoS-based classification of teleconsulting services and then dynamically allocate the bandwidth requirements. Three teleconsulting services with different bandwidth requirements are considered in order to evaluate the performance of the proposed approach: high-resolution teleconsulting, medium-resolution teleconsulting, and audio-only teleconsulting. Simulation results demonstrate that the proposed approach is able to reduce blocking probability by using different criteria for service modes within the admission control scheme.

In this paper, a new and an accurate symbol error probability's analytical model of Rectangular Quadrature Amplitude Modulation in α-µ fading channel is presented for single-user single-input multi-output environment, which can be easily extended to generalized fading channels. The maximal-ratio combining technique is utilized at the receiving end and unified moment generating functions are used to derivate the results. The fading mediums considered are independent and non-identical. The mathematical model presented is applicable for slow and frequency non-selective fading channels only. The final expression is presented in terms of Meijer G-function; it contains single integrals with finite limits to evaluate the mathematical expressions with numerical techniques. The beauty of the model will help evaluate symbol error probability of rectangular quadrature amplitude modulation with spatial diversity over various fading mediums not addressed in this article. To check for the validity of derived analytical expressions, comparison is made between theoretical and simulation results at the end.

The conventional generalized spatial modulation (GSM) simultaneously activates multiple transmit antennas in order to improve the spectral efficiency of the original SM. In this letter, to lessen the hardware burden of the multiple RF chains, we provide a new scheme that is designed by combining the GSM scheme using only two active antennas with quaternary quasi-orthogonal sequences of a length of two. Compared with the other SM schemes, the proposed scheme has significant benefits in average error performances and/or their hardware complexities of the RF systems.

An optimal design method for a sub-ranging Analog-to-Digital Converter (ADC) based on stochastic comparator is demonstrated by performing theoretical analysis of random comparator offset voltages. If the Cumulative Distribution Function (CDF) of the comparator offset is defined appropriately, we can calculate the PDFs of the output code and the effective resolution of a stochastic comparator. It is possible to model the analog-to-digital conversion accuracy (defined as yield) of a stochastic comparator by assuming that the correlations among the number of comparator offsets within different analog steps corresponding to the Least Significant Bit (LSB) of the output transfer function are negligible. Comparison with Monte Carlo simulation verifies that the proposed model precisely estimates the yield of the ADC when it is designed for a reasonable target yield of >0.8. By applying this model to a stochastic comparator we reveal that an additional calibration significantly enhances the resolution, i.e., it increases the Number of Bits (NOB) by ∼ 2 bits for the same target yield. Extending the model to a stochastic-comparator-based sub-ranging ADC indicates that the ADC design parameters can be tuned to find the optimal resource distribution between the deterministic coarse stage and the stochastic fine stage.

This letter investigates the performance of a legitimate surveillance system, where a wireless powered legitimate monitor aims to eavesdrop a suspicious communication link. Power splitting technique is adopted at the monitor for simultaneous information eavesdropping and energy harvesting. In order to maximize the successful eavesdropping probability, the power splitting ratio is optimized under the minimum harvested energy constraint. Assuming that perfect channel state information (CSI) or only the channel distribution information (CDI) is available, the closed-form maximum successful eavesdropping probability is obtained in Rayleigh fading channels. It is shown that the minimum harvested energy constraint has no impact on the eavesdropping performance if the minimum harvested energy constraint is loose. It is also shown that the eavesdropping performance loss due to partial knowledge of CSI is negligible when the eavesdropping link channel condition is much better than that of the suspicious communication link channel.

Feature visualization is of great significances in volume visualization, and feature extraction has been becoming extremely popular in feature visualization. While precise definition of features is usually absent which makes the extraction difficult. This paper employs probability density function (PDF) as statistical property, and proposes a statistical property guided approach to extract features for volume data. Basing on feature matching, it combines simple liner iterative cluster (SLIC) with Gaussian mixture model (GMM), and could do extraction without accurate feature definition. Further, GMM is paired with a normality test to reduce time cost and storage requirement. We demonstrate its applicability and superiority by successfully applying it on homogeneous and non-homogeneous features.

Word sense disambiguation (WSD) is to identify the right sense of ambiguous words via mining their context information. Previous studies show that classifier combination is an effective approach to enhance the performance of WSD. In this paper, we systematically review state-of-the-art methods for classifier combination based WSD, including probability-based and voting-based approaches. Furthermore, a new classifier combination based WSD, namely the probability weighted voting method with dynamic self-adaptation, is proposed in this paper. Compared with existing approaches, the new method can take into consideration both the differences of classifiers and ambiguous instances. Exhaustive experiments are performed on a real-world dataset, the results show the superiority of our method over state-of-the-art methods.

Sudoku is a pencil puzzle. The aim of the solver is to complete the 9×9 grid by filling in a digit in every cell according to a certain rule. In this study, we regard the process of solving Sudoku as a process of decoding a codeword from a received word, and show the expected decoding error probability for erasure channels obtained by experiments.

A circuit-aging simulation that efficiently calculates temporal change of rare circuit-failure probability is proposed. While conventional methods required a long computational time due to the necessity of conducting separate calculations of failure probability at each device age, the proposed Monte Carlo based method requires to run only a single set of simulation. By applying the augmented reliability and subset simulation framework, the change of failure probability along the lifetime of the device can be evaluated through the analysis of the Monte Carlo samples. Combined with the two-step sample generation technique, the proposed method reduces the computational time to about 1/6 of that of the conventional method while maintaining a sufficient estimation accuracy.

This letter treats the problem of lossless fixed-to-variable length source coding in moderate deviation regime. We investigate the behavior of the overflow probability of the Bayes code. Our result clarifies that the behavior of the overflow probability of the Bayes code is similar to that of the optimal non-universal code for i.i.d. sources.

In this paper, we propose a new spatial modulation (SM) scheme based on quaternary quasi-orthogonal sequences (Q-QOSs), referred to as Q-QOS-SM. First, the conventional SM and generalized-SM (GSM) schemes are reinterpreted as a new transmission scheme based on a spatial modulation matrix (SMM), whose column indices are used for the mapping of spatial-information bits unlike the conventional ones. Next, by adopting the SMM comprising the Q-QOSs, the proposed Q-QOS-SM that guarantees twice the number of spatial bits at a transmitter compared with the SM with a constraint of transmit antennas, is designed. From the computer-simulation results, the Q-QOS-SM is shown to achieve a greatly improved throughput compared with the conventional SM and GSM schemes, especially, for a large number of the receive antennas. This finding is mainly because the new scheme offers a much higher minimum Euclidean distance than the other schemes.

We previously proposed a lossless image coding scheme using example-based probability modeling, wherein the probability density function of image signals was dynamically modeled pel-by-pel. To appropriately estimate the peak positions of the probability model, several examples, i.e., sets of pels whose neighborhoods are similar to the local texture of the target pel to be encoded, were collected from the already encoded causal area via template matching. This scheme primarily makes use of non-local information in image signals. In this study, we introduce a prediction technique into the probability modeling to offer a better trade-off between the local and non-local information in the image signals.

The aim of this letter is to guarantee the ability of low probability of intercept (LPI) and anti-jamming (AJ) by maximizing the energy efficiency (EE) to improve wireless communication survivability and sustain wireless communication in jamming environments. We studied a scenario based on one transceiver pair with a partial-band noise jammer in a Rician fading channel and proposed an EE optimization algorithm to solve the optimization problem. With the proposed EE optimization algorithm, the LPI and AJ can be simultaneously guaranteed while satisfying the constraint of the maximum signal-to-jamming-and-noise ratio and combinatorial subchannel allocation condition, respectively. The results of the simulation indicate that the proposed algorithm is more energy-efficient than those of the baseline schemes and guarantees the LPI and AJ performance in a jamming environment.

In data stream analysis, detecting the concept drift accurately is important to maintain the classification performance. Most drift detection methods assume that the class labels become available immediately after a data sample arrives. However, it is unrealistic to attempt to acquire all of the labels when processing the data streams, as labeling costs are high and much time is needed. In this paper, we propose a concept drift detection method under the assumption that there is limited access or no access to class labels. The proposed method detects concept drift on unlabeled data streams based on the class label information which is predicted by a classifier or a virtual classifier. Experimental results on synthetic and real streaming data show that the proposed method is competent to detect the concept drift on unlabeled data stream.

In this letter, we consider a cognitive radio network where multiple secondary users (SUs) share the spectrum bands with multiple primary users (PUs) who are facing security threats from multiple eavesdroppers. By adopting the PU secrecy outage constraint to protect the PUs, we optimize the joint user and power allocation for the SUs to maximize the SU ergodic transmission rate. Simulation results are presented to verify the effectiveness of the proposed algorithm. It is shown that the proposed algorithm outperforms the existing scheme, especially for a large number of PUs and a small number of SUs. It is also shown that the number of eavesdroppers has negligible impact on the performance improvement of the proposed algorithm compared to the existing scheme. In addition, it is shown that increasing the number of eavesdroppers has insignificant impact on the SU performance if the number of eavesdroppers is already large.

In this letter, we focus on a system where N sources send n ≤ N different packets to one destination, through M ≥ N relays. Each relay employs random linear network coding to encode the packets it received by randomly choosing coefficients in a finite field Fq, then forwards it to the destination. Owing to the inherent errorprone nature of erasure channels, data packets received by the relay and the destination nodes may not be correct. We analyze the optimal throughput with respect to n, given a series of parameters and derive the upper and lower bounds of throughput performance. We also analyze the impact of the number of relays and the erasure probability on the throughput performance. Simulation results are well matched with the theoretical analysis.

In this paper, the performance of orthogonal space-time block codes (OSTBC) for distributed multiple-input multiple-output (MIMO) systems employing adaptive M-QAM transmission is investigated over independent but not necessarily identically distributed (i.n.i.d.) generalized-K fading channels with arbitrary positive integer-valued k(inversely reflects the shadowing severity) and m (inversely reflects the fading severity). Before this, i.n.i.d. generalized-K fading channel has never been considered for distributed OSTBC-MIMO systems. Especially, the effects of the shape parameter k on the distributed OSTBC-MIMO system performance are unknown. Thus, we investigate mainly the significance of the shape parameter k on the distributed OSTBC-MIMO system performance, in terms of the average symbol error probability (SEP), outage probability, and spectral efficiency (SE). By establishing the system model, the approximated probability density function (PDF) of the equivalent signal to noise ratio (SNR) is derived and thereafter the approximated closed-form expressions of the above performance metrics are obtained successively. Finally, the derived expressions are validated via a set of Monte-Carlo simulations and the implications of the shape parameter k on the overall performance are highlighted.

The biometrical identification system, introduced by Willems et al., is a system to identify individuals based on their measurable physical characteristics. Willems et al. characterized the identification capacity of a discrete memoryless biometrical identification system from information theoretic perspectives. Recently, Mori et al. have extended this scenario to list-decoding whose list size is an exponential function of the data length. However, as the data length increases, how the maximum identification error probability (IEP) behaves for a given rate has not yet been characterized for list-decoding. In this letter, we investigate the reliability function of the system under fixed-size list-decoding, which is the optimal exponential behavior of the maximum IEP. We then use Arimoto's argument to analyze a lower bound on the maximum IEP with list-decoding when the rate exceeds the capacity, which leads to the strong converse theorem. All results are derived under the condition that an unknown individual need not be uniformly distributed and the identification process is done without the knowledge of the prior distribution.