In wireless communication systems users compete for communication opportunities through a medium access control protocol. Previous research has shown that selfish behavior in medium access games could lead to inefficient and unfair resource allocation. We introduce a new notion of reciprocity in a medium access game and derive the corresponding Fairness Nash equilibrium. Further, using mechanism design we show that this type of reciprocity can remove unfair/inefficient equilibrium solutions.

This paper proposes an active steganalysis method for quantization index modulation (QIM)-based steganography. The proposed nonparametric steganalysis method uses irregularity (or randomness) in the test image to distinguish between the cover image and the stego image. We have shown that plain quantization (quantization without message embedding) induces regularity in the resulting quantized object, whereas message embedding using QIM increases irregularity in the resulting QIM-stego. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test image. The QIM-stego image is then analyzed to estimate secret message length. To this end, the QIM codebook is estimated from the QIM-stego image using first-order statistics of the image coefficients in the embedding domain. The estimated codebook is then used to estimate secret message. Simulation results show that the proposed scheme can successfully estimate the hidden message from the QIM-stego with very low decoding error probability. For a given cover object the decoding error probability depends on embedding rate and decreases monotonically, approaching zero as the embedding rate approaches one.

Abstract Traditional automatic repeat request (ARQ) protocols are channel unaware. That is, they react to channel errors by simply retransmitting erroneous packets and do not proactively decide whether or not to transmit a packet in a given slot based on past channel conditions. Clearly, ARQ protocols operating in this mode are not energy efficient. For example, continuously retransmitting erroneous packets when the wireless channel is in deep fade would cause significant wastage of transmission energy. In this paper, we present a stochastic learning automaton‐based wireless channel state aware ARQ protocol. The learning automaton learns to predict and track the time‐varying wireless channel conditions based on past observations. A Markov chain model for the channel state transitions is used. No a priori knowledge about the state transition probabilities is required by this predictor. Stochastic convergence of the learning algorithm is proved. The proposed ARQ protocol utilizes the predictions to compute transmission/retransmission policies in an online fashion. No pilot (training) symbols are used by the protocol for channel state prediction thereby avoiding any energy wastage due to the transmission of these symbols. Simulation results show that depending on the channel memory significant energy savings can be attained when compared with standard ARQ protocols. We also discuss the transmission energy versus delay trade‐off. Copyright © 2007 John Wiley & Sons, Ltd.

It is known that circuits exhibit multiple modes of power consumption due to various factors such as the presence of many feedback (or sequential) elements, RAM, large size, etc. Previous power-estimation techniques have largely ignored this fact. For example, Monte Carlo simulation-based power estimators tend to produce estimates for the average power consumption that corresponds only to the most probable power mode of the circuit. This can be a cause for trouble later in the design step. The aim of this paper is twofold. First, an algorithm is proposed that estimates the total number of power modes of a circuit based on simulated data. This is then followed by a maximum-likelihood estimation procedure that produces the average values of the power modes along with their probabilities of occurrence. Theoretical ideas are supported by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The proposed method is shown to perform well by capturing the multiple power modes for both large and small circuits even when the number of simulated samples is small while the Monte Carlo estimator does not. We conclude with a note that the proposed method is also applicable to other model selection problems in VLSI.

The estimation of average-power dissipation of a circuit through exhaustive simulation is impractical due to the large number of primary inputs and their combinations. In this work, two algorithms based on least square estimation are proposed for determining the average power dissipation in complementary metal-oxide-semiconductor (CMOS) circuits. Least square estimation converges faster by attempting to minimize the mean square error value during each iteration. Two statistical approaches namely, the sequential least square (SLS) estimation and the recursive least square estimation are investigated. The proposed methods are distribution independent in terms of the input samples, unbiased and point estimation based. Experimental results presented for the MCNC'91 and the ISCAS'89 benchmark circuits show that the least square estimation algorithms converge faster than other statistical techniques such as the Monte Carlo method and the DIPE.

A maximum-likelihood estimation procedure for computing the average power consumption of VLSI circuits is proposed. The method can handle data that has a mixture-density with multiple components unlike most of the previous approaches. An iterative computational procedure based on the expectation-maximization principle is also discussed. This can be used to estimate the parameters of an arbitrary (but finite) number of components of the probability distribution of the simulated power data. Experimental results for ISCAS '85 benchmark circuits and a large industrial circuit are given in order to validate the eciency and practicality of the algorithm. Comparisons show that the proposed method estimates the multiple components (even those with a low probability of occurrence) while the Monte Carlo estimate captures only the most probable component.

Optimal probability model selection for power estimation in low power VLSI applications is studied. Akaike's information criterion is used to estimate the optimal number of components in a mixture density model for the simulated power data. Theory behind the proposed algorithm is discussed followed by experimental results for ISCAS '85 benchmark circuits and a large industrial circuit. The method is shown to perform well for both large and small circuits even when the number of observed samples is small. The algorithm is promising as a pre-processing step to automatically compute the optimal probability model before any other power estimation procedure is applied. We also note that the method is applicable to other problems in VLSI for model selection.

In signal processing applications, it is often required to compute the integral of the bivariate Gaussian probability density function (PDF) over the four quadrants. When the mean of the random variables are nonzero, computing the closed form solution to these integrals with the usual techniques of integration is infeasible. Many numerical solutions have been proposed; however, the accuracy of these solutions depends on various constraints. In this work, we derive the closed form solution to this problem using the characteristic function method. The solution is derived in terms of the well-known confluent hypergeometric function. When the mean of the random variables is zero, the solution is shown to reduce to a known result for the value of the integral over the first quadrant. The solution is implementable in software packages such as MAPLE.

Accurate switching-activity estimation is crucial for power budgeting. It is impractical to obtain an accurate estimate by simulating the circuit for all possible inputs. An alternate approach would be to compute tight bounds for the switching activity. In this paper, we propose a nonsimulative decision theoretic method to compute the lower bound for switching activity. First, we show that the switching activity can be modeled as the decision error of an abstract two-class problem. It is shown that the Bayes error L* is a lower bound for the switching activity. Further, we improve L* to obtain a tighter bound L/sub 1/, which is based on the one-nearest neighbor classification error. The proposed lower bounds are used for switching-activity characterization at the register transfer (RT) level. Experimental results for the RT-level switching-activity estimates for ISCAS'85 circuits are presented. This technique is simple and fast and produces accurate estimates.

Summary form only given. Channel matched quantization for image transmission over time varying channels reduces the effects of channel errors. The presence of variable length codes in compression standards like the JPEG cause error propagation due to bit errors. Unequal error protection (UEP) schemes have emerged as an effective method to combat catastrophic loss in the received signal due to burst and random errors. An empirical channel matched quantizer design algorithm that jointly optimizes the distortion due to quantization-channel noise and a new efficient UEP scheme for image transmission are proposed. The baseline JPEG encoder is used to compress the 8-bit gray level images before transmission. A slow frequency non-selective Rayleigh fading channel is considered in this study. A quantization table optimized for human visual quality is used for very low channel bit error rates. For higher bit error rates, the quantization table is matched to the channel conditions by multiplying its entries by the optimal quantization multiplication factor, M/sup */ such that the average number of received image blocks in error is minimized. M/sup */ is computed for each bit error rate ranging from 10/sup -4/ to 10/sup -1/ through empirical modeling of the trade-off between the quantization and the channel noise. In order to enhance the performance of the proposed system, a new UEP scheme that limits the error propagation due to variable length encoding is used. This scheme works by packing the output bits of the JPEG coder into slots of fixed size.

Optimal quantization and channel estimation are one among the main issues in low bit-rate video transmission over time varying noisy channels. Previous approaches to these issues were mainly based on quantizers optimized for parametric channel models and pilot symbol aided techniques for channel identification. However, this optimality may not hold when the randomly varying channel behavior deviates from these models. Also, the cost involved in terms of delay could be large for pilot symbol based channel estimation. We propose a new empirically optimized channel matched quantizer and a stochastic learning algorithm that estimates and tracks the channel with minimal additional delay and overhead. Performance analysis of the algorithm shows that the new adaptive quantizer results in a better quality video. The learning algorithm converges very fast.

In this paper, a rate controller for a H.261 based video encoder is proposed. The rate controller adaptively chooses the optimal channel matched quantizer using a stochastic learning automaton. The automaton learns the channel characteristics based on a one bit feedback from the decoder. The rate control algorithm is shown to converge to the optimal choice of the quantizer very quickly for various channel bit error probabilities and for different video sequences. The adaptation can be achieved in real-time. The peak signal to noise ratio of the received video signal is seen to be better using the proposed approach.

It is known that for fixed error probabilities sequential signal detection based on the sequential probability ratio test (SPRT) is optimum in terms of the average number of signal samples for detection. But, often suboptimal detectors like the sequential sign detector are preferred over the optimal SPRT. When the additive noise statistic is independent and identically distributed (i.i.d.), the sign detector is preferred for its simplicity and nonparametric properties. However, in many practical applications such as the usage of high speed sampling devices the noise is correlated. A generalized sequential sign detector for detecting binary signals in stationary, first-order Markov dependent noise is studied. Under the i.i.d. assumptions, this reduces to the usual sequential sign detector. The optimal decision thresholds and the average sample number for the test to terminate are derived. Numerical results are given to show that the proposed detector exploits the correlation in the noise and hence results in quicker detection. The method can also be extended to Mth order Markov dependence by converting it to a first-order dependence in an extended state space.

There has been an outburst of research in image and video compression for transmission over noisy channels. Channel matched source quantizer design has gained prominence. Further, the presence of variable-length codes in compression standards like the JPEG and the MPEG has made the problem more interesting. Error-resilient entropy coding (EREC) has emerged as a new and effective method to combat catastrophic loss in the received signal due to burst and random errors. We propose a new channel-matched adaptive quantizer for JPEG image compression. A slow, frequency-nonselective Rayleigh fading channel model is assumed. The optimal quantizer that matches the human visibility threshold and the channel bit-error rate is derived. Further, a new fast error-resilient entropy code (FEREC) that exploits the statistics of the JPEG compressed data is proposed. The proposed FEREC algorithm is shown to be almost twice as fast as EREC in encoding the data, and hence the error resilience capability is also observed to be significantly better. On average, a 5% decrease in the number of significantly corrupted received image blocks is observed with FEREC. Up to a 2-dB improvement in the peak signal-to-noise ratio of the received image is also achieved.