On the Applicability of Average Channel Capacity in Log-Normal Fading Environment

The statistical characterization of channel capacity in slow fading environment modeled by log-normal probability density function is considered. The probability density function of channel capacity is found to be positively skewed and significantly departs from bell shaped curve for higher values of σ _dB . The computation of first two moments of channel capacity provides a measure for relative fluctuation in terms of coefficient of variation (CV). It is noted that the value of CV is quite high in lower range of SNR and starts declining with increasing SNR. This may question the applicability of average channel capacity as a performance measure in slow fading scenario and accordingly a more appropriate measure is based on outage capacity. A simple procedure based on three-point estimate is outlined to obtain the approximate expression for higher order moments of channel capacity and they are found to be in excellent agreement with exact results.     

On efficacy of Rayleigh‐inverse Gaussian distribution over K‐distribution for wireless fading channels

For studying performance characteristics of radio channels, the knowledge about the probability density function (pdf) of fading–shadowing effects is essential. K‐distribution corresponding to Rayleigh–gamma distribution (RGD) is widely used to approximate a more realistic Rayleigh–lognormal distribution (RLD) which does not have a closed form expression. A new composite Rayleigh‐inverse Gaussian distribution (RIGD), an alternative to K‐distribution, is analyzed with regards to its suitability and effectiveness in radio channels. Detailed investigations are made to study the performance characteristics of RIGD and K‐distribution (RGD) in terms of Kullback–Leibler (KL) measure of divergence. Based on these investigations, it is found that RIGD is better suited for capturing fading–shadowing aspects of radio channels instead of K‐distribution. Copyright © 2006 John Wiley & Sons, Ltd.     

Performance Analysis of Composite Fading Channel Based on Point Estimate Method

Existing spectrum sensing methods for cognitive radio do not consider the secondary network’s characteristics to obtain the frequency of spectrum sensing, i.e., the sensing period would be identical for secondary networks that have different traffic characteristics. In this paper, a hybrid sensing algorithm is proposed that finds the optimal sensing period based on both primary and secondary networks’ properties. A continuous-time Markov chain system is used to accurately model the spectrum occupancy, and a novel method is proposed that adaptively varies its parameters to avoid unnecessary sensing tasks, while guaranteeing the priority of the primary network. We conduct simulation work to evaluate the performance of the proposed method. It is shown that the proposed technique outperforms the non-hybrid approach with respect to sensing efficiency and energy consumption. A cognitive sensor network is also considered based on IEEE 802.15.4/ZigBee radios, and it is shown that significant energy savings can be achieved by the proposed method.     

Queue length distribution of network packet traffic: Tsallis entropy maximization with fractional moments

A framework based on maximization of Tsallis entropy constrained by fractional moments is proposed to model queue length distribution of number of packets in network traffic exhibiting long-range behavior. For appropriate range of the Tsallis entropy parameter q, it is found that the first moment of number of packets may not exist Based on Euler summation formula, explicit expressions for mean queue length and buffer overflow probability exhibiting power law behavior are obtained. It is shown that in the limiting case as q tends to 1, one recovers the asymptotic results for buffer overflow probability depicting Weibull-like tail.     

On population growth of cities in a region: a stochastic nonlinear model

"A stochastic migration model describing the population dynamics in a region is investigated. The model is described by a pair of coupled differential equations with state-dependent stochasticity. Explicit expressions for the time evolution of the moments for the population sizes of cities are obtained from the Fokker-Planck equation. The Stratonovich calculus is employed in the analysis."     

Adaptive mean queue size and its rate of change: queue management with random dropping

The random early detection active queue management (AQM) scheme uses the average queue size to calculate the dropping probability in terms of minimum and maximum thresholds. The effect of heavy load enhances the frequency of crossing the maximum threshold value resulting in frequent dropping of the packets. An adaptive queue management with random dropping algorithm is proposed which incorporates information not just about the average queue size but also the rate of change of the same. Introducing an adaptively changing threshold level that falls in between lower and upper thresholds, our algorithm demonstrates that these additional features significantly improve the system performance in terms of throughput, average queue size, utilization and queuing delay in relation to the existing AQM algorithms.