1. Prashant Bajpayee Advisor: Dr. Daniel Noneaker SURE 2005 Motivation Currently most radio-frequency spectrum is assigned exclusively to “primary” users (e.g. Broadcasting of T.V. channels) Assigned spectrum is often underutilized in many locations at specific times This “unused ”spectrum is lost opportunity for other potential communication systems General Solution Exploitation of unutilized spectrum on “secondary” user basis and Cognitive radio is viewed as a novel approach for efficient utilization Characteristic of CR Intelligent wireless communication system Aware of its environment Methodology of understanding-by-building to learn from environment Adapt to statistical variances in the input-stimuli Reconfigurable CR has the capability of reconfigurability which is provided by a platform known as Software- defined radio on which CR is built Key properties of Cognitive Radio This Spectrum monitoring capability Dynamic spectrum assignment protocols/rules Purpose of Research Development and simulation of a mathematical model of channel monitoring function of CR Analyze the effect of time-varying interference in each channel and design trade-offs that result Advantages Highly reliable communications whenever and wherever needed Efficient utilization of the radio spectrum Dynamically reuse available spectrum by changing its parameter System Details Multiple – channel communication system Channel monitoring approach used by destination: –When no data is transmitting, sequentially monitor channel to determine level of noise in each channel –When RTS is received,destination should send CTS specifying channel with least intereference. Assume DS Spread Spectrum binary antipodal signaling is used by transmitter so that During monitoring of traffic channel j its received signal is and are independent random process Two types of channel: Good channel with lesser interference Bad channel with more noise interference Decision Statistic Monitor channel 1 by forming statistic Where Detection Technique Similarly for all channels we generate chi-square random variable with degree 2n and pick channel with smallest value of Now each channel can be said good or bad depending upon its state at that time All Channels changes their states after each time interval = n New decision statistics are generated after n+n1 time interval but only for one channel at a time Efficiency of system = n1/n+n1 c = ratio of noise variances of good and bad channels Assume Pe = Probability of selecting BAD channel Channel = no. of channels in radio. Generalized Model CR alters between frequency monitoring and other tasks Each channel has time-varying interference/noise Now each channel is represented as two-state Markov random process = state of channel 1 at time k, may be 0 or 1 Conclusions On increasing n, error probability decreases. On increasing c, error probability decreases. Conclusions For large T-coh, increasing channel, error probability decreases. For moderate T-coh, there is an optimal number of channel. Conclusions On increasing n, error probability decreases. On increasing c, error probability decreases. Conclusions For fixed efficiency and T-coh, there is an optimal n. Increasing efficiency, increases error probability. Increasing efficiency, decreases n-optimal. Simulation Details n = time required to generate each statistics and qual to number of chi-square random variables which add up to form a test statistic n1 = the time radio spends on other tasks T_coh = measure of rate of change of states Future work Consider a more general Markov model with different parameter Examine the performance in terms of bit error probability Analyze the effect of mutual coupling amongst different radios on channel-noise distribution of each radio. Acknowledgement Dr. Daniel Noneaker Dr. Harlan Russell Dr. Xiao-Bang Xu Dr. Michael Pursley NSF 01 ----- t=0 n n+n1 2n+2n1 2n+n1 Monitor f1 work Ln+Ln1 n1 =q*n ----- Monitor f2