Analysis of radio frequency spectrum usage using cognitive radio

This paper presents the analysis of radio frequency (RF) spectrum usage using cognitive radio. The aim was to determine the unused spectrum frequency bands for efficiently utilization. A program was written to reuse a range of vacant frequency with different model element working together to produce a spectrum sensing in MATLAB/Simulink environment. The developed Simulink model was interfaced with a register transfer level - software defined radio, which measures the estimated noise power of the received signal over a given time and bandwidth. The threshold estimation performed generates a 1\0 output for decision and prediction. It was observed that some spectrum, identified as vacant frequency, were underutilized in FM station in Benin City. The result showed that when cognitive radio displays “1” output, which is decision H1, the channel is occupied and cannot be used by the cognitive radio for communication. Conversely, when “0” output (decision H0) is displayed, the channel is unoccupied. There is a gradual decrease in the probability of detection (Pd), when the probability of false alarm (Pfa) is increased from 1% to 5%. In the presence of higher Pfa, the Pd of the receiver maintains a high stability. Hence, the analysis finds the spectrum hole and identifies how it can be reuse

Cognitive Radio Made Practical: Forward-Lookingness and Calculated Competition

Cognitive radio is more than just radio environment awareness, but more importantly the ability to interact with the environment in the best way possible. Ideally, cognitive radios will form a selfregulating society of mobile radios achieving maximum spectrum utilization. However, challenges arise as mobile radios tend to compete with one another for spectrum, generating harmful interference and damaging performance individually and for the network as a whole. In this paper, we present a framework that allows competing radios to teach and learn from each other’s action so that a desirable equilibrium can be reached. The heart of cognition to establish this is the forward-looking ability, which enables competing radios to see beyond the present time, negotiate and optimize their actions towards a more agreeable equilibrium. Technically speaking, we adopt a belief-directed game where each mobile radio, regarded as player, formulates a belief function to project how the radio environment as a whole would respond to any of its action. This model facilitates engineering of the equilibrium by different choices of the players’ belief functions. Under this model, players will negotiate naturally through a sequence of calculated competition (i.e., cycles of teaching and learning with each other). We apply this methodology to a cognitive orthogonal frequency-division multiple-access (OFDMA) radio network where mobile users are free to access any of the subcarriers and thus compete for radio resources to maximize their rates. Results reveal that the proposed negotiation-by-forward-looking competition mechanism guides users to converge to an equilibrium that benefits not only individual users but the entire network approaching the maximum achievable sum-rate