26 research outputs found
Wideband Spectrum Sensing in Cognitive Radio Networks
Spectrum sensing is an essential enabling functionality for cognitive radio
networks to detect spectrum holes and opportunistically use the under-utilized
frequency bands without causing harmful interference to legacy networks. This
paper introduces a novel wideband spectrum sensing technique, called multiband
joint detection, which jointly detects the signal energy levels over multiple
frequency bands rather than consider one band at a time. The proposed strategy
is efficient in improving the dynamic spectrum utilization and reducing
interference to the primary users. The spectrum sensing problem is formulated
as a class of optimization problems in interference limited cognitive radio
networks. By exploiting the hidden convexity in the seemingly non-convex
problem formulations, optimal solutions for multiband joint detection are
obtained under practical conditions. Simulation results show that the proposed
spectrum sensing schemes can considerably improve the system performance. This
paper establishes important principles for the design of wideband spectrum
sensing algorithms in cognitive radio networks
Hybrid Spectrum Sensing Method for Cognitive Radio
With exponential rise in the internet applications and wireless communications, higher and efficient data transfer rates are required. Hence proper and effective spectrum is the need of the hour, As spectrum demand increases there are limited number of bands available to send and receive the data. Optimizing the use of these bands efficiently is one of the tedious tasks. Various techniques are used to send the data at same time, but for that we have to know which bands are free before sending the data. For this purpose various spectrum sensing approaches came with variety of solutions. In this paper the sensing problem is tackled with the use of hybrid spectrum sensing method, This new networking paradox uses the Centralized concept of spectrum sensing and creates one of the most trusted spectrums sensing mechanism. This proposed technique is simulated using MATLAB software.This paper also provides comparative study of various spectrum sensing methodologie
Cooperative Spectrum Sensing in Cognitive Radio Networks Using Multidimensional Correlations
In this paper, a multidimensional-correlation-based sensing scheduling algorithm, (CORN)2, is developed for cognitive radio networks to minimize energy consumption. A sensing quality metric is defined as a measure of the correctness of spectral availability information based on the fact that spectrum sensing information at a given space and time can represent spectrum information at a different point in space and time. The scheduling algorithm is shown to achieve a cost of sensing (e.g., energy consumption, sensing duration) arbitrarily close to the possible minimum, while meeting the sensing quality requirements. To this end, (CORN)2 utilizes a novel sensing deficiency virtual queue concept and exploits the correlation between spectrum measurements of a particular secondary user and its collaborating neighbors. The proposed algorithm is proved to achieve a distributed and arbitrarily close to optimal solution under certain, easily satisfied assumptions. Furthermore, a distributed Selective-(CORN)2 (S-(CORN)2) is introduced by extending the distributed algorithm to allow secondary users to select collaboration neighbors in densely populated cognitive radio networks. In addition to the theoretically proved performance guarantees, the algorithms are evaluated through simulations