Abstract
Implementation of dynamic spectrum access (DSA) in cognitive radio (CR) systems requires the unlicensed secondary users (SU) to implement spectrum sensing to monitor the activity of the licensed primary users (PU). Energy detection (ED) is one of the most widely used methods for spectrum sensing in CR systems, and in this paper we present a novel ED algorithm with an adaptive sensing threshold. The three-event ED (3EED) algorithm for spectrum sensing is considered for which an accurate approximation of the optimal decision threshold that minimizes the decision error probability (DEP) is found using Newton’s method with forced convergence in one iteration. The proposed algorithm is analyzed and illustrated with numerical results obtained from simulations that closely match the theoretical results and show that it outperforms the conventional ED (CED) algorithm for spectrum sensing.
Highlights
The expansion of wireless communication systems in all sectors of modern society over the past decade has prompted an increased demand in spectrum resources
dynamic spectrum access (DSA) allows the access of secondary users (SU) to licensed frequency bands when these are not actively used by licensed primary users (PU), and relies on spectrum sensing, which is performed by the SU to detect the presence of active PU
A new energy detection (ED) algorithm for spectrum sensing in cognitive radio (CR) systems is presented in the paper
Summary
The expansion of wireless communication systems in all sectors of modern society over the past decade has prompted an increased demand in spectrum resources. We denote by Ei the value of the received signal energy estimated during the i-th sensing slot that consists of N samples, λ the ED sensing threshold, and qi the binary decision variable {0,1} for the i-th spectrum sensing slot With these notations, in CED correct detection of the PU signal (active/inactive) implies setting qi = 1, that is, the channel is “busy”, if Ei > λ when y(n) is implied by an active PU signal corrupted by noise, and qi = 0, that is, the channel is “idle”, if Ei ≤ λ when y(n) corresponds to a noise only term. Analyzing expression (6) we note that the DEP is a function of the sensing threshold λ, the PU spectrum utilization ratio α, the average power of the PU signal σs , and the noise variance σn. We note that Equation (19) is transcendental, which makes it difficult to find a closed-form solution for the optimal threshold λopt , and the alternative is to obtain a numerical solution for it as discussed
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