Open AccessOptimal spectrum assignment for cognitive radio sensor networks under coverage constraint
Author(s) -
Eletreby Rashad M.,
Elsayed Hany M.,
Khairy Mohamed M.
Publication year - 2014
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2014.0423
Subject(s) - cognitive radio , computer science , mathematical optimization , transmission (telecommunications) , greedy algorithm , linear programming , integer programming , linear programming relaxation , node (physics) , optimization problem , base station , heuristic , channel (broadcasting) , wireless , computer network , algorithm , mathematics , telecommunications , structural engineering , engineering
Cognitive radios emerged as a solution to spectrum scarcity problem. The integration of cognitive radios and wireless sensor networks enables a new paradigm of communication, in which the sensor nodes can avoid heavily‐crowded transmission bands by tuning their transmission parameters to less‐crowded bands. The authors consider the problem of spectrum assignment for cognitive radio sensor network (CRSN) under coverage, interference, minimum data rate and power budget constraints. A mixed‐integer non‐linear programming problem formulation that addresses optimal power allocation, channel selection and node scheduling is presented. Following a practical assumption, that any CRSN node can only access one channel for its transmission with the CRSN base station, the problem is transformed to a binary linear programming (BLP) problem. Using the relaxation techniques, the problem is transformed to a linear programming problem that is solvable in polynomial time, and has the same optimal solution of the BLP problem. Hence, the minimum power algorithm that achieves the optimal solution of our problem is proposed. To further reduce the complexity of the solution, three heuristic lower‐complexity algorithms are proposed to solve the problem: random, greedy and two‐stage (decoupled) algorithms.