Optimal spectrum assignment for cognitive radio sensor networks under coverage constraint

Abstract

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.

Utility‐based opportunistic spectrum access for cognitive radio sensor networks: joint spectrum sensing and random access control

“…In [25], Han et al proposed an adaptive energy‐efficient channel management scheme for the CRSN, which optimally selects the operation mode based on the POMDP framework. In [26], Eletreby et al formulated the problem of spectrum assignment for CRSNs under coverage, interference, minimum data rate and power budget constraints as a mixed‐integer non‐linear programming problem and achieved its optimal solution in polynomial time by relaxation techniques.…”

“…To achieve this, CRSNs have to perform spectrum sensing besides the information collection, and to design access schemes which produce strictly limited interference to primary users (PUs). On the other hand, different from CR networks [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26], CRSNs inherit the fundamental limitations of traditional WSNs whose lifetime and capabilities (e.g. computation, communication and storage) are strictly limited due to constrains in energy resources and low-cost hardwares.…”

Optimal Spectrum Sensor Assignment in Multi-channel Multi-user Cognitive Radio Networks

“…The genetic algorithm is used in [10]- [11]. Non-linear mixed integer programming is used in [12]. A survey of various mathematical programming methods for solving channel selection in CRNs is given in [13].…”