Resource allocation in shared spectrum access communications for operators with diverse service requirements

In this paper, we study inter-operator spectrum sharing and intra-operator resource allocation in shared spectrum access communication systems and propose efficient dynamic solutions to address both inter-operator and intra-operator resource allocation optimization problems. For inter-operator spect...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing Vol. 2016; no. 1; pp. 1 - 15
Main Authors: Kibria, Mirza Golam, Villardi, Gabriel Porto, Ishizu, Kentaro, Kojima, Fumihide, Yano, Hiroyuki
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 29.07.2016
Springer
Springer Nature B.V
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ISSN:1687-6180, 1687-6172, 1687-6180
Online Access:Get full text
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Summary:In this paper, we study inter-operator spectrum sharing and intra-operator resource allocation in shared spectrum access communication systems and propose efficient dynamic solutions to address both inter-operator and intra-operator resource allocation optimization problems. For inter-operator spectrum sharing, we present two competent approaches, namely the subcarrier gain-based sharing and fragmentation-based sharing, which carry out fair and flexible allocation of the available shareable spectrum among the operators subject to certain well-defined sharing rules, traffic demands, and channel propagation characteristics. The subcarrier gain-based spectrum sharing scheme has been found to be more efficient in terms of achieved throughput. However, the fragmentation-based sharing is more attractive in terms of computational complexity. For intra-operator resource allocation, we consider resource allocation problem with users’ dissimilar service requirements, where the operator supports users with delay constraint and non-delay constraint service requirements, simultaneously. This optimization problem is a mixed-integer non-linear programming problem and non-convex, which is computationally very expensive, and the complexity grows exponentially with the number of integer variables. We propose less-complex and efficient suboptimal solution based on formulating exact linearization, linear approximation, and convexification techniques for the non-linear and/or non-convex objective functions and constraints. Extensive simulation performance analysis has been carried out that validates the efficiency of the proposed solution.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-016-0381-8