Resource Allocation in Shared Spectrum Access Communications for Operators with Diverse Service Requirements

TL;DR

This paper introduces dynamic inter- and intra-operator resource allocation strategies for shared spectrum access, balancing efficiency and computational complexity, and validates them through extensive simulations.

Contribution

It proposes novel spectrum sharing and resource allocation methods that improve throughput and reduce complexity in multi-operator communication systems.

Findings

Subcarrier gain based sharing yields higher throughput.

Fragmentation based sharing offers lower computational complexity.

The proposed intra-operator allocation method is efficient and less complex.

Abstract

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. Subcarrier gain based spectrum sharing scheme has been found to be more efficient in terms of achieved throughput. However, 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 nonlinear programming problem and nonconvex, 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 nonlinear and/or non-convex objective functions and constraints. Extensive simulation performance analysis has been carried out that validates the efficiency of the proposed solution.