Spatial spectrum sharing-based carrier aggregation for heterogeneous networks

Yong Xiao, Tim Forde, Irene Macaluso, Luiz A. DaSilva, Linda Doyle

2012 · 21 citations · 7 references

Concepts

TL;DR

Spatial spectrum sharing‑based carrier aggregation (SSS‑CA) lets operators transmit on their own licensed bands and also aggregate others’ bands for a fee, creating a heterogeneous network; this paper studies SSS‑CA from a game‑theoretic perspective. The study aims to determine when two operators are incentivized to form an SSS‑CA pair by modeling their pairing as a game. The authors formulate a hierarchical game‑theoretic framework comprising a pairing game, a power‑control game, and a pricing‑adjustment game, and solve for the Stackelberg equilibrium of the joint pricing and power optimization. The hierarchical game framework links pairing, power control, and pricing through the operators’ price function, and the derived Stackelberg equilibrium shows performance gains in numerical simulations.

Abstract

This paper considers spatial spectrum sharing-based carrier aggregation (SSS-CA) from a game theoretic perspective. In SSS-CA, a network operator can not only transmit on its own licensed spectrum but it can also access and aggregate the licensed spectrum of other operators on payment of a certain price. The difference between operators and the aggregators in each licensed spectrum makes this network heterogeneous. We first model the pairing problem between potential operators as a pairing game and then derive the condition for which both operators are incentivized to form an SSS-CA pair. We then introduce the power control game to derive the optimal transmit power of each spectrum aggregator. Finally, we consider the pricing optimization problem by forming a pricing adjustment game. We observe that these three problems are linked by the price function of the operators and hence can be jointly optimized by using a hierarchical game theoretic framework. We derive the Stackelberg equilibrium for the pricing and power joint optimization problem and present the numerical results to compare the performance improvement brought by our proposed joint optimization method.

References

7