Dynamic Profit Maximization of Cognitive Mobile Virtual Network Operator

TL;DR

This paper develops real-time control policies for a cognitive virtual network operator to maximize profit in a dynamic environment with uncertain spectrum resources, user demands, and channel conditions, without requiring prior statistical knowledge.

Contribution

It introduces a low-complexity online control framework that optimally balances profit and delay in a realistic, dynamic spectrum sharing scenario.

Findings

Achieves near-optimal profit with controllable delay.

Handles heterogenous users and imperfect sensing.

Operates without prior knowledge of network dynamics.

Abstract

We study the profit maximization problem of a cognitive virtual network operator in a dynamic network environment. We consider a downlink OFDM communication system with various network dynamics, including dynamic user demands, uncertain sensing spectrum resources, dynamic spectrum prices, and time-varying channel conditions. In addition, heterogenous users and imperfect sensing technology are incorporated to make the network model more realistic. By exploring the special structural of the problem, we develop a low-complexity on-line control policies that determine pricing and resource scheduling without knowing the statistics of dynamic network parameters. We show that the proposed algorithms can achieve arbitrarily close to the optimal profit with a proper trade-off with the queuing delay.