Distributed algorithm and pricing strategy for resource allocation and revenue maximization in communication networks

Abstract

With the rapid growth of communication networks and services, design of distributed algorithm for resource allocation in wired and wireless networks has attracted more and more research interests in recent years. Different from many technique-based centralized algorithms, pricing-based distributed algorithm exploits economic-driven behaviors of network users and thus is more transparent and incentive-compatible. A typical example is that pricing takes the form of “shadow price” to indicate the scarcity of limited resource and thus generates an efficient allocation distributedly. In this thesis, we study two applications of pricing scheme (and the corresponding distributed algorithm design) in communication networks, namely, (i) distributed resource allocation in Dynamic Spectrum Access (DSA) networks, and (ii) revenue maximization for Internet Service Providers (ISPs) with limited capability of utility-extraction. Facing with the severe problem of spectrum scarcity, the concept of dynamic spectrum access has been proposed to tackle inefficiency of the static spectrum management policy by leveraging a secondary spectrum utilization. In spite of its simple objective, implementation of DSA networks is much more challenging. The past decade has witnessed a joint effort from engineering, economics, and regulation communities on DSA networks research. By envisioning different capabilities of DSA networks, different models, namely, the Open Sharing model, the Hierarchical Access model, and the Dynamic Exclusive Usage model, have been proposed. In the first part of this thesis, we first study resource allocations (mainly the rate and power allocations) for DSA networks based on these different models. First, based on the Dynamic Exclusive Usage model, we study the joint pricing and power allocation for DSA networks with the Stackelberg game model. Specifically, Primary User (PU) has the power control flexibility and is willing to share its channel with Secondary User (SU) to obtain extra revenue by charging the cochannel interference. In response, SU pays the interference cost to obtain transmission opportunity. Pricing has two purposes in our model, namely, (i) to motivate PU’s channel sharing and (ii) to motivate SU’s efficient utilization of PU’s channel. Because of its Quality of Service (QoS) requirement, PU faces a tradeoff between charging SU and consuming its own power cost. We quantify the benefits that PU and SU can obtain from the sharing model and derive the stability condition for the sharing model. We further analyze two extensions. The first extension is the single PU multiple SUs scenario, where PU allows multiple SUs to share its channel simultaneously with the objective of revenue maximization. The second extension is the multiple PUs multiple SUs scenario, where each PU can only share its channel with one SU (and vice versa). Our objective is to maximize the entire network welfare by matching PUs and SUs distributedly. Second, based on the spectrum underlay approach of the Hierarchical Access model, we study the distributed multi-channel power allocation for DSA networks with QoS guarantee. By exploiting the Interference Temperature (IT) metric, we model this problem as a noncooperative power demand game with a coupled strategy space to address both PUs’ interference constraints and SUs’ QoS requirements. We analyze the properties of Nash Equilibrium (N.E.) of our proposed game and propose a distributed algorithm to find the N.E. Our distributed algorithm is based on a layered structure, where PUs and SUs first separately update their dual prices, and then all SUs form a power demand subgame. By incorporating the dual prices, each SU’s power demand is both interference-aware and QoS-aware. In the first part of this thesis, we mainly use pricing to motivate certain“desired” network performance in DSA networks, i.e., the efficient channel sharing between PUs and SUs, and the QoS-aware and interference-aware power demand of SUs. In the second part of this thesis, we focus on ISPs’ revenue maximization. Specifically, pricing serves as a bridge transferring customer’s utility into ISP’s revenue. Practical conditions, however, limit ISP’s capability of utility-extraction and thus result in ISP’s revenue loss. We study two of these factors, namely, (i) single ISP with“time-constrained” pricing strategy, and (ii) multiple ISPs inter-charging in a two-sided market. (Abstract shortened by UMI.)