Modeling wireless access and spectrum markets is challenging due to a plethora of technological and economic aspects that affect their performance. This work develops a modeling framework for analysing such markets using network economics, game theory, and queueing networks. The framework models the service selection of users as well as the competition and coalition among providers. It also develops tools and algorithms to analytically compute the Nash equilibriums (NEs) under the presence of discontinuities in the derivatives of the utility functions of providers. The analysis of different market scenarios reveals various interesting trends in the offered prices, market share, and revenue of providers depending on the user utility function, traffic demand, and mobility pattern. It also demonstrates the role of the quality of service (QoS) in the user utility function in reducing the intensity of competition and allowing for higher prices and revenue. However, the analysis of large-scale markets exhibits a high computational complexity. To improve the computational efficiency, we developed a network aggregation methodology based on the theorem of Norton. This aggregation allows the construction of equivalent networks for a specific region of interest, omitting the details of the entire networks. We demonstrate the aggregation algorithm in the context of capacity planning.

The rapid advances in networking, mobile computing, and virtualization, lead to a dramatic increase in the traffic demand. A cost-effective solution for serving it, while maintaining a good quality of service (QoS), would be to offload a part of the traffic originally targeted for cellular base stations (BSs) to a WiFi infrastructure. Related work on the WiFi offloading often considers markets with a single provider and omits parameters, such as the effect of the offloading on the perceived QoS by users, the capacity of the WiFi infrastructure, and competition of providers. In contrast to these approaches, this paper develops a detailed modeling framework for analysing the WiFi offloading using network economics, game theory, and queueing networks. It also proposes a novel network aggregation technique to reduce the computational complexity of the analysis. Using this framework, the performance of WiFi offloading was evaluated under various scenarios with respect to the bandwidth of BSs and APs, coverage of WiFi, and user preferences. Our results highlight that it is not always profitable for providers to invest in a large WiFi infrastructure. The limited capacity of the WiFi APs restricts the benefits of the offloading.

Advances in networking and regulatory changes on access and competition rules enable new network architectures, service paradigms, and partnerships, opening new opportunities for business cases. Unlike traditional cellular-based markets, new spectrum and wireless access markets are formed that have larger sizes, are more diverse, and can offer an improved set of services. The analysis of such markets is challenging due to a plethora of phenomena that manifest in different spatio-temporal scales. The main objective of this work is the development of a modular multi-layer modeling framework and simulation platform for analyzing wireless access markets. This framework employs game theory and queueing-theoretical models to instantiate a market at multiple spatio-temporal scales. At a microscopic layer, it models each entity of the market in a fine level of detail. By applying various aggregations, it also models the average behavior of certain clusters of entities. In that way, it can analyze a certain phenomenon at the appropriate level of detail, addressing the tradeoff between the loss of accuracy and computational complexity. The analysis then focuses on the flex service, a novel paradigm which allows users to select their provider dynamically. The proposed framework is used to model and analyze the performance of markets that offer the flex service. It employs various metrics, such as blocking probability, percentage of disconnected users, social welfare, and profit, to assess whether this service is beneficial to users, regulators, and providers, respectively. Furthermore, it highlights various challenges in modeling such markets and demonstrates the advantages in using the proposed multi-layer framework.

This paper focuses on a comparative statistical analysis of the performance of VoIP calls under various situations, namely, during a handover and under different background traffic conditions at a wireless access point (AP). Using empirical-based measurements, it demonstrates that these network conditions exhibit distinct statistical behaviour, in terms of SNR, packet losses and end-to-end delays, and thus, impact the VoIP user quality in a different manner. The analysis shows that both network conditions and codec type, as well as their interaction, have a significant effect on the PESQ MOS values. Moreover, it indicates statistically highly significant differences between the estimations of the PESQ and E-model. Finally, it highlights the benefits of the packet loss concealment of the AMR 12.2kb/s under these network conditions.