An Agent-Based Model for Secondary Use of Radio Spectrum

Wireless communications rely on access to radio spectrum. With a continuing proliferation of wireless applications and services, the spectrum resource becomes scarce. The measurement studies of spectrum usage, however, reveal that spectrum is being used sporadically in many geographical areas and times. In an attempt to promote efficiency of spectrum usage, the Federal Communications Commission has supported the use of market mechanism to allocate and assign radio spectrum. We focus on the secondary use of spectrum defined as a temporary access of existing licensed spectrum by a user who does not own a spectrum license. The secondary use of spectrum raises numerous technical, institutional, economic, and strategic issues that merit investigation. Central to the issues are the effects of transaction costs associated with the use of market mechanism and the uncertainties due to potential interference.The research objective is to identify the pre-conditions as to when and why the secondary use would emerge and in what form. We use transaction cost economics as the theoretical framework in this study. We propose a novel use of agent-based computational economics to model the development of the secondary use of spectrum. The agent-based model allows an integration of economic and technical considerations to the study of pre-conditions to the secondary use concept. The agent-based approach aims to observe the aggregate outcomes as a result of interactions among agents and understand the process that leads to the secondary use, which can then be used to create policy instruments in order to obtain the favorable outcomes of the spectrum management

Applications of Repeated Games in Wireless Networks: A Survey

A repeated game is an effective tool to model interactions and conflicts for players aiming to achieve their objectives in a long-term basis. Contrary to static noncooperative games that model an interaction among players in only one period, in repeated games, interactions of players repeat for multiple periods; and thus the players become aware of other players' past behaviors and their future benefits, and will adapt their behavior accordingly. In wireless networks, conflicts among wireless nodes can lead to selfish behaviors, resulting in poor network performances and detrimental individual payoffs. In this paper, we survey the applications of repeated games in different wireless networks. The main goal is to demonstrate the use of repeated games to encourage wireless nodes to cooperate, thereby improving network performances and avoiding network disruption due to selfish behaviors. Furthermore, various problems in wireless networks and variations of repeated game models together with the corresponding solutions are discussed in this survey. Finally, we outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Fostering Collaboration in Emerging Three-Tiered Spectrum Markets

Ensuring optimum spectral efficiency is a critical requirement for current wireless networks to cope with the ever-growing flow of wireless data traffic, using limited spectral resources. As such, spectrum sharing, which allows different grades of users, as well as multiple networking standards to co-exist and utilize in the same frequency band, has become a topic of great intrigue. Due to the inherent advantages of these schemes, the US government has opened up vast amounts of federal spectrum that supports spectrum sharing. The Citizens Broadband Radio Service (CBRS) proposed by the Federal Communications Commission (FCC) is one of them. A tiered spectrum sharing approach, CBRS allows end commercial users to share the radio spectrum with federal incumbent users in the 3,550-3,700 MHz range. Employing a light leasing approach, the FCC aims to encourage the licensed providers of CBRS called the Priority Access License (PAL), to lease/share their licensed spectrum with unlicensed users named the General Authorized Access (GAA) for limited duration, which is essential for the maximum utilization of the CBRS bandwidth, but the current approach proves ineffective for that purpose. In this thesis, we propose a novel clustered framework to facilitate this sharing, where GAA users are grouped into multiple distinct geographical clusters and request access to licensed spectrum through the clusters in a collaborative manner rather than individually. Each cluster will nominate a central entity denoted as the GAA leader to communicate their requests to the PAL operators, as well as establish temporary connections with PAL access points once granted permission for licensed CBRS access, to be used by GAAs outside the operators coverage range. The leaders will also receive information from the PAL operators regarding the number of requests they are willing to accept and transmit that to the GAAs within the cluster. This process reduces the amount of information flow between the licensed and unlicensed entities, thereby providing a convenient platform for CBRS spectrum sharing. In order to determine the leader, the role of which can be assumed by any of the GAA users within the cluster, we formulate a distributed leader selection algorithm algorithm called the LSA, which takes into account the signal strength of the PAL access points available the GAA users, as well as the network density of each GAA node, to assign a score called the leader evaluation score (LES) to each GAA user and nominate the user with the highest score as the leader. To encourage PAL operators to frequently share their licensed spectrum, we incorporate a government reward model, where operators are incentivized by gaining access to additional spectrum for limited periods based on their level of sharing

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system