Spectrum Trading: An Abstracted Bibliography

This document contains a bibliographic list of major papers on spectrum trading and their abstracts. The aim of the list is to offer researchers entering this field a fast panorama of the current literature. The list is continually updated on the webpage \url{http://www.disp.uniroma2.it/users/naldi/Ricspt.html}. Omissions and papers suggested for inclusion may be pointed out to the authors through e-mail (\textit{[email protected]})

Efficient Identification and Utilization of Spectrum Opportunities in Cognitive Radio Networks.

There has been an exponential increase in spectrum demands due to new emerging wireless services and applications, making it harder to find unallocated spectrum bands for future usage. This potential resource scarcity is rooted at inefficient utilization of spectrum under static spectrum allocation. Therefore, a new concept of dynamic spectrum access (DSA) has been proposed to opportunistically utilize the legacy spectrum bands by cognitive radio (CR) users. Cognitive radio is a key technology for alleviating this inefficient spectrum utilization, since it can help discover spectrum opportunities (or whitespaces) in which legacy spectrum users do not temporarily use their assigned spectrum bands. In a DSA network, it is crucial to efficiently identify and utilize the whitespaces. We address this issue by considering spectrum sensing and resource allocation. Spectrum sensing is to discover spectrum opportunities and to protect the legacy users (or incumbents) against harmful interference from the CR users. In particular, sensing is an interaction between PHY and MAC layers where in the PHY-layer signal detection is performed, and in the MAC-layer spectrum sensing is scheduled and spectrum sensors are coordinated for collaborative sensing. Specifically, we propose an efficient MAC-layer sensing scheduling algorithm that discovers spectrum opportunities as much as possible for better quality-of-service (QoS), and as fast as possible for seamless service provisioning. In addition, we propose an optimal in-band spectrum sensing algorithm to protect incumbents by achieving the detectability requirements set by regulators (e.g., FCC) while incurring minimal sensing overhead. For better utilization of discovered spectrum opportunities, we pay our attention to resource allocation in the secondary spectrum market where legacy license holders temporarily lease their own spectrum to secondary wireless service providers (WSPs) for opportunistic spectrum access by CR users. In this setting, we investigate how a secondary WSP can maximize its profit by optimally controlling the admission and eviction of its customers (i.e., CR users). In addition, we also focus on the price and quality competition between co-located WSPs where they contend for enticing customers by providing more competitive service fee while leasing the channels with best matching quality.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78741/1/hyoilkim_1.pd

Spectrum Sharing Optimization and Analysis in Cellular Networks under Target Performance and Budget Restriction

Dynamic Spectrum Sharing (DSS) aims to provide opportunistic access to under-utilised spectrum in cellular networks for secondary network operators. In this paper we propose an algorithm using stochastic and optimisation models to borrow spectrum bandwidths under the assumption that more resources exist for secondary access than the secondary network demand by considering a merchant mode. The main aim of the paper is to address the problem of spectrum borrowing in DSS environments, where a secondary network operator aims to borrow the required spectrum from multiple primary network operators to achieve a maximum profit under specific grade of service (GoS) and budget restriction. We assume that the primary network operators offer spectrum access opportunities with variable number of channels (contiguous and/or non-contiguous) at variable prices. Results obtained are then compared with results derived from an algorithm in which spectrum borrowing are random. Comparisons showed that the gain in the results obtained from our proposed stochastic-optimisation framework is significantly higher than random counterpart

White Space Network Management: Spectrum Quanti cation, Spectrum Allocation and Network Design

Philosophiae Doctor - PhD (Computer Science)The unused spectrum in the television broadcasting frequency bands (so-called TV white spaces) can alleviate the spectrum crunch, and have potential to provide broadband connection to rural areas of countries in the developing world. Current research on TV white spaces focuses on how to detect them accurately, and how they can be shared or allocated to secondary devices. Therefore, the focus of this research is three-fold: to investigate a novel distributed framework, which does not use propagation models in detecting TV white spaces, and suitable for use in countries of the developing world; to investigate a suitable spectrum sharing mechanism for short-time leasing of the TV white spaces to secondary devices; and extend the research to investigate the design of a TV white space-ware network in TV white space frequencies

Congestion control in multi-serviced heterogeneous wireless networks using dynamic pricing

Includes bibliographical references.Service providers, (or operators) employ pricing schemes to help provide desired QoS to subscribers and to maintain profitability among competitors. An economically efficient pricing scheme, which will seamlessly integrate users’ preferences as well as service providers’ preferences, is therefore needed. Else, pricing schemes can be viewed as promoting social unfairness in the dynamically priced network. However, earlier investigations have shown that the existing dynamic pricing schemes do not consider the users’ willingness to pay (WTP) before the price of services is determined. WTP is the amount a user is willing to pay based on the worth attached to the service requested. There are different WTP levels for different subscribers due to the differences in the value attached to the services requested and demographics. This research has addressed congestion control in the heterogeneous wireless network (HWN) by developing a dynamic pricing scheme that efficiently incentivises users to utilize radio resources. The proposed Collaborative Dynamic Pricing Scheme (CDPS), which identifies the users and operators’ preference in determining the price of services, uses an intelligent approach for controlling congestion and enhancing both the users’ and operators’ utility. Thus, the CDPS addresses the congestion problem by firstly obtaining the users WTP from users’ historical response to price changes and incorporating the WTP factor to evaluate the service price. Secondly, it uses a reinforcement learning technique to illustrate how a price policy can be obtained for the enhancement of both users and operators’ utility, as total utility reward obtained increases towards a defined ‘goal state’

Dynamic Fairness-Aware Spectrum Auction for Enhanced Licensed Shared Access in 6G Networks

This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism. Through comparison with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. We suggest using spectrum sensing and integrating UAV-based networks to enhance efficiency of the LSA system. This research employs two methods to solve the problem. We first propose a novel greedy algorithm, named market share based weighted greedy algorithm (MSWGA) to achieve better fairness compared to the traditional auction methods and as the second approach, we exploit deep reinforcement learning (DRL) algorithms, to optimize the auction policy and demonstrate its superiority over other methods. Simulation results show that the deep deterministic policy gradient (DDPG) method performs superior to soft actor critic (SAC), MSWGA, and greedy methods. Moreover, a significant improvement is observed in fairness index compared to the traditional greedy auction methods. This improvement is as high as about 27% and 35% when deploying the MSWGA and DDPG methods, respectively.Comment: 13 pages, 11 figure

Design and optimisation of a low cost Cognitive Mesh Network

Wireless Mesh Networks (WMNs) have been touted as the most promising wireless technology in providing high-bandwidth Internet access to rural, remote and under-served areas, with relatively lower investment cost as compared to traditional access networks. WMNs structurally comprise of mesh routers and mesh clients. Furthermore, WMNs have an envisaged ability to provide a heterogeneous network system that integrates wireless technologies such as IEEE 802.22 WRAN, IEEE 802.16 WiMAX, IEEE 802.11 Wi-Fi, Blue-tooth etc. The recent proliferation of new devices on the market such as smart phones and, tablets, and the growing number of resource hungry applications has placed a serious strain on spectrum availability which gives rise to the spectrum scarcity problem. The spectrum scarcity problem essentially results in increased spectrum prices that hamper the growth and efficient performance of WMNs as well as subsequent transformation of WMN into the envisaged next generation networks. Recent developments in TV white space communications technology and the emergence of Cognitive radio devices that facilitate Dynamic Spectrum Access (DSA) have provided an opportunity to mitigate the spectrum scarcity problem. To solve the scarcity problem, this thesis reconsiders the classical Network Engineering (NE) and Traffic Engineering (TE) problems to objectively design a low cost Cognitive Mesh network that promotes efficient resources utilization and thereby achieve better Quality of Service (QoS) levels

Multimedia in mobile networks: Streaming techniques, optimization and User Experience

1.UMTS overview and User Experience 2.Streaming Service & Streaming Platform 3.Quality of Service 4.Mpeg-4 5.Test Methodology & testing architecture 6.Conclusion

Oligopolies in private spectrum commons: analysis and regulatory implications

In an effort to make more spectrum available, recent initiatives by the FCC let mobile providers offer spot service of their licensed spectrum to secondary users, hence paving the way to dynamic secondary spectrum markets. This dissertation investigates secondary spectrum markets under different regulatory regimes by identifying profitability conditions and possible competitive outcomes in an oligopoly model. We consider pricing in a market where multiple providers compete for secondary demand. First, we analyze the market outcomes when providers adopt a coordinated access policy, where, besides pricing, a provider can elect to apply admission control on secondary users based on the state of its network. We next consider a competition when providers implement an uncoordinated access policy (i.e., no admission control). Through our analysis, we identify profitability conditions and fundamental price thresholds, including break-even and market sharing prices. We prove that regardless of the specific form of the secondary demand function, competition under coordinated access always leads to a price war outcome. In contrast, under uncoordinated access, market sharing becomes a viable market outcome if the intervals of prices for which the providers are willing to share the market overlap. We then turn our attention to how a network provider use carrier (spectrum) aggregation in order to lower its break-even price and gain an edge over its competition. To this end, we determine the optimal (minimum) level of carrier aggregation that a smaller provider needs. Under a quality-driven (QD) regime, we establish an efficient way of numerically calculating the optimal carrier aggregation and derive scaling laws. We extend the results to delay-related metrics and show their applications to profitable pricing in secondary spectrum markets. Finally, we consider the problem of profitability over a spatial topology, where identifying system behavior suffers from the curse of dimensionality. Hence, we propose an approximation model that captures system behavior to the first-order and provide an expression to calculate the break-even price at each network location and provide simulation results for accuracy comparison. All of our results hold for general forms of demand, thus avoid restricting assumptions of customer preferences and the valuation of the spectrum