Today's wireless networks are characterized by a fixed spectrum assignment policy. However, a large portion of the assigned spectrum is used sporadically and geographical variations in the utilization of assigned spectrum ranges from 15% to 85% with a high variance in time. The limited available spectrum and the ine#ciency in the spectrum usage necessitate a new communication paradigm to exploit the existing wireless spectrum opportunistically. This new networking paradigm is referred to as NeXt Generation (xG) Networks as well as Dynamic Spectrum Access (DSA) and cognitive radio networks. The term xG networks is used throughout the paper. The novel functionalities and current research challenges of the xG networks are explained in detail. More specifically, a brief overview of the cognitive radio technology is provided and the xG network architecture is introduced. Moreover, the xG network functions such as spectrum management, spectrum mobility and spectrum sharing are explained in detail. The influence of these functions on the performance of the upper layer protocols such as routing and transport are investigated and open research issues in these areas are also outlined. Finally, the cross-layer design challenges in xG networks are discussed.

Market-driven dynamic spectrum auctions can drastically improve the spectrum availability for wireless networks struggling to obtain additional spectrum. However, they face significant challenges due to the fear of market manipulation. A truthful or strategy-proof spectrum auction eliminates the fear by enforcing players to bid their true valuations of the spectrum. Hence bidders can avoid the expensive overhead of strategizing over others and the auctioneer can maximize its revenue by assigning spectrum to bidders who value it the most. Conventional truthful designs, however, either fail or become computationally intractable when applied to spectrum auctions. In this paper, we propose VERITAS, a truthful and computationally-efficient spectrum auction to support an eBay-like dynamic spectrum market. VERITAS makes an important contribution of maintaining truthfulness while maximizing spectrum utilization. We show analytically that VERITAS is truthful, efficient, and has a polynomial complexity of O(n 3 k) when n bidders compete for k spectrum bands. Simulation results show that VERITAS outperforms the extensions of conventional truthful designs by up to 200 % in spectrum utilization. Finally, VERITAS supports diverse bidding formats and enables the auctioneer to reconfigure allocations for multiple market objectives.

Abstract — We design truthful double spectrum auctions where multiple parties can trade spectrum based on their individual needs. Open, market-based spectrum trading motivates existing spectrum owners (as sellers) to lease their selected idle spectrum to new spectrum users, and provides new users (as buyers) the spectrum they desperately need. The most significant challenge is how to make the auction economic-robust (truthful in particular) while enabling spectrum reuse to improve spectrum utilization. Unfortunately, existing designs either do not consider spectrum reuse or become untruthful when applied to double spectrum auctions. We address this challenge by proposing TRUST, a general framework for truthful double spectrum auctions. TRUST takes as input any reusability-driven spectrum allocation algorithm, and applies a novel winner determination and pricing mechanism to achieve truthfulness and other economic properties while significantly improving spectrum utilization. To our best knowledge, TRUST is the first solution for truthful double spectrum auctions that enable spectrum reuse. Our results show that economic factors introduce a tradeoff between spectrum efficiency and economic robustness. TRUST makes an important contribution on enabling spectrum reuse to minimize such tradeoff. I.

Abstract—Existing capacity constrained cellular networks that operate in fixed spectrum bands can be enhanced with capacityon-demand services using the Coordinated Dynamic Spectrum Access (CDSA) model. In this model, a centralized spectrum broker coordinates access to spectrum in a given region and assigns short term spectrum leases to competing wireless service providers and/or end users. In contrast to existing multi-year cellular spectrum licenses that span large regions, a spectrum broker can grant spectrum leases that are for small regions (e.g.: per base station) and valid for short durations (e.g.: tens of minutes). Fast spectrum allocation algorithms are crucial to the design of scalable spectrum brokers that can provide such realtime spectrum access. In this paper, we address this challenge. Specifically, we formulate the spectrum allocation problem as two optimization problems: first with the objective of maximizing the overall demand (Max-Demand) satisfied among the various base stations and the second with the objective of minimizing the overall interference in the network (Min-Interference) when all the demands of the base stations are satisfied. We show that the optimization problems are NP-hard and design efficient algorithms to solve them. Our simulation results on sample network topologies show that our algorithms scale very well for large network sizes. I.

Abstract—In this paper, we address the spectrum allocation problem in cellular networks under the coordinated dynamic spectrum access (CDSA) model. In this model, a centralized spectrum broker owns a part of the spectrum and issues dynamic spectrum leases to competing base stations in the region it controls. We consider a dynamic auction based approach where the base stations bid for channels depending on their demands. The broker allocates channels to them with an objective to maximize the overall revenue generated subject to wireless interference in the network. This problem is known to be NP-hard and has been addressed before in limited context. We address this problem in a very generic context where (i) interference in the network is modeled using pairwise and physical interference models and (ii) base stations can bid for heterogeneous channels of different width using generic bidding functions. We propose efficient approximation algorithms that give near optimal solutions with provable analytical bounds. Detailed simulation studies using randomly generated and real base station networks show that our algorithms scale very well for large network sizes. I.

Open Spectrum systems offer an attractive solution to the reuse of under-utilized licensed spectrum. Existing proposals take a reactive sense-and-avoid approach to impulsively reconfigure spectrum usage without any knowledge of future dynamics. We propose a proactive spectrum access approach where secondary users utilize past observations to build predictive models on spectrum availability, and intelligently plan channel usage to maximize utilization and minimize disruptions to primary users. Based on the characteristics of TV-broadcast, we develop a simple availability metric and apply a usability filter to eliminate unreliable channels with heavy and frequent appearance of primary users. Our experimental results show that the proactive approach can significantly reduce the number of disruptions. We also observe a clear tradeoff between the disruption rate and the throughput at secondary users. By varying the usability filter threshold, we can control this tradeoff according to the constraints of primary users and the application requirements at secondary users.

Abstract — Wireless growth has been limited by the shortage of radio spectrum. While the spectrum assigned to legacy technologies remain unused, new prominent technologies such as Mesh/WiFi networks are forced to crowd into a small unlicensed band, suffering from significant interference and degraded performance. Using economic incentives, dynamic spectrum auctions redistribute spectrum to make it available to new technologies while providing financial benefits to legacy owners. In this paper, we investigate the performance of dynamic spectrum auctions under traffic dynamics. Using measured traffic traces from deployed WiFi access points, we evaluate the advantages and artifacts of dynamic auctions over plain channel sharing, and investigate the impact of bidding formats and auction intervals. Our results show that short-term dynamic auctions with traffic-aware bidding can significantly improve system throughput and provide bidders with cost-effective spectrum usage. I.

Abstract—Dynamic spectrum trading amongst small cognitive users is fundamentally different along two axes: temporal variation, and spatial variation of user demand and channel condition. We advocate that a spectrum secondary market, analogous to the stock market, is to be established for users to dynamically trade among themselves their channel holdings obtained in the primary market from legacy owners. We design a market mechanism based on dynamic double auctions, creating a marketplace in the air to match bandwidth demand with supply. In the analysis we prove important economic properties of the mechanism, notably its truthfulness and asymptotic efficiency in maximizing spectrum utilization. Complimentary simulation studies corroborate that spectrum utilization and user performance can be improved by establishing the spectrum secondary market. I.

In this paper, we propose a low-complexity auction framework to distribute spectrum in real-time among a large number of wireless users with dynamic traffic. Our design consists of a compact and highly-expressive bidding format, two pricing models to control tradeoffs between revenue and fairness, and fast auction clearing algorithms to achieve conflict-free spectrum allocations that maximize auction revenue. We develop analytical bounds on algorithm performance and complexity to verify the efficiency of the proposed approach. We also use both simulated and real deployment traces to evaluate the auction framework. We conclude that pricing models and bidding behaviors have significant impact on auction outcomes and spectrum utilization. Any efficient spectrum auction system must consider demand and spectrum availability in local regions to maximize system-wide revenue and spectrum utilization.

cognitive radio, power control, dynamic spectrum access, resource allocation, interference estimation, wireless networks