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.

A number of studies have shown the abundance of unused spectrum in the TV bands. This is in stark contrast to the overcrowding of wireless devices in the ISM bands. A recent trend to alleviate this disparity is the design of Cognitive Radios, which constantly sense the spectrum and opportunistically utilize unused frequencies in the TV bands. In this paper, we introduce the concept of a time-spectrum block to model spectrum reservation, and use it to present a theoretical formalization of the spectrum allocation problem in cognitive radio networks. We present a centralized and a distributed protocol for spectrum allocation and show that these protocols are close to optimal in most scenarios. We have implemented the distributed protocol in QualNet and show that our analysis closely matches the simulation results.

Wideband technologies in the unlicensed spectrum can satisfy the ever-increasing demands for wireless bandwidth created by emerging rich media applications. The key challenge for such systems, however, is to allow narrowband technologies that share these bands (say, 802.11 a/b/g/n, Zigbee) to achieve their normal performance, without compromising the throughput or range of the wideband network. This paper presents SWIFT, the first system where high-throughput wideband nodes are shown in a working deployment to coexist with unknown narrowband devices, while forming a network of their own. Prior work avoids narrowband devices by operating below the noise level and limiting itself to a single contiguous unused band. While this achieves coexistence, it sacrifices the throughput and operating distance of the wideband device. In contrast, SWIFT creates highthroughput wireless links by weaving together non-contiguous unused frequency bands that change as narrowband devices enter or leave the environment. This design principle of cognitive aggregation allows SWIFT to achieve coexistence, while operating at normal power, and thereby obtaining higher throughput and greater operating range. We implement SWIFT on a wideband hardware platform, and evaluate it in the presence of 802.11 devices. In comparison to a baseline that coexists with narrowband devices by operating below their noise level, SWIFT is equally narrowband-friendly but achieves 3.6 −10.5 × higher throughput and 6 × greater range.

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.

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has announced that it is willing to consider unlicensed operation in the TV broadcast bands. Compared to the ISM bands, this portion of the spectrum has several desirable properties for robust data communications. However, to make efficient use of this spectrum in a way that is non-disruptive to incumbents, there are a number of challenges that must be handled. For example, an unused portion of the spectrum must be found, and it is likely that its availability will vary over time. To address such challenges, we present KNOWS, a cognitive wireless networking system. KNOWS is a hardware-software platform that includes a spectrum-aware Medium Access Control (MAC) protocol and algorithms to deal with spectrum fragmentation. We describe our prototype and present evaluation results obtained from simulating our MAC protocol. We show that in common scenarios KNOWS accomplishes a remarkable 200 % throughput improvement over systems that use a IEEE 802.11 based MAC protocol. 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.

multi-objective optimization, artificial intelligence, genetic algorithms, case-based

Abstract—In an opportunistic dynamic spectrum access environment, individual nodes sense the local spectrum and choose their operating frequencies and bandwidth in collaboration with the other participating nodes. To make their spectrum access decisions, the nodes need to communicate with the existing nodes operating in the area. In this paper we propose an approach which establishes this first connection with a minimum risk of interference. We propose a link rendezvous strategy which relies on frequency domain decision statistics. Nodes wishing to join the network are emitting and scanning for a simple carrier with a small number of sidetones. We describe the strategy in the context of collaborative spectrum sensing. To validate our approach, we describe a series of experiments using the GNU Radio software defined radio toolkit. We show that an attention signal of length equivalent to a single FFT frame can be detected in a high noise environment using two sidetones. Index Terms—Dynamic spectrum allocation, link rendezvous and evolution I.

Abstract. In this paper, we present SURF, a distributed channel selection strategy for efficient data dissemination in multi-hop cognitive radio ad-hoc networks (CRNs). SURF classifies the available channels on the basis of primary radio unoccupancy and the number of cognitive radio neighbors using the channels. Through extensive NS-2 simulations, we compare the performance of SURF with three related approaches. Simulation results confirm that SURF is effective in selecting the best channels for efficient communication and for highest dissemination reachability in multi-hop CRNs. Key words: multi-hop cognitive radio networks, channel selection, data dissemination 1