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The effective utilization of the spectrum has become an essential goal in the communications field, which is addressed by the Cognitive Radio (CR) systems. The primary task in a CR system is to sense the spectrum to identify its holes to be exploited by the secondary users. In this paper, we tackle the compressed spectrum sensing problem in a cooperative manner. The CRs distributed in an area take the samples of the signal that has been reached to them through a wireless fading channel. The spectrum has the block-sparse structure. Moreover, the spectrum observed by different CRs in an area share the same block-sparse support. Therefore, we suggest to exploit the joint block-sparsity structure of the spectrum sensed by various CRs to reconstruct the underlying spectrum more efficiently. To this end, we have suggested a simultaneous block-sparse recovery technique based on the iterative adaptive thresholding scheme. In addition, we have given the convergence analysis of the proposed method. The suggested scheme has the advantage of reducing the number of required samples at each CR. The simulation results have also validated the superiority of the suggested scheme over the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper focuses on evaluating the gains obtained through cooperative spectrum sensing in the real world while using cyclostationary-based mobile sensors. In cooperative sensing (CS), different secondary users (SUs) in a geographical neighborhood cooperate to detect the presence of a primary user (PU). Compared with single-user sensing, cooperation provides diversity gains in the face of multipath fading and shadowing. The effectiveness of CS is demonstrated by analyzing data acquired in two extensive field measurement campaigns. The first measurement campaign (MC-I) focuses on measurements at fixed locations, whereas the second measurement campaign (MC-II) focuses on a scenario where measurements are taken inside a moving car. These measurements are carried out for DVB-T channels in the Capital Region of Finland, which consists of urban and suburban environments. Hard decision rules such as or, and, and majority and a soft decision rule such as sum of cyclostationary test statistics (sum) are employed, and their detection performances are compared with a cyclostationary-based single-user detector. A performance parameter of relative increase in probability of detection (RIPD) is used to efficiently demonstrate the cooperation gain obtained relative to local sensing. It is shown that cooperation can significantly improve the performance of a sensor severely affected by fading and shadowing effects. Furthermore, it is shown that increasing the number of collaborating users beyond few users (five to eight) does not, in practice, bring significant improvement in terms of the expected RIPD. The performances of CS schemes evaluated from MC-I are also compared with the corresponding simulated CS results using empirical channel models and terrain data for the same experimental parameters. It is shown that the use of empirical or theoretical models may result in detection errors in practical conditions, and measurements should be used to improve the accuracy in such scenarios. [ABSTRACT FROM AUTHOR]

Secondary users in a cognitive radio mesh network may select from a set of available channels, provided that they do not disrupt communications among primary users. This ability can improve the overall network performance but introduces the question of how to best use the channels. This paper first considers the problem of selecting the channels to use given a routing path such that the end-to-end throughput along the path is maximized. We show that a dynamic programming-based approach can optimally solve the problem and, if the path satisfies a natural condition, in time, be linear in the length (hop count) of the path. In addition, the algorithm can easily be implemented in a distributed fashion. We also examine the harder joint problem of finding the best routing path and channel selection that maximizes the end-to-end throughput. We prove that obtaining a (\2/3 + \epsilon) approximation to the joint problem is NP-hard. We then present a heuristic algorithm for the joint problem and a second heuristic channel-aware routing-only algorithm. Numerical results are provided to demonstrate the effectiveness of the methods on several experimental scenarios. [ABSTRACT FROM PUBLISHER]

We recently proposed the dynamic spectrum leasing (DSL) paradigm for dynamic spectrum access in cognitive radio networks. In this paper, we formalize this concept by developing a general game-theoretic framework for the DSL and by carefully identifying requirements for the coexistence of primary and secondary systems via spectrum leasing. In contrast to hierarchical spectrum access, spectrum owners in proposed DSL networks, which are denoted as primary users, dynamically adjust the amount of secondary interference that they are willing to tolerate in response to the demand from secondary transmitters. The secondary transmitters, in turn, opportunistically attempt to achieve maximum possible throughput, or another suitably defined reward, while not violating the interference limit that is set by the primary users. The new game-theoretic model, however, allows the secondary users to encourage the spectrum owners to push the interference cap upward based on demand. We have proposed a general structure for the utility functions of primary users and secondary users that allows the primary users to control the price and the demand for spectrum access based on their required quality of service (QoS). We establish that, with these utility functions, the DSL game has a unique Nash equilibrium to which the best response adaptation finally converges. Moreover, it is shown that the proposed coexistence and best response adaptations can be achieved with no significant interaction between the two systems. In fact, it is shown that the only requirement is that the primary system periodically broadcasts two parameter values. We use several examples to illustrate the system behavior at the equilibrium and use the performance at the equilibrium to identify suitable system design parameters. [ABSTRACT FROM AUTHOR]

Wireless mesh networks (WMNs) are considered to be an economical solution for last-mile broadband Internet access. In this paper, we study end-to-end bandwidth allocation in WMNs with cognitive radios, which involves routing, scheduling, and spectrum allocation. To achieve a good tradeoff between fairness and throughput, we define two fair bandwidth-allocation problems based on a simple max-min fairness model and the well-known lexicographical max-min (LMM) fairness model, respectively. We present linear programming (LP)-based optimal and heuristic algorithms to solve both problems. Extensive simulation results are presented to justify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]