Didem Gözüpek

A Spectrum Switching Delay-Aware Scheduling Algorithm for Centralized Cognitive Radio Networks

We formulate a scheduling problem that takes into account different hardware delays experienced by the secondary users (SUs) in a centralized cognitive radio network (CRN) while switching to different frequency bands. We propose a polynomial-time suboptimal algorithm to address our formulated scheduling problem. We evaluate the impact of varying switching delay, number of frequencies, and number of SUs. Our simulation results indicate that our proposed algorithm is robust to changes in the hardware spectrum switching delay and its performance is very close to its upper bound. We also compare our proposed method with the corresponding constant switching delay-based algorithm and demonstrate that our suggestion of taking into account the different hardware delays while switching to different frequency bands is essential for scheduling in CRNs.

Throughput and delay optimal scheduling in cognitive radio networks under interference temperature constraints

The fixed spectrum assignment policy in todays wireless networks leads to inefficient spectrum usage. Cognitive radio network is a new communication paradigm that enables the unlicensed users to opportunistically use the spatio-temporally unoccupied portions of the spectrum, and hence realizing a dynamic spectrum access (DSA) methodology. Interference temperature model proposed by Federal Communications Commission (FCC) permits the unlicensed users to utilize the licensed frequencies simultaneously with the primary users provided that they adhere to the interference temperature constraints. In this paper, we formulate two NP-hard optimal scheduling methods that meet the interference temperature constraints for cognitive radio networks. The first one maximizes the network throughput, whereas the second one minimizes the scheduling delay. Furthermore, we also propose suboptimal schedulers with linear complexity, referred to as maximum frequency selection (MFS) and probabilistic frequency selection (PFS). We simulate the throughput and delay performance of the optimal as well as the suboptimal schedulers for varying number of cognitive nodes, number of primary neighbors for each cognitive node, and interference temperature limits for the frequencies. We also evaluate the performance of our proposed schedulers under both additive white gaussian noise (AWGN) channels and Gilbert-Elliot fading channels.

A novel handover protocol to prevent hidden node problem in satellite assisted cognitive radio networks

The rapid growth in wireless technologies has intensified the demand for the radio spectrum. On the other hand, the research studies reveal that the spectrum utilization is unevenly distributed, which leads to the conclusion that there is a problem with the spectrum management and allocation rather than the scarcity of the spectrum itself. This inefficiency in spectrum usage in addition to the escalating demand for the radio spectrum fostered the research studies that focus on new communication paradigms referred to as dynamic spectrum access (DSA) and cognitive radio networks, which are based on opportunistically utilizing the radio spectrum. IEEE 802.22 is the first standard for cognitive radio networks, in which, however, network entry and initialization, as well as the hidden incumbent problem have not yet completely been addressed. On the other hand, mobility is also an unexplored issue in cognitive radio networks. In this paper, we propose a novel protocol that combats the hidden incumbent problem during network entry, initialization and handover, while at the same time taking the mobility pattern of the cognitive devices into consideration. Our proposed scheme is based on a satellite assisted cognitive radio architecture. Our model outperforms the current IEEE 802.22 scheme and other work in the literature in terms of connection setup delay.

An Auction Theory Based Algorithm for Throughput Maximizing Scheduling in Centralized Cognitive Radio Networks

In this letter we propose an auction theory based algorithm for throughput maximizing scheduling in centralized cognitive radio networks (CRN). In the considered CRN scheme, a centralized base station coordinates the assignment of frequencies and time slots to cognitive users with multiple antennas. Our proposed algorithm uses first-price sealed bid auction mechanism in which frequency and time slot pairs are considered as the auctioned resources and cognitive users are the bidders. The experimental results show that our computationally efficient algorithm yields very close throughput performance to the optimization software CPLEX values.

Throughput Satisfaction-Based Scheduling for Cognitive Radio Networks

Opportunistic scheduling algorithms in cognitive radio networks (CRNs) allocate resources by exploiting the variations in channel conditions and spectral activities of primary users. However, most of these scheduling algorithms ignore the per-user throughput requirements. In this paper, we formulate a scheduling problem called maximizing the number of satisfied users (MNSU), which maximizes the number of secondary users that are satisfied in terms of throughput in a centralized CRN. We show that MNSU is NP-hard in the strong sense and cannot be approximated within any constant factor better than 2 unless P = NP . We also prove that MNSU is at least as hard as the max-min fair scheduling problem, which has previously been proven to be a computationally very difficult problem in the literature. We then propose two heuristic algorithms: 1) best first resource assignment and 2) resource assignment with partial backtracking. We demonstrate that our proposed algorithms yield high performance while still achieving low computational complexity.

An interference aware throughput maximizing scheduler for centralized cognitive radio networks

In this paper, we propose an interference aware throughput maximizing scheduler for cognitive radio networks (CRNs) as part of a MAC layer resource allocation framework. In the considered CRN scenario, the cognitive users with multiple antennas are coordinated by a centralized cognitive base station. We evaluate the performance of our proposed scheme using analysis of variation (ANOVA) technique. We also show experimental results for the total throughput for varying number of cognitive users and frequencies. 1In this paper, we propose an interference aware throughput maximizing scheduler for cognitive radio networks (CRNs) as part of a MAC layer resource allocation framework. In the considered CRN scenario, the cognitive users with multiple antennas are coordinated by a centralized cognitive base station. We evaluate the performance of our proposed scheme using analysis of variation (ANOVA) technique. We also show experimental results for the total throughput for varying number of cognitive users and frequencies. 1

Enhancing quality of service provisioning in wireless ad hoc networks using service vector paradigm

Summary Emerging real-time communications and multimedia applications necessitate the provisioning of Quality of Service (QoS) in Internet. Recently, a new concept, referred to as service vector, has been introduced to enhance the end-to-end QoS granularity, and at the same time, maintain the simplicity and scalability feature of the current differentiated services (DiffServ) networks. This work extends this concept to wireless ad hoc networks and proposes a cross-layer architecture based on the combination of delay-bounded wireless link level scheduling and the network layer service vector concept, resulting in significant power savings and finer end-to-end QoS granularity. The impact of various traffic arrival distributions and flows with different QoS requirements on the performance of this cross-layer architecture is also investigated and evaluated. Copyright # 2006 John Wiley & Sons, Ltd.

On the complexity of constructing minimum changeover cost arborescences

The reload cost concept refers to the cost that occurs at a vertex along a path on an edge-colored graph when it traverses an internal vertex between two edges of different colors. This cost depends only on the colors of the traversed edges. Reload costs arise in various applications such as transportation networks, energy distribution networks, and telecommunications. Previous work on reload costs focuses on two problems of finding a spanning tree with minimum cost with respect to two different cost measures. In both problems the cost is associated with a set of paths from a given vertex r to all the leaves of the constructed tree. The first cost measure is the sum of the reload costs of all paths from r to the leaves. The second cost measure is the changeover cost, in which the cost of traversing a vertex by using two specific incident edges is paid only once regardless of the number of paths traversing it. The first problem is inapproximable within any polynomial time computable function of the input size [1], and the second problem is inapproximable within n1−ϵ for any ϵ>0 [2]. In this paper we show that the first hardness result holds also for the second problem. Given this strong inapproximability result, we study the complexity and approximability properties of numerous special cases of this second problem. We mainly focus on bounded costs, and consider both directed and undirected graphs, bounded and unbounded number of colors, and both bounded and unbounded degree graphs. We also present polynomial time exact algorithms and an approximation algorithm for some special case. To the best of our knowledge, these are the first algorithms with a provable performance guarantee for the problem. Moreover, our approximation algorithm shows a tight bound on the approximability of the problem for a specific family of instances.

Channel assignment problem in cellular networks: A reactive tabu search approach

The channel assignment problem (CAP) in cellular networks is concerned with the allocation and reuse of the frequency spectrum to the base stations in such a way that both the interference constraints and the traffic requirements of the cells are met. In this paper, we apply a reactive tabu search based method to solve the CAP and compare this approach with the classical tabu search as well as genetic algorithm based approaches in the literature.

Constructing minimum changeover cost arborescenses in bounded treewidth graphs

Given an edge-colored graph, an internal vertex of a path experiences a reload cost if it lies between two consecutive edges of different colors. The value of the reload cost depends only on the colors of the traversed edges. The reload cost concept has important applications in dynamic networks, such as transportation networks and dynamic spectrum access networks. In the minimum changeover cost arborescence (MinCCA) problem, we seek a spanning tree of an edge-colored graph, in which the sum of reload costs of all internal vertices, starting from a given root, is minimized. In general, MinCCA is known to be hard to approximate within factor n 1 - ? , for any ? 0 , on a graph of n vertices.We first show that MinCCA can be optimally solved in polynomial-time on cactus graphs. Our main result is an optimal polynomial-time algorithm for graphs of bounded treewidth, thus establishing the solvability of our problem on a fundamental subclass of graphs. Our results imply that MinCCA is fixed parameter tractable when parameterized by treewidth and the maximum degree of the input graph.