Performance Evaluation of Connectivity and Capacity of Dynamic Spectrum Access Networks

Recent measurements on radio spectrum usage have revealed the abundance of under- utilized bands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access (DSA) where secondary networks utilize unused spectrum holes in the licensed bands without causing interference to the licensed user. However, wide scale deployment of these networks have been hindered due to lack of knowledge of expected performance in realistic environments and lack of cost-effective solutions for implementing spectrum database systems. In this dissertation, we address some of the fundamental challenges on how to improve the performance of DSA networks in terms of connectivity and capacity. Apart from showing performance gains via simulation experiments, we designed, implemented, and deployed testbeds that achieve economics of scale. We start by introducing network connectivity models and show that the well-established disk model does not hold true for interference-limited networks. Thus, we characterize connectivity based on signal to interference and noise ratio (SINR) and show that not all the deployed secondary nodes necessarily contribute towards the network\u27s connectivity. We identify such nodes and show that even-though a node might be communication-visible it can still be connectivity-invisible. The invisibility of such nodes is modeled using the concept of Poisson thinning. The connectivity-visible nodes are combined with the coverage shrinkage to develop the concept of effective density which is used to characterize the con- nectivity. Further, we propose three techniques for connectivity maximization. We also show how traditional flooding techniques are not applicable under the SINR model and analyze the underlying causes for that. Moreover, we propose a modified version of probabilistic flooding that uses lower message overhead while accounting for the node outreach and in- terference. Next, we analyze the connectivity of multi-channel distributed networks and show how the invisibility that arises among the secondary nodes results in thinning which we characterize as channel abundance. We also capture the thinning that occurs due to the nodes\u27 interference. We study the effects of interference and channel abundance using Poisson thinning on the formation of a communication link between two nodes and also on the overall connectivity of the secondary network. As for the capacity, we derive the bounds on the maximum achievable capacity of a randomly deployed secondary network with finite number of nodes in the presence of primary users since finding the exact capacity involves solving an optimization problem that shows in-scalability both in time and search space dimensionality. We speed up the optimization by reducing the optimizer\u27s search space. Next, we characterize the QoS that secondary users can expect. We do so by using vector quantization to partition the QoS space into finite number of regions each of which is represented by one QoS index. We argue that any operating condition of the system can be mapped to one of the pre-computed QoS indices using a simple look-up in Olog (N) time thus avoiding any cumbersome computation for QoS evaluation. We implement the QoS space on an 8-bit microcontroller and show how the mathematically intensive operations can be computed in a shorter time. To demonstrate that there could be low cost solutions that scale, we present and implement an architecture that enables dynamic spectrum access for any type of network ranging from IoT to cellular. The three main components of this architecture are the RSSI sensing network, the DSA server, and the service engine. We use the concept of modular design in these components which allows transparency between them, scalability, and ease of maintenance and upgrade in a plug-n-play manner, without requiring any changes to the other components. Moreover, we provide a blueprint on how to use off-the-shelf commercially available software configurable RF chips to build low cost spectrum sensors. Using testbed experiments, we demonstrate the efficiency of the proposed architecture by comparing its performance to that of a legacy system. We show the benefits in terms of resilience to jamming, channel relinquishment on primary arrival, and best channel determination and allocation. We also show the performance gains in terms of frame error rater and spectral efficiency

Percolation in multi-channel secondary cognitive radio networks under the SINR model

In this paper, we use concepts and results from percolation theory to investigate and characterize the effects of primaries on the connectivity of a secondary cognitive radio network under the SINR model. The SINR requirements of the secondaries along with the interference tolerance of the primaries determine which secondary nodes can communicate and which ones are rendered invisible from each other. Such invisibility is even more pronounced when there are plenty of channels to choose from- a phenomenon which we define as channel abundance. With no node-to-node coordination and a naive channel rendezvous protocol, it becomes difficult for the nodes to select a common channel. Invisibility caused by interference and channel abundance is modeled using Poisson thinning. We study their combined effects on the formation of a communication link between two nodes and also on the overall connectivity of the secondary network. Representing multiple channels as parallel edges in a graph, we use the projection of multiple graphs on R2 and show how the network percolates in continuum R2 by coupling it with a typical discrete lattice percolation. We define and characterize the connectivity of the secondary network in terms of the available number of channels, deployment density, number of transceivers per node and interference cancellation coefficient, both in the presence and absence of primaries. When primaries are absent, we derive the number of channels for which the sub and super-criticality of the secondary network are achieved. When primaries are present, we identify the channel abundance region, the optimal point, and the channel deprivation region. Further, we show how cooperation between primary and secondary networks can increase the connectivity of both. © 2014 IEEE

A System Level Solution For Dsa Systems: From Low-Cost Sensing To Spectrum Database

In this paper, we present and implement a low-cost yet effective architecture that enables dynamic spectrum access (DSA) for any type of network. Our intention is to break the cost-scalability barrier and show that a complete system level solution for a database-assisted DSA system can be implemented with standard servers and inexpensive software configurable RF chips, thereby achieving economics of scale. First, we present the overall architecture that is capable of providing networks of any size to perform in-band and out of band channel access in a dynamic manner. The two main components of this architecture are the received signal strength indicator (RSSI) sensing network and the DSA server. For the RSSI sensing network, we built wired and wireless spectrum sensors that operate on 280-930 MHz using low-cost off the shelf software configurable RF (SCRF) chips. To get the RSSI values on a set of bands, we use generic micro-controllers to program the operating parameters (scan range, center frequency, bandwidth resolution, demodulation scheme and scan rate) of the SCRF chips. The wireless sensors transmit the sensed RSSI values to the nearest Ethernet-enabled hub using a light-weight communication protocol. The hub aggregates the data from multiple sensors and streams to the DSA server using UDP over IP. On receiving the real-time RSSI values from various sensors, the DSA server stores them in database engine with other meta data. Entries from the database are used by the channel allocation service that finds the best channel for the inquiring DSA nodes. To demonstrate the efficiency of the implemented database-assisted DSA system, we compare it to a legacy system and show the benefits in terms of resilience to jamming, channel relinquishment on primary arrival, and best channel determination and allocation. We also show the performance gains in terms of frame error rate (FER) and spectral efficiency. Finally, we compare the RSSI sensitivity of the low-cost sensors to that of a professional spectrum analyzer

Vector Quantization Based Qos Evaluation In Cognitive Radio Networks

In this paper, we characterize the QoS that secondary users can expect in a cognitive radio network in the presence of primaries. To that end, we first define a $$K$$K-dimensional QoS space where each point in that space characterizes the expected QoS. We show how the operating condition of the system maps to a point in the QoS space, the quality of which is given by the corresponding QoS index. To deal with the real-valued QoS space, we use vector quantization to partition the space into finite number of regions each of which is represented by one QoS index. We argue that any operating condition of the system can be mapped to one of the pre-computed QoS indices using a simple look-up in $$O(log\,N)$$O(logN) time—thus avoiding any cumbersome computation for QoS evaluation. The proposed technique takes the power vector as its input from the power control unit which we consider as a black box. Using simulations, we illustrate how a $$K$$K-dimensional QoS space can be constructed. We choose capacity as the QoS metrics and show what the expected capacity would be for a given power vector. We also show the effect of having large number of partitions on the distortion. As for the implementation feasibility of the proposed concept, we implement the QoS space on an 8-bit microcontroller and show how the mathematically intensive operations can be computed in a short time. Further we use binary search to achieve scalability as the dimensionality of the space increases

Capacity Of Finite Secondary Cognitive Radio Networks: Bounds And Optimizations

Though there are works that show the asymptotic capacity bounds in a wireless network considering interference constraints from all transmitting nodes, there are no such evaluation of capacity bounds for finite secondary cognitive radio networks where the primaries pose additional constraints. In this paper, we find the bounds for the maximum achievable capacity of a randomly deployed secondary cognitive radio network with finite number of nodes in the presence of primary users, i.e., in the underlay mode. Since solving the functional constrained optimization problem of maximizing the secondary network\u27s capacity subject to other radio constraints is computationally complex, we derive analytical bounds for the solution. That is achieved by deriving a pre-engineered deployment with the best possible pairings of transmitters and receivers, from the random deployment. The capacity of the former is used to upper bound the capacity of the latter. The bounds are based on the maximum signal to interference and noise ratio (SINR) of all transmitter-receiver pairs and their geometrical placement. The derived bounds provide an insight about the network\u27s maximum and minimum achievable capacities since solving the optimization problem shows in-scalability both in time and search space dimensionality. To this end, we reduce the optimizer\u27s search space by eliminating transmitters and receivers that do not contribute positively to the system capacity. Such reduction depends on primary transmit power, primary\u27s interference tolerance, and mutual distances between primaries and secondaries. Further, we propose a metric that gives the relative goodness of node pairs for being potential winners for power allocation from the global power optimizer. The metric also facilitates certain trade-offs such as achieving optimal capacity using all nodes or better execution time using a partial set of nodes (expected winners). We show how the metric along with the elimination schemes can be used for pre-processing (search space dimension reduction) the input power vector before it is fed to any power optimizer. Through simulation results we show the theoretical bounds and the capacity obtained via the proposed optimizations. We also show the gains obtained due to search space reduction

Percolation Condition For Interference-Limited Cognitive Radio Networks

In this paper, we characterize the percolation condition for a continuum secondary cognitive radio network under the SINR model. We show that the well-established condition for continuum percolation does not hold true in the SINR regime. Thus, we find the condition under which a cognitive radio network percolates. We argue that due to the SINR requirements of the secondaries along with the interference tolerance of the primaries, not all the deployed secondary nodes necessarily contribute towards the percolation process- even though they might particIPate in the communication process. We model the invisibility of such nodes using the concept of Poisson thinning, both in the presence and absence of primaries. Invisibility occurs due to nodes that i) cannot decode transmissions except from their nearest neighbors, ii) are always interfered, and iii) belong to isolated components. We find the thinning probability in terms of primary and secondary densities, communication radii, and interference cancellation coefficient. Further, we show how the effective coverage radius shrinks which also adds to the thinning. Theoretical findings are validated through simulations

Vector quantization based QoS evaluation in cognitive radio networks

In this paper, we attempt to characterize the QoS that secondary users can expect in a cognitive radio network. Using power control as a black-box, we propose a method that can help us evaluate the QoS for any given power vector based on past observations. To that end, we first define a k-dimensional QoS space where each point in that space characterizes the expected QoS. We show how the operating condition of the system maps to a point in the QoS space, the quality of which is given by the corresponding QoS index. To deal with the real-valued QoS space, we use vector quantization to partition the space into finite number of regions each of which is represented by one QoS index. We argue that any operating condition of the system can be mapped to one of the pre-computed QoS indices using a simple look-up in O(log n) time- thus avoiding any cumbersome computation for QoS evaluation. Using simulations, we illustrate how a 2-dimensional QoS space can be constructed. We choose capacity as the QoS metric and show what the expected capacity would be for a given power vector. © 2014 IEEE

KNOWLEDGE AND PERCEPTIONS OF VITAMIN D DEFICIENCY AMONG THE UNITED ARAB EMIRATES POPULATION

Objectives: The purpose of this study is to assess the Vitamin D deficiency awareness and perceptions between the United Arab Emirates (UAE) population. Methods: A cross-sectional study was done among the population of two emirates at the UAE: Abu Dhabi and Sharjah. Results: Overall, 434 participants completed the survey. Majority of people were aware of phenomenon of Vitamin D deficiency, but only 21.4% of them knew that sunlight is considered the main source of Vitamin D. Moreover, less than half of participants check their Vitamin D blood level regularly and around 55% of them follow-up with their physicians after completing the treatment. High proportion of participant females spend <1 h outdoors (60%) and use sunscreen daily (55%) that cause higher prevalence of Vitamin D deficiency among females than males (83% vs. 42%). Conclusion: This research gives some insights regarding the UAE population’s awareness and perceptions of Vitamin D insufficiency. Decreased awareness of sunlight exposure as a major source of Vitamin D, in addition to lifestyle, contributed to Vitamin D deficiency problem among the UAE population, in general, and in females, in particular

Capacity bounds of finite secondary cognitive radio networks

Though there are works that show the asymptotic capacity bounds in a wireless network considering interference constraints from all transmitting nodes, there are no such evaluation of capacity bounds for finite secondary cognitive radio networks where the primaries pose additional constraints. In this paper, we find the bounds for the maximum achievable capacity of a randomly deployed secondary cognitive radio network with finite number of nodes in the presence of primary users, i.e., in the underlay mode. Since solving the functional constrained optimization problem of maximizing the secondary network\u27s capacity subject to other radio constraints is computationally complex, we derive analytical bounds for the solution. We also show how a pre-engineered deployment with the best possible pairings of transmitters and receivers can help attain the best possible system capacity. The bounds are based on the maximum signal to interference and noise ratio (SINR) of all transmitter-receiver pairs and their geometrical placement. The derived bounds provide an insight about the network\u27s maximum and minimum achievable capacities since solving the optimization problem shows in-scalability both in time and search space dimensionality

Distributed Mac For Connectivity Maximization Of Interference Limited Un-Coordinated Dsa Networks

In this paper, we propose a medium access control (MAC) protocol to maximize the connectivity for an un-coordinated secondary dynamic spectrum access network under the signal to interference and noise ratio (SINR) regime. We use concepts from percolation theory to obtain the optimal deployment density for secondary nodes. We argue that the optimal connectivity under the SINR regime can be improved if a fraction of the nodes transmit at a given time. Thus, the proposed MAC is based on distributed Time Division Multiple Access (TDMA) where all nodes randomly choose a time slot to transmit on, a phenomenon similar to the Poisson blinking model. We find the optimal number of slots for the super-frame that includes the sensing, contention, and transmission phases. The performance of the proposed MAC is evaluated via simulations. We show how the proposed MAC adaptively adjusts the super-frame as the density of secondary varies. We also show the connectivity and throughput achieved for various network settings.