Multiuser Diversity Gain in Cognitive Networks

Dynamic allocation of resources to the \emph{best} link in large multiuser networks offers considerable improvement in spectral efficiency. This gain, often referred to as \emph{multiuser diversity gain}, can be cast as double-logarithmic growth of the network throughput with the number of users. In this paper we consider large cognitive networks granted concurrent spectrum access with license-holding users. The primary network affords to share its under-utilized spectrum bands with the secondary users. We assess the optimal multiuser diversity gain in the cognitive networks by quantifying how the sum-rate throughput of the network scales with the number of secondary users. For this purpose we look at the optimal pairing of spectrum bands and secondary users, which is supervised by a central entity fully aware of the instantaneous channel conditions, and show that the throughput of the cognitive network scales double-logarithmically with the number of secondary users ($N$) and linearly with the number of available spectrum bands ($M$), i.e., $M\log\log N$. We then propose a \emph{distributed} spectrum allocation scheme, which does not necessitate a central controller or any information exchange between different secondary users and still obeys the optimal throughput scaling law. This scheme requires that \emph{some} secondary transmitter-receiver pairs exchange $\log M$ information bits among themselves. We also show that the aggregate amount of information exchange between secondary transmitter-receiver pairs is {\em asymptotically} equal to $M\log M$. Finally, we show that our distributed scheme guarantees fairness among the secondary users, meaning that they are equally likely to get access to an available spectrum band.Comment: 32 pages, 3 figures, to appear in the IEEE/ACM Transactions on Networkin

Simultaneous Routing and Power Allocation using Location Information

To guarantee optimal performance of wireless networks, simultaneous optimization of routing and resource allocation is needed. Optimal routing of data depends on the link capacities which, in turn, are determined by the allocation of communication resources to the links. Simultaneous routing and resource allocation (SRRA) problems have been studied under the assumption that (global) channel state information (CSI) is collected at a central node. This is a drawback as SRRA depends on channels between all pairs of nodes in the network, thus leading to poor scalability of the CSI-based approach. In this paper, we first investigate to what extent it is possible to rely solely on location information (i.e., position of nodes) when solving the SRRA problem. We also propose a distributed heuristic based on which nodes can locally adjust their rate based on the local CSI. Our numerical results show that the proposed heuristic achieves near-optimal flow in the network under different shadowing conditions

Fast-Convergent Dynamics for Distributed Resource Allocation Over Sparse Time-Varying Networks

In this paper, distributed dynamics are deployed to solve resource allocation over time-varying multi-agent networks. The state of each agent represents the amount of resources used/produced at that agent while the total amount of resources is fixed. The idea is to optimally allocate the resources among the group of agents by reducing the total cost functions subject to fixed amount of total resources. The information of each agent is restricted to its own state and cost function and those of its immediate neighbors. This is motivated by distributed applications such as in mobile edge-computing, economic dispatch over smart grids, and multi-agent coverage control. The non-Lipschitz dynamics proposed in this work shows fast convergence as compared to the linear and some nonlinear solutions in the literature. Further, the multi-agent network connectivity is more relaxed in this paper. To be more specific, the proposed dynamics even reaches optimal solution over time-varying disconnected undirected networks as far as the union of these networks over some bounded non-overlapping time-intervals includes a spanning-tree. The proposed convergence analysis can be applied for similar 1st-order resource allocation nonlinear dynamics. We provide simulations to verify our results

A Convex Framework for Epidemic Control in Networks

With networks becoming pervasive, research attention on dynamics of epidemic models in networked populations has increased. While a number of well understood epidemic spreading models have been developed, little to no attention has been paid to epidemic control strategies; beyond heuristics usually based on network centrality measures. Since epidemic control resources are typically limited, the problem of optimally allocating resources to control an outbreak becomes of interest. Existing literature considered homogeneous networks, limited the discussion to undirected networks, and largely proposed network centrality-based resource allocation strategies. In this thesis, we consider the well-known Susceptible-Infected-Susceptible spreading model and study the problem of minimum cost resource allocation to control an epidemic outbreak in a networked population. First, we briefly present a heuristic that outperforms network centrality-based algorithms on a stylized version of the problem previously studied in the literature. We then solve the epidemic control problem via a convex optimization framework on weighted, directed networks comprising heterogeneous nodes. Based on our spreading model, we express the problem of controlling an epidemic outbreak in terms of spectral conditions involving the Perron-Frobenius eigenvalue. This enables formulation of the epidemic control problem as a Geometric Program (GP), for which we derive a convex characterization guaranteeing existence of an optimal solution. We consider two formulations of the epidemic control problem -- the first seeks an optimal vaccine and antidote allocation strategy given a constraint on the rate at which the epidemic comes under control. The second formulation seeks to find an optimal allocation strategy given a budget on the resources. The solution framework for both formulations also allows for control of an epidemic outbreak on networks that are not necessarily strongly connected. The thesis further proposes a fully distributed solution to the epidemic control problem via a Distributed Alternating Direction Method of Multipliers (ADMM) algorithm. Our distributed solution enables each node to locally compute its optimum allocation of vaccines and antidotes needed to collectively globally contain the spread of an outbreak, via local exchange of information with its neighbors. Contrasting previous literature, our problem is a constrained optimization problem associated with a directed network comprising non-identical agents. For the different problem formulations considered, illustrations that validate our solutions are presented. This thesis, in sum, proposes a paradigm shift from heuristics towards a convex framework for contagion control in networked populations

Optimization of Spectrum Allocation in Cognitive Radio and Dynamic Spectrum Access Networks

Spectrum has become a treasured commodity. However, many licensed frequency bands exclusively assigned to the primary license holders (also called primary users) remain relatively unused or under-utilized for most of the time. Allowing other users (also called secondary users) without a license to operate in these bands with no interference becomes a promising way to satisfy the fast growing needs for frequency spectrum resources. A cognitive radio adapts to the environment it operates in by sensing the spectrum and quickly decides on appropriate frequency bands and transmission parameters to use in order to achieve certain performance goals. One of the most important issues in cognitive radio networks (CRNs) is intelligent channel allocation which will improve the performance of the network and spectrum utilization. The objective of this dissertation is to address the channel allocation optimization problem in cognitive radio and DSA networks under both centralized architecture and distributed architecture. By centralized architecture we mean the cognitive radio and DSA networks are infrastructure based. That is, there is a centralized device which collects all information from other cognitive radios and produces a channel allocation scheme. Then each secondary user follows the spectrum allocation and accesses the corresponding piece of spectrum. By distributed architecture we mean that each secondary user inside the cognitive radio and DSA networks makes its own decision based on local information on the spectrum usage. Each secondary user only considers the spectrum usage around itself. We studied three common objectives of the channel allocation optimization problem, including maximum network throughput (MNT), max-min fairness (MMF), and proportional fairness (PF). Given different optimization objectives, we developed mathematical models in terms of linear programing and non-linear programing formulations, under the centralized architecture. We also designed a unified framework with different heuristic algorithms for different optimization objectives and the best results from different algorithms can be automatically chosen without manual intervention. We also conducted additional work on spectrum allocation under distributed architecture. First, we studied the channel availability prediction problem. Since there is a lot of usable statistic information on spectrum usage from national and regional agencies, we presented a Bayesian inference based prediction method, which utilizes prior information to make better prediction on channel availability. Finally a distributed channel allocation algorithm is designed based on the channel prediction results. We illustrated that the interaction behavior between different secondary users can be modeled as a game, in which the secondary users are denoted as players and the channels are denoted as resources. We proved that our distributed spectrum allocation algorithm can achieve to Nash Equilibrium, and is Pareto optimal

Adaptive resource allocation within three-stage OFDM relay networks

In this work we consider OFDM transmission, due to its potential for meeting the stringent quality of service (QoS) targets of next-generation broadband distributed wireless networks, over three-stage relay networks. In particular, we examine distributed adaptive space-frequency coding for generally asynchronous links composed of four transmit and/or receive antennas, i.e. exploiting quasi-orthogonal and extended-orthogonal coding schemes. The successful deployment of these closed-loop methods is dependent upon channel state information (CSI) being available for each stage of the network. Taking the maximum end-to-end data rate as the optimal criterion, an adaptive resource allocation (RA) scheme suitable for a wide range of signal-to-noise-ratios (SNRs) and a prescribed transmit power budget is proposed to distribute appropriate resources to each stage based on the channel state information (CSI) and knowledge of the network topology

Distributed rate allocation for multi-flow video delivery

We consider rate-distortion (RD) optimized multi-flow video delivery in unstructured overlay networks. We show that this problem can be studied as a distributed rate allocation. To solve the problem over the participating peers in the overlay, we apply classical decomposition techniques such that the network-wide utility of video distortion is minimized. Media packets are assumed to be piggy-backed with RD preambles that contain information regarding their impact on decoder video distortion and their size. This allows for converting the calculated optimal rate allocation at every node into simple forwarding or dropping actions. Furthermore, the proposed distributed media streaming framework employs a network inference algorithm for minimizing the flow of duplicate packets over the network and utilizing thus more efficiently the available resources. Our simulation results indicate that significant quality benefits can be achieved when the precise RD characteristics of a media presentation are taken into account