Hyang-Won Lee

Diverse routing in networks with probabilistic failures

We develop diverse routing schemes for dealing with multiple, possibly correlated, failures. While disjoint path protection can effectively deal with isolated single link failures, recovering from multiple failures is not guaranteed. In particular, events such as natural disasters or intentional attacks can lead to multiple correlated failures, for which recovery mechanisms are not well understood. We take a probabilistic view of network failures where multiple failure events can occur simultaneously, and develop algorithms for finding diverse routes with minimum joint failure probability. Moreover, we develop a novel Probabilistic Shared Risk Link Group (PSRLG) framework for modeling correlated failures. In this context, we formulate the problem of finding two paths with minimum joint failure probability as an integer nonlinear program (INLP) and develop approximations and linear relaxations that can find nearly optimal solutions in most cases.

Cross-layer survivability in WDM-based networks

In layered networks, a single failure at a lower layer may cause multiple failures in the upper layers. As a result, traditional schemes that protect against single failures may not be effective in multilayer networks. In this paper, we introduce the problem of maximizing the connectivity of layered networks. We show that connectivity metrics in layered networks have significantly different meaning than their single-layer counterparts. Results that are fundamental to survivable single-layer network design, such as the Max-Flow Min-Cut Theorem, are no longer applicable to the layered setting. We propose new metrics to measure connectivity in layered networks and analyze their properties. We use one of the metrics, Min Cross Layer Cut, as the objective for the survivable lightpath routing problem and develop several algorithms to produce lightpath routings with high survivability. This allows the resulting cross-layer architecture to be resilient to failures between layers.

Downlink resource allocation in multi-carrier systems: frequency-selective vs. equal power allocation

This paper revisits equal power allocation from the viewpoint of asymptotic network utility maximization (NUM) problem in multi-carrier systems. It is a well-known fact that the equal power allocation is near optimal to the sum capacity maximization problem in high SNR (signal-to-noise ratio) regime, i.e., optimal water-filling approximates to equal power allocation in that case. Due to this property together with its simplicity, the equal power allocation has been adopted in several researches, but its performance in other problems has not been clearly understood. We evaluate the suitability of equal power allocation in NUM problem which turns into various resource sharing policies according to utility functions. Namely, our conclusion is that in frequency selective channels, the equal power allocation is near optimal for efficiency-oriented resource sharing policy, but when fairness is emphasized, its performance is severely degraded and thus frequency-selective power allocation is necessary. For this, we develop a suboptimal subcarrier and frequency-selective power allocation algorithm for asymptotic NUM problem using the gradient-based scheduling theory and compare the performance of equal power allocation and the developed algorithm. Extensive simulation results are presented to verify our arguments.

Reliability in layered networks with random link failures

We consider network reliability in layered networks where the lower layer experiences random link failures. In layered networks, each failure at the lower layer may lead to multiple failures at the upper layer. We generalize the classical polynomial expression for network reliability to the multi-layer setting. Using random sampling techniques, we develop polynomial time approximation algorithms for the failure polynomial. Our approach gives an approximate expression for reliability as a function of the link failure probability, eliminating the need to resample for different values of the failure probability. Furthermore, it gives insight on how the routings of the logical topology on the physical topology impact network reliability. We show that maximizing the min cut of the (layered) network maximizes reliability in the low failure probability regime. Based on this observation, we develop algorithms for routing the logical topology to maximize reliability.

Virtual Cell Beamforming in Cooperative Networks

We study the coordinated transmission problem in cooperative cellular networks where a cluster of base stations forms a virtual cell to serve a mobile station (MS). The performance of such an MS-centric virtual cell network is dictated by the beamformer that enables to suppress interference; however, designing a beamformer is highly challenging due to the coupled nature of interference and desired signals under arbitrarily formed virtual cells. We develop a new formulation of the beamforming problem for sum-rate maximization in virtual cell networks and analyze the structure of its optimal solutions. Based on this analysis, we develop a beamforming algorithm that can balance between desired signal maximization and interference minimization, so as to maximize the sum-rate. We show through extensive simulations that our balanced beamforming algorithm mitigates edge user effect and outperforms existing algorithms in various scenarios where virtual cells are allowed to overlap.

Downlink Resource Allocation in Multi-Carrier Systems: Frequency-Selective vs. Equal Power Allocation

In this paper, dynamic subcarrier and power allocation problem is considered in the context of asymptotic utility maximization in multi-carrier systems. Using gradient-based resource allocation, we formulate an optimization problem involving subcarrier and transmit power allocation for each time slot, and propose an optimal algorithm solving the problem. Since the optimal algorithm is impractical due to its complexity, a simple suboptimal algorithm is also proposed based on generalized Benders decomposition. Furthermore, we identify the performance of equal power allocation policy by showing that equal power allocation is not always near optimal in general utility maximization problem and characterizing the optimality condition of equal power allocation. This result not only justifies the use of our dynamic power allocation, but also generalizes the performance of equal power allocation, which is well-known to be approximately optimal to the sum capacity maximization problem in high SNR (signal-to-noise ratio) regime. Our algorithms and analysis are verified through extensive simulations.

Opportunistic Relaying in Cellular Network for Capacity and Fairness Improvement

In this paper, we study how the cooperative relaying can improve both capacity and fairness in cellular network. The capacity and fairness have a trade-off relationship, so increasing cell throughput deteriorates fairness and vice versa. First, we show that the achievable average throughput region can be enlarged by using the cooperative relaying. This enlarged region means that capacity and fairness can be improved at the same time with an adequate scheduling algorithm. Thus, secondly we propose a generalized scheduling algorithm for cooperative relaying. The proposed scheduling algorithm can improve both capacity and fairness at the expense of cooperation among users. From simulations, we show that the trade-off relationship can be surpassed and the unfairness problem in the heterogeneous channel condition can be solved by the opportunistic relaying.

A robust optimization approach to backup network design with random failures

This paper presents a scheme in which a dedicated backup network is designed to provide protection from random link failures. Upon a link failure in the primary network, traffic is rerouted through a preplanned path in the backup network. We introduce a novel approach for dealing with random link failures, in which probabilistic survivability guarantees are provided to limit capacity over-provisioning.We show that the optimal backup routing strategy in this respect depends on the reliability of the primary network. Specifically, as primary links become less likely to fail, the optimal backup networks employ more resource sharing amongst backup paths. We apply results from the field of robust optimization to formulate an ILP for the design and capacity provisioning of these backup networks. We then propose a simulated annealing heuristic to solve this problem for largescale networks, and present simulation results that verify our analysis and approach.

Survivable Path Sets: A New Approach to Survivability in Multilayer Networks

We consider the problem of survivability in multilayer networks. In single-layer networks, a pair of disjoint paths can be used to provide protection for a source-destination pair. However, this approach cannot be directly applied to layered networks where risk-disjoint paths may not always exist. In this paper, we take a new approach, which is based on finding a set of paths that may not be disjoint but together will survive any single risk. We start with two-layered communication networks, where the risks are fiber failures. We prove that in general, finding the minimum survivable path set (MSPS) is NP-hard, whereas if we restrict the length of paths the problem can be solved in polynomial time. We formulate the problem as an integer linear program (ILP), and use this formulation to develop heuristics and approximation algorithms. Moreover, we study the minimum cost survivable path set problem, where the cost is the number of fibers used, and thus, nonadditive. Finally, we generalize the survivability problem to the networks with more than two layers. By applying our algorithms for survivable path set, we assess the survivability of communication networks that operate relying on power from a power grid.

Reliability in Layered Networks with Random Link Failures

We consider network reliability in layered networks where the lower layer experiences random link failures. In layered networks, each failure at the lower layer may lead to multiple failures at the upper layer. We generalize the classical polynomial expression for network reliability to the multi-layer setting. Using random sampling techniques, we develop polynomial time approximation algorithms for the failure polynomial. Our approach gives an approximate expression for reliability as a function of the link failure probability, eliminating the need to resample for different values of the failure probability. Furthermore, it gives insight on how the routings of the logical topology on the physical topology impact network reliability. We show that maximizing the min cut of the (layered) network maximizes reliability in the low failure probability regime. Based on this observation, we develop algorithms for routing the logical topology to maximize reliability.