Xiaoying Zheng

Self-configuration and self-optimization for LTE networks

With the rapid growth of mobile communications, deployment and maintenance of cellular mobile networks are becoming more and more complex, time consuming, and expensive. In order to meet the requirements of network operators and service providers, the telecommunication industry and international standardization bodies have recently paid intensive attention to the research and development of self-organizing networks. In this article we first introduce both the market and technological perspectives for SONs. Then we focus on the self-configuration procedure and illustrate a self-booting mechanism for a newly added evolved NodeB without a dedicated backhaul interface. Finally, mobility load balancing as one of the most important selfoptimization issues for Long Term Evolution networks is discussed, and a distributed MLB algorithm with low handover cost is proposed and evaluated.

Optimal Peer-to-Peer Technique for Massive Content Distribution

A distinct trend has emerged that the Internet is used to transport data on a more and more massive scale. Capacity shortage in the backbone networks has become a genuine possibility, which will be more serious with fiber-based access. The problem addressed in this paper is how to conduct massive content distribution efficiently in the future network environment where the capacity limitation can equally be at the core or the edge. We propose a novel peer-to-peer technique as a main content transport mechanism to achieve efficient network resource utilization. The technique uses multiple trees for distributing different file pieces, which at the heart is a version of swarming. In this paper, we formulate an optimization problem for determining an optimal set of distribution trees as well as the rate of distribution on each tree under bandwidth limitation at arbitrary places in the network. The optimal solution can be found by a distributed algorithm. The results of the paper not only provide stand-alone solutions to the massive content distribution problem, but should also help the understanding of existing distribution techniques such as BitTorrent or FastReplica.

A Class of Cross-Layer Optimization Algorithms for Performance and Complexity Trade-Offs in Wireless Networks

In this paper, we solve the problem of a joint optimal design of congestion control and wireless MAC-layer scheduling using a column generation approach with imperfect scheduling. We point out that the general subgradient algorithm has difficulty in recovering the time-share variables and experiences slower convergence. We first propose a two-timescale algorithm that can recover the optimal time-share values. Most existing algorithms have a component, called global scheduling, which is usually NP-hard. We apply imperfect scheduling and prove that if the imperfect scheduling achieves an approximation ratio rho, then our algorithm produces a suboptimum of the overall problem with the same approximation ratio. By combining the idea of column generation and the two-timescale algorithm, we derive a family of algorithms that allows us to reduce the number of times the global scheduling is needed.

PSS based time synchronization for 3GPP LTE downlink receivers

Time synchronization based on primary synchronization signal (PSS) is investigated for 3GPP Long Term Evolution (LTE) downlink receivers. Frequency offset and low SNR are two factors which degrades the detection performance during initial synchronization. In the proposed scheme, the receiver performs sliding differential mirror correlation and further accumulates correlated results of multiple PSS transmission periods. During these procedures, differential operations eliminate the detrimental effect of frequency offset and performance at low SNR is further improved by superimposing multiple correlated results. Simulation results show that the proposed scheme can provide robust capabilities under fading scenarios.

Content distribution by multiple multicast trees and intersession cooperation: Optimal algorithms and approximations

The paper addresses the problem of massive content distribution in a network where multiple sessions coexist. In more traditional approaches, the sessions form separate overlay networks and operate independently from each other. In this case, some sessions may suffer from insufficient resources (e.g., aggregate upload bandwidth) even though other sessions have excessive resources. To cope with this problem, we consider the universal swarming approach, which allows multiple sessions to cooperate with each other by forming a shared overlay network. We formulate the problem of finding the optimal resource allocation to maximize the sum of the session utilities under the network capacity constraints. The solution turns out to be optimal sharing of multiple minimum-cost multicast trees. We present a subgradient algorithm and prove that, although the algorithm uses a single multicast tree per session at each iteration and hence does not converge in the conventional sense, it converges to the optimal solution in the time-average sense. The solution involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope with this difficulty by using a column generation method, which reduces the number of Steiner-tree computations. Furthermore, we allow the use of approximate solutions to the Steiner-tree subproblem. We show that the approximation ratio to the overall problem turns out to be no more than that to the Steiner-tree subproblem. Simulation results demonstrate that universal swarming improves the performance of resource-poor sessions with negligible impact to resource-rich sessions.

Optimal Swarming for Massive Content Distribution

A distinct trend has emerged that the Internet is used to transport data on a more and more massive scale. Capacity shortage in the backbone networks has become a genuine possibility, which will be more serious with fiber-based access. The problem addressed in this paper is how to conduct massive content distribution efficiently in the future network environment, where the capacity limitation can equally be at the core or the edge. We propose a novel technique as a main content transport mechanism to achieve efficient network resource utilization. The technique uses multiple trees for distributing different file pieces, which at the heart is a version of swarming. In this paper, we formulate an optimization problem for determining an optimal set of distribution trees as well as the rate of distribution on each tree under bandwidth limitation at arbitrary places in the network. The optimal solution can be found by a distributed algorithm. The results of the paper not only provide stand-alone solutions to the massive content distribution problem, but should also help the understanding of existing distribution techniques such as BitTorrent or FastReplica.

Content distribution by multiple multicast trees and intersession cooperation

The paper addresses the problem of massive content distribution in a network where multiple sessions coexist. In more traditional approaches, the sessions form separate overlay networks and operate independently from each other. In this case, some sessions may suffer from insufficient resources (e.g., aggregate upload bandwidth) even though other sessions have excessive resources. To cope with this problem, we consider the universal swarming approach, which allows multiple sessions to cooperate with each other by forming a shared overlay network. We formulate the problem of finding the optimal resource allocation to maximize the sum of the session utilities under the network capacity constraints. The solution turns out to be optimal sharing of multiple minimum-cost multicast trees. We present a subgradient algorithm and prove that, although the algorithm uses a single multicast tree per session at each iteration and hence does not converge in the conventional sense, it converges to the optimal solution in the time-average sense. The solution involves an NP-hard subproblem of finding a minimum-cost Steiner tree. We cope with this difficulty by using a column generation method, which reduces the number of Steiner-tree computations. Furthermore, we allow the use of approximate solutions to the Steiner-tree subproblem. We show that the approximation ratio to the overall problem turns out to be no more than that to the Steiner-tree subproblem. Simulation results demonstrate that universal swarming improves the performance of resource-poor sessions with negligible impact to resource-rich sessions.

Algorithms and Stability Analysis for Content Distribution over Multiple Multicast Trees

The paper investigates theoretical issues in applying the universal swarming technique to efficient content distribution. In a swarming session, a file is distributed to all the receivers by having all the nodes in the session exchange file chunks. By universal swarming, not only all the nodes in the session, but also some nodes outside the session may participate in the chunk exchange to improve the distribution performance. We present a universal swarming model where the chunks are distributed along different Steiner trees rooted at the source and covering all the receivers. We assume chunks arrive dynamically at the sources and focus on finding stable universal swarming algorithms. To achieve the throughput region, universal swarming usually involves a tree-selection subproblem of finding a min-cost Steiner tree, which is NP-hard. We propose a universal swarming scheme that employs an approximate tree-selection algorithm. We show that it achieves network stability for a reduced throughput region, where the reduction ratio is no more than the approximation ratio of the tree-selection algorithm. We propose a second universal swarming scheme that employs a randomized tree-selection algorithm. It achieves the throughput region, but with a weaker stability result. Comprehensive simulation results support the stability analysis of the algorithms. The proposed schemes and their variants are expected to be useful for infrastructure-based content distribution networks with massive content and relatively stable network environment.

Optimizing network objectives in collaborative content distribution

One of the important trends is that the Internet will be used to transfer content on more and more massive scale. Collaborative distribution techniques such as swarming and parallel download have been invented and effectively applied to end-user file-sharing or media-streaming applications, but mostly for improving end-user performance objectives. In this paper, we consider the issues that arise from applying these techniques to content distribution networks for improving network objectives, such as reducing network congestion. In particular, we formulate the problem of how to make many-to-many assignment from the sending nodes to the receivers and allocate bandwidth for every connection, subject to the node capacity and receiving rate constraints. The objective is to minimize the worst link congestion over the network, which is equivalent to maximizing the distribution throughput, or minimizing the distribution time. The optimization framework allows us to jointly consider server load balancing, network congestion control, as well as the requirement of the receivers. We develop a special, diagonally-scaled gradient projection algorithm, which has a faster convergence speed, and hence, better scalability with respect to the network size than a standard subgradient algorithm. We provide both a synchronous algorithm and a more practical asynchronous algorithm.