Network planning and design

About: Network planning and design is a research topic. Over the lifetime, 12393 publications have been published within this topic receiving 229776 citations. The topic is also known as: network design.

A polynomial-time approximation scheme for base station positioning in UMTS networks

Abstract: We consider the following optimization problem for UMTS networks: For a specified teletraffic demand and possible base station locations, choose positions for base stations such that the construction costs are below a given limit, as much teletraffic as possible is supplied, the ongoing costs are minimal, and the intra-cell interference in the range of each base station is low. We prove that for a particular specification of teletraffic (the so called demand node concept), this problem has a polynomial-time approximation scheme, but cannot have a fully polynomial-time approximation scheme unless P = NP.

On the deployment of picocellular wireless infrastructure

Abstract: Wireless LANs are becoming increasingly common in both home and office networks. Additionally, wireless Internet service providers have begun installing public WLANs in airports, hotels, and other frequently trafficked areas. This article analyzes deployment strategies for such networks, specifically for possible future systems utilizing bands of up to 60 GHz. A major part of the installation cost stems from network planning, wiring, and manpower for setting up base stations. It is therefore crucial to find ways to simplify the network installation and reduce the deployment costs, while at the same time maintaining the desired system performance. We show that even simple installation rules can often achieve adequate coverage results. However, proper network planning is in some situations necessary to achieve adequate coverage. Therefore, we examine different combinatorial optimization methods for obtaining close to optimal positioning of WLAN access points and compare the performance of the proposed algorithms to the simple installation methods. The optimization algorithms used in this article evaluate an objective function that aims to maximize both the coverage area and the overall signal quality over a discrete search space. Random search algorithms can yield very good solutions, but often exhibit difficult convergence properties. Successive removal algorithms, such as pruning, converge in polynomial time, but usually produce suboptimal solutions. We therefore propose a combination of the two approaches, using pruning to obtain an initial set of the base station positions and refining these by using neighborhood search or simulated annealing.

Strong formulations for network design problems with connectivity requirements

Abstract: The network design problem with connectivity requirements lNDCr includes as special cases a wide variety of celebrated combinatorial optimization problems including the minimum spanning tree, Steiner tree, and survivable network design problems. We develop strong formulations for two versions of the edge-connectivity NDC problem: unitary problems requiring connected network designs, and nonunitary problems permitting nonconnected networks as solutions. We l1r present a new directed formulation for the unitary NDC problem that is stronger than a natural undirected formulation; l2r project out two classes of valid inequalities—partition inequalities, and combinatorial design inequalities—that generalize known classes of valid inequalities for the Steiner tree problem to the unitary NDC problem; and l3r show how to strengthen and direct nonunitary problems. Our results provide a unifying framework for strengthening formulations for NDC problems, and demonstrate the power of flow-based formulations for network design problems with connectivity requirements. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 45l2r, 61–79 2005

Fuzzy-Network Planning - FNET

Abstract: Currently used network planning techniques, such as PERT/CPM and their derivatives, assume that, in a graph of known structure, the duration of its activities is known either with certitude or at least with some probability. However, in many applications the structure of the graph and the duration of its activities are imprecise. This paper shows how the application of fuzzy sets to such problems can yield quasi-deterministic results obtained from imprecise Input data.

Minimizing Energy Consumption with Probabilistic Distance Models in Wireless Sensor Networks

Abstract: Minimizing energy consumption in wireless sensor networks has been a challenging issue, and grid-based clustering and routing schemes have attracted a lot of attention due to their simplicity and feasibility. Thus how to determine the \textit{optimal grid size} in order to minimize energy consumption and prolong network lifetime becomes an important problem during the network planning and dimensioning phase. So far most existing work uses the average distances within a grid and between neighbor grids to calculate the average energy consumption, which we found largely underestimates the real value. In this paper, we propose, analyze and evaluate the energy consumption models in wireless sensor networks with probabilistic distance distributions. These models have been validated by numerical and simulation results, which shows that they can be used to optimize grid size and minimize energy consumption accurately. We also use these models to study variable-size grids, which can further improve the energy efficiency by balancing the relayed traffic in wireless sensor networks.