Novel algorithms for efficient exploration of the tradeoffs between cell count and performance in wireless networks

Abusch-Magder, D.; Graybeal, J. M.

doi:10.1002/bltj.20095

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The channel capacity C for a channel perturbed by additive white Gaussian noise is a function of the average received signal power P Rx ϭ E{s(t)s(t)*}, the average noise power P N ϭ E{n(t)n(t)*}, and the bandwidth B, where s(t) and n(t) denote the signal and noise values at the time instant t. The well-known capacity relationship (Shannon-Hartley theorem [18]) can be expressed as (1) In order to write equation 1 in terms of transmitted power P Tx , the impact of the channel loss L ϭ L p L s , characterized as a combination of attenuations resulting from path loss L p and shadow fading L s , must be taken into account. Note that this requires knowledge of the positions of the connected mobile devices and knowledge of the environment (i.e., shadow fading properties).…”

Section: Power Requirement For a Link With Given Capacitymentioning

confidence: 99%

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Autonomous self-deployment of wireless access networks

Claußen¹

2009

Bell Labs Tech. J.

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The deployment and configuration of wireless access networks represent an increasingly significant cost factor which will force the telecommunications industry to adopt new methodologies for network deployment and configuration. In System (UMTS) or IEEE 802.11. In combination with the need for interoperability between heterogeneous systems (e.g., different access technologies), this will inevitably increase the relative costs for deployment and configuration to an end where current quasimanual methods, using a mixture of off-line planning tools, expensive drive testing, and economic rules of thumb, become uneconomical and unsustainable. Self-configuration of access nodes helps to control these costs. However, in addition, there is a strong need for the novel concept of a self-deploying network [13]. Figure 1 shows the future vision of a selfdeploying radio access network architecture. While the deployment of base stations could be performed IntroductionNext-generation cellular networks will have to support higher data rates to meet the demand for upcoming data services. One way to achieve this is to improve the signal processing capability of transmitters and receivers, up to a point where the channel efficiency is close to the Shannon bound. Other options are the use of multiple input-multiple output (MIMO) radio communications or beam forming to increase the achievable data rates in rich scattering or line-of-sight environments, respectively. A simpler, more pragmatic approach is a reduction of the cell size with a commensurate increase in the total number of cells. This trend is already emerging today with the increasing number of picocells for hot-spot coverage using Universal Mobile Telecommunications manually, based on the optimal positions identified by the network, a different approach is investigated here: mobile wireless access nodes that are able to plan and modify their positions without human interaction are considered. This allows the network to adapt autonomously to changes in user positions and demand. In the future, this concept may even be further extended to fully "robotic" base stations with a high degree of autonomy, intelligence, and powers of reasoning [8]. To enable base station mobility, a wireless backhaul is considered that supports both direct and peer-to-peer connections.While base station mobility might seem futuristic for commercial wireless communication systems today, this concept has near-term applications in the field of military and emergency communications, where fast network deployment is required in highrisk areas or in environments that are difficult to access. For example, using unmanned area vehicles (UAVs) as base station carriers, a self-deploying network could rapidly provide coverage over large areas.Base station positioning has been studied extensively in the past, using simulated annealing [11,19], evolutionary algorithms [21], integer linear programming [12], and greedy algorithms [20]. Other work has explored the trade-offs among coverage, cell count, and capac...

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Section: Power Requirement For a Link With Given Capacitymentioning

confidence: 99%

“…Other work has explored the trade-offs among coverage, cell count, and capacity [1]. It has been shown that the identification of globally optimum base station positions in a network is an NP-hard problem far too complex to solve computationally [12,20,21].…”

Section: Introductionmentioning

confidence: 99%

Autonomous self-deployment of wireless access networks

Claußen¹

2009

Bell Labs Tech. J.

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“…Our work uses similar techniques, albeit in a different context. In [7] and [8], a heuristic algorithm was developed to determine the cell sites selection problem during upgrading from 2G to 3G. The objective was to determine a solution which minimised cell count and associated costs while continuing to satisfy the user demand during the upgrade.…”

Section: IImentioning

confidence: 99%

“…Constraint (7) ensures every TP is assigned to either a BS or an RS. Constraint (8) ensures every RS assigned to only one BS; also if the RS is not installed, it cannot be assigned to a BS. Constraints (9), (10) and (11) ensure that TPs are not assigned to BSs that are not present, TPs are not assigned to RSs that are not present and RSs are not assigned to BSs that are not present respectively.…”

Section: A Problem Formulationmentioning

confidence: 99%

A Clustering Approach to Planning Base Station and Relay Station Locations in IEEE 802.16j Multi-Hop Relay Networks

Murphy

2008

2008 IEEE International Conference on Communications

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In this paper, a clustering approach to solve a network planning problem for 802.16j relay networks is considered. Our clustering approach consists of three basic steps: (1) divide the nodes into k distinct clusters, (2) solve the planning problem separately for each cluster, and (3) perform a final optimization to reduce issues arising at cluster boundaries. Simulation results show that our approach is more efficient than existing approaches: solutions of equivalent quality can be found in 40% of the time. Thus our technique can be used to solve larger problems with similar hardware, or similar size problems in less time.

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“…Other work has explored the trade-offs between coverage, cell count and capacity [8]. It has been shown that the identification of the globally optimum base station locations in a network of multiple base stations is an NP-hard problem, far too complex to solve computationally [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Self-deployment of Wireless Access Networks in an Airport Environment

Claußen¹

2006

Lecture Notes in Computer Science

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Abstract. In environments with highly dynamic user demand, for example in airports, high over-dimensioning of wireless access networks is required to be able to serve high user densities at any possible location in the covered area, resulting in a large number of base stations. This problem is addressed with the novel concept of a self-deploying network. Distributed algorithms are proposed, which autonomously identify the need of changes in position and configuration of wireless access nodes and adapt the network to its environment. It is shown that a self-deploying network can significantly reduce the number of required base stations compared to a conventional statically deployed network. In this paper, this is demonstrated in a specific test scenario at Athens International Airport, simulating a moving user hotspot after the arrival of an airplane.

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Novel algorithms for efficient exploration of the tradeoffs between cell count and performance in wireless networks

Cited by 6 publications

References 11 publications

Autonomous self-deployment of wireless access networks

Autonomous self-deployment of wireless access networks

A Clustering Approach to Planning Base Station and Relay Station Locations in IEEE 802.16j Multi-Hop Relay Networks

Autonomous Self-deployment of Wireless Access Networks in an Airport Environment

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