Green Small-Cell Networks

Hoydis, Jakob; Kobayashi, Mari; Debbah, Mérouane

doi:10.1109/mvt.2010.939904

Cited by 407 publications

(278 citation statements)

References 10 publications

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“…First, it is monotonically increasing with both the number of links that are able to satisfy their individual constraints inside the corresponding cell k; and with the value of the function ϕ k that determines the global performance of cell k. Second, as shown in Sec. IV, for a particular choice of the parameter β, there exists an algorithm whose stochastically stable points are both equilibria of the game G and solutions of the optimization problem in (1). The notion of equilibrium used in the following of this analysis is that proposed by Nash in [34].…”

Section: B Game Theoretical Modelingmentioning

confidence: 99%

“…The relevance of these networks lies on the fact that a formal network planning is not required, their deployment is easy, quick and, more importantly, capabilities such as self-healing and self-configuration are often present. Therefore, DSCNs span a large number of applications including military, law enforcement, disaster relief, space, and indoor/outdoor commercial applications [1], [2].…”

Section: Introductionmentioning

confidence: 99%

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Self-Organization in Decentralized Networks: A Trial and Error Learning Approach

Rose

Perlaza

Martret

et al. 2014

IEEE Trans. Wireless Commun.

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Abstract-In this paper, the problem of channel selection and power control is jointly analyzed in the context of multiplechannel clustered ad-hoc networks, i.e., decentralized networks in which radio devices are arranged into groups (clusters) and each cluster is managed by a central controller (CC). This problem is modeled by game in normal form in which the corresponding utility functions are designed for making some of the Nash equilibria (NE) to coincide with the solutions to a global network optimization problem. In order to ensure that the network operates in the equilibria that are globally optimal, a learning algorithm based on the paradigm of trial and error learning is proposed. These results are presented in the most general form and therefore, they can also be seen as a framework for designing both games and learning algorithms with which decentralized networks can operate at global optimal points using only their available local knowledge. The pertinence of the game design and the learning algorithm are highlighted using specific scenarios in decentralized clustered ad hoc networks. Numerical results confirm the relevance of using appropriate utility functions and trial and error learning for enhancing the performance of decentralized networks.

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Section: B Game Theoretical Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Organization in Decentralized Networks: A Trial and Error Learning Approach

Rose

Perlaza

Martret

et al. 2014

IEEE Trans. Wireless Commun.

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“…In general, there are two different approaches for improving EE, namely, small-cell networks [7], [8] and large-scale or massive multiple-input multiple-output (MIMO) systems [9]- [12]. Both technologies can improve the area throughput and reduce the radiated power, but at the cost of deploying more hardware infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of wireless networks for broadband access with minimum power consumption

Verenzuela

Björnson

Sanguinetti

2016

2016 IEEE International Conference on Communications (ICC)

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Abstract-The continuous rise in wireless data traffic brings forth an increase in power consumption and static users constitute a large fraction of these traffic demands. This work focuses on designing cellular networks to deliver a given data rate per area and user, while minimizing the power consumption. In particular we are interested in optimizing the transmission power, density of access points (APs), number of AP antennas and number of users served in each cell. To this end, we consider a network model based on stochastic geometry and a detailed power consumption model to derive closed form expressions and obtain insights on the interplay of the aforementioned design parameters. The results show that, in contrast with previous works on optimal network design for energy efficiency, having exceedingly high AP density does not bring the most benefits in terms of power savings. Instead the AP density should be chosen according to the area data rate that we want to deliver. In addition numerical results show that the minimum power consumption is obtained in the Massive MIMO regime with many antennas and users per AP.

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“…It is widely acknowledged that the future capacity needs of wireless cellular networks can only be satisfied by a significant network densification through the deployment of small cells (SCs) [1], [2]. While SCs are an efficient means to provide local capacity enhancements (e.g., hotspots in urban areas), they can not replace macro cells which ensure area coverage and support highly mobile terminals.…”

Section: Introductionmentioning

confidence: 99%

Massive MIMO and small cells: How to densify heterogeneous networks

Hosseini

Hoydis

Brink

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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Abstract-We propose a time division duplex (TDD) based network architecture where a macrocell tier with a "massive" multiple-input multiple-output (MIMO) base station (BS) is overlaid with a dense tier of small cells (SCs). In this context, the TDD protocol and the resulting channel reciprocity have two compelling advantages. First, a large number of BS antennas can be deployed without incurring a prohibitive overhead for channel training. Second, the BS can estimate the interference covariance matrix from the SC tier which can be leveraged for downlink precoding. In particular, the BS designs its precoding vectors to transmit independent data streams to its users while being orthogonal to the subspace spanned by the strongest interference directions; thereby minimizing the sum interference imposed on the SCs. In other words, the BS "sacrifices" some of its antennas for interference cancellation while the TDD protocol allows for an implicit coordination across both tiers. Simulation results suggest that, given a sufficiently large number of BS antennas, the proposed scheme can significantly improve the sum-rate of the SC tier at the price of a small macro performance loss.

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Green Small-Cell Networks

Cited by 407 publications

References 10 publications

Self-Organization in Decentralized Networks: A Trial and Error Learning Approach

Self-Organization in Decentralized Networks: A Trial and Error Learning Approach

Optimal design of wireless networks for broadband access with minimum power consumption

Massive MIMO and small cells: How to densify heterogeneous networks

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