Abstract
The performance of wireless networks will experience a considerable improvement by the use of novel technologies such as distributed antenna systems (DASs), multi-cell cooperation (MCC), and cognitive radio (CR). These solutions have shown considerable gains at the physical-layer (PHY). However, several issues remain open in the system-level evaluation, radio resource management (RRM), and particularly in the design of billing/licensing schemes for this type of system. This paper proposes a system-level simulator (SLS) that will help in addressing these issues. The paper focuses on the description of the modules of a generic SLS that need a modification to cope with the new transtnission/econotnic paradigms. An advanced RRM solution is proposed for a multi-cell DAS with two levels of cooperation: inside the cell (intra-cell) to coordinate the transmission of distributed nodes within the cell, and between cells (inter-cell or MCC) to adapt cell transmissions according to the collected inter-cell interference measurements. The RRM solution blends network and financial metrics using the theory of multi objective portfolio optimization. The core of the RRM solution is an iterative weighted least squares (WLS) optimization algorithm that aims to schedule in a fair manner as many terminals as possible across all the radio resources of the available frequency bands (licensed and non-licensed), while considering different economic metrics. The RRM algorithm includes joint terminal scheduling, link adaptation, space division multiplexing, spectrtun selection, and resource allocation.

Abstract

Abstract

Abstract
Disaster management is one of the most relevant application fields of wireless sensor networks. In this application, the role of the sensor network usually consists of obtaining a representation or a model of a physical phenomenon spreading through the affected area. In this work we focus on forest firefighting operations, proposing three fully distributed ways for approximating the actual shape of the fire. In the simplest approach, a circular burnt area is assumed around each node that has detected the fire and the union of these circles gives the overall fire's shape. However, as this approach makes an intensive use of the wireless sensor network resources, we have proposed to incorporate two in-network aggregation techniques, which do not require considering the complete set of fire detections. The first technique models the fire by means of a complex shape composed of multiple convex hulls representing different burning areas, while the second technique uses a set of arbitrary polygons. Performance evaluation of realistic fire models on computer simulations reveals that the method based on arbitrary polygons obtains an improvement of 20% in terms of accuracy of the fire shape approximation, reducing the overhead in-network resources to 10% in the best case.

Abstract

Abstract
The performance of wireless networks will experience a considerable improvement by the use of novel technologies such as distributed antenna systems (DASs), multi-cell cooperation (MCC), and cognitive radio (CR). These solutions have shown considerable gains at the physical-layer (PHY). However, several issues remain open in the system-level evaluation, radio resource management (RRM), and particularly in the design of billing/licensing schemes for this type of system. This paper proposes a system-level simulator (SLS) that will help in addressing these issues. The paper focuses on the description of the modules of a generic SLS that need a modification to cope with the new transmission/economic paradigms. An advanced RRM solution is proposed for a multi-cell DAS with two levels of cooperation: inside the cell (intra-cell) to coordinate the transmission of distributed nodes within the cell, and between cells (inter-cell or MCC) to adapt cell transmissions according to the collected inter-cell interference measurements. The RRM solution blends network and financial metrics using the theory of multi- objective portfolio optimization. The core of the RRM solution is an iterative weighted least squares (WLS) optimization algorithm that aims to schedule in a fair manner as many terminals as possible across all the radio resources of the available frequency bands (licensed and non-licensed), while considering different economic metrics. The RRM algorithm includes joint terminal scheduling, link adaptation, space division multiplexing, spectrum selection, and resource allocation. © 2015 IEEE.