An efficient genetic algorithm for large-scale transmit power control of dense and robust wireless networks in harsh industrial environments

The industrial wireless local area network (IWLAN) is increasingly dense, due to not only the penetration of wireless applications to shop floors and warehouses, but also the rising need of redundancy for robust wireless coverage. Instead of simply powering on all access points (APs), there is an unavoidable need to dynamically control the transmit power of APs on a large scale, in order to minimize interference and adapt the coverage to the latest shadowing effects of dominant obstacles in an industrial indoor environment. To fulfill this need, this paper formulates a transmit power control (TPC) model that enables both powering on/off APs and transmit power calibration of each AP that is powered on. This TPC model uses an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects, to enable accurate yet simple coverage prediction. An efficient genetic algorithm (GA), named GATPC, is designed to solve this TPC model even on a large scale. To this end, it leverages repair mechanism-based population initialization, crossover and mutation, parallelism as well as dedicated speedup measures. The GATPC was experimentally validated in a small-scale IWLAN that is deployed a real industrial indoor environment. It was further numerically demonstrated and benchmarked on both small- and large-scales, regarding the effectiveness and the scalability of TPC. Moreover, sensitivity analysis was performed to reveal the produced interference and the qualification rate of GATPC in function of varying target coverage percentage as well as number and placement direction of dominant obstacles. (C) 2018 Elsevier B.V. All rights reserved

Resource-on-demand schemes in 802.11 WLANs with non-zero start-up times

Increasing the density of access points is one of the most effective mechanisms to cope with the growing traffic demand in wireless networks. To prevent energy wastage at low loads, a resource-on-demand (RoD) scheme is required to opportunistically (de)activate access points as network traffic varies. While previous publications have analytically modeled these schemes in the past, they have assumed that resources are immediately available when activated, an assumption that leads to inaccurate results and might result in inappropriate configurations of the RoD scheme. In this paper, we analyze a general RoD scenario with N access points and non-zero start-up times. We first present an exact analytical model that accurately predicts performance but has a high computational complexity, and then derive a simplified analysis that sacrifices some accuracy in exchange for a much lower computational cost. To illustrate the practicality of this model, we present the design of a simple configuration algorithm for RoD. Simulation results confirm the validity of the analyses, and the effectiveness of the configuration algorithm

Optimal configuration of a resource-on-demand 802.11 WLAN with non-zero start-up times

Resource on Demand in 802.11 Wireless LANs is receiving an increasing attention, with its feasibility already proved in practice and some initial analytical models available. However, while these models have assumed that access points (APs) start up in zero time, experimentation has showed that this is hardly the case. In this work, we provide a new model to account for this time in the simple case, of a WLAN formed by two APs where the second AP is switched on/off dynamically to adapt to the traffic load and reduce the overall power consumption, and show that it significantly alters the results when compared to the zero start-up time case, both qualitatively and quantitatively. Our findings show that having a non-zero start up time modifies significantly the trade-offs between power consumption and performance that appears on Resource on Demand solutions. Finally, we propose an algorithm to optimize the energy consumption of the network while guaranteeing a given performance bound.The work of J. Ortín was partly supported by the Centro Universitario de la Defensa through project CUD2013-05, Gobierno de Aragon (research group T98) and the European Social Fund (ESF). The work of P. Serrano and C. Donato was partly supported by the European Commission under grant agreement H2020-ICT-2014-2-671563 (Flex5Gware) and by the Spanish Ministry of Economy and Competitiveness under grant agreement TEC2014-58964-C2-1-R (DRONEXT)

Fast Cell Discovery in mm-wave 5G Networks with Context Information

The exploitation of mm-wave bands is one of the key-enabler for 5G mobile radio networks. However, the introduction of mm-wave technologies in cellular networks is not straightforward due to harsh propagation conditions that limit the mm-wave access availability. Mm-wave technologies require high-gain antenna systems to compensate for high path loss and limited power. As a consequence, directional transmissions must be used for cell discovery and synchronization processes: this can lead to a non-negligible access delay caused by the exploration of the cell area with multiple transmissions along different directions. The integration of mm-wave technologies and conventional wireless access networks with the objective of speeding up the cell search process requires new 5G network architectural solutions. Such architectures introduce a functional split between C-plane and U-plane, thereby guaranteeing the availability of a reliable signaling channel through conventional wireless technologies that provides the opportunity to collect useful context information from the network edge. In this article, we leverage the context information related to user positions to improve the directional cell discovery process. We investigate fundamental trade-offs of this process and the effects of the context information accuracy on the overall system performance. We also cope with obstacle obstructions in the cell area and propose an approach based on a geo-located context database where information gathered over time is stored to guide future searches. Analytic models and numerical results are provided to validate proposed strategies.Comment: 14 pages, submitted to IEEE Transaction on Mobile Computin