Coverage In Indoor Environments Research Articles

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall.

Proposed APs Distribution Optimization Algorithm: Indoor Coverage Solution

Recently, Wireless systems for indoor environments have become very important and popular. With increasing the need for mobile devices like laptops, tabs and smartphones, the optimization of WLAN coverage became very necessary to provide efficient communication with less cost. This optimization is verified by decreasing number of WiFi Access Point coverage. A multi objectives algorithm is proposed in this paper to optimize WiFi coverage in indoor environment using MatLab software. The proposed algorithm employs (BPSO) to determine the optimal number and locations for WiFi APs distribution based on predefined received power thresholds value. it has been found that the proposed algorithm is flexible using weighted multi-fitness functions. In addition, conducting site survey in real environment verifies the reliability of the proposed algorithm. Applying APD-CS algorithm in the adopted real environment increase the coverage by 64.6% and the average received power 7 dBm at Pth equal to-55 dBm.

Effects of Femtocell Ultradense Deployment on Downlink Performance in LTE Heterogeneous Networks

With huge number of smart gadgets and wireless devices being interconnected to each other, the demand for very high data bandwidth is becoming critically challenging. With such density of nodes inside wireless networks, providing high‐quality service as well as wide coverage in indoor environment is a real challenge, which is due to the limited radio frequency and intense interference between nodes. As a one way to solve such problem and improve indoor service quality, femtocells have been introduced as an extension to the existing macrocell stations. Although femtocell is a promising technology, the pervasive deployment of huge number of femtocells without very tight network planning as well as coverage strategy may worsen the problem and degrade the service quality. One important problem that needs to be addressed when deploying femtocell technology in heterogenous networks (HetNets) is mitigating the various types of cross‐tier and cotier interferences in between wireless cells. This study investigates the effect of unplanned ultradensity femtocell deployment in the downlink performance of two‐tier heterogeneous networks in urban area based on LTE system. Instead of deploying femtocells one by one, grids of size either (3 × 3) or (5 × 5) of neighboring femtocell will be deployed inside each macrocell sector area. The simulation results show that femtocell deployment improves overall average user throughput in case of low and medium density scenarios. However, for ultradensity scenario, there is no enhancement in terms of fairness and throughput. The results confirm that this leads to high degradation for macrocell and femtocell user performance due to the severe interference between macrocells and femtocells, as well as among neighboring femtocells in each grid.

Energy Efficient and Delay Aware 5G Multi-Tier Network

Multi-tier heterogeneous Networks (HetNets) with dense deployment of small cells in 5G networks are expected to effectively meet the ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major concerns to mobile network operators such as complex distributed control plane management, handover management issue, increases latency and increased energy expenditures. Sleep mode implementation in multi-tier 5G networks has proven to be a very good approach for reducing energy expenditures. In this paper, a Markov Decision Process (MDP)-based algorithm is proposed to switch between three different power consumption modes of a base station (BS) for improving the energy efficiency and reducing latency in 5G networks. The MDP-based approach intelligently switches between the states of the BS based on the offered traffic while maintaining a prescribed minimum channel rate per user. Simulation results show that the proposed MDP algorithm together with the three-state BSs results in a significant gain in terms of energy efficiency and latency.

Interference and QoS aware cell switch-off strategy for software defined LTE HetNets

LTE-Heterogeneous Networks (HetNets) with dense deployment of small cells are expected to effectively meet unprecedented ever increasing data traffic demands and offer improved coverage in indoor environments. However, HetNets are raising major issues to Mobile Network Operators (MNOs) such as complex distributed control plane management and increased energy costs. Hence, MNOs are looking for flexible software defined mobile architectures to reduce their capital expenditure (CAPEX) and operational expenditure (OPEX). In this work, in order to reduce energy costs of the HetNets, we propose an interference and QoS aware cell switch-off strategy (IQ-CSOS) on a software defined LTE Radio Access Network (SD-LTE-RAN) framework. Unlike existing CSOSs, IQ-CSOS investigates both network energy costs and QoS satisfaction of sessions during CSOS decisions. In performance evaluation under various test scenarios, it is found that IQ-CSOS is able to provide 50–80% of network energy savings. Besides, it is able to provide 30% more energy savings compared to existing CSOSs with slight affect on network QoS.

Distributed Power and Channel Allocation for Cognitive Femtocell Network Using a Coalitional Game in Partition-Form Approach

The cognitive femtocell network (CFN) integrated with cognitive-radio-enabled technology has emerged as one of the promising solutions to improve wireless broadband coverage in indoor environments for next-generation mobile networks. In this paper, we study a distributed resource allocation that consists of subchannel- and power-level allocation in the uplink of the two-tier CFN comprised of a conventional macrocell and multiple femtocells using underlay spectrum access. The distributed resource allocation problem is addressed via an optimization problem, in which we maximize the uplink sum rate under constraints of intratier and intertier interference while maintaining the average delay requirement for cognitive femtocell users. Specifically, the aggregated interference from cognitive femtocell users to the macrocell base station (MBS) is also kept under an acceptable level. We show that this optimization problem is NP-hard and propose an autonomous framework, in which the cognitive femtocell users self-organize into disjoint groups (DJGs). Then, instead of maximizing the sum rate in all cognitive femtocells, we only maximize the sum rate of each DJG. After that, we formulate the optimization problem as a coalitional game in partition form, which obtains suboptimal solutions. Moreover, distributed algorithms are also proposed for allocating resources to the CFN. Finally, the proposed framework is tested based on the simulation results and shown to perform efficient resource allocation.

Comparison of GA and DDE for optimizing coverage in indoor environment

SUMMARYThis paper presents a method for determining the required number and locations of transmitting antennas to optimize wireless propagation coverage in indoor ultra‐wideband communication system. In the coverage prediction model, we use the three‐dimensional ray‐tracing technique associated to a genetic algorithm and a dynamic differential evolution for optimizing the transmitting antennas location in an indoor environment. The ray‐tracing method is employed to calculate the field strength from one or more transmitting antennas, and the optimization algorithm is used to determine the required number and locations of these antennas to achieve optimized wireless coverage in the indoor environment. The combined three‐dimensional ray‐tracing and optimization algorithm was applied in the indoor environment to find the best location of the transmitting antennas by maximizing the power in the coverage area. The use of deployments to minimize the transmitting antennas and maximize the power in the coverage area was proposed. Obtained simulation results illustrate the feasibility of using the integrated ray‐tracing and optimization method to find the optimal transmitter locations in determining the optimized coverage of a wireless network. The dynamic differential evolution has better optimization results compared with the genetic algorithm. The investigated results can help communication engineers improve their planning and design of indoor wireless communication. Copyright © 2013 John Wiley & Sons, Ltd.

Optimizing range finder sensor network coverage in indoor environment

This document states a method to optimize the coverage of a range finder multisensor network on an indoor environment. The problem arose from the need of a localization system for autonomous mobile robots, integrated with the building. The common approach for a localization system consists on sensors in the vehicle itself. In this case the vehicle is only an actuator and the sensory part is installed on the building. The localization system performance depends on the sensor placement, to enhance it, the sensor network must maximize the covered area. To solve the problem, an algorithm that maximizes the coverage areas is proposed. Given a scenario map, obtains the optimal coverage area multisensor placement. This paper presents also some results and proposals to future approaches to the problem.