A novel approach for radio propagation in indoor environment

In this tutorial survey the principles of radio propagation in indoor environments are reviewed. The channel is modeled as a linear time-varying filter at each location in the three-dimensional space, and the properties of the filter's impulse response are described. Theoretical distributions of the sequences of arrival times, amplitudes and phases are presented. Other relevant concepts such as spatial and temporal variations of the channel, large-scale path losses, mean excess delay and RMS delay spread are explored. Propagation characteristics of the indoor and outdoor channels are compared and their major differences are outlined. Previous measurement and modeling efforts are surveyed, and areas for future research are suggested.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The success of wireless local area networks (WLANs) has led to an intense interest among wireless engineers in understanding and predicting radio propagation characteristics within buildings. This paper present radio signal propagation measurement and modeling at 2.4GHz, within a building in the Faculty of Engineering, University of Benin. A base station built around SENAO access point, and a PA24 flat panel directional antenna of 19dBi gain and 17.50 beam-widths was setup for the experimentation. Signals were monitored from the fixed BS using LINKSYS, and NET STUMBLER version 4.0 software run on a Laptop. In this report, the effect of multi-path and delay spread was not considered. The result of the investigation revealed a significant attenuation of the radio signal within the small distance. A path loss exponent of 7.8 to 8.9 on the ground floor, and 2.9 to 5.2 for the 2nd floor were obtained for the period of investigation.

Study of radio-wave propagation into and through buildings has become important with the increase in the broadband mobile services. Currently ray tracing and ray launching techniques are used for modeling radio transmission but due to unavailability of detailed building databases, it is not possible to take into account all the edges and discontinuities in the buildings. To overcome this limitation a semi-deterministic technique is proposed in which walls and windows/doors in the buildings arc modeled as strips and slits respectively and their distribution is treated statistically. The model so developed takes into account all the edges and discontinuities in the buildings and doesn't rely heavily on the vector database. Simulations for field strength prediction in the vicinity of a room were carried out using the proposed model. The results show that to accurately model the scattering from buildings multiple interactions between the walls (strips) must be taken into account.

A multipath model of UWB pulse propagation in the indoor environment is addressed in this paper. The simulation utilizes the principles of geometrical optics (GO) for direct and reflected paths' tracing and time domain technique for describing the transient electromagnetic field reflected from wall, floor, ceiling, and objects. The closed-form time domain analytic solution for UWB impulse signal reflection, which takes into account the frequency range from 3. 1 to 10.6 GHz, is proposed, and it is suitable for dry concrete, wood, glass, etc. , which are envisioned as common building construction materials for indoor environment. A specific house with two cube-like objects, which is thought to be a more realistic environment, is investigated. The simulation results provide the three-dimension (3D) space-time distribution of received pulsed field, which give more intrinsically interpretations for the mechanics of UWB impulse signals propagation in indoor environment.

Objectives: The growing demand of Wireless connectivity supported in the 802.11 standard has caused a high demand of wireless networks by the users, due to the benefits of mobility and low-cost implementation. Such networks have been used in common places such as houses, offices, schools among others. The objective of this article is to propose an empiric propagation model, supported in the Log-Normal-Shadowing Path Loss model for 2.4 GHz and 5 GHz bands in Indoor environments, compatible with University campus building. Methods/Statistical Analysis: A scenario of a six-storey building on a university campus was proposed, with dimensions of 60 m long, 34 m wide and 24 m high. Additionally, such building has 24 Access Points (AP) distributed in such a way that allow network to serve connectivity to its students. In order to establish the expressions that describe the Log-Normal-Shadowing Path Loss propagation model, an experimental design of factorial mixed type was considered involving three factors: frequency band (2.4 GHz or 5 GHz), condition of the media (Free space or with obstacles) and the distance between the receiver and the AP. The experiment aims to assess how each factor influences on the Received Signal Strength Indicator RSSI. Topic Significance: The proposed model will be used to run analysis of radio propagation in indoor environments using institutional educational buildings and holding a capacity of carrying out prediction processes of RSSI, in the 2.4 GHz and 5 GHz bands. Findings: Based on obtained results, it was possible to evidence that the established models allow predicting the behavior the reception power and signal damping, with a level of confidence of 95%. Additionally, it was possible to verify, in the 5 GHZ band, the damping levels are bigger that those in 2.4 GHz band. Such aspect holds great importance when it comes to perform design processes of Wireless networks. Applications/Improvements: In future research papers, it would be significantly important to establish the expression for outdoor environments, supported in the Shadowing Path model. Keywords: Channels Allocation, Efficiency, Optimization, Performance, SINR, WLAN

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

A novel deterministic approach to model the radio wave propagation channels in complex indoor environments reducing computational complexity is proposed. This technique combines a neural network and a 3-D ray launching algorithm in order to compute wireless channel performance in indoor scenarios. An example of applying the method for studying indoor radio wave propagation is presented and the results are compared with a very high resolution fully 3-D ray launching simulation as the reference solution. The new method allows the use of a lower number of launched rays in the simulation scenario whereas intermediate points can be predicted using neural network. Therefore a high gain in terms of computational efficiency (approximately 80% saving in simulation time) is achieved.

For planning, analysis, and optimization of modern, self organizing radio networks field strength prediction plays an essential role. A huge number of predictions is needed for planning purposes while high quality and level of detail are important as well. In this paper, we present a flexible ray optical approach for radio wave propagation to meet those demands. This approach allows easy exchange of the path loss model and produces multi path information enabling for environment aware MIMO channel models.

Cross-polarization discrimination of MIMO antenna configuration at a mobile station in indoor propagation environment

Wireless signal propagation in indoor environments is generally affected by various factors such as multi-path fading, temperature and humidity. It is thus challenging to use the simple metric of Received Signal Strength (RSS) for indoor localization. Prior work often requires manual profiling of each indoor environment to achieve good accuracy. We, instead, propose a simple generic localization algorithm that can be applied in any indoor environment without profiling. Our algorithm makes use of a simple signal propagation model and accepts a broad characterization of the environmental effect. The algorithm's simplification results in some accuracy loss compared to profiling-based algorithms. We, however, believe that our algorithm's zero-profiling requirement would make it favorable in many scenarios, especially when extensive profiling is not affordable or it is favored to trade a little accuracy for saved human effort. Evaluations also show that deployment of additional landmarks can be combined with our algorithm to compensate the accuracy loss, thus achieving good accuracy without profiling.

Local positioning in buildings without access to the Internet is a crucial requirement in many scenarios, such as large corporate offices, hospitals, universities, and shopping malls. However, this need for local positioning also presents certain challenges and problems that must be addressed. One of the most significant challenges of local positioning in buildings without internet access is the lack of reliable and up-to-date indoor map data. Without accurate map data, it can be difficult to navigate through complex indoor environments or locate specific rooms or facilities within a building. Another challenge is the absence of GPS signals in indoor environments, which makes it challenging to obtain accurate location information. This is especially problematic when attempting to track the movement of people or objects within a building.Additionally, the lack of internet access can make it difficult to implement location-based services that rely on data transfer between devices. This includes applications like indoor navigation, asset tracking, and location-based marketing, which all require internet connectivity to function correctly. Despite these challenges, there is a growing need for local positioning in buildings without internet access. This is especially true for large organizations and institutions that rely on efficient and effective operations within their indoor environments. Some technologies can provide accurate location information and help to mitigate some of the challenges associated with local positioning in buildings without internet access. They allows to obtain the accurate location information without relying on GPS signals or internet connectivity.The need for local positioning in buildings without internet access is becoming increasingly important, especially for large organizations and institutions that require efficient and effective operations within their indoor environments.

Using WIFI fingerprint positioning is Vulnerable to indoor environment and various noise signals , positioning error in indoor environment to the wireless signal strength is in the controllable range, although the starting point can be obtained, but its accuracy is easily affected by complex indoor environment. Inertial positioning is not affected by the indoor environment, but there is a cumulative error. At present, the widespread application of intelligent mobile phone, the activities of various sensors embedded can indirectly describe pedestrians, with intelligent mobile phone to achieve indoor positioning can not only reduce the cost of positioning, which can meet the daily demand for indoor positioning. Indoor positioning method based on inertial navigation real-time location of pedestrian path and wireless signal fingerprint with the online positioning by using the neural network matching algorithm outperforms the KNN algorithm in the positioning accuracy, so it can avoid the cumulative error of inertial navigation and positioning, and also reduce the influence of environment interference factor on the positioning accuracy, finally verified by experiments two, positioning algorithm combined with neural network positioning result is obviously better than the traditional positioning accuracy.

Both empirical and ray-tracing models are powerful tools for studying propagation in indoor environments. Although the empirical models perform well in many cases, their physical significance is not clearly understood. Ray tracing is based on physics fundamentals, and its application as well as the interpretation of its results is more general. We compare the performance of both models for indoor environments. We found that the path-loss (PL) prediction error of the ray-tracing model is comparable to the error of the linear regression fit of the PL in a site-specific empirical model. Those empirical models provide some advantages when making estimations in the same environment in which they were trained, but their performance degrades in other environments. Ray-tracing models obtain better results when a wider range of application environments is sought. Ray tracing also provides a good insight into the specific propagation phenomena that take place in the wideband radio channel, so the relevant contributions can be identified.

Channel measurement and modeling is an important issue when designing ultra wideband (UWB) communication systems. A Precise model of the channel response is inevitable for designing an ultra wideband telecommunication system. In this article signal propagation in indoor environment and LOS condition is evaluated and the appropriate model of this scenario is presented. Parameters such as the power delay profile, mean excess delay, delay spread, “NP10dB” are analyzed and simulated. Based on analysis results, the proposed model is presented. This model is based on Two-cluster approach but its average power delay profile is described with power function and cluster time of arrival is modeled by the modified exponential distribution. Finally UWB channel parameters of proposed model, Saleh and Valenzuela (S-V) and Two-cluster models are compared. Measurement and simulation results show that considerable improvement for mean excess delay, delay spread and “NP10dB” of proposed model comparing with S-V and Two-cluster model, this means the channel is better described, which mean the channel is described more precisely.

An improved cohesive hierarchical clustering for indoor air quality monitoring in smart gymnasium with healthy sport areas