The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

Accurate channel models are essential to evaluate mobile communication system performance and optimize coverage for existing deployments. The introduction of various transmission frequencies for 5G imposes new challenges for accurate radio performance prediction. This paper compares traditional channel models to a channel model obtained using Deep Learning (DL)-techniques utilizing satellite images aided by a simple path loss model. Experimental measurements are gathered and compose the training and test set. This paper considers path loss modelling techniques offered by state-of-the-art stochastic models and a ray-tracing model for comparison and evaluation. The results show that 1) the satellite images offer an increase in predictive performance by ≈ 0.8 dB, 2) The model-aided technique offers an improvement of ≈ 1 dB, and 3) that the proposed DL model is capable of improving path loss prediction at unseen locations for 811 MHz with ≈ 1 dB and ≈ 4.7 dB for 2630 MHz.

/pdf/model-aided-deep-learning-method-for-path-loss-prediction-in-4muhxtlsdt.pdf

Model-Aided Deep Learning Method for Path Loss Prediction in Mobile Communication Systems at 2.6 GHz

The indoor positioning system (IPS) is becoming increasing important in accurately determining the locations of objects by the utilization of micro-electro-mechanical-systems (MEMS) involving smartphone sensors, embedded sources, mapping localizations, and wireless communication networks. Generally, a global positioning system (GPS) may not be effective in servicing the reality of a complex indoor environment, due to the limitations of the line-of-sight (LoS) path from the satellite. Different techniques have been used in indoor localization services (ILSs) in order to solve particular issues, such as multipath environments, the energy inefficiency of long-term battery usage, intensive labour and the resources of offline information collection and the estimation of accumulated positioning errors. Moreover, advanced algorithms, machine learning, and valuable algorithms have given rise to effective ways in determining indoor locations. This paper presents a comprehensive review on the positioning algorithms for indoors, based on advances reported in radio wave, infrared, visible light, sound, and magnetic field technologies. The traditional ranging parameters in addition to advanced parameters such as channel state information (CSI), reference signal received power (RSRP), and reference signal received quality (RSRQ) are also presented for distance estimation in localization systems. In summary, the recent advanced algorithms can offer precise positioning behaviour for an unknown environment in indoor locations.

https://www.mdpi.com/2076-3417/11/1/279/pdf

Review of Indoor Positioning: Radio Wave Technology

The Internet of Things (IoT), as a pervasive paradigm, is becoming an integral part of the tech industry and academic research in recent years. It forms a ubiquitous heterogeneous network connecting humans and things. The basic premise is acquiring data from the environment with sensors and remote intelligent management via actuators. For IoT service providers, time and place are functional parameters. Whereas most IoT scenarios are in indoor spaces and GPS cannot fully cover them, applying an indoor positioning system (IPS) is necessary. Besides, indoor enabling technologies can leverage the capability of IoT in context-aware services. In this article, we aim to provide a panoramic view of IPSs and localization services with the centrality of IoT. First, we explain the main concepts and review the latest positioning methods, techniques, and technologies with IoT remarks. Then, we discuss technical implementation challenges and open issues with feasible solutions. Finally, we mentioned location-based services (LBSs), real IoT applications, and active vendors in the realm of positioning services. This article provides a real insight into LBSs in IoT for future research.

A Survey on Indoor Positioning Systems for IoT-Based Applications

Millimeter wave technology will be dominating the fifth-generation networks due to the clear advantage of higher frequency bands and hence wider spectrum. In this paper, the indoor radio wave propagation at 28 GHz is studied by developing an efficient three-dimensional ray tracing (ETRT) method. The simulation software based on the ETRT model has been verified by measurement data. The received signal strength indication and path loss have shown significant agreement between simulation and measurement. Compared with the conventional shooting bouncing ray tracing method, the proposed ETRT method has better agreement with measurement data.

https://www.mdpi.com/2079-9292/8/3/286/pdf

An Efficient 3-D Ray Tracing Method: Prediction of Indoor Radio Propagation at 28 GHz in 5G Network

A comprehensive review of ray tracing (RT) techniques for wireless communication systems is presented in this paper. The conventional techniques are described with respect to the existing problems and solutions. The development of the computational speed in computer produced a significant change in electromagnetic theory by increasing the performance and by speeding up the numerical calculation method. Nowadays, it is effectively used in cellular wireless propagation investigation. Here, some ray- tracing techniques have been presented to remove the limitation of the uniform geometrical theory of diffraction (UTD) and geometrical optics(GO). Outlines to decrease the computational time and to increase accuracy are the main focus. A very clear synopsis of ray tracing is presented in this paper, with a very exceptional consideration to future high-frequency applications. New challenges in high frequency are described and some new smart ideas for solving those challenges are presented. First, ray-tracing elemental methods are described and the later recently upgraded methods are reviewed. Second, numerous ray-tracing progressive methods are reviewed and then grouped into several categories like heuristic, effective deterministic and special brute force. Finally, the hybrid ray tracing methods which used physical optics are reviewed along with the effective unevenness model to increase the outcome accuracy.

A Comprehensive Review of Efficient Ray-Tracing Techniques for Wireless Communication

This article introduces an efficient analysis of indoor 4.5 GHz radio wave propagation by using a proposed three-dimensional (3-D) ray-tracing (RT) modeling and measurement. The attractive facilities of this frequency band have significantly increased in indoor radio wave communication systems. Radio propagation predictions by simulation method based on a site-specific model, such as RT is widely used to categorize radio wave channels. Although practical measurement provides accurate results, it still needs a considerable amount of resources. Hence, a computerized simulation tool would be a good solution to categorize the wireless channels. The simulation has been performed with an in-house developed software tool. Here, the 3-D shooting bouncing ray tracing (SBRT) and the proposed 3-D ray tracing simulation have been performed separately on a specific layout where the measurement is done. Several comparisons have been performed on the results of the measurement: the proposed method, and the existing SBRT method simulation with respect to received signal strength indication (RSSI) and path loss (PL). The comparative results demonstrate that the RSSI and the PL of proposed RT have better agreements with measurement than with those from the conventional SBRT outputs.

https://www.mdpi.com/2079-9292/8/7/750/pdf

Indoor 3-D RT Radio Wave Propagation Prediction Method: PL and RSSI Modeling Validation by Measurement at 4.5 GHz

Prediction of energy consumption in campus buildings using long short-term memory

This study presents a design of dielectric resonator antenna with filtering capability at C-band. The antenna design consists of dielectric substrate on which a dielectric material with permittivity of 10 is designed to work as radiated element. A pair of separated aperture coupling slots are etched on the ground plane to couple the microstip feed line to the top dielectric material. Open stub with microstip feedline is designed for filtering function. The proposed antenna can achieve a wide bandwidth of 456.5 MHz in the range from 4.8911-5.3476 GHz. High order harmonic suppression and low order harmonic suppression are achieved which improve the antenna filtering response. The antenna can achieve a gain of 6.34 dBi, return loss <-25 dB and VSWR of 1.1 dB. The proposed dielectric resonator antenna has high performance with good filtering response which is useful feature in modern wireless communication systems.

Chia Pao Liew

Papers

Review of Indoor Positioning: Radio Wave Technology

A Comprehensive Review of Efficient Ray-Tracing Techniques for Wireless Communication

Indoor 3-D RT Radio Wave Propagation Prediction Method: PL and RSSI Modeling Validation by Measurement at 4.5 GHz

Prediction of energy consumption in campus buildings using long short-term memory

Dielectric Resonator Antenna Design with High Gain and Wide Bandwidth