A Multipath-Aided Localization Method for MIMO-OFDM Systems via Tensor Decomposition

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.

Compared with the Wi-Fi Received Signal Strength (RSS) commonly-used for the indoor localization, the Channel State Information (CSI) can be used for precise ranging to achieve the high Wi-Fi indoor localization accuracy since it includes the fine-grained physical-layer information such as the amplitude and phase of each subcarrier during the signal transmission. Due to the lack of the theoretical analysis of the localization error bound in the existing CSI ranging-based localization methods, it is not easy to compare the ideal performance of different localization methods. Therefore, this paper proposes a CSI ranging-based Wi-Fi indoor localization error bound analysis method, which is based on the indoor signal propagation model to derive out the CSI ranging-based localization error bound by considering the relationship between the localization accuracy and the path loss, shadow fading, multipath effect and asynchronous effect. Besides, through the experimental comparison, this paper analyzes the difference between the actual localization error and the derived localization error bound, as well as discusses the impact of different experimental parameters on the localization performance.

In recent years, Channel State Information (CSI)-based Human activity recognition (HAR) has received extensive attention for its low cost and privacy protection property. Existing CSI-based HAR systems utilize single classifier to accomplish recognition, which results in poor robustness and relatively low recognition accuracy. In this paper, a random subspace classifier ensemble method is proposed for classification, which utilizes the frequency domain feature instead of the time domain feature and each kind of feature is selected in the same amount. We evaluate its performance both in typical indoor and outdoor environment. Our results show, the recognition accuracy can reach 91.2% and 90.2% in outdoor and indoor environment, respectively.

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.

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

In this study, we investigate the fundamental limits for time delay based location estimation in decode-andforward (DF) relaying strategy based cooperative relay networks considering multipath environments. The limits are presented by obtaining Cramer-Rao Lower Bounds (CRLBs) expressions. Simulations are performed to verify the CRLB expressions and show the effects of the node topology of the network, number of relay nodes, and number of multipath components (MPCs) on the location estimation accuracy of source node. According to the results, the placement of the relay nodes is very important to lower the location estimation error. Furthermore, using more relay nodes decreases the location estimation error, dramatically. Finally, processing more MPCs at the destination node is crucial to improve the estimation accuracy in the cooperative relay networks.

Localization of the nodes in wireless networks is crucial for supporting the location-based services (LBSs). In this study, we investigate the fundamental limits for time delay based location estimation in amplify-and-forward (AF) cooperative relay networks considering multipath environment. The limits are presented by obtaining Cramer-Rao Lower Bounds (CRLBs) expressions. Simulations are performed to verify the CRLB expressions and show the effects of node topology of the network, number of relay nodes, and number of multipath components (MPCs) on the location estimation of source node. According to the results, the placement of the relay nodes is very important to lower the location estimation error. Furthermore, using more relay nodes decreases the location estimation error, dramatically. Finally, processing more MPCs at the destination node is crucial to improve the estimation accuracy for both relaying strategy.

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

Challenges for precise indoor geolocation cannot be overcome by using the existing RF models for multipath components (MPCs). These models were designed for communication applications without specific attention to relation among the physical distance between the transmitter and the receiver and characteristics of MPCs pertinent to indoor geolocation applications. In this paper, we propose a novel model for the spatial behavior of MPCs which addresses these issues. This model uses principles of ray optics to explain the spatial evolution of path gains, time-of-arrival, and angle-of-arrival of MPCs observed by a mobile terminal operating in an indoor environment. The relation between the transmitter–receiver separation and the number of paths as well as statistical modeling of lifetime and birth rate of the paths are also incorporated in our model based on empirical data obtained by using a measurement calibrated ray-tracing tool.

At present, there has been a booming interest in utilizing Channel State Information (CSI) extracted from MIMO-OFDM PHY layer to achieve precise indoor localization. Compared with Received Signal Strength Indicator (RSSI), CSI as a fine-grained feature has a better performance on expressing the spatial and temporal features of wireless signal. As a result, CSI is more sensitive to the noise interference and multi-path. In this paper, we present a direct-path based fingerprint extraction algorithm for indoor localization in noisy and multi-path indoor environment. Our proposed algorithm firstly extracts the amplitude and phase measurements of direct-path from the raw CSI, and then calculates the unique fingerprint feature according to the filtered CSI. The experimental results show that our proposed algorithm improves the positioning accuracy up to 23.5% in complex indoor multipath environment.

The design and performance of indoor communication systems are strongly dependent on the delay spread in the impulse response of the channel. The delay spread is due to the abundance of the multipath reflections in the indoor environment. Judicious system design can derive benefit from the availability of the multipath signal to improve the system performance. Such potential improvement is due mainly to the fact that the various multipath components are very weakly dependent on each other, while fading, and a natural temporal diversity situation occurs. For system performance evaluation it is desirable to have an estimate of how the delay spread varies while the received power decreases with increasing transmitter-receiver distance. The simplest possible model that retains the essentials of a dense multipath environment is proposed and analyzed. It assumes that the direct path is blocked and that only scattered signals are received. A small ellipse of focal distance r/sub min/ about the transmitter and receiver is assumed to be free of scatterers. The scatterers are assumed to be Lambertian uncorrelated point-scatterers, uniformly distributed in 2-dimensional space. Secondary reflections are neglected and primary reflections are assumed not to be hampered by the presence of the other point scatterers. This is basically the wide sense stationary uncorrelated scatterers (WSSUS) model often used in communications analysis.

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.

In this paper, we will propose a novel approach to the radio propagation channels in complex indoor environments. This technique is called the deterministic splitting method of ray tubes. The prediction of field strength has been done with this method in indoor environments. A good agreement is achieved between the simulated results and the published results.

This paper addresses channel state information (CSI) estimation in a wireless relay network, which consists of a base station (BS), a relay station (RS), and a mobile station (MS). We propose a method for the BS to obtain the instantaneous CSIs of the three links (BS-RS, BS-MS, and RS-MS links) with minimized overhead. The CSI availability plays an important role for the BS to adapt its transmission strategies to the given channel conditions. Our proposed method exploits the fact that the link condition between fixed BS and fixed RS tends to be stable, and the frequent CSI update is not necessary for this link. In order for the BS to obtain the CSI of the distant link (i.e. RS-MS link), RS amplifies a pilot signal received from MS with a pre-defined amplification factor, and forwards it to BS. This enables the BS to obtain the CSI of the RS-MS link based on the received pilot signal and pre-knowledge on the CSI of BS-RS link. We show by computer simulation that our proposed method can reduce the required overhead for CSI estimation with little degradation on the accuracy of the obtained CSI.

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.