Signal Channel State Information Research Articles

A Novel Passive Indoor Localization Method by Fusion CSI Amplitude and Phase Information.

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

A Survey on CSI-Based Human Behavior Recognition in Through-the-Wall Scenario

Recent years have witnessed increasing research interest in human behavior recognition as it provides attractive applications in various sensing scenarios. Among these encouraging implementations, device-free behavior recognition based on WiFi channel state information (CSI) has attracted significant attention due to the popularity of WiFi devices and abundant channel characteristics from CSI. Meanwhile, the CSI signal provides us with additional benefits because it can propagate through a wall. This through-the-wall and device-free scheme not only enables us to identify specific human actions but also to infer person activities by collecting the data from different rooms. This paper presents a survey on the state-of-art progresses in device-free through-the-wall human behavior recognition based on CSI. Specifically, this paper first introduces the basic concept of CSI and describes the signal variation caused by human behavior. Then, it illustrates that different human behaviors can cause signal transformation. Therefore, the unique map relationship between action and signal variation can be leveraged to recognize human behavior. Next, it provides the general architecture of through-the-wall behavior recognition and highlights its core characteristic. It investigates the state-of-art applications in various scenarios and analyzes specific design schemes and implementations. Afterward, it discusses the various across wall applications and makes a detailed comparison between non-through-the-wall and through-the-wall applications. Meanwhile, it analyzes many factors that affect recognition accuracy and emphasizes performance differences under across wall scenarios. Finally, this paper concludes by summarizing the issues and challenges faced and providing insights into the possible solution and future research trend.

Smartphone-Based Indoor Fingerprinting Localization Using Channel State Information

Indoor localization technology plays an important role in many indoor application scenarios. Existing WiFi-based indoor localization methods mainly obtain channel state information (CSI) through the personal computer, or obtain coarse-grained received signal strength (RSS) through the smartphone to finish the localization. Little work has been done on using smartphones to obtain fine-grained channel state information for localization. In this paper, we use the smartphone to collect fine-grained CSI that is more convenient and applicable, and propose a indoor fingerprinting localization. Compared with the CSI collected by the computer, the CSI signal collected by the smartphone fluctuates greatly. Hence, we corrects the CSI data through the signal processing technique and selects optimal subcarriers to obtain more stable and effective signals. In order to cope with the noisy WiFi environment, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is used to remove abnormal sample points to reduce environmental interference. Moreover, the support vector machine multi-classification method is used for training and classification to achieve localization. Finally, we use the Google Nexus 5 smartphone to conduct experiments in two typical indoor environments. The localization accuracy is 91% and 86%, respectively, and both average localization errors are less than 0.5m. Experimental results show that the proposed algorithm has higher localization accuracy compared with the typical algorithms.

On the capacity of some channels with channel state information

We study the capacity of some channels whose conditional output probability distribution depends on a state process independent of the channel input and where channel state information (CSI) signals are available both at the transmitter (CSIT) and at the receiver (CSIR). When the channel state and the CSI signals are jointly independent and identically distributed (i.i.d.), the channel reduces to a case studied by Shannon (1958). In this case, we show that when the CSIT is a deterministic function of the CSIR, optimal coding is particularly simple. When the state process has memory, we provide a general capacity formula and we give some more restrictive conditions under which the capacity has still a simple single-letter characterization, allowing simple optimal coding. Finally, we turn to the additive white Gaussian noise (AWGN) channel with fading and we provide a generalization of some results about capacity with CSI for this channel. In particular, we show that variable-rate coding (or multiplexing of several codebooks) is not needed to achieve capacity and, even when the CSIT is not perfect, the capacity achieving power allocation is of the waferfilling type.