Channel charting (CC) is an unsupervised machine learning framework for learning a lower-dimensional representation of Channel State Information (CSI), while preserving spatial relations between CSI samples. In this paper, we consider super-resolution features in the angle-delay domain in massive Multiple-Input Multiple-Output (MIMO) systems. We i) treat the angle and delay separately, ii) present the so-called “Normalized Polar Feature” utilizing the channel statistics of the CSI samples, iii) use the Euclidean distance to compute the dissimilarity matrix, and create the channel chart. Simulation results based on the DeepMIMO data-set show that the proposed super-resolution representation with the Euclidean distance leads to the state-of-the-art quality CC as compared to other CSI features and distances from the literature such as angle-delay-power features with earth mover distance.

We implement several low-cost ambient backscatter communication (AmBC) receivers for the first time in practice. Multi-antenna AmBC receivers have been actively investigated theoretically. Practical implementations face a challenge as low-cost antennas are prone to suffer from wideband effect and/or hardware impairments and propagation environment is complicated. For a successful implementation, we propose a signal processing method which avoid the wideband signal but extracts the reference signal which is narrowband. We evaluate the method using the data measured in a complicated environment. Measurement results show that the proposed signal processing tackles the above issues and gives acceptable bit-error-rate (BER) performance of AmBC systems. The implementation of low-cost multi-antenna AmBC receivers paves the way for realizing ubiquitous green communication systems.

Ambient backscatter communication (AmBC) is becoming increasingly popular as a green Internet-of-things technology by enabling ultra-low-power data exchanges among simple tags. The bit-error-rate (BER) performance of the AmBC receivers is hampered by the low signal-to-interference-plus-noise ratio (SINR), which limits either the range or the data rate that can be supported by these systems. Among the alternatives, the solutions provided by a multiantenna receiver enable several practical methods to improve the SINR using array signal processing. In this paper, the optimum multiantenna AmBC receiver for any binary modulated tag signal is presented. Two receivers are derived from the maximum-a-posterior probability criterion for deterministic-unknown and for Gaussian distributed ambient signals. The closed-form cumulative distribution functions are derived for both of the receivers for performance evaluation purposes. It is discussed that these two receivers are equivalent under practical conditions, and the optimum receiver is a generalization of the multiantenna receivers available in the literature. Several implementation details are discussed, and numerical evaluation is provided to validate the development. Therefore, the presented receiver accommodates different tag modulations and achieves the best possible BER performance, which opens up new application possibilities by improving AmBC system flexibility.

Ambient backscatter communication (AmBC) is an emerging communication technology enabling green Internet of Things deployments. The widespread acceptance of this paradigm is limited by the low signal-to-interference-plus-noise ratio (SINR) of the signal impinging on the receiver antenna due to the strong direct path interference and unknown ambient signal. The adverse impact of these two factors can be mitigated by using noncoherent multiantenna receivers, which are known to require a higher SINR to reach the bit-error-rate (BER) performance of coherent receivers. However, considering the unknown ambient signal, unknown location of AmBC tags, and varying channel conditions, coherent receivers for AmBC systems are rarely studied in the literature. In this article, a coherent multiantenna receiver, which requires no prior information of the ambient signal for decoding the binary-phase-shift-keying (BPSK) modulated signal, is presented. The performance of the proposed receiver is compared with an ideal coherent receiver that has perfect phase information, and also with the performance of a noncoherent receiver, which assumes distributions for ambient signal and phase offset caused by the excess length of the backscatter path. Comparative simulation results show the designed receiver can achieve the same BER performance of the ideal coherent receiver with 1-dB more SINR, which corresponds to 5-dB or more gain with respect to noncoherent reception of on-off-keying modulated signals. Variation in the detection performance with the tag location shows that the coverage area is in the close vicinity of the transmitter and a larger region around the receiver, which is consistent with the theoretical results.

Multi-antenna receivers are becoming popular in ambient backscatter communication (AmBC) because they help to improve the detection performance by improving the signal-to-interference-plus-noise ratio (SINR) of the backscatter signal. Recent solutions, however, suffer from high implementation complexity and they tend to require the receiver to have high dynamic range. In this paper, a simplified receiver is proposed, which is able to overcome the above limitations. The receivers for the deterministic-unknown ambient signal and the Gaussian signal are derived using maximum-a-posteriori (MAP) criterion. Then, their detection performances are derived for bit-error-rate analysis. It is shown that the test statistic of the receiver is independent of the ambient signal type. The components for implementation are halved compared with the optimum AmBC receiver at the cost of an SINR penalty as low as 1-dB to achieve the same detection performance for the deterministic ambient signal and 3.6-dB for the Gaussian ambient signal. The dynamic range problem can be mitigated by implementing it fully in the analog domain. By providing attractive practical advantages mentioned above, the proposed simplified receiver, therefore, enables cost-effective AmBC receiver implementations.

Received signal strength measurements of commodity radios can be utilized for sensing the surrounding environment. This work harnesses the signal strength measurements for estimating time periods when a person is stationary and moving. A novel reciprocal signal strength model is presented, and an energy detector is developed. It is shown that the decision threshold can be calculated in closed form for the proposed model. In addition, the observation time window can be minimized to one communication cycle which equals 58 milliseconds in our case. Using real-world experimental data from two different environments, it is demonstrated that movement can be correctly detected over 99% of the time.

The ambient backscatter communication (AmBC) system utilizes the existing ambient RF signals present in the atmosphere for backscattering the signal. One of the challenges for AmBC system is the interference at the receiver module caused by the direct path signal from the ambient source. The purpose of this paper is to study the coverage aspects of the bi-static backscatter communication system in a typical urban environment at sub-1GHz frequencies using simulations in MATLAB. For the simulation, 3rdgeneration partnership project (3GPP) urban microcellular and international telecommunication union (ITU) device-to-device (D2D) propagation models are used. Moreover, the dynamic range i.e., the difference in the received power level of the direct path and the backscatter path is investigated. For correctly decoding the backscatter signal at the reader, the target value set for the dynamic range is less than 30 dB. This paper studies the importance of direct path interference suppression for the successful deployment of a bi-static backscatter communication system.

Backscatter communication offers a reliable and energy-efficient alternative to conventional radio systems. With the additional capability of wireless power transmission, the backscatter tags can work in a completely battery-less manner. Due to these exciting features, the researchers from academia and industry are extensively investigating its utility as an enabler of massive Internet of Things (IoT) networks. However, before reaping the benefits of ambient backscatter communications, it is necessary to evaluate its feasibility and operability for large scale networks. To do so, this research paper provides an empirical study of the real city wide deployment of monostatic wireless powered backscatter tags in Helsinki region. The coverage and outage performance has been assessed for both indoor and outdoor conditions using a sophisticated 3D ray tracing simulations. Whereby, the indoor scenario consists of several story buildings with low and high loss materials. Moreover, an indepth evaluation of energy shortage at backscatter tags has also been provided which sheds the light on the importance of wireless power transmission for such networks. The results provided here would be helpful in upscaling the practical deployment of backscatter tags.

Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The model is numerically and empirically evaluated, and its properties are discussed in depth. The most important model implications are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.

Ambient backscatter communication (AmBC), a green communication technology, is hampered by the continuously and extremely fast varying, strong and unknown ambient radio frequency (RF) signals. This paper presents a machine learning-assisted method for extracting the information of the AmBC device. The information is modulated on top of the unknown Gaussian-distributed ambient RF signals. The proposed approach can decode the binary phase shift keying backscatter signals encoded using Hadamard codes. This method extracts the learnable features for the tag signal by first eliminating the direct path signal and then correlating the residual signal with the coarse estimate of ambient signal. Thereafter, the tag signals are recovered by using the k-nearest neighbors classification algorithm. The recovered signals are decoded by a Hadamard decoder to retrieve the original information bits. We validate the performance using simulations to corroborate the proposed approach.

Received signal strength based radio tomographic imaging is a popular device-free indoor localization method which reconstructs the spatial loss field of the environment using measurements from a dense wireless network. Existing methods achieve high accuracy localization using a complex system with many sophisticated components. In this work, we propose an alternative and simpler imaging system based on link level occupancy detection. First, we introduce a single-bounce reflection based received signal strength model, which allows relating received signal strength variations to a large region around the link-lines. Then, based on the model, we present methods for all system components including a classifier, a detector, a back-projection based reconstruction algorithm, and a localization method. The introduced system has the following advantages over the other imaging based methods: i) a simple image reconstruction method that is straightforward to implement; ii) significantly lower computational complexity such that no floating point multiplication is required; iii) each link's measured data are compressed to a single bit, providing improved scalability; and iv) physically significant and repeatable parameters. The proposed method is validated using measurement data. Results show that the proposed method achieves the above advantages without loss of accuracy compared to the other available methods.

A growing set of environmental sensing applications use received signal strength measurements of a static wireless network for unobtrusive monitoring purposes. The success of these systems, which typically process low-amplitude signals, depend strongly on the distribution of the measurements when there are no changes in the channel. In this letter, a statistical model for signal strength measurements acquired when the environment is static is studied. As previously empirically verified, it is shown that the measurements have log-normal distribution even in idealistic environments, which cannot be explained using log-normal shadow fading arguments. Quantization and round-off errors induced by different measurement system components are also considered, and their impact are analyzed. As a result, it is shown that the logarithmic received signal strength measurements under static channel conditions are samples from stationary Gaussian process independent of the environment.

The relative significance of available processing/memory resources and cost/power constraints for wireless sensor-actuator nodes depends on the application and use-case scenario. As an example, wireless automation applications usually have diverse processing/memory resource demands and energy constraints, and they bring forth development feasibility as one of the important decision criteria. For similar applications, adaptable platforms, supporting scaling between processing-memory resources and power consumption, and providing flexibility in use-case dependent hardware modifications are preferable. To the best of our knowledge, the available node platforms cannot be easily modified to support resource scaling and hardware flexibility simultaneously. In this paper, an adaptable node hardware architecture is introduced. The proposed platform can be utilized in several of roles for applications with diverse requirements, lowering the development-effort compared to alternative implementations. The practical problems of the architecture are elaborated and the details of their solutions are given. The feasibility of the introduced architecture is shown through an example implementation using off-the-shelf components, the pRoot node.

The packet delivery ratio (PDR) performance of a wireless communication network interfered by a coexisting network is determined by the collision-time. In this paper, we propose a stochastic collision-time model for coexisting wireless networks, and analyze it in particular for coexisting low-rate wireless personal area network (LR-WPAN) and WLAN. Although, the packet collision-time of the interfered network is a complex process, as it depends on the packet size and packet inter-arrival time distributions of both networks, its properties can be inferred by modeling the interference as an alternating renewal process, leading to a stochastic collision-time model. The proposed collision-time model is utilized to derive the theoretical collision-time distributions for periodic, exponential and gamma inter-arrivals. The comparison of the theoretical and simulation based collision-time distributions for the studied inter-arrivals suggests that the proposed collision-time model can be used for performance analysis of coexisting wireless networks. In order to investigate the effects on the collision-time distribution in a realistic multi-terminal WLAN traffic shaped by the CSMA/CA mechanisms, heavy tailed hyper-Erlang and gamma distributions are fitted to an experimental inter-arrival times data-set. The goodness-of-fit tests of both distributions show that the gamma distributed inter-arrivals model the observed interfering traffic good enough, which allows analytical evaluation of LR-WPAN link quality using the derived collision-time model.

Received signal strength based device-free localization (RSS-based DFL) is recently gaining momentum as an indoor localization technology, since it enables locating people that are not cooperating with the system by carrying a device. The technology is based on monitoring the signal strength measurements of the many wireless transceivers that are deployed in the monitored area. The measurement modality can be used to accurately localize people and recent works have shown that it can be used e.g. in residential monitoring. Despite the recent advances in enhancing the accuracy of RSS-based DFL, real-world requirements such as energy efficiency and adaptation to the changing communication conditions are often neglected in the related literature. In this paper we present a management framework for RSS-based DFL which enables not only monitoring the environment and network, but to also interact with the dynamic environment and varying wireless channel. With the proposed framework, it is possible to make a considerable step forward so that RSS-based DFL can be used in long-term and real-world deployments.