In this paper, we consider people counting and localization systems exploiting channel state information (CSI) measured from commodity WiFi network interface cards (NICs). CSI has useful information of amplitude and phase to describe signal propagation affected by the number of people or their locations in a designated space. However, due to hardware impairments of transceivers, CSI measurement suffers from offsets such as packet boundary detection uncertainty, sampling time difference, and carrier frequency difference. Moreover, an uncontrollable external environment where other WiFi devices communicate each other induces interfering signals, resulting in erroneous CSI captured at a receiver. In this paper, preprocessing of CSI is first proposed for offset removal, and it guarantees low-latency operation without any filtering process. The number of samples collected for each specific scenario is kept after packet-preserving preprocessing, which can be fully utilized to learn neural network models. Afterwards, we design people counting and localization models based on pre-training. To be adaptive to different measurement environments, meta-learning-based people counting and localization models are also proposed. We provide computational and space complexity analyses, confirming that the proposed meta-learning-based people counting and localization models require comparable resources to conventional adaptive models. Numerical results show that, compared with other learning-based benchmarks, the proposed scheme can achieve high sensing accuracy.

In this paper, a novel channel estimation technique for intelligent reflecting surface (IRS)-aided single-user multiple-input multiple-output (SU-MIMO) systems is proposed. Based on dominant single-path channel approximation for IRS-related channels, the proposed technique conducts parameter estimation instead of straightforward full channel estimation. This makes the proposed technique practical with low training overhead, compared to the typical channel estimations that require a large number of training signals. Through the simulations, we verify that, because of its low training overhead, the proposed estimation technique can provide a higher effective spectral efficiency than that of existing channel estimation methods requiring high training overhead.

Medical applications have driven many areas of engineering to optimize diagnostic capabilities and convenience. In the near future, wireless body area networks (WBANs) are expected to have widespread impact in medicine. To achieve this impact, however, significant advances in research are needed to cope with the changes of the human body’s state, which make coherent communications difficult or even impossible. In this paper, we consider a realistic noncoherent WBAN system model where transmissions and receptions are conducted without any channel state information due to the fast-varying channels of the human body. Using distributed reception, we propose several symbol detection approaches where on-off keying (OOK) modulation is exploited, among which a supervised-learning-based approach is developed to overcome the noncoherent system issue. Through simulation results, we compare and verify the performance of the proposed techniques for noncoherent WBANs with OOK transmissions. We show that the well-defined detection techniques with a supervised-learning-based approach enable robust communications for noncoherent WBAN systems.

In this paper, channel estimation techniques and phase shift design for intelligent reflecting surface (IRS)-empowered single-user multiple-input multiple-output (SU-MIMO) systems are proposed. The two novel channel estimation techniques proposed in the paper, single-path approximated channel (SPAC) and selective emphasis on rank-one matrices (SEROM), have low training overhead to enable practical IRS-empowered SU-MIMO systems. SPAC is mainly based on parameter estimation by approximating IRS-related channels as dominant single-path channels. SEROM exploits IRS phase shifts as well as training signals for channel estimation and easily adjusts its training overhead. A closed-form solution for IRS phase shift design is also developed to maximize spectral efficiency where the solution only requires basic linear operations. Numerical results show that SPAC and SEROM combined with the proposed IRS phase shift design achieve high spectral efficiency even with low training overhead compared to existing methods.

There has been a continuing demand for improving the accuracy and ease of use of medical devices used on or around the human body. Communication is critical to medical applications, and wireless body area networks (WBANs) have the potential to revolutionize diagnosis. Despite its importance, WBAN technology is still in its infancy and requires much research. We consider body channel communication (BCC), which uses the whole body as well as the skin as a medium for communication. BCC is sensitive to the body's natural circulation and movement, which requires a noncoherent model for wireless communication. To accurately handle practical applications for electronic devices working on or inside a human body, we configure a realistic system model for BCC with on-off keying (OOK) modulation. We propose novel detection techniques for OOK symbols and improve the performance by exploiting distributed reception and supervised-learning approaches. Numerical results show that the proposed techniques are valid for noncoherent OOK transmissions for BCC.