Tissue-mimicking phantom is a biological tissue replacement which has been used as a replacement to understand the relationship between the electromagnetic and the human body. However, many of the developed phantoms are produced for several MHz to several GHz region, and less in the kHz region. This research introduces a new phantom to understand the electromagnetic effect at kHz region. The phantom is a 500kHz phantom which mimics human muscle dielectric properties. The dielectric properties of the phantom are adjusted by aluminum powder content, which is 40% of the total phantoms content. In this research, we use different phantom dielectric properties measurement method compared to the one at higher frequencies. Furthermore, the phantom thermal properties and density are measured to be used in numerical calculations.
A folded dipole antenna with a class-F load, acting as a receiving antenna for microwave wireless power transfer, is proposed. Conventionally, a class-F load circuit and matching circuit are connected to a rectifier circuit. A capacitor is used to adjust the impedance for harmonic waves. The proposed antenna involves both the class-F load and matching circuit functions to decrease loss in the circuit and reduce space usage. The antenna design was optimized and demonstrated to verify the class-F function.
Subject area: Navigation, Guidance and Control Systems
2020 Volume 9 Issue 11 Pages
Published: November 01, 2020
Released: November 01, 2020 [Advance publication] Released: August 21, 2020
The reflections and diffractions of global navigation satellite system (GNSS) signals from buildings may produce large measurement errors. Detecting non-line-of-sight signals using 3D maps is a means to detect and exclude satellites with large measurement errors. However, the true position is typically needed for using 3D maps. In this study, we verify the assumption that an approximate user position can be used when using 3D maps. We found that the correct fixed position of real-time kinematic GNSS (RTK-GNSS) could be achieved when approximate positions for RTK-GNSS assisted by 3D maps were within 5-15m from the true position.
Selected mapping (SLM) is one of the most widely used peak-to-average power ratio (PAPR) reduction schemes-it selects and transmits a signal having the smallest PAPR among candidate signals by performing a full search (FS). In this Letter, a simple modification to reduce the computational complexity of SLM-based PAPR schemes is proposed that terminates searching process when it meets a candidate signal not causing a clipping, instead of having the smallest PAPR.
This letter presents a multiple antenna based spectrum sensing via a sequential detection technique. A log-likelihood ratio test of a low computational complexity statistic is defined and we develop the sequential detection based spectrum sensing using the test. The presented technique can reduce the computational complexity by overlapping samples for the calculation of each statistic while almost maintaining the accuracy of signal detection. Numerical examples are shown to validate the effectiveness of the presented technique.
This paper reports the error effects of actual mmWave RF devices on beam pattern by monte carlo simulation. A mmWave beamforming by array antenna is one of the most promising technique to realize access methods which meet requirements of URLLC, mMTC, and eMBB communication simultaneously. The accuracy of analog beamforming is necessary to be increased to realize 5G or beyond 5G access. Our simulation shows individual effects of frequency, phase, and gain non-linearity characteristics on beamforming accuracy. We conclude that a proper calibration on the phase difference between array antenna elements improve the beamforming accuracy effectively and guarantee providing sufficient beam gain required in the entire communication area.
In order to improve the efficiency of spectrum use, systems that share spectrum while avoiding interference between different systems are being investigated. In the millimeter-wave band, which is expected to be utilized in the future, the received power fluctuates due to quasi-static obstructions such as people and vehicles, but such temporal variations have not been taken into account in conventional methods. In this paper, we use a variety of machine learning algorithms for comparative evaluation to forecast the temporal fluctuations of radio propagation due to changes in the number of people and vehicles in order to achieve more dynamic spectrum access.