Adaptive Antenna Array with weight and antenna space control

Abstract A typical adaptive antenna array based on weight control (AAA-W) with M antennas can suppress M-1 interference signals. In this paper, we propose AAA based on not only weight but also antenna spacing control (AAA-WS) and investigate the basic performance of AAA-WS under the line-of-sight. At first, we show that AAA-WS with two antennas (M=2) can sufficiently suppress more than two interference signals while the desired signal is enhanced. The inherent interference suppression capability of AAA-WS can be determined by the maximum antenna spacing and this fact is exhibited by analysis and Monte Carlo simulations. In addition, we will show that AAA-WS with two antennas can outperform AAA-W with more than two antennas. It is notable that the additional gain in AAAWS compared to AAA-W can be around 18 dB

Energy detection for M-QAM signals

Abstract In this paper, we address energy detection for M-ary quadrature amplitude modulation (QAM) signals. In the literature deterministic signal model is widely used and detection probability is a function of signal energy. Unlike constant amplitude signals, the QAM signal is not deterministic since the energy in each QAM symbol can randomly vary. For random signals, model where both signal and noise are Gaussian has been widely used. However, this approximation may not provide accurate detection probability for QAM signals. Instead the detection probability should be averaged over the distribution of the energy. Previous work has considered calculating exact detection probability for given M analytically. However, the method presented previously has complexity that increases as a function of M and the number of samples. In this paper, we show that the distribution of observed energy for any M can be accurately approximated by one distribution which is derived analytically. Multiple numerical results showing probability density function, Kolmogorov-Smirnov distance, and detection probability are shown. Based on these results, a range where the proposed approximation is applicable is obtained

Threshold-setting for spectrum sensing based on statistical information

Abstract The use of prior information related to the spectrum usage of primary users can enhance spectrum sensing performance. However, to the best of our knowledge, no analytical threshold-setting method to achieve target sensing performance, such as detection probability, is reported in the literature. In this letter, a threshold-setting method based on approximate analysis for achieving target detection probability or false alarm probability is proposed. Numerical results confirm that the proposed threshold-setting method is effective, when the number of samples for the spectrum sensing is large or signal-to-noise power ratio is high

A study on channel model for THz band

Abstract Impulse response of the terahertz band (0.1–10 THz) for wireless nanosensor networks is considered. For wireless communication analysis and modeling, the impulse response is very important. In the earlier works, the impulse response has been derived from the transmittance by assuming a linear phase shift. However, the linear phase shift only leads to a symmetric impulse response before and after the LoS propagation delay. Physically, it is impossible for a signal to arrive before the LoS propagation delay since this violates causality. In this paper, a phase shift function leading to an impulse response satisfying causality is derived. The validity of the derived model is shown by comparison between measurements and results predicted by the theory

Time domain channel model for the THz band

Abstract Time domain channel model based on impulse response is introduced for wireless nanosensor networks which is used in the terahertz band (THz band: 0.1—10 THz) and short range (1—100 cm). We assume two-ray ground-reflection model. In THz band, rough surface on the reflector has effect on the impulse response due to very short wavelength. This paper focuses on frequency domain and time domain channel models and, for wireless communication analysis the impulse response is very important. Both models represent molecular absorption and rough surface effect which are unique aspects in THz band. The time domain channel model has multiple delayed taps even in LoS path. Reflected path has significantly strong effect to received signal from LoS path at long distance between transmitter and receiver than at short distance, relatively. These channel models are essential for a development of THz band communication techniques

Energy detection for M-QAM signals

Abstract Accurate threshold setting for energy detector is important for example in dynamic spectrum access. This requires accurate statistical distribution models of the observed energy. In this paper, we consider energy detection (ED) for $M$-ary quadrature amplitude modulation (QAM) signals. The derivation of the exact solution of the distribution model (ES) requires all combinations of QAM signals in the observed signals based on the brute-force search and it leads to a significant computational cost. For this issue, this paper proposes three statistical distribution models which assume $M$=∞ to avoid the brute-force search. Due to the assumption of $M$, the proposed models are independent of $M$ and can handle adaptive modulation where $M$ can be changed dynamically. In the numerical evaluations, we compare the three proposed models with the other typical approximation models under additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. In addition, the proposed models are extended for more realistic scenario where imperfect synchronization is considered. The comprehensive numerical evaluations show that the first proposed model is most accurate among all considered models except ES but requires relatively high computational cost. The second proposed model where the observed energy is assumed to follow Gaussian distribution is the least complexity but can have reduced accuracy. The third proposed model based on skew-normal distribution can achieve comparable accuracy and less complexity compared to the first model