In this paper, an architecture of MIMO mesh network which avoids co-channel interference and supplies link multiplexing simultaneously, namely MIMO spatial spectrum sharing, is proposed. As a MIMO transmission scheme, linear (such as zero-forcing) and nonlinear (such as dirty paper coding and successive interference cancellation) MIMO algorithm are developed for the proposed mesh network. It is found from numerical analysis that the proposed MIMO mesh network achieves significantly higher channel capacity than that of conventional mesh networks.
In this paper, we propose a soft decision based cooperative sensing method for cognitive radio (CR) in ad-hoc networks. A CR can sense a surrounding radio environment and adapt to the environment by the reconfigurability to achieve more effective frequency utilization. Observing the environment, CR should detect presence or absence of primary user and exploits this information to opportunistically provide communication among secondary users while performance of primary user should not be deteriorated by the secondary users. Because of multi- path fading or shadowing, the detection of primary users may be significantly difficult. For this problem, cooperative sensing (CS), where gathered observations obtained by multiple users is utilized to achieve higher performance of detection, has been investigated. We propose a soft decision based CS where test statistic used in traditional single user energy detection is treated as soft decision information. The detector is designed analytically, i.e., the statistic of the performance, detection probability and given false alarm, can be obtained analytically. By computer simulations, we have presented as follows; validity of analysis and that the CS has better performance compared to non-cooperative sensing. In addition, we have presented the detection performance of cooperative sensing when there are considerable differences regarding signal to noise ratio among users.
Long-range (LoRa) wireless communication technology has been widely used in many Internet-of-Things (IoT) applications in industry and academia. Radio wave propagation characteristics in forested areas are important to ensure communication quality in forest IoT applications. In this study, 920 MHz band propagation characteristics in forested areas and tree canopy openness were investigated in the Takakuma experimental forest in Kagoshima, Japan. The aim was to evaluate the performance of the LoRa 920 MHz band with spreading factor (SF12) in a forested hilly area. The received signal strength indicator (RSSI) was measured as a function of the distance between the transmitter antenna and ground station (GS). To illustrate the effect of canopy openness on radio wave propagation, sky view factor (SVF) and a forest canopy height model were considered at each location of a successfully received RSSI. A positive correlation was found between the RSSI and SVF. It was found that between the GS and transmitter antenna, if the canopy height is above 23 m, the signal diffracted and RSSI fell to −120 to −127 dBm, so the presence of the obstacle height should be considered. Further research is needed to clarify the detailed tree density between the transmitter and ground station to propose an optimal propagation model for a forested environment.
In this paper, we propose a soft decision based cooperative sensing method for cognitive radio (CR) networks for opportunistic frequency usage. To identify unused frequency, CR should exploit sensing technique to detect presence or absence of primary user and use this information to opportunistically provide communication among secondary users while performance of primary user should not be deteriorated by the secondary users. Because of multipath fading or shadowing, the detection of primary users may be significantly difficult. For this problem, cooperative sensing (CS), where gathered observations obtained by multiple secondary users is utilized to achieve higher performance of detection, has been investigated. We design a soft decision based CS analytically and analyze the detector in several situations, i. e., signal model where single-carrier case and multi-carrier case are assumed and two scenarios; in the first scenario, SNR values of secondary users are totally equal and in the second scenario, a certain SNR difference between secondary users is assumed. We present numerical results as follows. The first scenario shows that there is little difference between the signal models in terms of detection performance. The second scenario shows that CS is superior to non-cooperative sensing. In addition, we presents that detection performance of soft decision based CS outperform detection performance of hard decision based CS.
