Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

Spatial Wireless Channel Prediction under Location Uncertainty

Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties.\textcolor{blue}{{} }In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel

実観測に基づく電波環境データベースを用いた空間的周波数共用に関する研究

The growth in demand for mobile communication systems has exponentially increased data traffic during the last decade. Because this exponential growth consumes finite spectrum resources, traditional spectrum utilization policies with exclusive resource allocation faces a limit. In order to develop novel spectrum resources, many researchers have shown an interest in spectrum sharing with cognitive radio (CR). This method allows secondary users (SUs) to share spectrum bands with primary users (PUs) under interference constraints for PUs. SUs are required to take into consideration the interference margin to the estimated interference temperature at PUs in order to protect communication quality of PUs. On the other hand, an excess interference margin decreases the spectrum sharing opportunity; therefore, it is important to manage the interference power properly. Spectrum estimation techniques in spectrum sharing can be categorized into two methods: spectrum sensing and spectrum database. Spectrum sensing uses the detection of PU signals to characterize radio environments. To provide good protection, signal detection must be performed under the (strict) condition that the PU signal strength be below the noise floor, even under low signal-to-noise ratios (SNRs) and fading conditions. These fluctuations make it difficult for the SUs to achieve stable detection; thus, it is very challenging to accurately estimate the actual activity of the PU. The second method is based on storing information about spectrum availabilities of each location in spectrum databases. In this method, afterSUs query the database before they utilize the spectrum, the database provides spectrum information to the SUs. Current databases usually evaluate white space (WS) based on empirical propagation models. However, it is well known that empirical propagation models cannot take into account all of the indeterminacies of radio environments, such as shadowing effects. Because SUs must not interfere toward PUs, the conventional database requires the SUs to set large margins to ensure no interference with PUs.In this dissertation, we propose and comprehensively study a measurement-based spectrum database for highly efficient spectrum management. The proposed database is a hybrid system, combining spectrum sensing and a spectrum database. The spectrum database consists of radio environment information measured by mobile terminals. After enough data are gathered, the database estimates the radio environment characteristics by statistical processing with the large datasets. Using the accurate knowledge of the received PU signal power, spectrum sharing based on PU signal quality metrics such as the signal-to-interference power ratio (SIR) can be implemented.We first introduce the proposed database architecture. After we briefly discuss a theoretical performance of the proposed database, we present experimental results for the database construction using actual TV broadcast signals. The experimental results show that the proposed database reduces the estimation error of the radio environment. Next, we propose a transmission power control method with a radio environment map (REM) for secondary networks. The REM stores the spatial distribution of the average received signal power. We can optimize the accuracy of the measurement-based REM using the Kriging interpolation. Although several researchers have maintained a continuous interest in improving the accuracy of the REM, sufficient study has not been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary Kriging for the spectrum cartography. According to the predicted distribution of the estimation error, the allowable interference power to the PU is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically, and an increase in the number of measurement datasets improves the spectrum sharing capability. After that, we extend the proposed database to the radio propagation estimation in distributed wireless links in order to accurately estimate interference characteristics from SUs to PUs. Although current wireless distributed networks have to rely on an empirical model to estimate the radio environment, in the spectrum sharing networks, such a path loss-based interference prediction decreases the spectrum sharing opportunity because of the requirement for the interference margin. The proposed method focuses on the spatial-correlation of radio propagation characteristics between different wireless links. Using Kriging-based shadowing estimation, the radio propagation of the wireless link that has arbitrary location relationship can be predicted. Numerical results show that the proposed method achieves higher estimation accuracy than path loss-based estimation methods. The methods discussed in this thesis can develop more spatial WSs in existing allocated bandwidth such as TVWS, and can provide these WSs to new wireless systems expected to appear in the future. Additionally, these results will contribute not only to such spectrum sharing but also to improvement of the spectrum management in existing systems. For example, in heterogeneous networks (HetNets), a suitable inter-cell interference management enables transmitters to reuse the frequency efficiently and the user equipment can select the optimum base station. We anticipate that this dissertation strongly contributes to improvingthe spectrum utilization efficiency of the whole wireless systems.電気通信大学201