Primary channel statistics have recently gained increasing attention due to its remarkable role in the performance improvement of Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems. These statistics can be calculated from the outcomes of spectrum sensing, which is the well-known method used to identify the available instantaneous opportunities in the spectrum. Computing statistical information from spectrum sensing, however, may sometimes be unreliable due to the fact that spectrum sensing is imperfect in the real world and errors are likely to occur in the sensing decisions. In this context, this work provides a detailed analysis of a broad range of primary channel statistics under Imperfect Spectrum Sensing (ISS) and finds a set of closed-form expressions for the calculated statistics under ISS as a function of the original primary channel statistics, probability of error, and the employed sensing period. In addition, the obtained mathematical expressions are employed to find and propose novel estimators for the primary channel statistics, which outperform the existing estimators in the literature and can provide accurate estimations of the original statistics even under high probability of error of spectrum sensing. The correctness of the obtained analytical expressions and the accuracy of the proposed estimators are corroborated with both simulation and experimental results.

Impulse response is vital for wireless communication analysis and modeling. This paper considers the impulse response of the terahertz band (THz band: 0.1–10 THz) for short range (1–100 cm) wireless communication. Earlier works derived the impulse response from transmittance by assuming a linear phase, which corresponds to a line-of-sight (LoS) propagation delay to a receiver. However, the linear phase 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. To address this issue, this study derives a phase function leading to an impulse response that satisfies causality. The validity of the derived model is verified with experimental THz band measurements (up to 2 THz), which show excellent agreement with the results predicted by the theory. From the impulse response, coherence bandwidth is found for both the entire THz band and its subbands. The results show significant variations in the coherence bandwidth as a function of the center frequency. Knowledge of these variations supports selection of the proper center frequency for wireless communications in the THz band.

This paper discusses a spreading factor allocation for Long Range Wide Area Network (LoRaWAN). Because Long Range (LoRa) is based on chirp spread spectrum that each spreading factor is approximately orthogonal to each other, the performance of LoRaWAN can be enhanced by allocating the spreading factor appropriately to end devices (EDs). Several spreading factor allocation techniques have been reported. Techniques shown in existing studies can improve some characteristics (e.g. throughput or packet reception probability (PRP)); however, there are a few studies that have focused on the energy consumption of the EDs. The LoRa communication offers a low power communication and this enables the improvement of the performance in exchange for the energy consumption. This paper presents a performance improvement technique via spreading factor allocations for LoRaWAN. We define the optimization problem for the spreading factor allocation to maximize the PRP under a constraint for the average energy consumption of all the EDs. It enables for the performance improvement under the constraint of the average energy consumption of all the EDs by solving the problem. This study further develops a method to solve the defined problem based on a distributed genetic algorithm, which is metaheuristics method. Although the techniques shown in the existing studies give the average energy consumption as a result of the performance improvement by the spreading factor allocation, the presented technique can enhance the LoRaWAN performance by allocating the spreading factor to EDs under the constraint for the average energy consumption of all the EDs. Numerical examples validate the effectiveness of the presented technique. The PRP performance of the presented technique is superior to that of the techniques shown in the existing studies despite that the average energy consumption of all the EDs of the presented technique is less than that of the techniques shown in the existing studies.

To enable widespread use of electric vehicles (EVs), large-scale public charging stations with fast chargers are being planned in places such as shopping malls and office car parks. Operators of public charging stations need to utilize EV scheduling algorithms that can satisfy charging demands with a minimum number of simultaneous charging sessions. In this paper, we propose EV charging scheduling algorithms that meet the charging demands and deadlines of EV users while minimizing the number of simultaneous charging sessions. An uncertainty-aware deep learning (DL) framework is also used to predict EV arrivals at a charging station. The predicted EV arrivals in turn are used to help the charging operator estimate how many charging sessions to order from the grid. Our DL model not only predicts the mean EV arrival rates but also the upper limits of EV arrivals, which enhances robustness against uncertainty in EV arrivals and helps estimate the maximum charging demand for a given interval. Moreover, to overcome the challenge of insufficient EV charging data for DL models, we construct a synthetic data model that takes into account multiple factors influencing EV arrivals, such as weather, events, weekdays, and weekends. Both online and offline approaches in the design of EV scheduling algorithms are utilized. The performances of the proposed algorithms are evaluated in terms of active charging sessions used to serve EV users. We also compare their performance with a baseline algorithm which is an offline optimal algorithm based on a mixed integer linear problem formulation.

For achieving efficient spectrum sharing in a context of dynamic spectrum access, understanding the spectrum usage by licensed users (primary user: PU), is important for secondary users (SU). Duty cycle (DC) has been used to express the deterministic and stochastic aspects of spectrum usage. Specifically, a deterministic model for the mean of the duty cycle (M-DC) has been proposed in a previous work. The deterministic aspect of M-DC is affected by social behavior, and common habits of users, which can be confirmed in cellular systems. On the other hand, the observed DC (O-DC) during short time duration has randomness and a stochastic model is more suitable, e.g. distribution of O-DC. In this paper, we extend the conventional approach, in which only either the deterministic or stochastic aspect is considered, to a combined deterministic-stochastic (DS) model which represents both the deterministic and stochastic aspects at once. For the distribution of the O-DC, the beta distribution has been used as stochastic model, but we employ a mixture of beta distributions. The mixture-beta distribution can achieve higher accuracy but requires more capacity for data storage in spectrum usage measurements since it has a higher number of parameters than the beta distribution. For this issue, we employ regression analysis in DS-model since this approach can reduce the number of parameters while retaining the accuracy. We show the validity of DS-model based on exhaustive spectrum measurements in IEEE 802.11-based wireless local area networks and Long-Term Evolution uplink.

The use of prior information related to spectrum usage of primary users can enhance spectrum sensing performance. However, to the best of our knowledge, no analytical thresholdsetting 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 thresholdsetting method is effective when the number of samples for the spectrum sensing is large or signal to noise power ratio is high.