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This paper documents a simple parametric polynomial line-of-sight channel model for 100–450 GHz band. The band comprises two popular beyond fifth generation (B5G) frequency bands, namely, the D band (110–170 GHz) and the low-THz band (around 275–325 GHz). The main focus herein is to derive a simple, compact, and accurate molecular absorption loss model for the 100–450 GHz band. The derived model relies on simple absorption line shape functions that are fitted to the actual response given by complex but exact database approach. The model is also reducible for particular sub-bands within the full range of 100–450 GHz, further simplifying the absorption loss estimate. The proposed model is shown to be very accurate by benchmarking it against the exact response and the similar models given by International Telecommunication Union Radio Communication Sector. The loss is shown to be within ±2 dBs from the exact response for one kilometer link in highly humid environment. Therefore, its accuracy is even much better in the case of usually considered shorter range future B5G wireless systems.

In traditional radio propagation, the arrival time of the signal at the receiver from the transmitter is the propagation delay, which is given by the distance divided by the light speed, in the line of sight (LoS) scenario. However, in THz, the observable arrival time would be more than the propagation delay, and the extra delay is denoted as a molecular absorption (MA) delay . At first, this paper shows the presence of such MA delay based on the causal channel model that can provide an accurate impulse response for the target frequency band in THz. The comprehensive investigations of the impulse responses reveal that the MA delay is related to the frequency selectivity due to molecular absorption in THz. The causal channel model can also provide an appropriate phase response, and the MA delay can be approximated by the group delay. In addition, an estimated impulse response from the experimental tests also indicates the validity of the MA delay at the THz band.

The concept of signal area (SA), defined as the rectangular time–frequency region in a spectrogram where a signal is detected, plays an important role in spectrum usage measurements. The need for signal area estimation (SAE) is justified by its role in the process of allocating white space spectrum to secondary users in dynamic spectrum access systems as well as in other interesting applications such as compliance verification and enforcement of spectrum regulations, signal interception, and network planning and optimisation. Existing SAE methods are far from perfect and therefore new solutions capable to provide more accurate estimations are thus required. In this study, a novel approach based on image processing techniques is explored. Concretely, the feasibility of using morphological operations (MOs) is explored to examine its usefulness in the context of SAE. By means of extensive simulations, the performance of different MOs (erosion, dilation, opening, and closing) in the context of SAE is investigated under various configurations, including different shapes and sizes of the structuring element (SE), when used both as standalone SAE methods and in combination with other SAE methods from the literature. Based on the obtained results, an MO-based SAE method is formulated based on the optimum MO and SE configuration for each specific SNR regime, which can improve substantially the performance of other proposed SAE methods when used as a pre- or postprocessing technique. Concretely, the accuracy improvement in terms of F1 score is up to 40% in the low-SNR regime while achieving a perfect accuracy of 100% in the high-SNR regime. This is achieved without having a noticeable impact on the associated computational cost (and even reducing it by up to 15% at high SNR). The performance improvement is thus particularly significant in the low-SNR regime, where most methods’ performances are limited, and as a result the proposed SAE approach can extend the operational SNR range of the existing SAE methods.

Spectrum sensing plays a significant role in enabling utilization of spectrum holes by unlicensed secondary users (SUs) in cognitive radio networks (CRNs). Most of the related work concerning spectrum sensing has focused on sensing carried out by incoming secondary users (SUs) aiming at locating spectrum opportunities. However, in order to appropriately protect returning licensed primary users (PUs), SUs should continuously perform spectrum sensing during their ongoing transmissions. An important issue associated with the continuous sensing is the false alarm rate (FAR), which is defined as the average number of false alarms per unit of time and can be modeled by a Poisson process with Poisson parameter λ FAR . In this paper, we address this issue and develop a continuous time Markov chain (CTMC)-based analytical model to evaluate the effect of the false alarm rate on the performance of CRNs. A major feature of the proposed analytical framework is that it takes into account the effects of sensing errors by both incoming SUs looking for free channels to transmit on and the already transmitting SUs expecting the presence of returning PUs. The analytical model also examines the interference tolerance among PUs and SUs as well as the impact of SUs residual self-interference. The performance results show that high λ FAR can severely degrade PUs performance and reduce the overall system resource utilization. However, with increasing PU interference tolerance, PUs performance improves as well. SU residual interference was found to decrease the detection probability resulting in a low PU performance. Extensive simulations validate the analytical model, demonstrating excellent agreement with the theoretical results.

We investigate a method to set the maximum allowable transmit power for a secondary base station in dynamic spectrum sharing among secondary users and primary users. In conventional methods, location information is assumed. Thus, the maximum allowable transmit power can be set by considering the shadowing between the secondary base station and the primary user receivers to satisfy a constraint. Specifically, the probability that the interference from secondary base station exceeds the acceptable level must be less than the constraint target probability. We assume that the location information is not available at the secondary network. Instead, the secondary base station uses the received signal strength from the primary user transmitter for distance estimation. In this case, we have to consider shadowing not only between the secondary base station and the primary user receivers, but also between the primary user transmitter and the secondary receiver(s). We also need to account for the uncertainty of the distance. In order to satisfy the constraint target probability, we proposed a two-step approach to setting the maximum allowable transmit power where a transmission decision margin and a transmit power margin are utilized. To reduce these margins, we also propose cooperative maximum allowable transmit power setting method utilizing also received signal strength values from several secondary users. Simulation results confirm the validity of the analysis and show the effectiveness of the proposed cooperative maximum allowable transmit power setting method, i.e., the capacity based on cooperative maximum allowable transmit power setting method is significantly better than that of non-cooperative maximum allowable transmit power setting method. In addition, we show a proper size of radius of additional separation area to protect primary users by the numerical results.

We propose a framed slotted Aloha-based adaptive method for robust communication between autonomous wireless nodes competing to access a channel under unknown network conditions such as adversarial disruptions. With energy as a scarce resource, we show that in order to disrupt communications, our method forces the reactive adversary to incur higher energy cost relative to a legitimate node. Consequently, the adversary depletes its energy resources and stops attacking the network. Using the proposed method, a transmitter node changes the number of selected time slots and the access probability in each selected time slot based on the number of unsuccessful transmissions of a data packet. On the receiver side, a receiver node changes the probability of listening in a time slot based on the number of unsuccessful communication attempts of a packet. We compare the proposed method with two other framed slotted Aloha-based methods in terms of average energy consumption and average time required to communicate a packet. For performance evaluation, we consider scenarios in which: (1) Multiple nodes compete to access a channel. (2) Nodes compete in the presence of adversarial attacks. (3) Nodes compete in the presence of channel errors and capture effect.

The backward and forward consecutive mean excision (CME/FCME) algorithms are diagnostic methods for outlier (signal) detection. Since they are computationally simple, they have applications for both narrowband signal detection in cognitive radios and interference suppression. In this paper, a theoretical performance analysis framework of the CME algorithms is presented. The analysis provides simple tests of the detectability of the signals based on their shape in the considered domain (e.g., spectrum). As a consequence, results can be used to quickly check whether the CME/FCME algorithms are usable for a given problem or not without the need to resort to time consuming computer simulations. The computer simulations for random and orthogonal frequency division multiplexing (OFDM) signals show that the presented analysis is able to predict the detectability of signals well.

We formulate the problem of distributed throughput-efficient sensing in cognitive radio (CR) networks as a dynamic coalition formation game based on a Markovian model. The proposed coalition formation enables the CRs to increase their achievable throughput, under the detection probability constraint, while also taking into account the overhead in sensing reports combining. The dynamic model of coalition formation is used to express and model the behavior of the coalition forming CRs over time. In the proposed game, CRs form coalitions either to increase their individual gains (selfish coalition formation) or to maximize the overall gains of the group (altruistic coalition formation). We show that the proposed coalition formation solutions yield significant gains in terms of reduced average false alarm probability and increased average throughput per CR as compared to the non-cooperative solutions. Given a target detection probability for a coalition, we adopt a weighted target detection probability for individual CRs in a coalition. We find that the weighted target detection probability for individual CRs results in increased average throughput per CR as compared to when each CR is assigned the same target detection probability in a coalition.

Infostation, hotspot, and drive-thru internet are examples of sparse coverage-based wireless networks. These wireless communication networks provide low-cost, delay insensitive high data rate services intermittently with discontinuous coverage. Radio propagation parameters, velocity of the user, distance between the user, and access point are the key factors that affect the throughput and the amount of information downloaded from such sparse coverage-based wireless networks. To evaluate the performance of such wireless communication networks analytically the impact of above mentioned factors can be modeled with simplified relationship model such as received signal strength versus distance or signal to noise ratio versus throughput. In the paper, we exploit the relationship between throughput and distance and develop two throughput distance relationship models to evaluate the performance of multirate wireless networks. These two throughput distance relationship models are used in calculation of average throughput as well as downloaded file size. Numerical values are presented for the IEEE 802.11n standard. The numerical results verify that the new proposed technique can be used as an alternative to the simulations to evaluate the performance of sparse coverage-based wireless networks.