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One of the key parameters that plays a major role in enabling the data rate requirements is spectrum or bandwidth, which is scarce and expensive. Therefore, spectrum management policies are required to optimize its usage to meet all the requirements and the promised data rate growth. Another strategy to deal with spectrum scarcity is to move toward higher frequency bands (terahertz bands), which are expected in the next 6G communication standard. It is therefore important to develop not only new techniques that enable efficient dynamic spectrum access and sharing at such bands, but also suitable channel models for the terahertz bands. Meanwhile, offloading mechanisms are very promising for cellular networks where a plethora of options have been proposed in the research arena in terms of device-to-device, licensed assisted access, or WiFi offloading, among others; but their behavior, when operated at high frequencies (terahertz band) remains unclear. Therefore, this article will tackle two technologies that will shape future networks: terahertz channel modeling/communications and offloading mechanisms.

In this paper, we investigate a transmitter and receiver design for a single-user massive SIMO (single-input multiple-output) system with 1-bit analog-to-digital converters (ADCs) at the base station (BS), where the user adopts higher-order modulation, e.g., 16-quadrature amplitude modulation (16-QAM), for the data transmission. For the channel estimation and the signal detection, linear least-squares (LS) estimation and maximum ratio combining (MRC) are respectively employed. In this context, we first introduce closed-form formulas for the mean of the estimated symbols and for the correlation matrix between their real and imaginary parts considering the effect of 1-bit quantization. The study of the distribution of the estimated symbols indicates that, in presence of 1-bit ADCs, the conventional 16-QAM detector and the typical square 16-QAM modulation are not adequate. In light of this, we propose three novel symbol detectors and re-design the 16-QAM modulation in order to improve the symbol error rate (SER). Furthermore, the upper bound on the SER is analyzed based on the pair-wise error probability and the boundary equation between two regions is also studied. Through numerical results, the proposed framework, i.e., the symbol detector and the transmit constellation design, shows a significant enhancement in the SER performance against the conventional detector and the typical square 16-QAM modulation.

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.

This paper investigates the prediction of spectrum usage by wireless local area networks based on recurrent neural networks (RNNs). The prediction results can be used to enhance the spectrum efficiency in dynamic spectrum sharing, and accuracy of spectrum sensing in cognitive radio networks. Observed time series of duty cycle (DC), which indicates the spectrum usage trend, is utilized to predict the future DC by the RNN. At first, we reveal a drawback of prediction by a single RNN, and this approach is denoted by a conventional approach in this paper. Specifically, the prediction results may have a significant biased error if the observed DCs are biased to either high or low values. For this problem, we propose the DC prediction with two RNNs and each of them designed for high DC case and low DC case, respectively. The prediction algorithm at first identifies the state of current DC either high or low. Then, the RNN for the identified state is performed for an accurate DC prediction. Numerical evaluations based on comprehensive measurement experiments of spectrum usage have presented that the proposed DC prediction can improve the accuracy of DC prediction compared to the conventional approach.

One of the main approaches for distance estimation is the received signal strength (RSS) based techniques. Their drawbacks include the requirement of accurate knowledge of the transmit power and antenna gains. Also, the traditional RSS-based techniques do not take into account the molecular absorption that occurs at terahertz frequencies. In this paper, we propose a distance estimation method for the line-of-sight case that actually takes advantage of the molecular absorption. The proposed method measures the RSS at two frequencies and does not require known transmit powers and antenna gains, which are assumed to be almost the same at the two frequencies. Therefore, the two frequencies have to be relatively close to each other. The proposed distance estimation method calculates the difference of the received powers (in the dB domain) at the two frequencies and finds the link distance based on it and the humidity level.

Spectrum sharing in radar bands with interference forecasting for enhanced radar protection can help design proactive resource allocation solutions which can achieve high data rates for wireless communication networks on one hand and help protect the incumbent radar systems. We consider radar spectrum sharing in 5.6GHz where a weather radar operates as a primary system and the dominant secondary system is an enterprise network consisting of access points (APs) in a university campus. Our work models transmit the power of the APs as a time series with multinomial distribution based on real collected data. The aggregated interference due to the transmissions from the APs at the radar is forecasted using a long short-term memory (LSTM) based neural network. Monte Carlo dropout is utilized to generate prediction intervals that capture the uncertainties in the interference from the APs. Finally, by using both average and upper limits of predicted interference time series a cloud-assisted efficient sharing and radar protection algorithm is proposed. Tracking the rotating radar is not required in the proposed system. The results show that the proposed efficient sharing and radar protection system ensures better radar protection and increased throughput for wireless communication users.

Statistics in terms of spectrum occupancy are useful for efficient and smart dynamic spectrum sharing, and the statistics can be obtained by long-term and wide-band spectrum measurements. In this paper, we investigate noise floor (NF) estimation for energy detection (ED)-based long-term and wide-band spectrum measurements since the NF estimation heavily affects the ED performance and eventually the accuracy of the statistics in terms of spectrum occupancy. Specifically, we address the following NF estimation problems simultaneously for the first time in the spectrum measurement field: (1) slow time-varying property of the NF, (2) frequency dependency of the NF, (3) the NF estimation in the presence of the signal, and (4) the computational cost of the NF estimation. Firstly, we apply Forward consecutive mean excision (FCME) algorithm-based NF estimation to deal with the above three problems ((1), (2) and (3)) successfully. Second, we propose and apply an NF level change detection on top of the FCME algorithm-based NF estimation to deal with the fourth problem. The proposed NF level change detection exploits the slow time-varying property of the NF. Specifically, only if the significant NF level change is detected, the FCME algorithm-based NF estimation is performed to reduce the redundant NF estimations. In numerical evaluations, we show the efficiency and the validity of the NF level change detection for the NF estimation problems, and compare the NF estimation performance with the method without the NF level change detection.

This paper considers a massive single-input single-output (SIMO) system with 1-bit analog-to-digital converters (ADCs) at the base station (BS). In this context, the stochastic resonance phenomenon can be exploited together with the massive number of antennas to enable the symbol detection. In this regard, we investigate the statistics of the estimated symbols when maximum ratio combining (MRC) is adopted at the BS. In addition, assuming 16 quadrature amplitude modulation (16-QAM), we propose three different symbol detectors that are designed based on the mean and variance of the MRC outputs. Through numerical evaluations, we show that the proposed symbol detectors can significantly enhance the symbol error rate performance of massive SIMO systems with 1-bit ADCs.

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.

In recent years, as more devices are connected to wireless communication systems, the demand for spectrum has increased. As conventional spectrum resources are limited, the THz band becomes an interesting option of more spectrum for wireless communication. However, the channel in THz band has different characteristics compared to the channels in typical frequency bands, and therefore, it is necessary to perform more research to understand the THz channel propagation. In this paper, we focus on the time domain THz channel model under line of sight (LoS) propagation conditions and investigate the channel propagation characteristics in time domain. Firstly, in the full frequency band (FFB) scenario, the time domain impulse responses, which correspond to the time domain THz channel model, are presented for different distances. In the impulse responses, there are significantly delayed paths due to the molecular absorption which causes significant frequency selectivity. Secondly, we extend the model to the limited frequency band (LFB) scenario by applying the root raised cosine filters. The results indicate that the richness of the delayed paths in the impulse response depends on the selected frequency band. In addition, the results indicate that the time delay and total energy strongly depend on the distance whereas the delay spread varies as a function of frequency.

Spectrum occupancy prediction is a key enabling technology to facilitate a proactive resource allocation for dynamic spectrum management systems. This work focuses on the prediction of duty cycle (DC) metric that reflects spectrum usage (in the time domain). The spectrum usage is typically measured on a shorter time scale than needed for prediction. Hence, data thinning is required and we apply block averaging. However, averaging operation results in flattening the DC data and losing essential features to assist deep neural network (DNN) to predict the spectrum usage. To improve DC prediction after block averaging, a feature-based deep learning framework is proposed. Namely, long short-term memory (LSTM) and gated recurrent unit (GRU) are selected and enhanced by using features of the data, such as the variance of DC data in addition to DC data themself. The proposed model is capable of proactively predicting the spectrum usage by capturing complex relationships among various input features for the measured spectrum, thus providing higher prediction accuracy with an average improvement of 5% in RMSE compared with traditional models. Moreover, to have a better understanding of the proposed model, we quantify the effect of input features on the predicted spectrum usage values. Based on the most significant input features, a simpler and more efficient model is proposed to estimate DC with similar accuracy to when using all features.

Prediction of wireless network parameters, such as traffic (TU) and channel utilization (CU) data, can help in proactive resource allocation to handle the increasing amount of devices in an enterprise network. In this work, we examined the medium-to-long-scale forecasting of TU and CU data collected from an enterprise network using classical methods, such as Holt-Winters, Seasonal ARIMA (SARIMA), and machine learning methods, such as long short-term memory (LSTM) and gated recurrent unit (GRU). We also improved the performance of conventional LSTM and GRU for time series forecasting by proposing features-like grid training data structure which uses older historical data as features. The wireless network time series pre-processing methods and the verification methods are presented as time series analysis steps. The model hyper-parameters selections process and the comparison of different forecasting models are also provided. This work has proven that physical layer data has more predictive power in time series forecasting aspect with all forecasting models.

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.

A pro-active spectrum usage prediction is a key technique in decision making and spectrum selection for dynamic spectrum access systems. This work focuses on the estimation of the duty cycle (DC) metric to reflect spectrum usage. The prediction is formulated as a time-series regression problem. Deep neural networks (DNNs) is selected to obtain accurate predictions of channel usage. Namely, Multilayer perceptron (MLP), Long short term memory (LSTM) and a hybrid model based on convolutional neural network followed by an LSTM (CNN-LSTM) layer are selected. The hyper-parameters selection has been optimised utilising both grid search and multi-stage grid search. Moreover, in many cases, the spectrum usage is measured on a smaller time scale from the actual required one. Hence, down-sampling and averaging is required. Averaging operation results in flattening the data and losing essential features to assist DNN to predict the channel usage. We show what is the minimum required time resolution to have a pro-active prediction system. Then, we propose utilising feature engineering to improve prediction accuracy. All the proposed DNNs approaches are trained on real-life measurements. The experimental evaluation demonstrated a high potential of DNNs to learn from previous spectrum usage and accurately predict the spectrum usage. Moreover, adding input features significantly assists the system to achieve accurate predictions in a pro-active manner.

The problem of weighted sum-rate maximization for hybrid architecture in millimeter wave communications with dynamic time division duplexing is considered. We propose an over the air pilot- aided direct beamforming estimation technique for the considered problem, by exploiting the channel reciprocity. Proper selection of analog precoder/combiner not only suppresses large part of interference, but also ensures that less coordination is required for digital precoder/combiner design. We design analog precoder/combiner for each data stream in such a way that its signal strength is maximized; this strategy also minimizes large part of inter-stream interference that may arise due to the main lobe of analog beamformer. Then we design digital beamformer such that inter-stream interference is further mitigated, maximizing the overall weighted sum-rate of the network.

We investigate noise floor (NF) estimation for FFT-ED (Energy Detection based on FFT)-based spectrum usage measurement in the context of smart spectrum access (SSA), in which spectrum usage information of primary users (PUs), such as channel occupancy rate (COR), will be exploited by secondary users (SUs). In FFT-ED, the NF has to be estimated to set a decision threshold for ED appropriately. In general, the NF is frequency-dependent and its level changes with time leading to the need of estimating the NF regularly while performing the spectrum usage measurement. In this paper, we propose an NF estimation method which exploits prior information regarding the shape of NF and forward consecutive mean excision (FCME) algorithm. Numerical and experimental evaluations show the proposed method enables an accurate NF estimation considering the time and frequency dependencies of the NF. Moreover, we show the proposed method can obtain the almost desired detection performance, but can not the comparative method (the original FCME method).

An accurate estimation of the noise floor is of paramount importance for an optimum performance of spectrum sensing in Cognitive Radio (CR). The most common approach followed by existing noise floor estimation methods is to attempt to isolate a set of noise-only samples based on a given energy/power threshold. However, this approach is unreliable and in general unable to provide accurate estimations of the noise floor, in particular under low SNR conditions where the power of the Primary User (PU) signal is comparable to the noise floor of the CR device. In this context, this work considers a different approach where the power observed by the CR device is modelled as a Gaussian mixture. Based on a mathematical analysis of the relation among the parameters of the obtained Gaussian mixture, a modified version of the well-known Expectation Maximisation (EM) algorithm is proposed to fit the Gaussian mixture to the observed power values and provide an estimation of the noise floor, something that the general EM algorithm fails to achieve in this scenario. The obtained results demonstrate that the proposed method provides a highly accurate estimation of the noise floor in the presence of PU signals over the whole range of SNR values.

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.

We investigate the design of signal processing in wideband spectrum usage (SPU) measurements for efficient and smart dynamic spectrum access (DSA). In particular, we focus on spectrum usage detection (SPUD) in the experimental measurements. The detection results can be exploited to estimate statistics of the SPU. An appropriate design of the SPUD depends on the actual SPU in the target frequency band. There is a broad range of wireless systems in a considered broad measurement frequency band, such as from 60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the proposed design framework, we at first define two models in terms of the SPU and the SPUD process. In addition, the proposed design procedure determines the adequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target frequency band. Numerical evaluation based on computer simulations shows the validity of the design framework and design procedure. Moreover, a modified duty cycle (DC) estimation method is proposed, which can remove bias errors caused by low time resolution in the SPUD. Numerical evaluation based on experimental measurements demonstrates the practicality of the detection framework and procedure proposed in this work.

Dynamic spectrum access (DSA)/cognitive radio (CR) systems can benefit from the knowledge of the activity statistics of primary channels, which can use this information to intelligently adapt their spectrum use to the operating environment. Particularly relevant statistics are the minimum, mean and variance of the on/off period durations, the channel duty cycle and the governing distribution. However, most DSA/CR systems have limited resources (power consumption, memory capacity, computational capability) and an important question arises of how many on/off period observations are required (i.e., the number of observed on/off periods, referred to as observation sample size in this paper) to estimate the statistics of the primary channel to a certain desired level of accuracy. In this paper, closed-form expressions to link such sample size with the accuracy of the observed primary activity statistics are proposed. A comprehensive theoretical analysis is performed on the required number of observed on/off periods to obtain a specific estimation accuracy. The accuracy of the obtained analytical results is validated and corroborated with both simulation and experimental results, showing a perfect agreement. The analytical results derived in this paper can be used in the design and dimensioning of DSA/CR systems in which the spectrum awareness function relies on spectrum sensing.

Primary channel activity statistics (such as the minimum idle/busy periods of the channel, the moments or the underlying distributions) can be exploited by cognitive radio (CR) systems to adapt their operation and improve their performance. Such statistics can be directly estimated from periodic observations of the instantaneous idle/busy state of the primary channel (i.e., periodic spectrum sensing). However, the periodicity of such observations (i.e., the sensing period) imposes a fundamental limit on the time resolution in which idle/busy periods can be observed, and consequently on the accuracy of any subsequent estimated statistics. In this context, this paper provides a comprehensive analysis on the estimation of the primary activity statistics based on periodic channel state observations performed with a finite sensing period. In particular, this paper provides a comprehensive set of closed-form expressions for the estimated statistics as a function of the true primary activity statistics and the employed sensing period. These expressions can find a wide range of applications in the analysis, design, and simulation of CR systems. Moreover, several methods to minimize the estimation errors and improve the accuracy are proposed and validated with both simulations and hardware experiments.

Impulse response based channel model 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 reflected path in case of short range (1–100 cm) wireless communication. In indoor application, it is necessary to consider multipath channel. In analysis of reflected path, rough surface of reflector is considered with Rayleigh roughness factor. The validity of the model is investigated with experimental THz band measurements (up to 2THz).

Dynamic Spectrum Access (DSA) / Cognitive Radio (CR) systems utilize spectrum sensing to monitor spectrum status and decide transmission time in an opportunistic manner. This results in an increase in wireless spectrum efficiency. Spectrum sensing can also be used to monitor the statistics of primary users to gain information on occupation patterns and estimate the statistics of the primary traffic activity, a useful knowledge that can be exploited in many ways. In this research, three novel algorithms are proposed to enhance the estimation of primary user activity statistics under imperfect spectrum sensing given the knowledge of minimum transmission time. Simulation results show that the proposed methods enable accurate estimation for the primary user statistics. Moreover, the proposed methods are compared to previously proposed methods and it is shown they provide a significantly better estimation accuracy.

An accurate estimation of the primary statistics is essential for Cognitive Radio (CR) systems. This knowledge can be exploited to enhance CR performance and reduce the interference with the primary users. In this work, we propose a method based on the Method of Moments (MoM) to improve the distribution estimation. A Modified Method of Moments (MMoM) with a correction factor is proposed to improve the estimation of moments and thus the resulting primary distribution. The simulation and experimental results show that the MMoM approach is notably more accurate. Finally, we study the importance of having a sufficiently large sample space and the effect of sample size on the moments and the primary distribution estimation.

We introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less.

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 investigates a signal area (SA) estimation method for spectrum measurements. The spectrum measurements is for estimating statistical information of spectrum usage and required latency is not very critical compared to spectrum sensing. SA denotes the area (in time/frequency domain) occupied by the primary user’s signal. The traditional approach, which utilizes Fourier transform (FT) and energy detector (ED) for SA estimation, can achieve low complexity, but its estimation performance is not very high. For this issue, we propose a post-processing, simple SA (S-SA) estimation. S-SA estimation exploits the correlation of the spectrum states among the neighboring tiles, i.e., time/frequency grids, and the fact that SA typically has a rectangular shape to estimate SA with high accuracy and relatively low complexity compared to a conventional method, contour tracing based SA (CT-SA) estimation. Numerical results will show that the S-SA estimation method can achieve better detection performance than CT-SA. Furthermore, it can reduce the computation time compared to the CT-SA estimation.

Cognitive Radio (CR) systems utilize spectrum sensing to decide transmission time in an opportunistic manner. Spectrum sensing can also be used not only to determine the instantaneous on/off state of the channel but also to monitor the statistics of primary user to gain information on occupancy pattern. This knowledge can be exploited in many ways to improve CR systems. In this paper, we propose an analytical model to link the sensing period with the observed spectrum occupancy. Moreover, the effect of spectrum sensing periods on the estimated primary activity pattern is analysed. Simulation results show that the proposed model captures with reasonable accuracy the spectrum occupancy observed at the CR.

Dynamic spectrum access/cognitive radio (DSA/CR) systems can benefit from an accurate knowledge of the primary channel ON/OFF activity statistics. This information can be readily obtained from spectrum sensing observations. However, given the practical limitations of spectrum sensing, an accurate and realistic evaluation of estimation methods is required before they can be implemented in real systems. This paper presents the design and implementation of a low-cost Prototype for the Estimation of Channel Activity Statistics (PECAS) in DSA/CR systems. An in-depth description of the hardware design (based on common and inexpensive components) and software implementation (based on free open source code) is provided along with an illustrative applicability example. The developed platform provides researchers and engineers with a low-cost fully functional tool for proof-of- concept, validation and optimisation of algorithms and designs.

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.

Two-layer smart spectrum access (SSA) consists of spectrum sharing based on dynamic spectrum access (DSA: first layer) and spectrum awareness system (SAS: second layer). A main role of SAS is providing useful statistical information in terms of spectrum usage by long term, wide-band and wide-area measurements. In this paper, we focus on signal area (SA) estimation which is a core signal processing in SAS to understand the spectrum usage. Specifically, SA estimation is used as post processing for energy detector outputs. It has been shown that Simple-SA (S-SA) estimation can enhance the spectrum usage measurement performance, but it inherently increases false alarms. For this issue, efficient false alarm cancellation technique, L-shaped false alarm cancellation (L-FC), is proposed in this paper. Numerical evaluations show that the proposed method can achieve proper detection performance while the computational cost is small compared to other methods.

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, noninterfering manner with the primary network. As a result, CR performance depends on the primary channel occupancy pattern. The occupancy pattern of primary network is affected by multiple factors including time, location and frequency band. This work focuses on the time domain of spectrum sharing. The objective of this work is to study how the primary user activity pattern in the time domain (i.e., statistical distribution of the durations of idle/busy periods) affects the ability of the CR system to obtain accurate statistical information based on spectrum sensing observations. In this research, we model the primary activity pattern as a Continuous-Time Semi-Markov Chain (CTSMC). Different distributions to imitate occupancy patterns of primary network are tested by means of simulation, first when having a perfect spectrum sensing, then in the presence of imperfect spectrum sensing. It is shown that every occupancy pattern (i.e., distribution) actually leads to different levels of accuracy in the estimated statistics. A new algorithm to palliate the degrading effects of spectrum sensing errors is proposed and evaluated. The new and considered algorithms can improve the prediction of primary network statistics, however with different levels of effectiveness depending on the primary activity pattern.

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.