In disaster scenarios, ensuring both reliable communication and situational awareness becomes a critical challenge due to the partial or complete collapse of terrestrial networks. This paper proposes an integrated sensing and communication (ISAC) over non-terrestrial networks (NTN) architecture— referred to as ISAC-over-NTN—that integrates multiple uncrewed aerial vehicles (UAVs) and a high-altitude platform station (HAPS) to maintain resilient and reliable network operations in post-disaster conditions. We aim to achieve two main objectives: i) provide a reliable communication infrastructure, thereby ensuring the continuity of search-and-rescue activities and connecting people to their loved ones, and ii) detect users, such as those trapped under rubble or those who are mobile, using a Doppler-based mobility detection model. We employ an innovative beamforming method that simultaneously transmits data and detects Doppler-based mobility by integrating multiuser multiple-input multiple-output (MU-MIMO) communication and monostatic sensing within the same transmission chain. The results show that the proposed framework maintains reliable connectivity and achieves high detection accuracy of users in critical locations, reaching 90% motion detection sensitivity and 88% detection accuracy.

Short channel coherence time and oscillator phase noise are two major impairments in millimeter-wave (mmWave) communication systems. Several studies indicate that a substantial fraction of the available bandwidth may be required as overhead to compensate for these impairments, potentially exceeding one third of the total capacity. In this paper, we study Direction-Shift Keying (DSK), a variant of Spatial Modulation (SM), which encodes information in the Direction-of-Arrival (DoA) rather than in the signal amplitude or phase. DSK is implemented over a Distributed Antenna System (DAS), enabling angular resolvability of the transmitted signals. We first derive the structure of the optimal detector for a mobile device equipped withMantennas. We then introduce and characterize the Direction Coherence Time (DCT), defined as the temporal interval over which the DoA remains approximately invariant. Our analysis shows that DCT scales withd/v(transmitter-receiver distance over velocity), whereas the conventional Channel Coherence Time (CCT) scales with λ/v, revealing a coherence-time gain proportional tod/λ, which can exceed several orders of magnitude in mmWave systems. Furthermore, we show that the proposed detector inherently cancels receiver phase noise, eliminating the need for explicit phase-noise tracking. Simulation results validate the analytical findings and demonstrate the robustness of DSK in mobile mmWave environments in the presence of phase noise.

Network slicing is a key enabler for providing differentiated service support to heterogeneous use cases and applications in 5G and beyond networks through creating multiple logical slices. Resource management for satisfying diverse requirements of slices is a highly challenging task under time-varying traffic and wireless channel conditions. This paper presents a deep reinforcement learning (DRL) based xApp for dynamically managing the service level agreements (SLAs) of network slices, eliminating the need for human-in-the-loop decision-making. The proposed xApp is implemented within an open source mobile network emulator to create an O-RAN compliant end-to-end 5G network capable of dynamic resource management capabilities. The intelligent resource management xApp operates on the RAN Intelligent Controller (RIC), enabling monitoring and dynamic resource control of the gNodeB through the E2 interface. The proof-of-concept experiment results demonstrate that the trained DRL model deployed on the RIC platform is successfully utilized to manage the key performance indicators of the network slices.

This paper evaluates the performance of reconfigurable intelligent surface (RIS) optimization algorithms, which utilize channel estimation methods, in ray tracing (RT) simulations within urban digital twin environments. Beyond Sionna's native capabilities, we implement and benchmark additional RIS optimization algorithms based on channel estimation, enabling an evaluation of RIS strategies under various deployment conditions. Coverage maps for RIS-assisted communication systems are generated through the integration of Sionna's RT simulations. Moreover, real-world experimentation underscores the necessity of validating algorithms in near-realistic simulation environments, as minor variations in measurement setups can significantly affect performance.

There is a growing interest in codebook-based beam-steering for millimeter-wave (mmWave) systems due to its potential for low complexity and rapid beam search. A key focus of recent research has been the design of codebooks that strike a trade-off between achievable gain and codebook size, which directly impacts beam search time. Statistical approaches have shown promise by leveraging the likelihood that certain beam directions (equivalently, sets of phase-shifter configurations) are more probable than others. Such approaches are shown to be valid for static, non-rotating transmission stations such as base stations. However, for the case of user terminals that are constantly changing orientation, the possible phase-shifter configurations become equally probable, rendering statistical methods less relevant. On the other hand, user terminals come with a large number of possible steering vector configurations, which can span up to six orders of magnitude. Therefore, efficient solutions to reduce the codebook size (set of possible steering vectors) without compromising array gain are needed. We address this challenge by proposing a novel and practical codebook refinement technique, aiming to reduce the code book size while maintaining array gain within$\gamma\ \mathbf{dB}$of the maximum achievable gain at any random orientation of the user terminal. We project that a steering vector at a given angle could effectively cover adjacent angles with a small gain loss compared to the maximum achievable gain. We demonstrate experimentally that it is possible to reduce the codebook size from 102416to just a few configurations (e.g., less than ten), covering all angles while maintaining the gain within$\gamma=3\ \mathbf{dB}$of the maximum achievable gain.

Terahertz (THz) frequencies play a crucial role in the advancement of next-generation wireless systems, primarily owing to their substantial available bandwidths. The inherent limitation of limited range, attributed to high attenuation in these frequencies, can be effectively addressed by implementing densely deployed heterogeneous networks, complemented by Unmanned Aerial Vehicles (UAVs) within a three-dimensional hyperspace. Yet, the success of THz communications relies on the precise alignment of beams. Inadequate handling of beam alignment can lead to diminished signal strength at the receiver, significantly affecting THz signals more than their conventional counter-parts. This research underscores the paramount importance of meticulous alignment in THz communication systems. The profound impact of proper alignment is substantiated through comprehensive measurements conducted using a state-of-the-art measurement setup, facilitating accurate data collection across the 240 GHz to 300 GHz spectrum. These measurements encompass varying angles and distances within an anechoic chamber to eliminate reflections. Through a meticulous analysis of the channel frequency and impulse responses derived from these extensive measurements, this study pioneers quantifiable results, providing an assessment of the effects of beam misalignment in THz frequencies.

The utilization of millimeter wave frequency bands is expected to become prevalent in the following communications systems. However, generating and transmitting communication signals over these frequencies are not as straightforward as in sub-6 GHz frequencies due to complex transceiver structures. As an alternative to conventional transmitter architectures, this paper investigates the implementation of time modulated arrays to effectively modulate and transmit high-quality communication signals at millimeter wave frequencies. By exploiting the array structures and analog beamformers, which are the fundamental components of millimeter wave transmitters, secure and low-cost transmission can be achieved. Though, harmonics of theoretically infinite bandwidth arise as fundamental problem in this approach. Thus, this paper presents a frequency analysis tool for the time-modulated arrays with hardware impairments and shows how controlling the sampling period can reduce the harmonics. Furthermore, the derived results are experimentally verified at 25 GHz with two important remarks. First, the phase error of received signals can be reduced by 32% using the proposed architecture. Second, the harmonics can be significantly suppressed by the correct choice of sampling period for the given hardware.

Reconfigurable holographic surfaces (RHSs) for multi-beam steering applications have been receiving significant attention due to their low fabrication cost and power consumption. RHSs have the advantage of exploiting more antenna elements by configuring only their radiation levels, as opposed to the typical phased antenna arrays which enable the control of each element's phase and amplitude. RHSs can be utilized to realize holographic beamforming with single-feed or multi-feeds, where digital beamforming can also be implemented using multi-feeds. In this paper, first, the holographic beamforming problem is formulated by means of the conventional beamforming expressions using the array factor and the holographic beamforming weights. Then, a zero-forcing beamforming method is developed for a single-feed RHS to generate multi-beam and multi-null patterns. Additionally, a bisectional null scanning approach for beam sweeping is presented to exploit the advantages of RHS for forming wide nulls in the beamspace. Simulation results imply a substantial advantage of such single-feed RHS for high-accuracy beam sweeping.

In this work, extensive propagation characteristics of short-range 240 to 300 GHz terahertz (THz) channels are mapped based on a measurement campaign conducted utilizing a novel, task-specific measurement system. The measurement system allows collecting measurements from different distances and orientations in a very-fine grained resolution, which is a particular issue in achieving realistic THz channel estimation. After the accurate measurement results are obtained, they are investigated in terms of channel impulse and channel frequency response. Furthermore, the fading channel amplitude histograms are modeled with the Gamma mixture model (GMM). The expectation-maximization (EM) algorithm is utilized to determine the corresponding mixture parameters. Also, to demonstrate the flexibility of the GMM, the Dirichlet process Gamma mixture model (DPGMM) is utilized in cases where the EM algorithm fails to represent histograms. Moreover, the suitability of the GMM is evaluated utilizing Kolmogorov–Smirnov tests. Results verify that the GMMs can simulate the fading channel of micro-scale THz wireless communication in a realistic way, providing important implications regarding the achievable capacity in these channels. Finally, the average channel capacity of each link is evaluated using the probability density function of GMMs to gain deeper insight into the potential of micro-scale THz communications.

Reconfigurable intelligent surface (RIS)-empowered communications represent exciting prospects as one of the promising technologies capable of meeting the requirements of the sixth generation networks such as low-latency, reliability, and dense connectivity. However, validation of test cases and real-world experiments of RISs are imperative to their practical viability. To this end, this paper presents a physical demonstration of an RIS-assisted communication system in an indoor environment in order to enhance the coverage by increasing the received signal power. We first analyze the performance of the RIS-assisted system for a set of different locations of the receiver and observe around 10 dB improvement in the received signal power by careful RIS phase adjustments. Then, we employ an efficient codebook design for RIS configurations to adjust the RIS states on the move without feedback channels. We also investigate the impact of an efficient grouping of RIS elements, whose objective is to reduce the training time needed to find the optimal RIS configuration. In our extensive experimental measurements, we demonstrate that with the proposed grouping scheme, training time is reduced from one-half to one-eighth by sacrificing only a few dBs in received signal power.

The terahertz (THz) band radio access with larger available bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modeling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a real image denoising network (RIDNet) and orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including far-field and near-field constituents. The simulation experiments are performed using the ray-tracing tool. The results demonstrate that the proposed RIDNet-based method consistently provides lower channel estimation errors than the conventional OMP algorithm for all signal-to-noise ratio (SNR) regions. The performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.

Reconfigurable Intelligent Surfaces (RISs) are becoming one of the fundamental building blocks of next-generation wireless communication systems. To that end, RIS phase configuration optimization is an important issue, where finding the most suitable configuration becomes a challenging and resource-consuming task, especially as the number of RIS elements increases. Since exhaustive search is not practical, iterative algorithms are utilized to determine the RIS configuration by sequentially considering all RIS elements, where the best-performing phase shift configuration is obtained for each element. However, each configuration attempt requires receiver performance feedback, leading to higher delay and signaling overhead. Thus, in this paper, a convolutional neural network (CNN) based solution is formulated to rapidly find the phase configurations of the RIS elements. The simulation results for a RIS with 40×40 elements imply that the proposed algorithm reduces the number of steps dramatically e.g., from 3200 to 160 for the particular setup. Furthermore, such improvement in complexity is achieved with a slight degradation in performance.

This paper presents field measurement-based channel characterization for air–to–ground (A2G) and air–to– air (A2A) wireless communication systems using two drones equipped with lightweight software-defined radios. A correlation-based channel sounder is employed such that the transmitting drone broadcasts the sounding waveform with a pseudo-noise sequence and the receiving drone captures the sounding waveform together with the location information for the post-processing analysis. The path loss results demonstrate that the measurement and flat-earth two-ray results have similar trends for A2G while the measurement and free space path loss are similar to each other for A2A. The time delays between the direct path and multipath components are widely spread for A2A while the multipath components are mostly concentrated around the direct path for A2G generating a more challenging communication environment. We observe that the reflections from several buildings having metal roofs and claddings on the measurement site cause sudden peaks in the root-mean-square delay spread. The results indicate that the A2A channel has better characteristics than the A2G under similar mobility conditions.

The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.

There have been recently many studies demonstrating that the performance of wireless communication systems can be significantly improved by a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low power requirement and low complexity. This paper presents a measurement-based characterization of RISs for providing physical layer security, where the transmitter (Alice), the intended user (Bob), and the eavesdropper (Eve) are deployed in an indoor environment. Each user is equipped with a software-defined radio connected to a horn antenna. The phase shifts of reflecting elements are software controlled to collaboratively determine the amount of received signal power at the locations of Bob and Eve in such a way that the secrecy capacity is aimed to be maximized. An iterative method is utilized to configure a Greenerwave RIS prototype consisting of 76 passive reflecting elements. Computer simulation and measurement results demonstrate that an RIS can be an effective tool to significantly increase the secrecy capacity between Bob and Eve.

Automatic modulation classification (AMC) facilitates adaptive modulation schemes, leading to the minimization of pilot signals, thus affecting spectral efficiency and reducing the power consumption in wireless communications systems. Since high-frequency heterogeneous and adaptive networks are established as future projections, AMC will also play a critical role in the millimeter-wave (mmWave) band communications. This study proposes multi-channel convolutional long short-term deep neural network (MCLDNN) model for AMC in mmWave bands. The performance of the proposed method is evaluated under real conditions based on a measurement campaign. 802.11ad signals are utilized for the measurements in 57.24 GHz to 59.40 GHz band. The classification performance of the proposed model is compared with that of well-known deep-learning methods, i.e., convolutional neural network and convolutional long short-term deep neural network. The measurement results imply the robustness of the proposed method to real-life conditions and its superiority against contemporary networks, especially in low signal-to-noise ratio (SNR) region.

There is no doubt that we need to keep our eyes on the sky as satellite networks aim to address the demands of 6G and beyond communications systems. On the other hand, the existence of millions of space debris pieces, large or small, poses a threat to the new space communications systems which consist of large number of small satellites, especially in the low-orbit. In this study, a dual-functioning pulsed linear frequency modulated (LFM) waveform at Terahertz (THz) bands is proposed for both wireless communications and space debris sensing over low-orbit inter-satellite links (ISLs). Initially, the ambiguity function of the proposed waveform is derived. Then, velocity and range estimation performance for the radar function and bit error rate performance for the communications function are investigated. Simulation results indicate significant performance gains in the THz-bands compared to the legacy LFM systems.

Automatic modulation classification (AMC) with deep learning (DL) based methods has been studied in recent years and improvements have been shown in many studies; however, it has been difficult to design a classifier that can distinguish modulation orders such as 16-QAM and 64-QAM, with high accuracy. In this study, the distinction performance of 16-QAM and 64-QAM modulation orders increased by feeding the features obtained during the preprocessing stage to the multi-channel convolutional long short-term deep neural network (MCLDNN). Simulation results indicate performance improvements, particularly at the low SNR region. Furthermore, the proposed method can be extended for the separation of other orders of QAM and other digital modulations.

Unmanned aerial vehicles (UAVs) play a critical role in the fifth-generation and beyond communications. Since UAVs have an important potential in terms of getting deployed as base stations in next generation wireless networks, an extensive elaboration is required to model UAV wireless communication channels both in terms of large-scale and small-scale characteristics. To this end, the main objective of this study is to reveal alterations of air-to-ground wireless propagation channels for possible base station allocation at the height of 50 m, based on real measurements. The large-scale characteristics of the channel are analyzed by log-distance path loss model and the flat earth two-ray model. The small-scale characteristics of the propagation channel are examined considering the Rician K factor and root mean square delay spread. The measurement results also include channel impulse response and power delay profile results make it possible to observe the multi-path components on the wireless communication channel. The measurement campaign is conducted in a setting of hilly terrain layout at 2.57 GHz which is one of the 5G mid-band frequencies.

Frequency scarcity implies the utilization of higher frequencies for wireless communications; however, spreading loss becomes a dominating issue as the frequency increases to the level of and beyond millimeter waves. To this end, massive multiple-input multiple-output structures introduce mitigation alternatives. However, to make these solutions possible, the channel estimation approach strives to be modified: since Rayleigh distance is very short for conventional systems, the only far-field channel is examined in that context. On the other hand, the implementation of massive antenna arrays in high frequencies increases Rayleigh distance; thus, both near-field and far-field analyses become necessary. Instead of a dual estimation process, it would be effective and efficient to develop hybrid-field channel estimation techniques. Therefore, in this study, a new channel estimation method which is based on convolutional autoencoder (CAE) and orthogonal matching pursuit (OMP) approach, is proposed for hybrid channel estimation. The results indicate that the proposed OMP-CAE method has much better error performance when compared to the conventional OMP algorithm, especially at low signal-to-noise ratio regimes.

The identification of spectrum opportunities is a pivotal requirement for efficient spectrum utilization in cognitive radio systems. Spectrum prediction offers a convenient means for revealing such opportunities based on the previously obtained occupancies. As spectrum occupancy states are correlated over time, spectrum prediction is often cast as a predictable time-series process using classical or deep learning-based models. However, this variety of methods exploits time-domain correlation and overlooks the existing correlation over frequency. In this paper, differently from previous works, we investigate a more realistic scenario by exploiting correlation over time and frequency through a 2D-long short-term memory (LSTM) model. Extensive experimental results show a performance improvement over conventional spectrum prediction methods in terms of accuracy and computational complexity. These observations are validated over the real-world spectrum measurements, assuming a frequency range between 832-862 MHz where most of the telecom operators in Turkey have private uplink bands.

Powered by the advances in microelectronics technologies, unmanned aerial vehicles (UAVs) provide a vast variety of services ranging from surveillance to delivery in both military and civilian domains. It is clear that a successful operation in those services relies heavily on wireless communication technologies. Even though wireless communication techniques could be considered to reach a certain level of maturity, wireless communication links including UAVs should be regarded in a different way due to the peculiar characteristics of UAVs such as agility in 3D spatial domain and versatility in modes of operation. Such mobility characteristics in a vast variety of environmental diversity render links including UAVs different from those in traditional, terrestrial mobility scenarios. Furthermore, UAVs are critical instruments for network operators in order to provide basic voice and short messaging services for narrow band communication in and around disaster areas. It is obvious that such widespread use of UAVs under different scenarios and environments requires a better understanding the behavior of the communication links that include UAVs. Therefore, in this study, details of a measurement campaign designed to collect data for large-scale propagation characterization of air-to-ground links operated by UAVs at 446MHz under narrowband assumption are given. Data collection, post-processing, and measurement results are provided.

The applications of the unmanned aerial vehicles (UAVs) increase rapidly in everyday life, thus detecting the UAVs and/or its pilot is a crucial task. Many UAVs adopt frequency hopping spread spectrum (FHSS) technology to efficiently and securely communicate with their radio controllers (RCs) where the signal follows a hopping pattern to prevent harmful interference. In order to realistically distinguish the frequency hopping (FH) RC signals, one should consider the real-world radio propagation environment since many UAVs communicate with RCs from a far distance in which signal faces both slow and fast fading phenomenons. Therefore, in this study different from the literature, we consider a system that works under real- conditions by capturing over-the-air signals at hilly terrain suburban environments in the presence of foliages. We adopt the short-time Fourier transform (STFT) approach to capture the hopping sequence of each signal. Furthermore, time guards associated with each hopping sequence are calculated using the autocorrelation function (ACF) of the STFT which results in differentiating the each UAV RC signal accurately. In order to validate the performance of the proposed method, the results of normalized mean square error (MSE) respect to different signal-to-noise ratio (SNR), window size and Tx-Rx separation values are given.

Automatic modulation classification (AMC) has been studied for more than a quarter of a century; however, it has been difficult to design a classifier that operates successfully under changing multipath fading conditions and other impairments. Recently, deep learning (DL)-based methods are adopted by AMC systems and major improvements are reported. In this paper, a novel convolutional neural network (CNN) classifier model is proposed to classify modulation classes in terms of their families, i.e., types. The proposed classifier is robust against realistic wireless channel impairments and in relation to that, when the data sets that are utilized for testing and evaluating the proposed methods are considered, it is seen that RadioML2016.10a is the main dataset utilized for testing and evaluation of the proposed methods. However, the channel effects incorporated in this dataset and some others may lack the appropriate modeling of the real-world conditions since it only considers two distributions for channel models for a single tap configuration. Therefore, in this paper, a more comprehensive dataset, named as HisarMod2019.1, is also introduced, considering real-life applicability. HisarMod2019.1 includes 26 modulation classes passing through the channels with 5 different fading types and several number of taps for classification. It is shown that the proposed model performs better than the existing models in terms of both accuracy and training time under more realistic conditions. Even more, surpassed their performance when the RadioML2016.10a dataset is utilized.

Recent research considers the application of a lens antenna array in order to provide efficient beam selection in beamspace massive MIMO. Achieving the advantages of this beam selection paradigm requires efficient channel estimation in the beamspace. Along this line, beamspace sparsity is an efficient regularizer to this problem. In this paper, we propose using a dictionary trained over a set of example beam selection matrices, as a beam selection tool. In this context, a learned dictionary can more effectively guarantee the sparsity of the representation at the specified sparsity level, owing to the dictionary learning process. This means that it gives a better sparse representation, and, consequently, a better channel estimation quality. Simulations validate that using a trained dictionary improves the quality of channel estimation, as tested over two channel models with different operating scenarios.

Despite the well-known success of sub-Nyquist sampling in reducing the hardware and computational costs of spectrum sensing, it still has the shortcoming of requiring a pre-determined spectrum sparsity level. This paper proposes an algorithm for sub-Nyquist wide-band spectrum sensing addressing this shortcoming. The proposed algorithm divides the spectrum into narrow, contagious frequency subbands and learns a subband dictionary for each subband. A subband dictionary is well-suited for the representation of signals in its corresponding subband. A compressed version of the received signal is sparsely coded over each subband dictionary. We show that the convergence patterns over a specific dictionary can be used for identifying the occupancy of its underlying subband. Therefore, the convergence patterns obtained by the gradient operator are used as distinctive classifying features. Then, a machine learning-based classifier is trained over these features and used to make the decision about spectrum occupancy. As the interest is only to characterize sparse coding convergence patterns, we alleviate the need for a specific or an estimated sparsity level. Besides, using subband dictionaries at different frequencies omits the need for a frequency-splitting filterbank. The proposed algorithm achieves significant performance improvements in terms of the probability-of-detection and false-alarm-rate measures. This result is validated through simulations with various operating scenarios.

Underutilized spectrum constitutes a major concern in wireless communications especially in the presence of legacy systems and the prolific need for high-capacity applications as well as consumer expectations. From this perspective, Terahertz frequencies provide a new paradigm shift in wireless communications since they have been left unexplored until recently. Such a vast frequency spectrum region extending all the way up to visible light and beyond points out significant opportunities from dramatic data rates on the order of tens of Gbps to a variety of inherent security and privacy mechanisms, and techniques that are not available in the traditional systems. Thus, in this paper, we investigate statistical parameters for short-range line- of-sight channels of Terahertz communication. Short-range measurement campaign within the interval of [3cm, 20cm] are carried out between 275GHz to 325GHz range. Path loss model is examined for different frequencies and distances to provide the insight regarding the effect of the operating frequency. Measurement results are provided with relevant discussions and future directions.

Signal identification is an important notion that leads to significant performance improvements for adaptive wireless spectrum access techniques. Besides identifying the modulation types and other features, standard-based identification has also an important place in signal identification domain. In this paper, a generalized identification method which utilizes the outputs of spectral correlation function as the training inputs for the support vector machines to distinguish wireless signals is introduced. The proposed method eliminates the dependence on the distinct features to identify different signals. The method's performance is tested using the measurements taken in the laboratory environment and various wireless signals are successfully distinguished from each other. The comparative performance of the proposed method is also quantified by the classification confusion matrix.

Spectrum sensing is one of the most important features of Cognitive Radio (CR) systems. Matched-filter based spectrum sensing techniques provide optimum sensing performance given that a number of characteristics of the transmitted signal are known by the sensors. Assuming that the received signal pertains to one communication standard from a given set of wireless technologies, conventional spectrum sensors employ separate filters corresponding to each standard which gives rise to increased power consumption and ciruit size. A novel reconfigurable matched-filter based spectrum sensor to be deployed in CR systems is proposed in order to overcome the disadvantages of conventional design methods. This approach proposes a spectrum of design qualities which trade-off area for reconfiguration overhead. We will show that our approach is capable of designing reconfigurable filter for standards with widely varying filter characteristics.

Inefficient spectrum usage is a crucial issue in wireless communications and methods for dynamic spectrum access are proposed based on spectrum sensing methodology of the cognitive radio systems. Beside the detection and estimation methods, spectrum sensing procedures can also benefit from the modeling and prediction of the wireless spectrum usage. Markovian, regressive and other approaches are introduced for time or frequency domain channel modeling however, the research on the spectrum allocation methods indicates that location information has also an important influence on the spectrum occupancy characterization. In this paper, linear autoregressive prediction approach for binary time series is employed to investigate channel occupancy prediction performance based on spectrum measurements conducted in four different locations synchronously. Through the modeling procedure, dependency in frequency domain is also taken into consideration by modeling the adjacent frequency bands together. The model order is selected based on mean residual magnitudes and Akaike information criterion, mode order parameters are tabulated, and comparative prediction analysis considering the observation time is given for each location. The performance of the proposed linear modeling method is also compared with continuous-time Markov chain modeling in one of the locations.

Cognitive Radio (CR) systems with spectrum awareness feature is a promising approach to use spectrum effectively. However, accurate modeling of spectrum utilization is crucial for the development and performance evaluation of such systems. Hence, in this paper, a wideband multidimensional spectrum occupancy measurement campaign is conducted to study the evolution of RF spectrum over time, frequency, and space dimensions simultaneously. The measurements are performed over three consecutive days considering 700-3000 MHz frequency band at four different locations concurrently. The measurement results show low utilization of the 700-3000 MHz frequency band with different utilization percentage at each location. Furthermore, the measurements confirm that the spectrum occupancy highly depends on the time, frequency, and location. As a result, multidimensional spectrum measurement and analysis are vital for accurate spectrum utilization modeling and performance evaluation of CR systems.