In wireless security, the phrase ‘seeing is believing’ takes on a profound meaning as we explore the practical application of machine learning in enhancing the security of wireless networks. This paper focuses on a specific type of denial-of-service attacks, called as the reactive injection attack, for which a modified signalling strategy in non-coherent communications is proposed. This strategy harnesses the potential of active intelligent reflecting surface-based transmitter (AIRS-T), secret-key-based strategy, and deep learning-based techniques, i.e. convolutional neural networks (CNN). Based on the joint use of these schemes, the proposed strategy shows a significant covertness gain in terms of Kullback-Leibler divergence (KLD), followed by a rich pattern distribution of scatterplots for the decoded signals. The worst case (1-element AIRS-T) and the best case (N-element AIRS-T) are also evaluated, and it is found to be consistent with the achieved covertness results. The covertness gain translates into enhanced performance when CNN is used for classification, leading to faster model convergence and higher accuracy when compared to transfer learning using models like Resnet50, VGG16, and InceptionV3.

This paper investigates the impact of Reconfigurable Intelligent Surface (RIS) orientation on the throughput performance of Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems. Specifically, we study how physical rotation of the RIS, through controlled azimuth and elevation adjustments, influences the effective channel and data rate. A UAV-mounted RIS enables directional alignment to serve ground users in scenarios where the direct Base Station (BS)-to-user path is blocked. Using the SimRIS channel simulator, we analyze the system under various rotation angles and present performance heatmaps that highlight optimal RIS orientations. The study shows that RIS alignment has a substantial effect on achievable rates, thereby motivating orientation-aware optimization in practical deployments.

This paper investigates unmanned aerial vehicles (UAVs)-based free space optical (FSO) communications under sandstorm conditions, using different wavelengths of 1550 nm and 850 nm. We propose a single UAV relay model and formulate simplified closed form expression for end-to-end (E2E) outage probability. We also obtain asymptotic outage probability for the overall system. Then, we evaluate the outage performance across various parameters, including, transmitted power, visibility, receiver field of view (FOV), and optical beamwidth. Furthermore, a dynamic single UAV relay is investigated over various distances to minimize the E2E outage probability. Our findings show that the minimum E2E outage probability occurs when a single UAV relay is positioned equidistantly between the source and destination UAVs, and the E2E outage probability increases as the single UAV relay moves farther away. Besides, in sandstorm conditions, we observe that a smaller wavelength, specifically 850 nm, provides a minimum E2E outage probability, which may be beneficial for use in UAVs-based FSO links.

Indoor positioning systems (IPS) are gaining higher attention recently due to the increased demand for indoor location aware services. Visible light communication (VLC) is a promising technology to use for IPS. In particular, received signal strength (RSS) based visible light positioning (VLP) systems are gaining high attention due to their low complexity and cost, in addition to higher positioning accuracy compared to their radio frequency (RF) counterparts. One of the main challenges in RSS based VLP systems is encountered when the receiver (the target) is tilted and not placed in parallel with the transmitters (the anchors). RSS based trilateration techniques require a computationally expensive and time-consuming process to solve the nonlinear problem of tilted receivers. Fingerprint based systems generally provide high positioning accuracy with short positioning time, and maybe used to circumvent the need to deal with the high complexity associated with tilted receivers. However, the design of a fingerprinting VLP system for tilted receiver has not been explored yet as far as receivers with a single photodetector (PD) are concerned. In this work, a fingerprint based VLP system for tilted receivers using artificial neural networks (ANN) is proposed, where different types of input features for training the positioning algorithm are studied. We show that using the components of the normal vector to the PD's surface in addition to RSS values provides an excellent positioning accuracy with an average positioning error of 25.41 cm and a remarkably low average positioning time less than$\mathbf{5} \boldsymbol{\mu} \boldsymbol{s}$. In addition, important research directions for future work are discussed.

A novel framework is introduced in this paper, wherein Software-Defined Radio (SDR) technology is utilized to deploy a Frequency Modulation Continuous Wave (FMCW) radar system in conjunction with Long Range (LoRa) modulation. The system acquires reflected signals and detects micro-Doppler signatures using SDR-based FMCW radar, enabling the identification of individual profiles even when obstructed by visual barriers. Reliable communication among various stakeholders is ensured by incorporating the LoRa modulation scheme for data transmission within the framework. Improved collaboration between rescue teams and surveillance units is fa-cilitated by this integration, thereby enhancing overall response efficiency. The efficacy of the approach is validated through experimental implementation utilizing USRP N210 with GNU Radio software and the LoRa AB01 module.

In digital communication, nonlinearity is a frequent source of signal and channel distortion. Such distortions often need equalization techniques or devices to correct them. An equalization technique for recovering nonlinear multichannel signals in convolutive mixture is presented in this article. The proposed work uses the mutually referenced equalization technique to estimate the equalizer and the transmitted signal from the nonlinear convolutive mixture while in the present of quadratic nonlinearities. The proposed semi-blind model builds a cost function that offers an equalization solution by combining data, pilots, and the mutually referenced equalizer approach. The proposed method offers a number of benefits, including ease of implementation, resistance against channel order misspecification, and the ability to provide several equalization delays with a single solution. The simulation findings demonstrate that the proposed approach has fascinating performance characteristics and is resilient to low SNR.

We address the portfolio optimization problem using the global minimum variance portfolio (GMVP). The GMVP gives the weights as a function of the inverse of the covariance matrix (CM) of the stock net returns in a closed-form. The matrix inversion operation usually intensifies the impact of noise when the matrix is ill-conditioned, which often happens when the sample covariance matrix (SCM) is used. A regularized sample covariance matrix (RSCM) is usually used to alleviate the problem. In this work, we address the regularization issue from a different perspective. We manipulate the expression of the GMVP weights to convert it to an inner product of two vectors; then, we focus on obtaining accurate estimations of these vectors. We show that this approach results in a formula similar to those of the RSCM based methods, yet with a different interpretation of the regularization parameter’s role. In the proposed approach, the regularization parameter is adjusted adaptively based on the current stock returns, which results in improved performance and enhanced robustness to noise. Our results demonstrate that, with proper regularization parameter tuning, the proposed adaptively regularized GMVP outperforms state-of-the-art RSCM methods in different test scenarios.

Antenna arrays have many applications in direction-of-arrival (DOA) estimation. Sparse arrays such as nested arrays, super nested arrays, and coprime arrays have large degrees of freedom (DOFs). They can estimate large number of sources greater than the number of elements. They also have closed form expressions for antenna locations and the achievable DOFs. The multi-level prime array (MLPA) uses multiple uniform linear subarrays where the number of elements in the subarrays are pairwise coprime integers. The array achieves large DOFs and it has closed form expressions for the antenna locations and the required aperture size. For a given number of subarrays and total number of elements, there are different design alternatives. This paper finds the optimum number of elements within each subarray and the optimized ordered inter-element spacing. In almost all cases, we have found that a unique configuration jointly realizes the maximum number of unique lags and the maximum number of consecutive lags.

The objective of this work is to design a wireless communicating heliostat system using microcontrollers and Zigbee communications protocol. The Arduino microcontrollers which are connected to the Zigbee modules (XBee) communicate wirelessly which eliminates the use of cables to transfer data between the heliostats and the central controller. This wireless feature is highly desired in full scale heliostat systems. The constructed prototype features four mirrors. Given the coordinates of the heliostat and the source of heat, a tracking algorithm is implemented to direct the heliostats towards the source of heat for optimum reflection of light beam into the tower. In addition, temperature sensors and photoresistors are installed in the tower to monitor the temperature and light intensity, respectively. Finally, a graphical user interface was developed to allow the user to control the operation and monitor the sensors reading.

In this paper, an unmanned aerial vehicle (UAV) enabled secure downlink communication is considered, where a single-antenna UAV serves multiple ground users facilitated by orthogonal frequency-division multiple access (OFDMA), in the presence of an eavesdropper (EV) with imperfect channel state information. To enhance the secrecy rate (SR), we employ a power splitting approach, where part of the transmit power is used for communication while the rest is used for jamming. We maximize the average secrecy rate (ASR) by jointly optimizing the bandwidth, trajectory, power allocation, and power splitting ratio. To tackle this non-convex and computationally intractable optimization problem, we propose a novel algorithm by employing successive convex approximation, block coordinate descend and$S$-procedure. Numerical results show that our proposed joint optimization algorithm outperforms the benchmark schemes.

Wide area networks for surveying applications, such as seismic acquisition, have been witnessing a significant increase in node density and area, where large amounts of data have to be transferred in real-time. While cables can meet these requirements, they account for a majority of the equipment weight, maintenance, and labor costs. A novel wireless network architecture, compliant with the IEEE 802.11ad standard, is proposed for establishing scalable, energy-efficient, and gigabit-rate backhaul across very large areas. Statistical path-loss and line-of-sight models are derived using real-world topographic data in well-known seismic regions. Additionally, a cross-layer analytical model is derived for 802.11 systems that can characterize the overall latency and power consumption under the impact of co-channel interference. On the basis of these models, a Frame Aggregation Power-Saving Backhaul (FA-PSB) scheme is proposed for near-optimal power conservation under a latency constraint, through a duty-cycled approach. A performance evaluation with respect to the survey size and data generation rate reveals that the proposed architecture and the FA-PSB scheme can support real-time acquisition in large-scale high-density scenarios while operating with minimal power consumption, thereby enhancing the lifetime of wireless seismic surveys. The FA-PSB scheme can be applied to cellular backhaul and sensor networks as well.

Modern seismic surveys can obtain subsurface images of superior quality and depth, albeit requiring wireless acquisition systems to keep up with higher data rate requirements. Data communication protocols have been studied primarily for links between the geophones and the gateway nodes, leaving a dearth of analysis for the communication links between the gateway nodes and the sink node. In this paper, a novel wireless geophone network architecture based on the IEEE 802.11ad standard is proposed with the objective of providing gigabit rates in order to support real-time seismic acquisition. A performance analysis reveals that the latency and power consumption are dependent on the ratio of the data generation rate to the data transfer rate. An additional Power-Saving Geophone Relay (PSGR) scheme is described, which exploits idle periods of operation to conserve power at the cost of an increased latency. The trade-off between the latency and power consumption under the PSGR scheme is evaluated for a variety of scenarios in seismic acquisition.

Modern geophysics methods for oil and gas exploration are able to generate subsurface images of superior quality and depth, albeit making real-time data acquisition a more challenging task. While current literature addresses data transfer issues primarily between the geophones and the gateway nodes, a communication scheme for the transfer of data from the gateway nodes to the sink largely remains unsolved. A novel wireless geophone network architecture for seismic data acquisition is described, with the objective of eliminating cable-based systems and providing Gigabit rates in order to support real-time acquisition. A performance analysis is conducted for various mesh networks employing IEEE 802.11ac, IEEE 802.11ad, and free space optical communication, from the geophysics perspective. Our findings suggest that the bottleneck links can be shifted from the top to the bottom of existing architectures, and a scalable approach requiring minimal number of gateway devices can be designed for high-density seismic surveys.

Direction of arrival (DOA) estimation has many applications in localization and interference mitigation. The estimation performance can be improved by increasing the number of antennas at the receiving array or by using different antenna array configurations. Sparse arrays such as coprime arrays were proposed assuming isotropic antennas, which have unit gain and fixed phase in all directions. In practice, antennas have gain and phase which are functions of the DOA. In this paper, we experimentally examine the impact of antenna directionality on the performance of DOA estimation using directional fabricated sparse arrays of monopole and patch elements based on a software defined radio (SDR) platform. Under equal aperture size and using fewer antenna elements, sparse array DOA estimation performance matches the performance of uniform arrays.

Seismic surveys are conducted by oil exploration companies to keep up with the global demand for oil and gas. Although seismic cables offer fast and reliable data transfer, they account for a majority of the equipment weight, logistics, and labor costs. A novel wireless geophone network architecture, compliant with the IEEE 802.11af standard, is described. Operation in television white spaces can achieve long transmission ranges, allowing for scalable coverage of large seismic survey areas. Two methods for data collection are proposed: a Geophone-Polling (GP) scheme and an Adaptive Geophone Time Division Multiple Access Scheduling (AGTS) scheme. The two schemes are analyzed and evaluated in terms of the total time taken for data acquisition and the average power consumption, in comparison to the default IEEE 802.11 channel access schemes. The problem of hexagonal clustering for orthogonal deployment of geophones is also tackled, and the impact of co-channel interference is considered. The proposed schemes not only reduce data acquisition time, but also the average power consumption of the geophones, thereby improving the lifetime of wireless seismic surveys.

Traditional seismic data acquisition systems used for surveying during the exploration of oil and gas rely on cables between geophones and data collection center. Despite the fact that cable-based systems provide reliable seismic data transfer, their deployment and maintenance costs increase substantially as the survey area increases in scale. This paper investigates the aggregate data throughput and transmission time from wireless geophones to gateway node in a wireless geophone network architecture based on IEEE802.11af standard. The analytical expressions of throughput and transmission time are derived using Markov chain models. Two Markov models are considered for this purpose: one for modeling carrier sense multiple access method with collision avoidance (CSMA/CA) behavior and the other for representing a buffer in a wireless geophone. Furthermore, the physical layer path-loss models are taken into account.

Sensor array configurations such as coprime and nested arrays have attracted many researchers because they increase the degree of freedom (DOF). For example, in the direction of arrival estimation, the number of sources that can be estimated is greater than the total number of sensors. This study proposes a multi‐level prime array (MLPA) configuration for sparse sampling that can further increase the DOF. The proposed array uses multiple uniform linear subarrays where the number of sensors in the subarrays is pairwise coprime integers. The inter‐element spacing between the sensors is formulated as a scaled multiple of half‐wavelength where the subarrays share only their first element. For a fixed number of sensors, multiple MLPA configurations can be constructed by controlling the number of sensors in the subarrays or by adjusting the inter‐element spacing. For a given number of sensors, the proposed array has a smaller aperture and achieves more number of unique and consecutive lags compared with coprime arrays. The proposed configuration has limited holes in the difference coarray. The analytical expressions of both the difference coarray and the aperture size are derived. Simulation results confirm the advantage of the proposed configurations compared with the two level coprime arrays.

Multicarrier transmission in the form of orthogonal frequency division multiplexing (OFDM) is an efficient method to handle inter‐symbol interference resulting from the frequency selectivity of the underwater acoustic (UWA) channels. OFDM can be further combined with cooperative transmission to benefit from the spatial diversity advantages and improve the performance over conventional point‐to‐point UWA communication systems. In this study, the authors consider a cooperative OFDM UWA communication system and investigate the outage performance for both amplify‐and‐forward relaying and decode‐and‐forward (DF) relaying. They derive expressions for bounds on outage probability and outage capacity. Monte Carlo simulations corroborate and quantify the enhanced performance of cooperative OFDM UWA communication compared with direct transmission systems. Through numerical simulations, they investigate the impact of geometry on outage probability and signal‐to‐noise ratio requirements in cooperative OFDM UWA communication with DF relaying. Furthermore, the authors analyse the effects of relay location and carrier frequency on the outage probability for both relaying schemes.

Due to its diverse applications, through-the-wall radar imaging (TWRI) is rapidly growing with much attention on the application of compressive sensing (CS) and multipath ghost suppression. If not properly handled, ghosts can adversely affect the image interpretation. This paper proposes efficient ghost suppression method which exploits aspect dependence feature of the ghost under CS framework while takes advantage of single-view bistatic radar configuration. The efficacy of the proposed method was scrutinized based on the generalized signal model which mimics the real TWRI scenario that accommodates almost all possible scatterers. Duo sensing matrices are developed such that the ghost's aspect dependence on the resulting images is noticeable. Both qualitative and quantitative results demonstrate the strength of the proposed methods.

Ultra-wideband (UWB) radios are expected to be the next generation of the transmission systems that can support high data rate and power-constrained applications. Since the UWB signals have wide bandwidth, two problems arise, namely the high speed analog-to-digital-converter (ADC) required at the receiver side and the coexistence with other narrowband interference (NBI) systems that share the same part of the spectrum. The two problems can be addressed using compressive sensing (CS) based on the fact that narrowband signals have sparse representation in the discrete cosine transform (DCT) domain. It becomes more complicated when multiuser case is considered. For training based NBI mitigation with CS, three groups of pilot symbols are used to estimate the NBI signal subspace, the UWB signal subspace and to provide information about the channel. This work aims to evaluate the system performance in the presence of multiuser interference in addition to the NBI. The paper extends the derivations to include the effect of the multiuser interference together with the NBI and the additive white Gaussian noise. Direct sequence combined with time hopping coding is applied for the multiple access technique. Simulation illustrates that as more users being active the performance of the system degrades. Additionally, when the number of the secondary users increases, the multiuser becomes the most dominant factor that affects the system performance irrespective of the NBI.

In through-the-wall radar imaging, ghost targets can arise because of multipath reflections. Having a model that compensates for the multipath returns solves this problem; however this requires the knowledge of the room geometry. If wrong information about the room geometry is used in the model, the obtained image will be denser than the image of the true scene. This paper proposes a multipath exploitation method that utilizes this fact. A greedy search is performed to find the wall positions that produce the sparsest image of the desired scene. Hence, detecting the true wall positions and obtaining a ghost-free image. Simulation results demonstrate the effectiveness of the proposed method.

Compressive sensing (CS) is proposed by many researchers as a solution to the formidable sampling requirements of promising ultra-wideband (UWB) technology. Previous research was evaluated by simulating the IEEE UWB channel model. This study examines the behaviour of compressive sensing for practical UWB signals and proposes approaches to enhance signal reconstruction performance. Four practical dictionaries are proposed to increase the sparsity of the realistic UWB signals. Those dictionaries account for the practical effects of channel-like pulse dispersion and the unavoidable effects of antenna directivity. Both measured data and a more practical directional model are used for evaluation. It is shown that CS based on the new dictionaries is able to reconstruct the practical UWB signals more efficiently with reasonable complexity. Performance improvement of the proposed practical dictionaries in channel estimation for two different receivers is quantified.

Ultra-Wideband (UWB) signaling is finding many applications in through-wall imaging, target positioning, and indoor communications. In this context, signal propagation through walls has to be modeled accurately since the different building materials have varying attenuation and dispersion properties that impact UWB signal propagation. One of the interesting properties of a UWB signal is its ability to penetrate walls and obstacles which comes from the lower frequency components of the signal. However, as the signal propagates through these obstacles, it gets attenuated, slows down, and gets dispersed. In this paper, we present experimental results on the frequency-domain measurements of UWB signal transmission and reflection over a frequency range of 1-18 GHz. The results presented give a thorough characterization of UWB wave propagation through walls made of common building materials (including glass, wood and gypsum). This is done by measuring the insertion transfer function given as the ratio of two signals measured in presence and absence of walls. The materials' dielectric constant and propagation loss are extracted from the measured insertion transfer function. Double layer walls and three layer walls are also investigated. Results from transmission and reflection measurements give good agreement with each other and with other related work in the literature.

Ultra wideband (UWB) propagation is antenna-dependent, and multipath components experiencing specular diffraction or arriving at different angles have different shapes. To utilise the diversity of the multipath components, this study proposes a statistical model which incorporates directional parameters, such as elevation and azimuth angles by extending the IEEE802.15.3a model. Based on this model, a UWB directional simulator is developed. A novel multi-template subtractive deconvolution is applied to an extensive measurement campaign and simulated profiles. The use of multi-templates makes possible the directional estimation of the multipath components with a single receive antenna, and the use of subtractive deconvolution allows for the resolution of overlapping components and higher energy capture. Directional characterisation of UWB channels facilitates performance evaluation studies and achieves more accurate positioning, imaging and Rake receiver design.

The propagation of ultra wideband (UWB) signals in indoor environments is an important issue with significant impacts on the future direction and scope of the UWB technology and its applications. The objective of this work is to obtain a better assessment of the potentials of UWB indoor communications by characterizing the UWB indoor communication channels. Channel characterization refers to extracting the channel parameters from measured data. An indoor UWB measurement campaign is undertaken. Time-domain indoor propagation measurements using pulses with less than 100 ps width are carried out. Typical indoor scenarios, including line-of-sight (LOS), non-line-of-sight (NLOS), room-to-room, within-the-room, and hallways, are considered. Results for indoor propagation measurements are presented for local power delay profiles (local PDP) and small-scale averaged power delay profiles (SSA-PDP). Site-specific trends and general observations are discussed. The results for path-loss exponent and time dispersion parameters are presented.