In this paper, we investigate the information piggyback capability of the unmanned aerial vehicle (UAV) by encoding observed motion states. Specifically, at specific moments throughout the holistic navigation process, the distance between the UAV and the starting point, the position expressed by the three-dimensional (3D) Cartesian coordinates, the linear velocities, and the attitude angles are encoded into 16-bit digital symbols through a proposed codebook. In this way, covert data communications can be enabled, complementing conventional radio frequency (RF) communications in harsh electromagnetic environments. To achieve a well-designed flight controller that is necessary to enable fluent movement against external disturbances and accurate motion state encoding, we introduce the whole flight control structure and perform system dynamic analysis. The proposed motion control mechanism can mitigate the jitter and oscillation during the journey while ensuring the required motion status for information encoding purposes.

On behalf of the entire Organizing Committee, we welcome you to IEEE WCNC 2024 in Dubai, UAE! We are delighted to gather in one of the most vibrant cities and attractive destinations in the Middle East for the 25th edition of one of the most successful conferences of IEEE Communication Society (ComSoc).

This article examines two chirp spread spectrum techniques specifically devised for low-power wide-area networks (LPWANs) to optimize energy and spectral efficiency (SE). These methods referred to as layered CSS (LCSS) and layered dual-mode CSS (LDMCSS), involves utilizing multiple layers for multiplexing symbols with varying chirp rates. These waveform designs exemplify a high degree of SE compared to existing schemes. Additionally, LDMCSS necessitates a lesser number of layers than LCSS to attain comparable SE, thereby reducing computational complexity. These proposed techniques can employ coherent and non-coherent detection and can be adjusted to achieve various spectral efficiencies by altering the number of multiplexed layers. Unlike our proposed LCSS and LDMCSS, other CSS alternatives for LPWANs cannot provide the same level of flexibility and SE. The performance of these techniques is evaluated in terms of bit error rate under different channel conditions, as well as with phase and frequency offsets.

Vehicular Ad Hoc Networks (VANET) is an emerging technology that enables a comfortable, safe, and efficient travel experience by providing mechanisms to execute applications related to traffic congestions, road accidents, autonomous driving, and entertainment. The mobile vehicles in VANET are characterized by low computational and storage capabilities. In such scenarios, to meet applications’ performance requirements, requests from vehicles are offloaded to edge and cloud servers. The high energy consumption of these servers increases operating costs and threatens the environment. Energy-aware offloading strategies have been introduced to tackle this problem. Existing works on computation offloading focus on optimizing the energy consumption of either the IoT devices/mobile/vehicles and/or the edge servers. This paper proposes a novel offloading algorithm that optimizes the energy of edge–cloud integrated computing platforms based on Evolutionary Genetic Algorithm (EGA) while maintaining applications’ Service Level Agreement (SLA). The proposed algorithm employs an adaptive penalty function to incorporate the optimization constraints within EGA. Comparative analysis and numerical experiments are carried out between the proposed algorithm, random and genetic algorithm-based offloading, and no offloading baseline approaches. On average, the results show that the proposed algorithm saves 2.97 times and 1.37 times more energy than the random and no offloading algorithms respectively. Our algorithm has 0.3% of violations versus 52.8% and 62.8% by the random and no offloading approaches respectively. While the energy-non-SLA-aware genetic algorithm saves, on average, 1.22 times more energy than our approach, however, it violates SLAs by 159 times more than our proposed approach.

Every year, doctors diagnose skin cancer in around 3 million or more patients across the globe. Currently, it is one of the most widely recognized kinds of cancers for human health. Hence, we need an early diagnosis to prevail any critical condition of the infected patients. Apparently, it can treat with topical drugs, if it diagnoses in an early stage. Hence as an outcome, skin cancer is responsible for less than 1% of all cancer deaths. There are two types of tumors in the skin cancer diseases domain, such as benign and malignant. To develop a robust and early screening system to diagnose skin cancer, it requires an efficient algorithm for prediction, trained with a large dataset. The primary aim of this research is to develop an efficient skin cancer screening process using a robust deep neural network with a large dataset. In this paper, we intend to determine considerate and dangerous types of skin cancer tumors using dermoscopic images from a publicly available dataset. We proposed an efficient and fast scaled 2D-CNN based on EfficientNet-B7 deep neural architecture with image preprocessing. This paper also uses two different pre-trained deep neural architectures, such as VGG19, and ResNet-50 to compare the performance with the proposed architecture. The proposed architecture outperformed the other pre-trained CNN models whereas the proposed architecture achieved higher AUC and accuracy compared to other architectures.

In vehicular communications, reliable channel estimation is critical for the system performance due to the doubly-dispersive nature of vehicular channels. IEEE 802.11p standard allocates insufficient pilots for accurate channel tracking. Consequently, conventional IEEE 802.11p estimators suffer from a considerable performance degradation, especially in high mobility scenarios. Recently, deep learning (DL) techniques have been employed for IEEE 802.11p channel estimation. Neverthe-less, these methods suffer either from performance degradation in very high mobility scenarios or from large computational complexity. In this paper, these limitations are solved using a long short term memory (LSTM)-based estimation. The proposed estimator employs an LSTM unit to estimate the channel, followed by temporal averaging (TA) processing as a noise alleviation technique. Moreover, the noise mitigation ratio is determined analytically, thus validating the TA processing ability in improving the overall performance. Simulation results reveal the performance superiority of the proposed schemes compared to the recently proposed DL-based estimators, while recording a significant reduction in the computational complexity.

Recently, the deployment of Wireless Sensor Networks (WSNs) increases in various applications. These networks are dispersed in an inaccessible area and have to operate on small batteries. Energy is one of the most critical resources since sensor nodes sense, collect, process and send data to its base station. Many researches have demonstrated the importance of clustering to extend the lifetime of wireless sensor networks. Clustering algorithms consider several parameters like the density, the residual energy, and the distance separating the base station and the nodes to select the appropriate cluster heads. In this paper, K-means algorithm is first used to select a set of Cluster Heads (CHs) from all the nodes. Then we propose the Kruskal algorithm to define the optimal path between CHs and BS. Simulation results show that the proposed combination allows improved lifetime extension compared with existing algorithms.

Anomaly detection techniques are very crucial in multiple business applications, such as cyber security, manufacturing and finance. However, developing anomaly detection methods for high-dimensional data with high speed and good performance is still a challenge. Generative Adversarial Networks (GANs) are able to model the complex high-dimensional data, but they still require large computation in inference stage. This paper proposes an efficient method, known as Mahalanobis Distance-based Adversarial Network (MDAN), for anomaly detection. The proposed MDAN models the data using generative adversarial network (GAN) and detects anomalies by using the Mahalanobis distance. The proposed MDAN outperforms conventional GAN-based methods considerably and has a higher inference speed, when applied to several tabular and image datasets.

A dual-band frequency reconfigurable monopole antenna is presented in this paper. Reconfiguration of frequency bands is achieved using RF PIN diodes. An already published antenna design is used as a starting point and is then modified to achieve frequency reconfiguration mechanism by incorporating RF PIN diodes. The antenna resonates at center frequency of 2.4 GHz and 4.25 GHz when RF PIN diodes are switched OFF. However, RF PIN diodes ON state resulted in 1.84 and 3.66 GHz frequency bands. The 1.84 and 2.45 GHz bands are controlled by altering the electrical length of inverted L-shaped resonator. Whereas, C-shaped radiator is used to resonate the proposed antenna at 3.66 GHz and 4.56 GHz frequency bands. Overall, the simulation and measurement results showed a good agreement.

A compact Ultra-Wideband (UWB) MultipleInput Multiple-Output (MIMO) antenna prototype is proposed in this paper. The proposed prototype is designed using Rogers 4533, which has dielectric constant of 3.3. Radiating patch of both antennas is loaded with an inverted U-shaped slot to notch upper WLAN band. Isolation for whole UWB band is achieved using modified ground plane, while keeping both radiating elements close to each other. Parametric analysis is also presented in this paper along with other results. Results for S-parameter and radiation pattern prove that this antenna is good candidate for UWB MIMO systems.

A compact UWB MIMO antenna composed of two elements is presented in this paper. The two antenna elements are fed by asymmetric coplanar strip (ACS). The radiator of the single antenna consists of half elliptical shape radiator. The two antenna elements are located side by side on FR4 substrate with size equal to 48 × 28 mm2. The edge to edge distance between the antenna elements is 0.23 λο at3 GHz. The two antenna elements are designed to operate in the UWB frequency band from 3 GHz to 11.5 GHz with return loss lower than -10 dB and isolation lower than -15 dB without using decoupling structures between the elements. The envelope correlation coefficient, diversity gain, and capacity loss which define the performance of MIMO antenna are within the required limits. The proposed antennas has been fabricated and measured. Experimental measurements were obtained and are in good agreement with simulation results, which verify that the proposed antenna is a good candidate for UWB MIMO applications. Moreover, the performance of the MIMO antenna is verified by computing the envelope correlation coefficient (ECC), diversity gain (DG), and channel capacity loss (CCL).

For single-input multiple-output (SIMO) systems, Maximum Ratio Combining (MRC), employed at the receiver, achieves the best performance compared to other combining schemes in the literature, such as Selection Combining (SC) and Equal Gain Combining (EGC). However, for cooperative relay-based systems, MRC has limited performance due to the lack of relay decision information awareness at the destination combiner. To overcome this limitation, this paper proposes a new optimum combining scheme, which is being demonstrated for Hybrid-Decode-Amplify-Forward (HDAF) cooperative system. This scheme utilizes relay decision information in the form of the number of errors per received packet over the source-relay link. The derivation of the optimum combining scheme is based on a mathematical model that utilizes conditional error probability. The improved performance of the proposed optimum combining scheme is demonstrated through analytical results and Monte Carlo simulations.

Diversity combining is a form of spatial diversity which is of primary importance in wireless communications. Maximum Ratio Combining (MRC) is known as the best combining scheme because it effectively uses the Channel State Information (CSI) at the receiver in the combining process. However, in cooperative relay-based systems employing relay detection, MRC performance is limited by the fact that the CSI is insufficient to optimize the combining process due to hard decisions performed by some signaling methods at the relay such as Decode-and-Forward (DF). In this paper, we propose a new optimum combining scheme for DF cooperative systems in which the performance gain is achieved through the use of relay decision information along with the CSI in the combining process. The proposed optimum combining is a general scheme, where MRC and other combining schemes are considered to be special cases of optimum combining. Results show that optimum combining significantly improves the system throughput and reduces the error probability. Moreover, it enhances the system robustness due to the adaptivity of the combiner with the change in cooperative channel conditions and packet size.

In this paper, we address the problem of MultiPoint Relay (MPR) node disconnection due to mobility in Vehicular ad-hoc networks (VANETs) using the cluster-based Quality of Service Optimized Link State Routing (QoS-OLSR) protocol. The protocol uses MPR nodes to establish communication among the clusters. MPR disconnection represents a major challenge in VANETs, due to the frequent change in the network topology. Consequently, the performance of the routing protocol will be weakened as it adversely affects the connectivity level of the network. Thus, our solution is a new cluster-based QoS-OLSR protocol based on intelligent water drop algorithm that is capable of (1) selecting the best set of MPR in terms of QoS (2) dealing with the MPR disconnection as it selects alternatives to assure a connected network (3) maintaining a reliable MPR failure management process. Simulation results demonstrate that the proposed model succeeds in improving the network connectivity and stability, reducing both the overhead and the path length, and increasing the packet delivery ratio compared to the original QoS-OLSR.

The error probability is one of the important metrics used to measure the performance of cooperative relay-based systems. Conventionally, the error probability analysis assumes no information about the condition of the Source-Relay link. In this paper, we show that this assumption could result in over or under estimation of the error probability and therefore affects the accuracy of the analysis which is essential to design optimum relay-based system. Therefore, we propose the mathematical model of conditional error probability, in which relay decision information is considered in the performance analysis of cooperative relay-based systems. The mathematical model for Hybrid Decode-Amplify-Forward cooperative system is utilized in order to validate the necessity of modeling conditional error probability and evaluate the overall system performance. The accuracy of the derived analytical expressions are validated via Monte Carlo simulations. Results show that, there is substantial difference between the conventional and the conditional error probability, which depends on relay decision information. Finally, the derived expressions provide accurate analytical tool to analyze and design relay-based cooperative systems for various Source-Relay channel conditions.

This paper presents a fast approach for detecting coherent signals encountered in mul-tipath propagation environment. The developed approach utilizes a Toeplitz matrix reconstruction method along with DOA estimation using Root-MUSIC. The proposed approach exhibits several features such as robustness, high accuracy, and computational efficiency. Nevertheless, the primary advantage of the proposed Toeplitz-based de-correlation scheme is the fast detection of the maximum possible number of coherent signals, without the need of iteration and re-estimation.

This paper develops both theoretical and experimental models for the prediction of attenuation and reflectivity of satellite signals at Ku-band in the UAE. The theoretical model gives a detailed study on the effect of the attenuation and the reflectivity due to the increase of the frequency or the density of different hydrometers. The experimental model, which is based on actual measurements obtained from NASA, was used to extract the required precipitation radar (PR) products. By applying the image analysis techniques on the PR data for one whole year (2000), a graphical representation of attenuation at Ku-band in the UAE was obtained. Results of this paper are vital for the successful design of satellite TV broadcast systems in the UAE operating at Ku-band.

This paper presents a performance analysis of the sample matrix inversion (SMI) adaptive beamforming algorithm for spatial interference rejection by smart antenna systems. A simulation tool which implements the SMI algorithm is developed along with a graphical user interface. Performance of the SMI beamformer is investigated with respect to the variation of a number of parameters related to the signal environment and sensor array. Results of numerical simulation are used to design smart antennas systems with optimal performance