The article introduces an innovative wireless backhauling approach employing non-orthogonal multiple access (NOMA) and automatic repeat request (ARQ) mechanisms. In this novel scheme, power allocation follows a round-robin (RR) method, ensuring equitable performance among paired users. To address the potential packet loss afterARQ, an intelligent packet repair technique is incorporated to recover the dropped packets. A key feature involves storing dropped data packets for subsequent processing before forwarding to their respective IoT devices (IoDs). The proposed methodology hinges on recognizing that interference within a dropped packet may correspond to a packet retrievable in a forthcoming transmission, facilitating recovery through iterative successive interference cancellation (SIC). Significantly, the scheme enhances data reliability without necessitating an increase in the ARQ retransmission limit, which makes it particularly suited for certain Internet of things (IoT) applications. Empirical results confirm a substantial success rate in recovering dropped packets. Notably, the iterative interference cancellation (IIC) technique demonstrated a noteworthy reduction in the packet drop rate (PDR) from 10 −1 to 10 −3 , representing a 100-fold improvement. This implies the successful recovery of 99% of the packets initially dropped in specific scenarios, showcasing the efficacy of the proposed approach.

The bit error rate (BER) analysis of non-orthogonal multiple access (NOMA) has been widely considered in the literature with the assumptions of perfect and imperfect successive interference cancellation (SIC). For both cases, exact closed-form formulas were derived under various channel models, number of users, and modulation orders. However, all the analysis reported overlooked the transformations that affect the probability density function (PDF) of additive white Gaussian noise (AWGN) after the SIC process. Therefore, the signal model after the SIC process is generally inaccurate, which makes the analysis just approximations rather than exact because the noise after SIC is not Gaussian anymore. Therefore, this letter derives the exact noise PDF after the SIC process and evaluates its impact on the BER analysis. The analytical results obtained show that the noise PDF after SIC should be modeled as a truncated Gaussian mixture. Moreover, the PDF after successful and unsuccessful SIC should be modeled differently.

The bit error rate (BER) analysis of non-orthogonal multiple access (NOMA) has been widely considered in the literature with the assumptions of perfect and imperfect successive interference cancellation (SIC). For both cases, exact closed-form formulas were derived under various channel models, number of users, and modulation orders. However, all the analysis reported overlooked the transformations that affect the additive white Gaussian noise (AWGN) probability density function (PDF) after the SIC process. Therefore, the signal model after the SIC process is generally inaccurate, which makes the analysis just approximations rather than exact. The same discussion applies to the analysis with perfect SIC assumption because the noise after successful SIC is not Gaussian anymore. Therefore, this letter derives the exact noise PDF after the SIC process and evaluates its impact on the BER analysis. The analytical results obtained show that the noise PDF after SIC should be modeled as a truncated Gaussian mixture. Moreover, the PDF after successful and unsuccessful SIC should be modeled differently. Comparing the BER of the legacy perfect SIC formula and the exact one shows that the BER using the exact PDF is generally less than the Gaussian case, particularly for low signal-to-noise ratios (SNRs)and low far-user power allocation.

This work presents a new framework that utilizes power-domain (PD) nonorthogonal multiple access (NOMA) as a multiplexing scheme to improve the throughput of point-to-point (P2P), or single user, communications. The proposed framework synergizes PD-NOMA and automatic repeat request (ARQ) to enable multiplexing and transmitting multiple packets that belong to the same user simultaneously. To overcome channel estimation and feedback limitations, and to reduce the system complexity, a simple adaptation scheme is proposed select the appropriate number packets to be transmitted within a given transmission slot. Moreover, the number of transmitted packets is limited to a maximum of two to allow the receiver to blindly identify the number of transmitted packets in a particular transmission slot. The obtained results show that the proposed NOM scheme can eventually double the system throughput at high signal-to-noise ratios (SNRs), and hence, reduce the delay by 50%. The system complexity and overhead are generally comparable to conventional ARQ systems.

Road accidents caused by human error are among the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper, a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method for practical applications in intelligent transportation systems

Non-orthogonal multiple access (NOMA) has received an enormous attention in the recent literature due to its potential to improve the spectral efficiency of wireless networks. For several years, most of the research efforts on the performance analysis of NOMA were steered towards the ergodic sum rate and outage probability. More recently, error rate analysis of NOMA has attracted massive attention and sparked massive number of researchers whose aim was to evaluate the error rate of the various NOMA configurations and designs. Therefore, the large number of publications that appeared in a short time duration made highly challenging for the research community to identify the contribution of the different research articles. Therefore, this work aims at surveying the research work that considers NOMA error rate analysis and classifying the contributions of each work. Therefore, work redundancy and overlap can be minimized, research gabs can be identified, and future research directions can be outlined. Moreover, this work presents the principles of NOMA error rate analysis.

This work considers the design and performance analysis of multirate non-orthogonal multiple access (MR-NOMA) with arbitrary rates and various modulation orders, where closed-form bit error rate (BER) expressions are derived for the joint-multiuser maximum likelihood sequence detector (JMLSD) and low-complexity optimal successive interference cancellation (SIC) receiver. The derived expressions are used to optimize the power assignment (PA) at the base station to minimize the BER while strictly satisfying certain BER requirements. The presented results show that MR-NOMA can increase the trade-off freedom between spectral efficiency and robustness to errors due to its ability to control the energy by varying the power and symbol duration, simultaneously, while single-rate non-orthogonal multiple access (SR-NOMA) can only control the power only. The mathematical derivations are validated via Monte Carlo simulation.

This paper proposes a multiple targets localization scheme using a clustered wireless sensor network (WSN) for terrestrial and underwater environments. In the considered system, sensors measure the total energy emitted by the targets and transmit quantized versions of their measurements to a data central device (DCD) with the assistance of intermediate cluster-heads (CHDs), which employ decode-and-forward relaying (DFR). Upon data collection from the sensors, the DCD performs the localization process, which involves estimating the number and positions of the targets. The data transmission from the sensors to the CHDs takes place through an imperfect medium, which is characterized by a Rician fading model. The penalized maximum likelihood estimator (PMLE), also known as regularized maximum likelihood estimation (MLE), is applied at the DCD to provide optimal estimates for the number and locations of targets. Furthermore, a suboptimal estimator is derived from PMLE, which can offer comparable performance under certain operating conditions with significantly reduced computational complexity. Cramer-Rao lower bound (CRLB) is derived to serve as an asymptotic benchmark for the root mean square error (RMSE) of the estimators in addition to the centroid-based localization benchmark. Monte Carlo simulation is used to evaluate the performance of the proposed estimation techniques under various system conditions. The results show that PMLE is an effective tool for estimating the number and locations of the targets. Additionally, it is demonstrated that the RMSE of the proposed estimation approaches the CRLB for a large number of sensors and high signal-to-noise ratio.

This paper introduces a new analytical framework to evaluate the capacity of intelligent reconfigurable surface (IRS)-aided wireless networks in the presence of a direct link (DL). The obtained analysis is used to characterize the signal-to-noise ratio (SNR) at the user equipment (UE) while using adaptive power and rate transmission. In particular, we consider the channel inversion with a fixed rate, optimum power and rate adaptation, and the truncated channel inversion with a fixed rate. The obtained expressions are derived in a unified closed-form. All the single-hop channel gains are modeled as independent and identically distributed Nakagami-m fading channels. Consequently, the channels’ gains at the receiver become independent and non-identically distributed. The moment generating function (MGF) is used to derive an accurate approximation of the probability density and cumulative distribution functions of the instantaneous SNR, which are used to evaluate the channel capacity at low and high SNRs to quantify the achievable multiplexing gain. The obtained analytical and simulation results indicated that a strong DL may significantly enhance the channel capacity gain obtained using the IRS. In particular scenarios, the capacity improved by about 30% for a large number of IRS elements when the DL Nakagami fading parameter m is increased from 2 to 6.

In non-orthogonal multiple access (NOMA), user pairing, power allocation, and performance evaluation are typically performed while assuming that all users have equal symbol rates. However, such an assumption may significantly limit the design flexibility of NOMA and devalue its potential. Therefore, this paper considers a generalized scenario where the user-paring process may include users with different symbol rates, and thus, the proposed configuration is denoted as multi-rate NOMA (MRNOMA). In MR-NOMA, the relation between the symbol rate and energy is exploited to add a new degree of freedom when assigning power to the paired users. That is, the fact that the symbol energy is proportional to the symbol duration extends the range of power values that can be allocated to the high symbol rate user while satisfying the quality-of-service requirements for all users. Consequently, the number of served users can be increased, or such a feature can be used to increase the link throughput. The obtained results show that with optimal power selection, both the high and low symbol rate users can achieve lower bit error rates (BERs), which in turn increases the system throughput as a result of the improved transmission reliability.

In intelligent reflecting surface (IRS)-assisted communications, the ultimate gain is achieved when the phases of the reflected signals are optimally selected to maximize the signal-to-noise ratio (SNR). However, practical hurdles, particularly the imperfect phase estimation and quantization can reduce the potential gain. Therefore, this work aims at evaluating the impact of applying a quantized phase in the presence of phase estimation errors. Towards this goal, we derive the probability density function (PDF) of the estimated quantized phase, then using the sinusoidal addition theorem (SAT), the PDF of the received signal envelope is derived and used to derive closed-form expressions of the symbol error rate (SER) and outage probability (OP). The obtained analytical and simulation results show that the SER and OP jointly depend on the SNR, phase estimation accuracy, number of IRS elements, and number of quantization levels. The imperfect phase and quantization demonstrated several counterintuitive results. In particular, it is shown that increasing the number of IRS elements or the number of quantization levels may degrade the system performance. Moreover, the results reveal that the impact of phase quantization increases as the phase estimation accuracy decreases. The results also show that the performance is susceptible to phase errors with an even number of reflectors and binary quantization levels.

Physical layer security (PLS) can be adopted for efficient key sharing in secured wireless systems. The random nature of the wireless channel and channel reciprocity (CR) are the main pillars for realizing PLS techniques. However, for applications that involve air-to-air (A2A) transmission, such as unmanned aerial vehicle (UAV) applications, the channel does not generally have sufficient randomness to enable reliable key generation. Therefore, this work proposes a novel system design to mitigate the channel randomness constraint and enable high-rate secret key generation (SKG) process. The proposed system integrates physically unclonable functions (PUFs) and CR principle to securely exchange secret keys between two nodes. Moreover, an adaptive and controllable artificial fading (AF) level with interleaving is used to mitigate the impact of low randomness variations in the wireless channel. The proposed system can operate efficiently even when the channel is nearly flat or time invariant. Consequently, the time required for generating and sharing a key is significantly shorter than conventional techniques. We also propose a novel bit extraction scheme that reduces the number of overhead bits required to share the intermediate keys. The obtained Monte Carlo simulation results show that a key agreement can be reached at the first trial for moderate and high signal-to-noise ratios (SNRs), which is substantially faster than other PLS techniques. Moreover, the results show that inducing AF into static channels reduces the mismatch ratio between the generated secret sequences and degrades the eavesdropper’s capability to predict the secret keys.