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.

This work proposes an efficient two-stage jamming detection and channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based unmanned aerial vehicles (UAVs) communications. The proposed scheme is designed based on the unique time and frequency domain statistical characteristics of OFDM signals. In the time domain (TD), a likelihood ratio test (LRT)-based decision rule is derived as a function of the inherent correlation between the cyclic prefix (CP) samples and their counterparts in the OFDM symbol. In addition, in the frequency domain (FD), a closed-form joint jamming detection and channel estimation scheme is derived using the maximum a posteriori probability (MAP) principle as a function of the statistics of the received pilots and virtual subcarriers (VSCs) signals, which is then re-expressed using generalized MAP ratio test (GMAPRT). The system’s complexity is reduced by applying the two stages sequentially, where the possible implementation of the second stage is conditioned on the outcome of the first stage. The performance of the proposed algorithm is evaluated using Monte Carlo simulations, where the results demonstrate its effectiveness compared to the TD-only and FD-only approaches. The results confirm the superior performance of the proposed scheme compared to the cyclostationary feature (CF)-based technique under various operating scenarios.

The limitations of fifth generation (5G) networks in simultaneously addressing the growing demands of diverse applications, including ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), and high-throughput services, drive the exploration of 6G technologies. Given the multifaceted nature of 6G use cases and the proliferation of edge functions, such as computing nodes, charging nodes, and sensing targets, selecting an optimal multiple access (MA) technique becomes paramount. This article reviews the literature to identify key milestones and insights from the recent progression of MA technology, exploring robust research directions and optimization aspects of developing an intelligent, unified MA framework for 6G. The article examines promising access schemes such as rate-splitting multiple access (RSMA) and non-orthogonal multiple access (NOMA) and discusses the pivotal role of the latest paradigms, such as artificial intelligence (AI) and Open radio access network (RAN), in optimizing the MA framework.

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.

Wireless sensor networks (WSNs) were originally envisioned to consist of a massive number of diminutive, lowcost, and long-life wireless sensors. However, this vision has faced significant obstacles, particularly in transferring sensing information to data centers. This article proposes a novel protocol, termed relaying-as-a-service (RaaS), to leverage public smart devices to relay sensing information to data centers. By offloading major communications and networking operations to these more powerful smart devices and well-developed fifth generation (5G) or sixth generation (6G) networks, the size and cost of sensors can be significantly reduced, and the need for extensive network infrastructure can be eliminated. This reduction in complexity can greatly streamline the development and implementation of WSNs and Internet of Things (IoT) solutions. Furthermore, using low-power sensors enables large-scale frequency-reuse, which can help alleviate the load on the already congested spectrum.

This work presents a novel signal design based on time-domain interleaving (TDI) of orthogonal frequency division multiplexing (OFDM). Unlike conventional OFDM-TDI that introduces a one-dimensional frequency diversity, the proposed design introduces a two-dimensional frequency diversity leading to a superior symbol error rate (SER) performance for various channel conditions. The performance of the proposed signal design is evaluated using zero-forcing (ZF) and minimum-meansquare-error (MMSE) equalizers where the signal-to-noise ratio (SNR) and signal-to-interference-plus-noise ratio (SINR) are derived and used to evaluate the outage probability (OP), SER, and diversity order. The analytical SER results obtained, corroborated by Monte Carlo simulation, demonstrate that the proposed signal model with MMSE equalizer achieves ∼40 dB gain over OFDM and ∼ 7 dB over conventional TDI in Rayleigh fading channels, and it is only 2 dB from the Gaussian channel SER in Rician channels. The proposed system architecture generally follows the conventional OFDM system model, which emphasizes the compatibility and efficiency of the proposed system. Finally, a novel approach is developed to compute the inverse-moments of non-central Chi-squared with two degrees of freedom.

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.