DL-NOMA-CancelNet: A Deep Learning Framework for Interference Cancellation Using CNN and BiLSTM

Deep learning models have recently been proven to be successful in various natural language processing tasks, including sentiment analysis. Conventionally, a deep learning model’s architecture includes a feature extraction layer followed by a fully connected layer used to train the model parameters and classification task. In this paper, we employ a deep learning model with modified architecture that combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, with Support Vector Machine (SVM) for Arabic sentiment classification. In particular, we use a linear SVM classifier that utilizes the embedded vectors obtained from CNN and Bi-LSTM for polarity classification of Arabic reviews. The proposed method was tested on three publicly available datasets. The results show that the method achieved superior performance than the two baseline algorithms of CNN and SVM in all datasets.

Postpartum depression (PPD) is a serious and under-diagnosed mental health condition affecting mothers after the birth of a child, with severe consequences for the well-being of the mother and the infant. Early identification is difficult because it is multifactorial, reported subjectively, and the interactions between biological, psychological, and social factors are complex. This research suggests an intelligent and reliable machine learning framework for early prediction of PPD using multidimensional maternal health data. The dataset contains the demographic, clinical, psychosocial, and psychological attributes obtained through structured bilingual questionnaires and Edinburgh Postnatal Depression Scale (EPDS) assessments for 64 postpartum women with 28 mixed-type features. Advanced preprocessing, feature selection, and class balancing using SMote were exploited to resolve the problem of data heterogeneity and imbalance. The proposed hybrid deep learning model combines Convolutional Neural Networks (CNN) for feature extraction at different levels, Bidirectional Long Short-Term Memory (Bi-LSTM) for modeling in time, and an attention mechanism to pay Attention to clinically relevant indicators. Model evaluation was done using a 10-fold CV based on accuracy, sensitivity, specificity, and ROC-AUC metrics. The results show the proposed model is superior to conventional approaches as it has 96.8% prediction accuracy, 96.1% sensitivity, 97.4% specificity, and ROC-AUC 0.98. Ablation analysis validates the role of each component (CNN, Bi-LSTM, Attention, feature fusion, and SMote), which has a significant contribution to the prediction performance. The study further includes explainable AI tools (SHAP and LIME) for improved interpretability for clinical applications. These results suggest that artificial intelligence-enhanced prediction of PPD can identify high-risk mothers early, facilitate early psychological interventions, and improve maternal mental health outcomes. The proposed framework provides a scalable and clinically relevant solution to supporting evidence-based maternal healthcare decision-making and could reduce undiagnosed PPD in a real-world setting.

In order to accurately predict the lane‐changing trajectory of the vehicle and improve the driving safety of the vehicle, a lane‐changing trajectory prediction model based on the combination of convolutional neural network (CNN) and bidirectional long short‐term memory (BiLSTM) neural network is proposed by comprehensively considering the historical driving behavior, the spatial characteristics of surrounding vehicles and the bidirectional time sequence information of the vehicle trajectory. Firstly, the vehicle trajectory data are filtered and smoothed, and it is divided into three categories: left lane change, right lane change, and straight driving, and a lane change trajectory sample set is constructed. Secondly, CNN‐BiLSTM model is constructed to identify the sample set of lane‐changing trajectory. Considering the interaction between vehicles in the driving process, the information of predicted vehicle, and surrounding vehicles is taken as the input of the model. The extracted feature vector is input to the BiLSTM layer for prediction after the CNN layer feature extraction, and the horizontal and vertical coordinates of the target vehicle at the next time are output. Thirdly, the trajectory data of the US‐101 dataset in NGSIM is selected to verify the performance of the CNN‐BiLSTM model, and at the same time, it is compared with models such as CNN‐LSTM, long short‐term memory (LSTM), BiLSTM, and CNN‐GRU‐Att. Finally, the verification result shows that the overall fitting degree of the vehicle lane change trajectory prediction of the proposed model reaches 99.50%, and the mean square error and mean absolute error are 0.0003076 and 0.01417, which are improved compared with other models. In the meanwhile, the research on multistep trajectory prediction in different prediction time domains is carried out. It was found that the longer the prediction time domain is, the lower the prediction performance of the model decreases, but the prediction accuracy still reached more than 96%, and it was able to accurately predict the lane change trajectory.

The electric energy consumption prediction (EECP) is an essential and complex task in intelligent power management system. EECP plays a significant role in drawing up a national energy development policy. Therefore, this study proposes an Electric Energy Consumption Prediction model utilizing the combination of Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM) that is named EECP-CBL model to predict electric energy consumption. In this framework, two CNNs in the first module extract the important information from several variables in the individual household electric power consumption (IHEPC) dataset. Then, Bi-LSTM module with two Bi-LSTM layers uses the above information as well as the trends of time series in two directions including the forward and backward states to make predictions. The obtained values in the Bi-LSTM module will be passed to the last module that consists of two fully connected layers for finally predicting the electric energy consumption in the future. The experiments were conducted to compare the prediction performances of the proposed model and the state-of-the-art models for the IHEPC dataset with several variants. The experimental results indicate that EECP-CBL framework outperforms the state-of-the-art approaches in terms of several performance metrics for electric energy consumption prediction on several variations of IHEPC dataset in real-time, short-term, medium-term and long-term timespans.

In this article, we explore the use of neural networks (NNs) to enhance the performance evaluation of Universal Filtered Multi-Carrier (UFMC) systems, a key technology for modern communication networks such as 5G and beyond. Traditional methods of evaluating the Bit Error Rate (BER) and system efficiency in UFMC system can be computationally intensive and less accurate under dynamic conditions. To address these challenges, we propose a NN based approach that not only improves the accuracy of BER prediction but also significantly optimizes the system's overall efficiency. Channel state estimation (CSE) plays a major role for UFMC system to address the phenomenon of multipath channel fading. In order to achieve a high data rate using UFMC technology, it is necessary to have an effective CSE and very accurate signal detection. Recently, there has been significant interest in utilizing deep learning (DL) to enhance channel estimations. This article introduces a new method for channel estimation (CE) in UFMC system. The suggested approach utilizes DL models to improve the CE. For the UFMC system, we propose a detector based on bidirectional long short-term memory (Bi-LSTM). To identify the transmitted symbols, the suggested detector uses DL training data directly. Currently, a significant drawback of UFMC systems is the presence of a high peak-to-average power ratio (PAPR). The approach aims to reduce the BER and enhances the efficiency of the UFMC system. This is achieved by dynamically setting the constellation mapping and symbol damping on each subcarrier and sub-symbol. The results illustrate that the proposed model can accurately and efficiently recognize UFMC signals. The suggested model is being compared to Least Square (LS), LSTM, and Minimum Mean Square Error (MMSE) channel estimators. Through extensive simulations, our results demonstrate that the NN model reduces BER and enhances efficiency. The proposed model gives more effective performance in terms of enhanced efficiency and reduction of BER. The findings offer valuable insights for the design and optimization of next-generation communication systems, where accurate and efficient performance evaluation is critical.

The multiuser detection is a signal processing tech- nique which enhances the performance and capacity in DS- CDMA technology. Since its complexity issue was too expensive to handle, there was focus on the suboptimal detectors. In this paper we propose a hybrid method called adaptive differential interference cancellation method (ADIC) which is a hybrid of the differential interference cancellation method and which is then combined in a adaptive way to determine a weight matrix which will map the outcome of the differential interference cancellation method to come out with a better performance. This paper also looks into the computational cost of different methods using the floating point operation (flops) which is a good estimation of the comparative computational cost if not the exact one. I. INTRODUCTION The conventional DS-CDMA match filter detector fails to combat these issues due three prominent drawbacks: unrealis- tic ucorrelated code, near-far problem (even with strict power control (1)), and tough multiple access interference (MAI). Many advanced signal processing approaches have been made to improve the performances of these kinds of detectors. In this paper we will study the different approaches that were made in this field and implement these methods to reach a conclusion about the detection methods. As the name of the topic suggests the focus of our work will be study and propose cost effective better performing multiuser detection method. We will explore Multiuser Detection Techniques in Synchronous CDMA environment. Multi-user detection deals with the development and ap- plication of joint demodulation and interference cancellation cancellation techniques for improved detection of a desired set of digital signals in the presence of multiple access interfer- ence, intersymbol interference and noise. Since veterbi forward dynamic algorithm (2) is used in case of Optimum multiuser detection (3) the complexity grows in with the increase in the number of users. Hence the complexity is immense which increases exponentially with the increase in the number of users. Looking ahead for lower complexity hence lesser cost of implementation, the paper proposes the use of sub-optimal linear detectors (4). The sub-optimal detector the paper next focuses on is Decorrelator type of Detector (5) which is optimal in least squares. This detector out performs the optimal detector in many respects. Then the Minimum Mean Square Error (MMSE) linear detector is proposed which tries to minimize the squared error from the required transformation. It is one of the most popular detectors in use due to its easiness to implement and its lower complexity. But then in the above detection methods the decisions made were based on certain linear transformation criterion and there are missing efforts to check the performance in between the detection process to improvise the transformation method better detection. So then the paper proposes Adaptive multiuser detection. Then the paper focuses on Interference cancellation methods which utilize the feedback to reduce Multiple Access Interference (MAI) in the detected bits. Under the Interference Cancellation umbrella the Successive Interference Cancellation (SIC) (6), (7) method is explored and simulated to observe the results. Then the next method which performs better than SIC and more commonly used in real life, called Parallel Interference Cancellation (PIC) (6), (8) is also implemented. Before going ahead to implement the final proposed detection method, this paper implements Differential Interference Cancellation (DIC) (9) method. And then this paper finally proposes an Inter- ference Cancellation method called the Adaptive Differential Interference Cancellation Multiuser Detection method which is cost effective in comparison to other Interference Cancellation methods as we will require lesser hardware (multipliers as in the SIC or Parallel Interference Cancellation (PIC)) for that matter. More ever since we are using adaptive method the desired BER can be further reduced. This proposed method will be discussed in detail in the following section.

Performance analysis of multicell multiuser MIMO MC DS/CDMA system with MMSE user-ordered SIC technique

NOMA (Non- orthogonal Multiple Access) is an early candidate for 5G that concentrates on networks capacity expansion. It can be broadly classified into two major categories; Power-Domain NOMA (PD-NOMA) and Code-Domain NOMA (CD-NOMA). This research focuses on PD-NOMA. The significance of PD- NOMA is that concurrent transmission and reception can be accomplished on the same frequency and with this spectral efficiency can be enhanced. The objective of this research is to investigate the impact of error propagation caused due to AWGN (Additive White Gaussian Noise) channel in PD-NOMA 5G system. A 2 × 2 PD-NOMA system is simulated in Matlab version 2019a. In the Uplink, the raw data is BPSK modulated and transmitted to the Base Station (BS). In the downlink, from the BS, User 1 data is transmitted with a power level of 0.25 watts and User 2 with 0.75 watts based on their placement. In the Reception section, Successive Interference Cancellation (SIC) is executed to segregate the data which is further recovered through decoding process. To analyze the performance, AWGN channel is introduced in the proposed simulated system model. The Signal to Noise Ratio is varied from 5 to 25 dB. From the results obtained, it is inferred that, Bit Error Rate experienced by the users varies from 0.1 to 1.

Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

We present a successive interference cancellation (SIC) scheme for a multicarrier (MC) DS CDMA system. In every subband, the SIC receiver successively detects the interferers' signals and substracts them from the user-of-interest. A comparison is made among SIC, matched filter detection with an equal gain combiner (MF-EGC), matched filter followed by maximal-ratio combining (MF-MRC) and minimum mean-squared error (MMSE) receivers with optimal tap coefficients. For this latter receiver, we determine the performance both when the number of taps equals the processing gain, denoted by N, and when the number of taps is roughly one-half of the processing gain. By analysis and simulation, it is shown that the SIC has better ability to suppress multiple-access interference (MAI) than do MF and [N/2]-tap MMSE in single carrier (SC) CDMA, and MF-EGC, MF-MRC and [N/2]-tap MMSE in MC CDMA, and this translates to increased capacity and better fading resistance. Further, SIC and N-tap MMSE yield approximately the same performance in SC CDMA, but SIC has a poorer performance than does an N-tap MMSE in MC CDMA. In addition, we derive results for the performance degradation that a SIC scheme experiences in MC CDMA due to both phase and timing tracking errors. It is shown that with small tracking errors, SIC can still retain a performance advantage over MF-EGC and MF-MRC, although the advantage of SIC decreases with increasing tracking errors.

We present a successive interference cancellation (SIC) scheme for a multicarrier (MC) direct-sequence code-division multiple-access (CDMA) system, using band-limited spreading waveforms to prevent self-interference. In every subband, the SIC receiver successively detects the interferers' signals and substracts them from the user-of-interest. A comparison is made among SIC, a minimum mean-squared error (MMSE) receiver, and matched filter (MF) detection with maximal-ratio combining (MRC). We also consider suboptimal combining using pilot symbol-assisted modulation (PSAM) to make the system more realistic. Analytic bit-error probabilities for SIC, MMSE, and MF in correlated Rayleigh fading channels are derived. The theoretical results for SIC, MMSE, and MF are shown to agree well with simulations. In particular, SIC and MMSE are shown to achieve better performance than MF with MRC; when the number of users is small, SIC provides better performance than does MMSE. Further, the correlation among different subcarriers is studied, and only large subcarrier correlation coefficients result in an obvious worsening of performance. Finally, we derive results for the performance degradation that an SIC scheme experiences in MC CDMA due to phase and timing tracking errors. It is shown that SIC can still retain a performance advantage over MF-MRC, although the advantage of SIC decreases with increasing tracking errors, especially when subcarrier correlation coefficients are small.

A Hybrid Interference Cancellation technique for overloaded CDMA with timing offset error

Precoding for multiple-input multiple-output (MIMO) spatial multiplexing generally requires high feedback overhead and/or high-complexity processing. Simultaneous reduction in transmitter complexity and feedback overhead is proposed by imposing a diagonal structural constraint to precoding, i.e., power allocation. Minimum bit-error rate (MBER) is employed as the optimization criterion, and an approximate MBER (AMBER) power-allocation algorithm is proposed for a variety of receivers, including zero-forcing (ZF), successive interference cancellation (SIC), and ordered SIC (OSIC). While previously proposed precoding schemes either require ZF equalization for MBER, or use a minimum mean-squared error (MMSE) criterion, we provide a unified MBER solution to power allocation for ZF, SIC, and OSIC receiver structures. Improved error-rate performance is shown both analytically and by simulation. Simulation results also indicate that SIC and OSIC with AMBER power allocation offer superior performance over previously proposed MBER precoding with ZF equalization, as well as over MMSE precoding/decoding. Performance under noisy channels and power feedback is analyzed. A modified AMBER algorithm that mitigates error propagation in interference cancellation is developed. Compared with existing precoding methods, the proposed schemes significantly reduce both transmit processing complexity and feedback overhead, and improve error-rate performance

In this work, resource allocation of Cooperative Power Domain Non-Orthogonal Multiple Access (PD-NOMA) with energy harvesting technique using Multiple Input Single Output (MISO) is examined. This paper focuses on improvement of bit error rate (BER), spectral efficiency and capacity compared to conventional Non-Orthogonal Multiple Access (NOMA). Two users scenario is considered, where an user is near to the base station (BS) while the other one is far away from the base station. As both users with different power levels exist in the same spectrum, co-channel interference is inevitable. To alleviate this, an interference cancellation technique known as Successive Interference Cancellation (SIC) is used. The simulation results of cooperative NOMA with energy harvesting are compared with conventional NOMA and it can be inferred that cooperative NOMA performance is better than NOMA.

Small Modular Reactors (SMRs) are gaining increasing attention as flexible and low-emission energy sources capable of supporting the transition to a sustainable power grid. Their ability to perform load-following operations is critical for ensuring grid reliability in systems with high shares of variable energy resources, such as wind and solar. This paper presents a seasonal assessment of SMR load-following capabilities using forecasted load demand profiles obtained from hybrid deep learning models. Multiple forecasting models were evaluated to first identify the most accurate short-term electricity load demand forecast model. A hybrid deep learning model based on the combination of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), i.e., hybrid CNN+BiLSTM, achieved the best performance by generating more accurate week-ahead load forecasts based on historical temperature and load data. These forecasts were used to simulate the response of a dynamic SMR model developed in Simulink, incorporating realistic ramp-rate and control constraints. Performance of the models is evaluated across four seasonal scenarios and test results show that the SMR is able to effectively track forecasted load profiles year-round, with Mean Absolute Percentage Error (MAPE) below 1.5%. These findings demonstrate the potential of forecast-informed SMR operation as a reliable and dispatchable clean energy solution for future grid architectures.