User Interference Research Articles

Analysis of emission of volatile organic compounds and thermal degradation in investment casting using fused deposition modeling (FDM) and three-dimensional printing (3DP) made of various thermoplastic polymers.

The study examined the degradation process of various types of polymers used to form models using the fused deposition modeling (FDM) and three-dimensional printing (3DP) methods and used in the investment casting method. Commercial filaments made of polylactide (PLA), acrylonitrile butadiene styrene terpolymer (ABS), high-impact polystyrene (HIPS), polyamide 12 (PA12), poly(methyl methacrylate) (PMMA), and polypropylene (PP) were used to produce gypsum molds. The assessment included organic volatile compounds (VOCs) released during mold heating and model degradation, which is characteristic of this technological process. The screening qualitative chromatographic analysis of decomposition products sampled from various points of the production hall made it possible to define potential threats related to the processing of selected polymers and the necessary preventive measures. Furthermore, passive diffusion-type samplers were used at the sampling stage of VOCs emitted to the gaseous phase to reduce nuisance and user interference. Studies have been completed to characterize the thermoplastic polymer degradation process. The coupled thermogravimetry (TGA) with analysis of gaseous products by infrared spectroscopy with Fourier transformation (FT-IR) has been used. The presented results are the first to compare and rate the use of this still-developing novel aspect of castings by the method of fused models.

Joint User Association, Interference Cancellation, and Power Control for Multi-IRS Assisted UAV Communications

Joint User Association, Interference Cancellation, and Power Control for Multi-IRS Assisted UAV Communications

Hidden Markov models for predicting cell-level mobile networks performance degradation

The ubiquitous deployment of Fourth-generation (4G) and Fifth-generation (5G) networks, while expanding user connectivity over a larger geographic area, presents significant challenges for mobile network management. The surge in connected devices increase cell congestion, while user mobility, fading, and interference further deteriorate channel quality. The combined effect of these factors contribute to a gradual degradation in cell and network performance, straining the capabilities of Mobile Network Operators (MNOs) to maintain optimal service quality. The degradation progresses through distinct stages, and ultimately leading to cell outages. Existing cell degradation detection methods, such as key performance indicators (KPIs) monitoring and drive tests, rely on indicators such as signal strength (like reference signal received power (RSRP)), network measurements (like traffic volume and handover requests), and user complaints. However, these methods may not provide real-time insights, leading to delays in identifying and addressing performance issues. This paper proposes a Hidden Markov Model (HMM) approach for proactive cell degradation prediction using real-time time-series KPIs data collected from a telecom operator’s network management system. The NetMax system, a network management system specifically designed for ZTE networks, was used for KPIs data collection. RSRP, 3GPP-recommended signal strength metrics for degradation measure, defines hidden states, while traffic volume and handover requests represent the observable states. The HMM achieves an average prediction accuracy of 93.12%, F1 score of 91.81%, and precision of 92.82% with 23 observation lengths, demonstrating the models’s effectiveness in predicting mobile network’s cell degradation severity.

Security-reliability trade-off analysis for transmit antenna selection in cognitive ambient backscatter communications

Massive deployment of IoT devices raises the need for energy-efficient spectrum-efficient low-cost communications. Ambient backscatter communication (AmBC) technology provides a promising solution to achieve that. Moreover, incorporating AmBC with cognitive radio networks (CRNs) achieves better spectrum efficiency; however, this comes with performance drawbacks. In this work, we investigate the security and reliability performance of an underlay CRN with AmBC, where the backscattering device (BD) exploits the radio frequency (RF) signals of the secondary transmitter (ST), and both the ST and the BD share a common receiver. Different from previous work, we consider an ST with multiple antenna. The ST employs a transmit antenna selection (TAS) scheme to enhance the ST performance and overcome the performance degradation caused by the BD interference. TAS exploits multiple antenna diversity with lower hardware complexity and power consumption. Considering the Nakagami-m fading model, closed-form expressions are derived for the outage probability (OP) and intercept probability (IP) of both the ST and the BD transmissions at the legitimate receiver and the eavesdropper. Moreover, the asymptotic behavior of OPs and IPs is also investigated in the high signal-to-noise ratio regime and the high main-to-eavesdropper ratio regime, respectively. Monte Carlo simulations are performed to validate the derived closed-form expressions. Numerical results show that employing TAS enhances the ST and BD reliability performance by percentages up to 98% and 80%, respectively, at high primary user interference threshold values. Moreover, it results in a better security-reliability trade-off for the ST and the BD.

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

SummaryTraditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra‐reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single‐carrier‐non‐orthogonal multiple access (SC‐NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time‐frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC‐NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC‐NOMA, this paper introduces a new method called user‐paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near‐far/far‐near (NF‐FN) and near‐near/far‐far (NN‐FF). NF‐FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN‐FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade‐offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC‐NOMA balances performance but experiences interference. NN‐FF prioritizes eMBB and offers best latency, while NF‐FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

Subchannel assignment for social-assisted UAV cellular networks using dynamic hypergraph coloring

Device-to-Device (D2D) communication when used in conjugation with Unmanned Aerial Vehicle (UAV) Femtocell and unlicensed spectrum can effectively tackle the ever-increasing mobile data demands. However, D2D communication raises security and privacy concerns among users due to the absence of a centralized entity such as a base station. Therefore, exploring social connections among users becomes imperative to enable secure and trustworthy D2D communication. This paper seeks to improve the performance of a social-assisted UAV cellular network augmented by D2D communication by proposing a novel subchannel assignment technique employing hypergraph coloring. Considering the real-world scenario, we incorporate the user/UAV mobility by using dynamic hypergraph coloring for subchannel assignment instead of the static one. Our proposed technique shifts a set of cellular users from the licensed to the unlicensed band based on their social connection with other co-channel D2D users. Additionally, we assign subchannels to different users to optimize the throughput while minimizing overall interference. Our proposed technique demonstrates significant improvements in system throughput, energy efficiency, and interference efficiency compared to conventional techniques. For our proposed technique, we observed improvements of 69%, 42%, and 15% in the per user system throughput when compared with the conventional techniques (graph, hypergraph, and dynamic-hypergraph, respectively). Moreover, our technique achieves higher per user energy efficiency by 120%, 70%, and 25%, and higher per user interference efficiency by 92%, 43%, and 22%, respectively, compared to graph, hypergraph, and dynamic-hypergraph techniques.

Optimization of gamified Learning Education Model based on XR + 5G technology

The new intelligent technology represented by 5G and XR has become the core driving force of the fourth industrial revolution, and the integrated application of the two in the field of education is also promoting the continuous transformation of teaching to gamification. Based on XR + 5G fusion technology, this paper proposes the optimization design of a gamified education model. Firstly, the problem of how to improve the network performance through D2D communication in a full-load 5G network is studied, and a D2D communication resource allocation framework based on cross-layer optimization design is proposed. In D2D user access control, a D2D user access criterion based on the distance between the CU user and D2D user receiver is designed according to the QoS requirement, maximum transmit power limit, and user channel information. XR simulation platform was designed based on a 5G network with optimized communication resource allocation. UMa and InH simulation scenarios were set according to XR business model working scenarios, and Wrap Around algorithm was used to model two circles of interference for users, ensuring the reliability of results while improving simulation efficiency. Finally, the platform channel was modeled strictly according to the channel modeling method in technical document 38.901, and the channel was calibrated with the channel provided by 3GPP. Based on the simulation platform, gamified education design is optimized. Including class platform and class platform gamification design, system demand analysis, and database design. In the gamification design, a relatively novel classroom interaction mode is introduced to increase students’ enthusiasm in class. In the demand analysis of the system, the main problems in class are listed, based on which the business and functional requirements of the gamification platform are determined. Experiments show that in the IID scenario, the proposed XR + 5G fusion technology can achieve 1.09 times the training speed and obtain 0.83 % learning accuracy gain compared with the individual learning algorithm. In the Non-IID scenario, the training speed of the proposed fusion technique is 1.03 times that of the individual learning algorithm, and there is a learning accuracy gain of 2.27 %.

Enhanced resource allocation strategies to improve the spectral efficiency in massive MIMO systems

The accuracy of the channel state information is important for correct channel estimation. However, when conducting channel estimation, more resources are allocated to pilots for estimation compared to data transmission. Furthermore, when the number of users increases, the number of pilots for estimation increases. Subsequently, there is an increase in the transmission overhead and hence reduces the spectral efficiency. Therefore, the advantage of obtaining channel state information is significantly reduced. To improve the performance of massive MIMO systems, the study analyses the tradeoff between the number of resources required to correctly estimate the channel using pilots to avoid interference while maintaining optimum spectral efficiency in massive MIMO antennas. Therefore, this study proposes an algorithm to address the challenge of optimum resource allocation in a massive MIMO. Pilot Frequency reuse, max–min fairness algorithm, and Zadoff–Chu sequences were adopted to achieve optimal allocation of resources and reduce interference for users in different cells using the same frequencies. The results reveal improved performance in terms of spectral efficiency with the adoption of the resource optimization approach. The study contributes to the performance improvement of massive MIMO antennas for 5 G communications.

Joint downlink user association and interference avoidance with a load balancing approach in backhaul-constrained HetNets.

In heterogeneous networks (HetNets), different lower-power base stations are added in a typically unplanned manner to the well-planned macro-only network, bringing new challenges to the network functions. Small cells experience limited backhaul capacity since cost-effective backhaul is not easily accessible to them. This study focuses on the issue of user association in backhaul-constrained HetNets. It shows that it is necessary to associate users with cells using a load balancing approach in order to fully leverage the addition of small cells. The cell association needs to be done jointly with an interference management technique that protects offloaded users and those prone to harmful interference. After modeling the system and describing the interference model, the problem of cell and subband allocation is formulated. We first examine the problem in a time-sharing mode and present a centralized heuristic solution to the cell and subband allocation problem. This is accomplished by solving the convex problem using the gradual removal method. The importance of providing distributed algorithms for HetNets leads to the development of a new algorithm through the application of the dual decomposition method to a reformulated problem and the use of an admission control mechanism. In the achieved algorithm, all computations are performed locally, with each user and base station relying only on local information. This algorithm obtains near-optimal answers, as confirmed by the simulation results. Compared with conventional cell allocation methods, our distributed algorithm prevents intensive interference for all users and achieves better load balance between network tiers, resulting in improved network utility.

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

The Convergence of Islamic Law and Technology in The Enforcement of Citizen's Constitutional Rights in The Era Society 5.0

Globalization spreading across life spheres, especially in the fields of science and technology, also affects the digitalization of law through media convergence. This digitalization of the law is not exempt from the interference of Internet users in the form of criticism, dissemination of cases or social and legal issues, as well as advice to governments or the legal system through social media platforms used by the public, either in a national or international scope. The influence of people’s opinions on the Internet through social media has been remarkable in recent times on the law enforcement ruling of a case. In few cases, the decision was made due to the pressure of internet citizens on social media. The purpose of this study is to describe the convergence of Islamic law and technology in enforcing the constitutional rights of citizens in the era of Society 5.0 as an effort to create democracy and enforcement of the law that is fair and transparent through independence and freedom to express opinions, as well as the right to share and receive information by citizens of the Internet in electronic media, social media, in particular. This research is qualitative with methods of Law approach, conceptual approach, and case approach. The source of legal material used is secondary legal material, which is subsequently analyzed by deductive methods. The result of this study is that when society cannot be regulated primarily related to the use of social media, then the law can act as a law as a tool of social engineering.
 Keywords: convergence, Islamic law, constitutional rights, era Society 5.0

Beyond TIN: GDoF of K-User Interference Channel with Successive Interference Cancellation and Power Control

Beyond TIN: GDoF of <i>K</i>-User Interference Channel with Successive Interference Cancellation and Power Control

Semantic speech analysis using machine learning and deep learning techniques: a comprehensive review

Human cognitive functions such as perception, attention, learning, memory, reasoning, and problem-solving are all significantly influenced by emotion. Emotion has a particularly potent impact on attention, modifying its selectivity in particular and influencing behavior and action motivation. Artificial Emotional Intelligence (AEI) technologies enable computers to understand a user's emotional state and respond appropriately. These systems enable a realistic dialogue between people and machines. The current generation of adaptive user interference technologies is built on techniques from data analytics and machine learning (ML), namely deep learning (DL) artificial neural networks (ANN) from multimodal data, such as videos of facial expressions, stance, and gesture, voice, and bio-physiological data (such as eye movement, ECG, respiration, EEG, FMRT, EMG, eye tracking). In this study, we reviewed existing literature based on ML and data analytics techniques being used to detect emotions in speech. The efficacy of data analytics and ML techniques in this unique area of multimodal data processing and extracting emotions from speech. This study analyzes how emotional chatbots, facial expressions, images, and social media texts can be effective in detecting emotions. PRISMA methodology is used to review the existing survey. Support Vector Machines (SVM), Naïve Bayes (NB), Random Forests (RF), Recurrent Neural Networks (RNN), Logistic Regression (LR), etc., are commonly used ML techniques for emotion extraction purposes. This study provides a new taxonomy about the application of ML in SER. The result shows that Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) are found to be the most useful methodology for this purpose.

Mobile communication channel resource allocation technology in interference environment based on clustering algorithm

The rapid development of urbanization has led to the gradual increase of urban residential density. Relatively speaking, the spectrum resources are increasingly scarce, which leads to the increasingly serious interference between communities, and the system performance is also greatly limited. Therefore, in order to improve the efficiency of spectrum resources and solve the problem of user interference between cells, the experiment combines the advantages of clustering by fast search and find of Density Peaks Clustering (DPC), and proposes a two-step clustering algorithm. This method is proposed based on the core idea of DPC after in-depth study of the downlink multi-cell orthogonal frequency division multiplexing system architecture. The proposed model is compared with Matching Pursuit (MP) algorithm and Graph-based algorithm with the sum of clustering distance Rs, 1.5⁢Rs. The results show that the two-step clustering algorithm can significantly improve the spectrum efficiency and network capacity while ensuring good quality of service under the condition of channel tension or not. In addition, the minimum SINR value of the two-step clustering algorithm can reach 75 dB. Compared with the 220 dB of the Graph-based algorithm with the clustering distance Rs, it has extremely obvious advantages. Therefore, the two-step clustering algorithm constructed in this study can effectively reduce system interference, and has certain research and application value in solving the problem of mobile communication channel resource shortage.

Design and analysis of quantized feedback based user-antenna joint scheduling scheme for ongoing 5G and beyond multi-user massive MIMO FDD communication systems

For the efficient user scheduling (US) and interference mitigation, channel state information (CSI) is mandatory at the base station (BS), particularly for the frequency division duplexing (FDD) systems where the users need to provide some CSI back to the BS for performing the scheduling process. As the feedback overhead increases drastically with increase of antennas as well as users, so, US with limited/reduced feedback is essential. Deploying massive multiple-input multiple-output frequency division duplexing (mMIMO FDD) systems remains an open area for research with major difficulties of associated uplink CSI feedback load as the downlink and uplink channels are not reciprocal. Therefore, smart balancing is required between feedback data and achievable throughput while scheduling the users. In order to handle the above mentioned issue, we employed a selective 4-bit quantized CSI feedback based user-antenna paired/joint scheduling scheme for the multi user (MU) FDD mMIMO systems. The key idea involves in this process is that, only a limited set of users qualifying a predefined selection threshold feed back the 4-bit quantized signal-to-interference-plus-noise ratio (SINR) values based on angle-of-departure (AoD) along with the antenna indices for the scheduling process. The BS schedules the users with respective best antenna. Furthermore, using this scheme the multi user diversity gain is also achieved. This dynamic adjustment of antenna/user selection and collision free antenna sharing between the users majorly helps in improving the throughput of the system with this selective 4-bit quantized limited feedback. The mathematical sum rate performance analysis has been demonstrated along with simulation results for different system parameters.

Integrated optimization of geometrical parameters and flow variables for industrial incinerator performance improvement

Industrial incinerators play a vital role in waste disposal, yet their operations carry significant environmental and efficiency consequences. The current study aimed to optimize an industrial incinerator using a multi-objective algorithm in which the influence of geometrical parameters and flow variables were considered simultaneously. While the initial optimization reduced the waste temperature non-uniformity from 14.63 % to 5.14 % and the waste disposed temperature from 1510 to 1760.23 [K], the disadvantages of the noted approach were found to be the direct user interference as well as optimizing just one parameter per case. Therefore, the Central Composite Design (CCD) is employed to generate the necessary experiments from a statistical perspective instead of the user preference. Later, based on the obtained results from the CCD experiments, the sensitivity analysis calculated the degree of importance for each input parameter. As an instance, the location corresponding to the air injector was found to have 84 % influence on NOX generation, 61 % on the waste temperature uniformity, 90 % on the waste disposal temperature, 11 % impact on the incinerator efficiency, and 13 % COX generation. Other parameters, such as inlet fuel location and equivalence ratio, have a comparably lower impact (3–20 %) in different conditions. By further acknowledging the flow situation, the Genetic Algorithm (GA) was applied to consider the effective parameters from a multi-objective optimization perspective. Employing the input values proposed by GA, the waste disposed of temperature increased from 1760.23 to 2800 [K] while the temperature non-uniformity of waste surface reduced from 5.14 % to 3.17 %. Moreover, the efficiency was improved by 2 % compared to the initial optimization by producing lower environmental emissions by 43 % compared to the equivalent cases in the Design Of Experiment (DOE) chart.

Coalitional game based sub-channel allocation for full-duplex-enabled mmWave IAB network in B5G

The combination of full-duplex communication (FDC) and millimeter wave integrated access and backhaul (mmWave IAB) is a highly implementable technique in beyond 5G era. However, the technical challenges such as sub-channel allocation and interference coordination must be effectively handled before the attractive benefits of the commercial implementation are enjoyed. In particular, the multiple user interference (MUI) is caused when the same sub-channel is shared by the concurrent links sourced from different transmitters, and the residual self-interference (RSI) is resulted from self-interference cancellation (SIC) in FDC. To this end, a coalitional game based sub-channel allocation mechanism is proposed for the full-duplex-enabled mmWave IAB network to maximize the total actual achievable rate. Firstly, the maximization problem of the sum actual achievable rate is formulated as an integer non-convex and non-linear optimization problem under the constriction of MUI and RSI, which is a non-polynomial hard (NP-Hard) problem, and it is intractable to obtain the optimal solution. Secondly, the formulated problem is transformed as a cooperative game, and a coalitional game based sub-channel allocation (CGSA) algorithm with lower computational complexity is proposed to obtain the sub-optimal solution. Thirdly, the properties of the proposed CGSA algorithm are discussed and analyzed from the aspects of stability and convergence, and it is proved that the final coalition partition formed by the proposed CGSA algorithm is Nash-stable. Fourthly, the proposed CGSA algorithm is compared with the reference algorithms under different scenarios, extensive simulations show that superiorities of the proposed CGSA algorithm are obvious including sum of achievable rate, number-ratio of user equipments with satisfied quality-of-service (QoS)requirement and iterations of algorithm convergence.

The Extremal GDoF Gain of Optimal Versus Binary Power Control in K User Interference Networks is Θ (√K)

Using ideas from Generalized Degrees of Freedom (GDoF) analyses and extremal network theory, this work studies the extremal gain of optimal power control over binary (on/off) power control, especially in large interference networks, in search of new theoretical insights. Whereas numerical studies have already established that in most practical settings binary power control is close to optimal, the extremal analysis shows not only that there exist settings where the gain from optimal power control can be quite significant, but also bounds the extremal values of such gains from a GDoF perspective. As its main contribution, this work explicitly characterizes the extremal GDoF gain of optimal over binary power control as Θ(√K) for all K. In particular, the extremal gain is bounded between ⌊√K⌋ and 2.5√K for every K. For K = 2, 3, 4, 5, 6 users, the precise extremal gain is found to be 1, 3/2, 2, 9/4 and 41/16, respectively. Networks shown to achieve the extremal gain may be interpreted as multi-tier heterogeneous networks. It is worthwhile to note that because of their focus on asymptotic analysis, the sharp characterizations of extremal gains are valuable primarily from a theoretical perspective, and not as contradictions to the conventional wisdom that binary power control is generally close to optimal in practical, non-asymptotic settings.

Fully automated operational modal identification of regular and irregular buildings with ensemble learning

Modal identification, a type of system identification that employs modal analysis, can be automated to reduce user interference in extracting modal parameters. Modal analysis is a valuable tool for obtaining a structure's modal parameters, which define its dynamic characteristics. Operational modal identification is a specific type of modal analysis that uses only system outputs to extract modal parameters. The present study aims to introduce a fully automated modal identification algorithm to identify modal parameters for an unknown system based on its output information. The algorithm uses a stochastic subspace algorithm to extract modal parameters, requiring only the appropriate degree of the state-space model estimated using the Singular Value Criterion. Several features are used to create a feature space that separates physical and spurious modes. The Improved Distance Evaluation algorithm selects appropriate features, making the identification process more accurate and flexible. The identification algorithm employs clustering algorithms with different approaches and the Dempster-Shafer data fusion algorithm to integrate clustering algorithm results and avoid divergence among them. Due to limited data diversity for identification in cases where only one dataset is available, statistical resampling methods are used as an alternative. The proposed algorithm can identify modal parameters using only one dataset of the building structure. Signal processing, data collection, clustering, feature extraction, model reduction, and data fusion are all aspects of this algorithm. To assess its performance, four regular and irregular buildings are considered as case studies. The presented modal identification algorithm is highly accurate and robust with the repeatable result as compared to similar algorithms, with extracted modal parameters demonstrating high stability.

An Optimized Reinforcement Learning-Based Routing Protocol Using Spider Monkey Optimization Algorithm

In Wireless Sensor Networks, numerous machine-learning algorithms are used in the packet routing strategy. The recommended routing approach is implemented using adaptive reinforcement learning and a spider monkey optimization algorithm. Data packets are sent over the network via optimum route establishment—the above attempts to save energy by changing the Q-value at each instance. Technology enables adaptive mechanisms to demonstrate intelligent behavior in complex and dynamic situations such as Wireless Sensor Networks (WSN). Recently, new routing methods for WSNs have been devised in response to such a notion. Due to user interference and overall network performance, the numerous routing pathways cause frequent channel-switching occurrences. The spider monkey optimization method obtains routing metrics such as bandwidth, end-to-end latency, throughput, overhead, and packet delivery ratio. Compared to other machine learning algorithms, such as RLBR, the performance of WSN is improved and obtained better performance results.