The emergence of 6G wireless communication networks has directed the evolution of cell-free networks as the upcoming standard technology for future-generation networks. The communication network consists of a variety of users moving at various speeds, maintaining seamless connectivity, and achieving ideal resource allocation is a notable challenge in the upcoming generation wireless networks. However, in indoor communication scenarios, interference management, multipath propagation, and high user density pose challenges for optimal performance. To address such limitations under changing channel conditions, this paper presents adaptive resource allocation across conventional base-station-based networks and the emerging 6G cell-free network, with analysis of synchronization, latency, interference, pilot contamination, and the impact of user mobility. Three communication scenarios are considered, including a highly mobile scenario, a hybrid scenario and an indoor scenario. A comparative simulation analysis has been performed to recognize the balance of the optimization strategy pertaining to data rates, spectrum efficiency, energy efficiency and complexity. Overall, this paper provides a thorough understanding of adaptive resource optimization while leveraging emerging cell-free networks and the base stations at the frequencies with the sub-GHz band (6GHz) and mmWave bands (28GHz and 54GHz) to serve the dynamic requirements of next-generation communication networks.

Summary The stock price fluctuation of one country can be influenced by the movement of the stock price of other countries implying that there exists some relationship among various stock markets. This study examines the interrelationship among Asian stock markets and forecasts the stock market on the basis of the relationship that exists among Asian stock markets. The interrelationship is tested by using the Granger causality (GC) test and Pearson's correlation (PC) matrix. Further, a deep learning model namely a long short term memory (LSTM) neural network is utilized to forecast the stock price of one country by using the price of other countries that have a correlation and causal relationship with the target stock market. PC matrix shows that there exists a strong correlation among Asian stock markets. Results from the GC show that there exists a unidirectional relationship between Sensex and NIKKEI 225 to SSE composite index, Sensex to NIKKEI 225, and Sensex and TSEC weighted index to KOSPI composite index and a bi‐directional relationship among Sensex, TSEC weighted index and Hang Seng index. Experimental results show that GC and LSTM‐based model namely GC‐LSTM shows better forecasting performance in comparison to PC and LSTM‐based model termed as PC‐LSTM.

In recent decades, gait has become an important topic in biometric. Gait attains popularity in human authentication because of its non-cooperation and sensing of gait patterns from a distance. However, in the real-time environment, recognizing based on gait is a challenging task when multiple people walk in a group. Therefore, this paper focused on recognizing people in a multi-gait (MG) scenario. Multi-gait means that more than one person are walking in a group. Here, our work is divided into two phases. In the first phase, we reconstruct the occluded regions. Here, we present a model-based approach to recognize a person in a multi-gait scenario. Therefore, five dynamic regions of interest (ROIs), such as Ankle, Knee, Wrist, Elbow, and Shoulder, are taken, and a numerical interpolation approach is applied to regenerate the occluded ROIs. Then, in the second phase, we extract linear kinematic features and propose a hybrid classifier for model-based multi-gait identification. Finally, the experimental results of the hybrid classifier, i.e., PSO-NN, demonstrate that the proposed classifier performs better in multi-gait identification than the state-of-the-art classifier, such as k-NN and ANN.

Smartphones with the platforms of applications are gaining extensive attention and popularity. The enormous use of different applications has paved the way for numerous security threats. The threats are in the form of attacks such as permission control attacks, phishing attacks, spyware attacks, botnets, malware attacks, and privacy leakage attacks. Moreover, other vulnerabilities include invalid authorization of apps, compromise on the confidentiality of data, and invalid access control. In this article, an application-based attack modeling and attack detection are proposed as a novel attack vulnerability is identified based on the app execution on the smartphone. The attack modeling involves an end-user vulnerable application to initiate an attack. The vulnerable application is installed at the background end on the smartphone with hidden visibility from the end-user, thereby accessing the confidential information. The detection model involves the proposed technique of an application-based behavioral model analysis (ABMA) scheme to address the attack model. The model incorporates application-based comparative parameter analysis to perform the process of intrusion detection. The ABMA is estimated by using the parameters of power, battery level, and data usage. Based on the source Internet accessibility, the analysis is performed using three different configurations, Wi-Fi, mobile data, and a combination of the two. The simulation results verify and demonstrate the effectiveness of the proposed model.

The advancement in wireless communication technologies is becoming more demanding and pervasive. One of the fundamental parameters that limit the efficiency of the network are the security challenges. The communication network is vulnerable to security attacks such as spoofing attacks and signal strength attacks. Intrusion detection signifies a central approach to ensuring the security of the communication network. In this paper, an Intrusion Detection System based on the framework of graph theory is proposed. A Layerwise Graph Theory-Based Intrusion Detection System (LGTBIDS) algorithm is designed to detect the attacked node. The algorithm performs the layer-wise analysis to extract the vulnerable nodes and ultimately the attacked node(s). For each layer, every node is scanned for the possibility of susceptible node(s). The strategy of the IDS is based on the analysis of energy efficiency and secrecy rate. The nodes with the energy efficiency and secrecy rate beyond the range of upper and lower thresholds are detected as the nodes under attack. Further, detected node(s) are transmitted with a random sequence of bits followed by the process of re-authentication. The obtained results validate the better performance, low time computations, and low complexity. Finally, the proposed approach is compared with the conventional solution of intrusion detection.

Trust assessment is a crucial parameter in the next generation WCN (Wireless Communication Network) for the secure collaboration of nodes with each other and the detection of the possible security menaces in the network. The trust ranking of nodes especially in the platforms of defense networks and healthcare networks is of vital importance to the security threats inside the network. In this paper, we have suggested an RM dataset based on the current possible attacks along with advanced authentication attributes for the next generation WCN (B5G/6G). Further, we proposed a trust-ranking model based on the SVM machine learning technique to define the trust ranks of the users present in the network. The model consists of five trust rankings. The fifth trust rank is defined as the highest level of trust and the first trust rank designates the lowest level of trust. The attained rank of trust determines the specific services offered to the corresponding node. The proposed prediction model based on the SVM (Support Vector Machine) algorithm maintains the triple-off balance between the computational time, accuracy, and security. The simulation results indicate that the proposed SVM-based trust ranking model intelligently identifies possible malicious threats and provides an advancement in network security.

In this paper, a hybrid evolutionary intelligent system is proposed for dimensionality reduction and tuning the learnable parameters of artificial neural network (ANN) that can forecast the future (1-day-ahead) close price of the stock market using various technical indicators. Although the ANN possesses the ability to model highly uncertain and complex nonlinear data but the key challenge in ANN is tuning its parameters and minimizing the feature set that can be used in the input layer. The backpropagation approach used for training the ANN has a limitation to get trapped in local minima and overfitting the data. Motivated by this, we proposed a hybrid intelligent system for optimizing the initial parameters and for reducing the dimensions of the feature set. The proposed model is a combination of feature extraction technique, namely principal component analysis (PCA), particle swarm optimization (PSO), and Levenberg-Marquardt (LM) algorithm for training the feed-forward neural networks (FFNN). This paper also compares the forecasting efficiency of the proposed model with PSO-FFNN, regular FFNN, two standard benchmark approaches viz. GA and DE and another hybrid model obtained by the combination of PCA and a time series econometric model viz. auto-regressive distributed lag model. The presented technique has been tested to predict the close price of three stock indices viz. Nifty 50, Sensex, and S&P 500. Simulation results indicate that the proposed model shows superior forecasting accuracy as compared with other methods.

With an unprecedented amount of sensitive data and private information generated by mobile devices, security becomes critical for the emerging ultradense network. The conventional approach of encryption is not suitable to provide desired level of security as a result of increased scalability. Thus, physical layer protection is stressed to ensure secure transmission to users in dense environment. In this paper, we examine the security challenges of high speed users for ultradense network under dense picocells deployment. Initially, we consider a dense condition where users are randomly distributed within the picocell. Targeting the problem of security for vehicular users, we propose a beam broadening (BB) and beam merging (BM) techniques that ensure reliable transmission between source and destination. We prove the effectiveness of proposed approach through mathematical and simulation analysis that guarantees high QoS and secure communication. Based on the above formulations, we examine the reliability and potential capability of BB and BM with the measurement of fairness security assessment (FSA). Numerical results demonstrate that BM and BB can achieve higher value of secrecy rate and FSA, in comparison to conventional beamforming. To end this, the complexity analysis is performed in terms of beam generations as a function of speed of user.

This paper considers a novel non-orthogonal multiple access (NOMA) enabled device-to-device (D2D) communication fifth generation cellular network, practicing sectorization. Though successive interference cancellation (SIC) is the key aspect of NOMA in the current wireless communication systems, the proposed NOMA-based approach introduces the concept of multiple interference cancellation (MIC), which stimulates the phenomenon of cancellation of interference levels in the network, involving optimal resource allocation. The interference reduction achieved with MIC is way below SIC. Such a framework improves the receiver's signal-to-interference-plus-noise ratio (SINR), as shall be examined. Promising gains are provided by the proposed scheme, in terms of sum rate of the network, total energy-efficiency (EE), and fairness factor (FF). MIC reduces the power levels at the regenerators, achieving considerable circuit power saving. Another important redressal of the proposed scheme is the reduced level of complexity achieved with MIC. The implications of proposing MIC in the NOMA-D2D networks are evaluated through simulation results.

Weight and bias connection are important features of neural networks, which is still challenging for researchers. In this work, we focus on initial weights and bias connection of counter propagation network (CPN) using modified chaotic bat algorithm (MCBA) i.e., MCBA-CPN for uncertain nonlinear systems and compare it with CPN using chaotic bat algorithm (CBA) i.e., CBA-CPN. Chaotic function is used for pulse frequency of bats in MCBA. We have implemented CBA and MCBA, which are based on the consideration of the global solution in the sound intensity adjustment. MCBA-CPN is applied on different uncertain nonlinear systems and Mackey–Glass time series data to test the concert in terms of prediction accuracy. Proposed method is validated through statistical testing like chi-square and [Formula: see text]-test demonstrate that the difference between target and output of proposed method are acceptable. Finally, MCBA-CPN is applied to a real world problem for prediction of milk production data.

This paper proposes a high-performance CMOS voltage-controlled oscillator (VCO) with two subfrequency bands. The VCO consists of two cross-coupled VCOs coupled by a pair of mode-switched inductors. Two pairs of shunt varactors are used to switch highand low-frequency bands. With the varactors as the mode switches, the odd mode VCO operates at the high frequency (C-band) and the even mode VCO operates at the low frequency (S-band). The proposed VCO has been implemented with the TSMC 0.18-μm 1P6M CMOS technology and it can generate differential signals in the frequency range of 5.21-6.66 GHz (IEEE 802.11a) and 3.26-3.84 GHz (IEEE 802.16a) and it also has high output voltage swings at both lowand high-frequency bands. The die area of the dual-band VCO is 0.976 mm × 1.092 mm. At the supply voltage of 0.75 V, the high(low)-band figure of merit is -190.5(-190.4) dBc/Hz.