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.

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.

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.

In this era, the number of users in a network is increasing tremendously at a faster rate; as a consequence, quality of service (QoS) is drastically deteriorating. To compensate such kinds of problems, we attempted to enhance the QoS of the network, which leads to an improvement in throughput, link quality, spectral efficiency, and many more. To meet the requirements mentioned above, many researchers intervene to advance and propose different techniques with an appropriate design methodology. In this work, we try to emphasize symbol error rate (SER) and frame error rate (FER) by implementing some of the existing space-time coding techniques like Space-Time Trellis Coding (STTC), multilevel space-time trellis coding (MLSTTC), and grouped multilevel space-time trellis coding (GMLSTTC). Though all these techniques are proved to be efficient enough, we explicitly included a powerful method of cooperative diversity-based spectrum sensing in cognitive radio scenario. From this analysis, we landed on to the conclusion that this technique is far better to deal with all these parameters, which can improve the QoS of the network. This paper has also analyzed the effect of the proposed model of GMLSTTC with cognitive radio on various deployment setups such as urban, suburban, and rural macrodeployment setup of the ITU-R M.2135 standard.

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.