As future-generation networks evolve, attention mechanisms find extensive applications in high-dimensional channel estimation scenarios. Especially on multisource heterogeneous spatial big data, attention-aware fusion methods have achieved remarkable channel estimation performance relative to that of attention-unaware fusion schemes. However, achieving better channel estimation effects with intertiered attention remains a challenge for smart edges. To address this issue, this paper presents a novel cascaded intertiered intelligent information fusion (IN3Fusion) scheme. The proposed scheme comprises a unique transformer attention encoder (TAE) and an inventive triplet attention module as its principal functional components. Additionally, we solve the noise overfitting problem by introducing a technique at the transformer attention decoder (TAD) level. Some auxiliary modules, including attention-augmented convolution (AttaCon), residual networks (ResNet), and squeeze-and-excitation (S & E), also contribute to the performance of the developed scheme. The efficacy of the proposed scheme in comparison with that of the state-of-the-art approaches is supported by experimental results.

The challenges and resilience of vehicular ad hoc network (VANET) and deep neural network (DNN) hybrid architectures in terms of reliability in smart cities have attracted much global interest stemming from the rollout of the next generation of intelligent networks. In this paper, we propose a novel distributed DNN (D-DNN) scheme with blockchain to support the Internet of Intelligent Things (IoIT) infrastructure in the VANET environment of the future. In particular, because the communication links between edge nodes are very unstable in VANETs, a new neuro-fuzzy server that serves the dual roles of finding reliable links between edge nodes and performing optimal routing path selection is proposed. Next, a blockchain layer is employed at the edge nodes, which are initially scrutinized before establishing communication links to ensure reliability during data transfer. Then, the proposed D-DNN (PD-DNN) scheme is applied to enhance the performance of the VANET-IoIT architecture by improving the data flow and convergence rate and mitigating erratic variations in output. To address reliability concerns, the coverage probability (CP) metric is investigated as a measure of network connectivity. Furthermore, we present an analysis of the PD-DNN scheme in comparison with the traditional DNN (T-DNN) scheme. Finally, simulation results for VANET-IoIT scenarios show that, subject to data protection and privacy constraints, the CP values corresponding to different communication links are improved to a greater extent under our scheme than under the traditional scheme, demonstrating the feasibility of the proposed scheme.

Reconfigurable intelligent surfaces (RISs) and multiuser gigantic multiple-input multiple-output (MU-gMIMO) systems are key technologies for enabling sixth-generation (6G) networks. Their numerous advantages include minimal path losses, high energy efficiency (EE), high spectrum efficiency (SE), high data rates, and compatibility with line-of-sight (LoS) and non-LoS (NLoS) paths. However, RIS-gMIMO faces numerous challenges, including pilot overhead during beam training due to a combined radiation field, high training overhead due to the cascaded channels between transceivers, inaccurate channel state information (CSI) due to the rapidly changing RIS-user equipment (UE) channel, and low-accuracy channel estimation caused by semipassive RISs. With semipassive RIS-gMIMO communications, we present a novel quantized deep learning (qDL) channel model. This proposed channel model is constructed via a radio frequency (RF) chain matrix, a combined radiation field, and a truncated activation output. To enhance the feature extraction performance and reduce the loss of the model, a novel qDL-based channel estimation scheme is also proposed to concurrently utilize denoising multilayer perceptron (DnMLP) units to satisfy the imposed sparsity constraint. The qDL scheme outperforms the previously developed benchmark schemes in terms of accuracy and performance according to the normalized mean squared error (NMSE) of the simulation results.

Nowadays, several technical colleges and universities are started for the purpose of increasing educational system of the country. It helps to increase the overall ratio of education based on different sectors of the country. Most of the institutes are based on total number of student. So, this number is used to deal with total number of teachers required in the institute. Apart from teachers, there are several manpower required to run the institute based on the requirement. Some of the manpower are named as examination department, academic staff, library staff, canteen staff, lab technician, etc. These members are known as nonteaching staff. The combination of teaching and nonteaching staff helps to run the institute for the purpose of smooth 198education system. But it is difficult to map the ratio of student to teacher. Because number of students increases day by day, but according to this enhancement, the number of teachers does not increase. Hence, it is difficult to deal and map this strategy. Therefore, in this chapter, an efficient technique is proposed for manpower management system in technical college. The main key element of this chapter is linear programming with fuzzy logic that helps to optimize the model and produce optimal solution.

Multiuser gigantic-multiple-input multiple-output (MU-gMIMO) and nonorthogonal multiple access (NOMA) are jointly seen as important enabling technologies for sixth generation (6G) networks. They have many benefits, such as spatial multiplexing, spatial diversity, massive connectivity, and spectral efficiency (SE). However, gMIMO-NOMA suffers from many inherent challenges. In this paper, we propose an MU-gMIMO-hybrid multiple-access (HMA) heterogeneous network architecture to address the ‘nearly same channel gain’ issue. Then, an iterative minimal mean squared error (IMMSE) scheme is applied along with quadrature amplitude modulation (QAM) for maximal ratio transmission in the proposed transceiver design to address the ‘residual error’ caused by imperfect successive interference cancellation (ISIC). Finally, to assess the performance of the proposed architecture for the ‘time offsets’ issue, we investigate the asynchronous mode with an MMSE detector matrix following imperfect channel state information (ICSI) to provide a new analysis for HMA transmission and formulate an optimization problem for energy efficiency (EE).

The wireless sensor network (WSN) is a collection of multiple nodes along with the base station (BS). These two components of the WSN make the difficult situation easy and helps sense environmental phenomenon and process it based on the user requirement. But each sensor node consists of the limited energy capacity of the battery. It creases problems in any operation or data transmission. Hence, the result is several types of interference and noise in the WSN. BS receives the processed information along with noise and imprecise information. Hence, the overall network performance decreases simultaneously and the proposed operation fails. In this paper, an intelligent strategy management technique is proposed for WSN. In this proposal, the game theory acts as an intelligent technique for managing and controlling different conflicting strategies of the network efficiently. The proposed method is simulated in the Python programming language for validating game theory technique with fusion of WSN.

We present a unified system model and framework for the analytical performance study of two heterogeneous and physically-distinct, but coexisting, networks that work harmoniously at the same time, space, and frequency domains. The two-tier network model considered in this paper is an overlaying of femtocells on a macrocell. Overlaying femtocells improves the performance by offloading traffic from macrocells and providing spatial diversity. The mmWave channel model employed considers the number of clusters and rays within each cluster to vary due to the end-user mobility. This is a new and different model compared to the widely used channel models for mmWave two-tier networks. Optimal power control is formulated as a sum-rate maximization problem for downlink and uplink transmissions at two-tier networks and a power allocation scheme is proposed by following Shannon-Hartley theorem. A comprehensive and interesting performance investigation is provided, where it is shown that the upper bound on the number of admitted secondary users has a linear relationship with the outage probability threshold, logarithmic relationship with SINR and exponential relationship with channel gain factors. Simulation results show that the proposed scheme with sub-channel iterative Lagrange multipliers search algorithm is very effective at managing the cross-tier interference and can outperform a competitive scheme from literature that is based on cognitive radio technology. The computational complexity analysis of proposed algorithms are also given, since the complexity of second algorithm can be a performance-complexity trade-off issue for systems with limited computation power and time requirements.

Non-orthogonal multiple access (NOMA) is a better multiple access technique than orthogonal multiple access (OMA), precisely orthogonal frequency division multiple access (OFDMA) scheme, at the conceptual level for fifth-generation (5G) networks and beyond 5G (B5G) networks. We investigate the potentials of the schemes by comparing the proposed NOMA scheme with the traditional cooperative communication NOMA (CCNOMA) scheme, rather than the comparison between NOMA and OMA only. To probe the effectiveness of NOMA as a multiple access technique, we propose a novel NOMA scheme considering two adjacent BSs with a special design of the transceiver architecture. The proposed scheme provides a reasonable data rate to both near user (NU) and far user (FU) without compromising the quality of service (QoS) to anyone of them. The conclusive analyses on the optimization framework of multi-user sum rate, capacity, transmit power, spectral efficiency (SE), and energy efficiency (EE) trade-off for NOMA and OFDMA schemes have been established to a succession of derivations. Under the analytical optimization framework, we also prove quite a few properties for them. Simulation results confirm the theoretical findings and show that the two schemes can efficiently approach the optimal power allocation, minimization of power consumption, and optimal SE-EE trade-off, and the proposed NOMA scheme provides comparatively better data sum rates than the baseline OMA scheme.

Non-orthogonal multiple access (NOMA) is shown to be the optimal channel access method and a strong candidate to be employed at the fifth generation (5G) and beyond networks. This paper studies direct transmission (DT) and cooperative transmission (CT) modes of operations in NOMA communications and proposes an investigation on evolving a cooperative transmission NOMA (C-NOMA) into a Hybrid transmission NOMA (H-NOMA) that can be used for design and deployment of relay based wireless networks, such as networks for Internet of Things (IoT) applications.

In modern era, the number of college increases rapidly due to simultaneously growing number of students. Although, number of student is vary day by day but according to number of students, number of teachers are less. The proposed method is used to maintaining manpower in technical college based on mathematical optimization. In this paper, there are two mathematical optimization techniques are used such as quadratic programming and fuzzy logic. Quadratic programming is a mathematical optimization model where objective function is a format of nonlinear. Fuzzy logic is used to reduce uncertainty of the problem by reducing imprecise information efficiently. The combination of both efficiently manages the manpower of the technical college. The proposed method is validated in LINGO optimization software for analysing and managing different faculty members and staffs.

Wireless sensor network (WSN) is a part of wireless network which has flexible and dynamic nature in context of real-life applications. It has several usages in terms of user requirements. It consists of several nodes having limited energy capacity. Energy capacity of the nodes does not completely fulfil the requirement of the services. During transaction or transmission, data is dropped and fails to reach the destination node or base station (BS). This BS also suffers several types of difficulties for sending or receiving data packets. So, there is need of some techniques or modelling that help to protect this issue. Apart from energy, distance is also one important parameter for transmitting data successfully. Although energy is the crucial parameter, but, combination of both energy and distance plays an important role for managing efficient route of the network. The proposed method is the combination of intelligent technique as well as mathematical modelling that uses fuzzy logic as an intelligent technique and quadratic programming as a mathematical modelling for solving the proposed goal. The combination of both provides a robustness technique that uses two basic parameters, energy and distance, for selecting optimal route of the WSN. The proposed method is validated in LINGO optimization software for formulating and validating the model efficiently.

The fifth-generation (5G) of cellular technology is currently being deployed over the world. In the next decade of mobile networks, beyond 5G (B5G) cellular networks with the under-development advanced technology enablers are expected to be a fully developed system that could offer tremendous opportunities for both enterprises and society at large. B5G in more ambitious scenarios will be capable to facilitate much-improved performance with the significant upgrade of the key parameters such as massive connectivity, ultra-reliable and low latency (URLL), spectral efficiency (SE) and energy efficiency (EE). Equipping non-orthogonal multiple access (NOMA) with other key drivers will help to explore systems’ applicability to cover a wide variety of applications to forge a path for future networks. NOMA empowers the networks with seamless connectivity and can provide a secure transmission strategy for the industrial internet of things (IIoT) anywhere and anytime. Despite being a promising candidate for B5G networks a comprehensive study that covers operating principles, fundamental features and technological feasibility of NOMA at mmWave massive MIMO communications is not available. To address this, a simulation-based comparative study between NOMA and orthogonal multiple access (OMA) techniques for mmWave massive multiple-input and multiple-output (MIMO) communications is presented with performance discussions and identifying technology gaps. Throughout the paper, aspects of operating principles, fundamental features and technological feasibility of NOMA are discussed. Also, it is demonstrated that NOMA not only has good adaptability but also can outperform other OMA techniques for mmWave massive MIMO communications. Some foreseeable challenges and future directions on applying NOMA to B5G networks are also provided.

A novel channel model has been proposed for mobile millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communications to evaluate the effect of end-user mobility. In this model the variance of number of clusters and number of rays generated from each cluster is taken into account that is novel and different from widely used channel models. Two optimum codebook based beam-tracking schemes-multi-objective joint optimization codebook (MJOC) and linear hybrid combiner (LHC)- have been proposed for the novel channel model and their performance for spectral efficiency (SE) is presented. Performance for the two most commonly used channel state information (CSI) estimation approaches is investigated. Finally, the relationship between the beamforming training blocks and optimal beam tracking scheme is presented.

This paper presents a two-tier cellular structure with femtocells in open access mode. The addition of these base stations extends the offered system capacity and coverage area of the network. This two network parameters nowadays becomes rather important to provide Quality of Service (QoS) for data-hungry applications. Femto base stations (FBS) are deployed randomly to extend the coverage of conventional base stations. Since the femtocells are operating here in open access mode, so that the handover probability parameter becomes important concern. In this article, we declare the handover probability using the fundamentals of stochastic geometry.

In downlink orthogonal frequency division multiple access (OFDMA) networks, an effective way of using the limited wireless spectrum resources can significantly improve network response. This paper presents a game-theoretic scheme with anticoordinated players by incorporating adaptation of femto base station (FBS) transmit power, attenuation of interference and utility function for open access mode and closed access mode respectively. The deployment of femtocells in the networks is to produce improved energy efficiency (EE) and optimized reponse of payoff function. In open access mode, each user belongs to the operator’s network can connect to the FBS and in closed access case, only a specified set of users can privately couple to the FBS whereas in the early access scenario it only allows authentic subscribers to take the advantage of femtocell networks. Additionally, the operating principle of spectrum sharing scheme has been discussed in which FBS as a player acquire knowledge from utility responses of their strategic communications and revise their strategies at each level of the game process. Here, an FBS is regarded as a player in the game to select the users who are satisfied to a greatest extent and an FBS plays a role of mentor. Thereafter, the equilibrium concept has been invoked to aid the anti-coordinated players for the strategies. Besides, a femtocell power adaptation algorithm has also been introduced based upon the set of enabled femtocells who can be used to retain its blocking probability that guarantees convergence to the stable strategy of the game, where the FBS monitors the subscribers’ actions and gives only limited data exchange. The simulations demonstrate that the proposed algorithm attains a high quality performance such as rapid convergence, interference attenuation to a greatest extent, noticeable EE improvement etc. Finally, validate the simulation results with its rarely studied extension in cognitive femtocell networks.

This study investigates a wireless sensor network to determine an optimal path from source node to destination node while minimizing energy consumption during data transmission. To this end, the problem is formulated as Mixed Integer Programming Problem and solved using Simple Branch and Bound Algorithm. Model variants is also developed to gain crucial insights into the problem structure by considering network dynamics and uncertainty. This allows to focus on key problem aspects such as minimization of energy consumption, improvement of transmission delays, and prevention of network failures. A modified Grey Wolf Optimization technique is developed to solve 30 large size problems with 50, 100, and 200 sensor nodes. Results obtained suggest that the Grey Wolf Optimizer is scalable, robust and efficient, when obtaining optimal solutions in a resource constraint environment.

The fifth generation (5G) networks and internet of things (IoT) promise to transform our lives by enabling various new applications from driver-less cars to smart cities. These applications will introduce enormous amount of data traffic and number of connected devices in addition to the current wireless networks. Thus 5G networks require many researches to develop novel telecommunication technologies to accommodate these increase in data traffic and connected devices. In this paper, novel power constraint optimization and optimal beam tracking schemes are proposed for mobile mmWave massive MIMO communications. A recently published novel channel model that is different from other widely used ones is considered. The channel model considers the number of clusters and number of rays within each cluster as varying due to user mobility. The proposed power constraint optimization scheme harmonizes conventional total power constraint (TPC) and uniform power constraint (UPC) schemes into a new one called allied power constraint (APC) that can significantly improve the system performance in 5G networks while achieving fairness among users. TPC and UPC have major drawbacks with respect to fairness and achieving quality-of-service (QoS) for users in dense networks. Thus APC aims to harmonize TPC and UPC by adjusting each antenna element’s constraint to adapt for some power resilience to a specific antenna element, hence proposing an intermediate solution between the two extreme case power constraint optimization schemes. Three optimal beam tracking schemes: (i) conventional exhaustive search (CES), (ii) multiobjective joint optimization codebook (MJOC), and (iii) linear hybrid combiner (LHS) scheme, have been provided for the mobile mmWave massive MIMO system with the proposed APC scheme. For the proposed APC scheme a comprehensive performance analysis is provided and compared with TPC and UPC. Spectral efficiency (SE), bit-error-rate (BER), Jain’s fairness index, channel occupancy ratio (COR) and instantaneous interfering power metrics are investigated. It has been shown that the proposed scheme can significantly outperform conventional schemes.

In the rapid development of wireless communications Femtocells provide tremendous improvement in coverage and quality of service for users. Macro-Femto based networks are envisioned to be the de-facto solution for providing ultrahigh speed communications in next generation mobile wireless networks. This paper studies two-tier Macro-Femto networks and proposes a collection of novel technologies to address the interference problems. First, a novel user association scheme is proposed that aims to optimize the load among Femto base stations (FBSs). Second, a near-optimal ergodic search algorithm is proposed to regulate the power consumption at Macro base stations (MBSs) and improve energy efficiency. Third, a channel access mechanism is proposed for FBSs that aims to minimize inter-tier interference. For the proposed system, CDF of SINR is derived and used for performance investigation. Simulation results show that the proposed system can significantly outperform a popular, conventional cognitive radio based system for all the considered simulation scenarios.

This paper presents a game-theoretic scheme with anti-coordinated players by incorporating adaptation of femto base station (FBS) transmit power, attenuation of interference and utility function for open access mode and closed access mode, respectively. The deployment of femtocells in the networks is to produce improved energy efficiency (EE) and optimized response of payoff function. Additionally, the operating principle of the spectrum sharing scheme has been discussed in which FBS as a player acquire knowledge from utility responses of their strategic communications and revise their strategies at each level of the game process. Here, an FBS is regarded as a player in the game to select those users who are satisfied to a greatest extent and besides an FBS plays a role of mentor. Thereafter, the equilibrium concept has been invoked to aid the anti-coordinated players for the strategies. Finally, validated the simulation results are with its rarely studied extension in cognitive-femtocell networks.