As the commercial implementations of 5G networks have been initiated in different regions of the world, the focus of the researchers is bending towards the next generation of wireless communication. This research study intends to investigate the requisites of the fast establishment of the theoretical and practical measures for sixth generation (6G) wireless communication. To this end, this paper first outlined the existing research works that have considered different aspects of 6G, and then based on this existing works, the future vision is established. Then, the 6G vision is based on four types of connectivity and is summarized as “Wherever you think, everything follows your heart.” To fill the gap between the market requirements after one decade and the limited capabilities of 5G, different specifications of 6G that make it an appropriate replacement are discussed. Furthermore, different candidate technologies that can potentially realize the 6G communication are studied, followed by discussion on different challenges in the realization and possible research directions to cope with these challenges. By exploring the vision of future, its specification, and key candidate technologies, this paper attempts to summarize the general 6G framework. In addition, with mentioned challenges in realization of 6G, the aim of this paper is to guide the researcher and attract their interest to consider them.

Fifth-Generation (5G) networks have adopted a multi-tier structural model which includes femtocells, picocells, and macrocells to ensure the user quality-of-service (QoS). To meet these QoS demands, the system requires optimization of different resources in different network dynamics carefully. However, if ignored, this will lead to long processing delays and high computational burdens. To avoid this, we proposed Deep Learning (DL) based resource allocation (RA) as a promising solution to meet the network requirements. DL is an effective mechanism where neural networks can learn to develop RA techniques. Thus, an optimized RA decision can be achieved using DL without exhaustive computations. Further, DL uses DL to achieve solutions for joint RA and remote-radio-head (RRH) association problems in multi-tier Cloud-Radio Access Networks (C-RAN). Initially, a summary of existing literature on DL-based RA techniques is provided, followed by a deep neural network (DNN) description, its architectures, and the data training method. Then, a supervised DL technique is presented to solve the joint RA and RRH-association problem. An efficient subchannel assignment, power allocation, and RRH-association (SAPARA) technique are used to generate the training data for the DNN model using the iterative approach where the seed data for the SAPARA technique is taken using a uniform power allocation and path-loss based association (UPA-PLBA) model. After training the DNN model, the accurateness of the presented model is tested. Simulation outcomes demonstrate that our proposed scheme is capable of providing an efficient solution in the considered scenario.

In this paper, we investigate energy efficient resource allocation for uplink multi-carrier non orthogonal multiple access (MC-NOMA) based two-tier heterogeneous networks (HetNets) with wireless backhaul. We have exploited channel diversity by assigning multiple subchannels to various MC-NOMA users (NUs), while allowing multiple NUs to use the same subchannel. We formulate a joint NU clustering, and power and subchannel allocation problem for system's energy efficiency (EE) maximization under the constraints of minimum quality of service (QoS) requirement, maximum allowable transmit power for each NU, and successive interference cancelation. The formulated problem is non-convex with fractional objective function, and therefore, to convert it into a tractable from, it is divided into two subproblems, i.e., subchannel allocation and NU clustering subproblem, and power allocation subproblem. The subchannel allocation and NU clustering subproblem is solved through a suboptimal algorithm, which exploits the channel diversity by maintaining maximum possible difference in transmit power levels of clustered NUs. Whereas for the power allocation subproblem, the EE optimization problem is convexified through Dinkelbach and successive convex approximation techniques and a closed-form suboptimal solution is obtained. The simulation results show that the proposed energy efficient algorithm outperforms the existing NOMA and orthogonal multiple access (OMA) algorithms. The proposed approach also guarantees user fairness by giving priority to the NUs with unfulfilled throughput requirements.

Wireless Sensor Networks (WSNs) consist of multiple sensor nodes, each of which has the ability to collect, receive and send data. However, irregular data sources can lead to severe network congestion. To solve this problem, the Proportional Integral Derivative (PID) controller is introduced into the congestion control mechanism to control the queue length of messages in nodes. By running the PID algorithm on cluster head nodes, the effective collection of sensor data is realized. In addition, a fuzzy control algorithm is proposed to solve the problems of slow parameter optimization, limited adaptive ability and poor optimization precision of traditional PID controller. However, the parameter selection of the fuzzy control algorithm relies too much on expert experience and has certain limitations. Therefore, this manuscript proposes the Cuckoo Fuzzy-PID Controller (CFPID), whose core idea is to apply the cuckoo search algorithm to optimize the fuzzy PID controller’s quantization factor and PID parameter increment. Simulation results show that in comparison with the existing methods, the instantaneous queue length and real-time packet loss rate of CFPID are better.

The Internet of Things (IoT) is a world of connected networks and modern technology devices, among them vehicular networks considered more challenging due to high speed and network dynamics. Future trends in IoT allow these inter networks to share information. Also, the previous security solutions to vehicular IoT (VIoT) much emphasize on privacy protection and security related issues using public keys infrastructure. However, the primary concern about efficient trust assessment, authorized users malfunctioning, and secure information dissemination in vehicular wireless networks have not been explored. To cope with these challenges, we propose a trust enhanced on-demand routing (TER) scheme, which adopts TrustWalker (TW) algorithm for efficient trust assessment and route search technique in VIoT. TER comprised of novel three-valued subjective logic (3VSL), TW algorithm, and ad hoc on-demand distance vector (AODV) routing protocol. The simulated results validate the accuracy of the proposed scheme in term of throughput, packet drop ratio (PDR), and end to end (E2E) delay. In the simulation, the execution time of the TW algorithm is analyzed and compared with another route search algorithm, i.e., Assess-Trust (AT), by considering real-world online datasets such as Pretty Good Privacy and Advogato. The accuracy and efficiency of the TW algorithm, even with a large number of vehicle users, are also demonstrated through simulations.