Rendezvous is the fundamental challenge in cognitive radio networks to find each other on a specific channel and establish a communication link. The primary focus of this paper is to design an algorithm for blind rendezvous, i.e., a rendezvous without a coordinator or common control channel. Channels to jump are assumed to be homogeneous in prior work; however, in the real world, channels exhibit different conditions. Therefore, we propose an adaptive jumping pattern such that a superior channel is considered more frequently based on its noise level and users have a greater possibility to rendezvous on such a channel. Furthermore, in this paper, we investigate the rendezvous problem with multiple radio interfaces. When multiple radio interfaces are available for users, the possibility of rendezvous increases; however, the number of channels to jump for each interface may not be the same as other users' interfaces. We propose an adaptive rendezvous algorithm that can function for multiple interfaces and different sizes of channel lists and an adaptive jumping pattern for different channel conditions. We prove that the proposed algorithm provides guaranteed rendezvous and derive the maximum time-to-rendezvous (TTR), which is also verified via simulation results. Further, our algorithm outperforms jump-and-stay in terms of TTR while enabling users to rendezvous on a better channel.

Unmanned Aerial Vehicles (UAVs) are abundantly becoming a part of society, which is a trend that is expected to grow even further. The quadrotor is one of the drone technologies that is applicable in many sectors and in both military and civilian activities, with some applications requiring autonomous flight. However, stability, path planning, and control remain significant challenges in autonomous quadrotor flights. Traditional control algorithms, such as proportional-integral-derivative (PID), have deficiencies, especially in tuning. Recently, machine learning has received great attention in flying UAVs to desired positions autonomously. In this work, we configure the quadrotor to fly autonomously by using agents (the machine learning schemes being used to fly the quadrotor autonomously) to learn about the virtual physical environment. The quadrotor will fly from an initial to a desired position. When the agent brings the quadrotor closer to the desired position, it is rewarded; otherwise, it is punished. Two reinforcement learning models, Q-learning and SARSA, and a deep learning deep Q-network network are used as agents. The simulation is conducted by integrating the robot operating system (ROS) and Gazebo, which allowed for the implementation of the learning algorithms and the physical environment, respectively. The result has shown that the Deep Q-network network with Adadelta optimizer is the best setting to fly the quadrotor from the initial to desired position.

Five decades have passed since the first bit was transmitted over the internet. Although the internet has improved our lives and led to the digital economy, currently only 51% of the world’s population have access to it. Currently, consumers mostly access the internet via mobile broadband, 2G, 3G, and 4G services. Regulatory bodies such as the Federal Communications Commission (FCC) of the US are responsible for ensuring that consumers receive an adequate service from Mobile Network Operators (MNOs). Usually, regulators evaluate the performance of each MNO in terms of service quality yearly and publish a report. To evaluate performance, metrics such as coverage, download/upload speed, and the number of subscribers can be used. However, the evaluation process and the metrics used by each regulatory body are inconsistent, and this makes it hard to determine which nations are providing adequate services to their citizens. Furthermore, it is not clear as to which performance evaluation is the right path. In this case study, we analyzed the reports released from eight nations (United States of America, United Kingdom, France, South Korea, Japan, Singapore, and Australia) as of the year 2020. We then point out the advantages and the drawbacks of the current evaluation process and metrics. Furthermore, a discussion on why the current methods are not sufficient to evaluate 5G services is presented. Our findings indicate that there is a great need for a unified metric and that this process becomes more complex with the rollout of 5G.

The substantial number of wearable devices in the healthcare industry and the continuous growth of the market procreates the demand for computational offloading. Despite major development of wearable devices and offloading techniques, there are several concerns such as latency, battery power, and computation capability that requires significant development. In this paper, we focus on the fact that most smart wearable devices have Bluetooth pairing with smartphones, and Bluetooth communication is significantly energy-efficient compare to 3G/LTE or Wi-Fi. We propose a computation offloading technique that offloads from the smartphone to the cloud server considering the decision model of both wearable devices and smartphones. Mobile cloud computing can elevate the capacity of smartphones considering the battery state and efficient communications with the cloud. In our model, we increase the energy efficiency of smart devices. To accomplish this, a Dhrystone Millions of Instructions per Second (DMIPS)-based workload measurement model along with a computation offloading decision model were created. According to the performance evaluation, offloading from wearable devices to smartphones and offloading once to cloud server can reduce energy consumption significantly.

Vehicular networks have stringent quality of service (QoS) requirements in terms of reliability, throughput, and latency. With the emergence of diverse services for autonomous driving, the resource contention in vehicle-to-vehicle communication can cause unavoidable packet loss and, therefore, must be handled for safety. Moreover, different levels of QoS should be defined for each service and task; however, existing solutions can neither provide a resource allocation scheme for any level of QoS requirement nor serve new stringent services without configuration or modification. We propose a distributed hierarchical deep Q-network (DH-DQN) to handle resource contention specifically. Thus, an intelligence resource management (I-RM) scheme is designed to serve on-demand QoSs. We first formulate the problem to address multiple QoS requirements, which extends the coverage of resource management tasks for on-demand stringent services. From the perspective of transmission pattern, we designed a hierarchical DQN structure that deals with resource block contention in a fully distributed manner and a state-action framework that enables a numerically defined service demand. In addition, a target $\epsilon$ -greedy is proposed to accelerate convergence, and a modified transfer learning algorithm is used to enhance learning performance for various levels of service. Through extensive simulations, we demonstrated that the proposed DH-DQN can learn successful transmission patterns to meet different levels of multiple QoS requirements.

In cognitive radio networks (CRNs), two secondary users (SUs) need to meet on a channel among multiple channels within a finite time to establish a link, which is called rendezvous. For blind rendezvous, researchers have devised ample well-grounded channel hopping (CH) sequences that guarantee smaller time-to-rendezvous. However, the best part of these works lacks the impact of network factors, particularly channel availability and collision during rendezvous. In this study, a new CH scheme is investigated by jointly considering the medium access control (MAC) protocol for single-hop multi-user CRNs. The analysis of our new variable hopping sequence (V-HS) guarantees rendezvous for the asymmetric channel model within a finite time. Although this mathematical concept guarantees rendezvous between two SUs, opportunities can be overthrown because of the unsuccessful exchange of control packets on that channel. A successful rendezvous also requires the exchange of messages reliably while two users visit the same channel. We propose a MAC protocol, namely ReMAC, that can work with V-HS and CH schemes. This design allows multiple rendezvous opportunities when a certain user visits the channel and modifies the conventional back-off strategy to maintain the channel list. Both simulation and analytical results exhibited improved performance over the previous approaches.