Device-to-device (D2D) communication is an essential part of the future fifth generation (5G) system that can be seen as “network of networks”, consisting of multiple seamlessly integrated radio access technologies (RATs). Public safety communications, autonomous driving, social-aware networking, and infotainment services are example use cases of D2D technology. High data rate communications and use of several active air interfaces in the described network create energy consumption challenges for both base stations and the end user devices. In this paper, we review the status of 3GPP standardization and define a set of application scenarios. We use the recent models of 3GPP Long Term Evolution (LTE) and WiFi interfaces in analyzing the power consumption both from the infrastructure and user device perspectives. The results indicate that the number of active interfaces should be minimized.

Device to Device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between active MDs and idle MDs in the D2D-MEC (Mobile Edge Computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D-MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently addressed in existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and coupling of actions across time slots, we model the problem as a Markov Decision Process (MDP) and perform the multi-agent DRL through Multi-Agent Proximal Policy Optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison with the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%.

Device-to-device (D2D) communication is a promising paradigm for the fifth generation 2 (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, 3 including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot 4 of technical challenges such as network architecture, and neighbor discovery, etc. The complexity 5 of configuring D2D links and managing their interference, especially when using millimeter-wave 6 (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address 7 these problems towards boosting the performance of D2D networks. In this paper, a comprehensive 8 survey about recent research activities on D2D networks will be explored with putting more 9 emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions 10 accompanied with their existing conventional solutions, we will show how different ML techniques 11 can be applied to enhance the D2D networks performance over using conventional ways. Then, still 12 open research directions in ML applications on D2D networks will be investigated including their 13 essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool 14 to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks 15 will be presented. This case study will put emphasis on the high potency of using ML solutions 16 over using the conventional non-ML based methods for highly improving the average throughput 17 performance of mmWave NDS.

In this paper, we propose a multi-zone service control scheme to maximize the performance of each service zone when a large number of cellular service zones and D2D (Device-to-Device) service zones are composed to 5G cellular network. This paper also improves performance of service zone by dividing traffic into real-time traffic and non-real-time traffic in order to minimize traffic interference. Real-time traffic and non-real-time traffic have a significant impact on communication performance. We propose a new self-detection traffic interference control technique to improve the QoS and throughput of D2D and C2D communication in a cellular network, STICS (Self-detecting Traffic Interference Control Scheme). The proposed STICS scheme distinguishes between short-term traffic congestion process and long-term traffic congestion process according to traffic characteristics to detect and control traffic. When the proposed scheme is applied to the 5G-based cellular network environment, it is expected that the traffic type will be efficiently classified by self-detecting the traffic according to the flow. Such classified traffic is less sensitive to communication between the D2D and C2D links, thereby reducing traffic overload. We evaluate the performance of the proposed scheme through simulation and show that the proposed scheme is more efficient than other comparison schemes.

In several emergency situations, during natural or human-caused disasters, frontline responders need to be able to communicate and collaborate to properly carry out the relief mission. Some countries are building their national Public Safety Mobile Broadband based on cellular LTE technology to provide fast, safe, and secure emergency services. Otherwise, in several emergency situations, the cellular antennas can be overloaded or partially damaged in manner to affect the group communication services. Since few last years, direct device-to-device (D2D) communications have been proposed by 3GPP as an underlay to Long Term Evolution (LTE) networks based on proximity, reuse, and hop gains. This paper focuses on the loosely coupled model based on D2D direct communication for Public Safety context. Many scenarios related to user membership and network management are detailed. Both “less cost” and “optimized tree” approaches are proposed, implemented, and their performance evaluated in terms of network updates number and resulting average Channel Quality Indicator (CQI). Other optimization approaches with different CQI threshold and optimization interval values have been implemented, simulated and their performance are compared with the “optimized tree”.

Internet of Things (IoT) is what we have as a great breakthrough in 5G network. Although 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6 addressing which is the foundation of the Network Slicing for the 5G core network. Comparing to other related papers, we provide in depth information on how the Internet of Things (IoT) is going to affect our lifes and how this technology is handled as Internet of Everything (IoE) in 6G network. Finally, a full reference is made to the 6G network with its challenges comparing to the 5G network.

As most Korean OTT subscribers use mobile devices as their primary medium for watching OTT video content, mobile network traffic is soaring. However, the existing build-out of mobile communication technology – such as deploying more base stations or expanding the network bandwidth – is struggling to keep pace with the enormous increased demands of mobile network traffic. Accordingly, wireless device-to-device (D2D) caching networks are considered one of the most reliable alternatives for reducing the overload from mobile network traffic caused by streaming video content online. However, wireless D2D caching networks have pros and cons, like any other technology. Thus, this study aims to examine the practical decision-making process of mobile OTT service users in response to the application of wireless D2D caching networks on their mobile devices. An online survey of 94 respondents was conducted in South Korea during 19–23 May 2022; the data obtained were analyzed using the analytic hierarchy process (AHP) approach. The study finds that it was the risks, rather than the situational conditions and benefits, that mostly affected mobile OTT service users’ behavioral intentions and strategies. Streaming video content using mobile data or Wi-Fi was perceived as the most viable option. Mobile OTT service users were also willing to take the leap of shifts towards downloading video content in advance rather than adopting wireless D2D caching networks on a mobile device. However, mobile OTT service users’ decisions are heavily dependent on situational conditions related to use of mobile OTT services that make the quality of service consumption different. As one of the first to examine the responses of mobile OTT service users, this study provides meaningful implications for a timely and sensitive issue.

Enabling D2D communication in the mm-Wave band has many obstacles which must be mitigated. The primary concern is the introduction of interference from various sources. Thus, we focused our work on the performance of decode and forward (DF) relay assisted D2D communication in the mm-Wave band for increasing the coverage probability and energy efficiency (EE). Three modes are proposed for D2D communication to prevail. Bit-wise binary XOR operation is executed at the relay node which increases the security feature. Radius of coverage expression is derived which indicates the switching of the modes. The diffused incoherent scattering power is also considered as a part of power consumption. Furthermore, a unique relay selection scheme, Dynamic relay selection (DRS) method is proposed to select the optimal relay for information exchange. Comparison of the proposed DF relay scheme with amplify and forward (DF) scheme is also shown. Finally, simulation results prove the efficacy of the proposed work.