D2D Links Research Articles

A Dynamic Algorithm for Interference Management in D2D-Enabled Heterogeneous Cellular Networks: Modeling and Analysis.

To supporting a wider and diverse range of applications, device-to-device (D2D) communication is a key enabler in heterogeneous cellular networks (HetCNets). It plays an important role in fulfilling the performance and quality of service (QoS) requirements for 5G networks and beyond. D2D-enabled cellular networks enable user equipment (UE) to communicate directly, without any or with a partial association with base stations (eNBs). Interference management is one of the critical and complex issues in D2D-enabled HetCNets. Despite the wide adoption of D2D communications, there are very few researchers addressing the problems of mode selection (MS), as well as resource allocation for mutual interference in three-tier cellular networks. In this paper, we first identify and analyze three key factors, namely outage probability, signal-to-interference and noise ratio (SINR), and cell density that influence the performance of D2D-enabled HetCNets. We then propose a dynamic algorithm based on a distance-based approach to minimize the interference and to guarantee QoS for both cellular and D2D communication links. Results obtained show that outage probability is improved by 35% and 49% in eNB and SCeNB links, respectively, when compared with traditional neighbor-based methods. The findings reported in this paper provide some insights into interference management in D2D communications that can help network researchers and engineers contribute to further developing next-generation cellular networks.

Multi-Objective Parallel Task Offloading and Content Caching in D2D-aided MEC Networks

In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

AI-Based Channel Prediction in D2D Links: An Empirical Validation

Device-to-Device (D2D) communication propelled by artificial intelligence (AI) will be an allied technology that will improve system performance and support new services in advanced wireless networks (5G, 6G and beyond). In this paper, AI-based deep learning techniques are applied to D2D links operating at 5.8 GHz with the aim at providing potential answers to the following questions concerning the prediction of the received signal strength variations: i) how effective is the prediction as a function of the coherence time of the channel? and ii) what is the minimum number of input samples required for a target prediction performance? To this end, a variety of measurement environments and scenarios are considered, including an indoor open-office area, an outdoor open-space, line of sight (LOS), non-LOS (NLOS), and mobile scenarios. Four deep learning models are explored, namely long short-term memory networks (LSTMs), gated recurrent units (GRUs), convolutional neural networks (CNNs), and dense or feedforward networks (FFNs). Linear regression is used as a baseline model. It is observed that GRUs and LSTMs present equivalent performance, and both are superior when compared to CNNs, FFNs and linear regression. This indicates that GRUs and LSTMs are able to better account for temporal dependencies in the D2D data sets. We also provide recommendations on the minimum input lengths that yield the required performance given the channel coherence time. For instance, to predict 17 and 23 ms into the future, in indoor and outdoor LOS environments, respectively, an input length of 25 ms is recommended. This indicates that the bulk of the learning is done within the coherence time of the channel, and that large input lengths may not always be beneficial.

Stable Matching Relay Selection (SMRS) for TWR D2D Network With RF/RE EH Capabilities

The green Internet of Things (IoT) has emerged as a promising paradigm to reduce the energy consumed by nodes in dense networks. To ensure energy efficiency (EE) operation, network devices are equipped with energy harvesting (EH) batteries that can further prolong the network lifetime. This study investigates a two-way relaying (TWR) device-to-device (D2D) model sharing the same resources with the underlying cellular network where all devices can harvest renewable energy (RE) from the surrounding environment. Each relay can assist one D2D link and harvest part of the received signal using the power splitting (PS) protocol. The radio frequency (RF) harvested energy is modeled using a non-linear EH model to match the behavior of the practical energy harvester. The main objective is to maximize the data rate (DR) of D2D links while preserving the quality of service (QoS) constraints. Therefore, a joint optimization solution based on particle swarm optimization (PSO) for power allocation (PA) and one-to-one stable matching (SM) for best relay selection (RS) is performed to untangle the mixed integer non-linear programming (MINLP) problem. Simulation results illustrate the behavior of the proposed model under different parameters as well as it is superiority over the most recent algorithm in terms of D2D link rate and EE.

Social-Content-Aware Scalable Video Streaming in Internet of Video Things

The Internet of Things (IoT) is evolving into the Internet of Video Things (IoVT) that supports massive smart devices and multiple video applications. However, how to effectively control massive devices and transmit large-volume video data have become challenges in the current IoVT. Inspired by device-to-device (D2D) communications and coalitional game, this article constructs a self-organized D2D collaborative video content sharing framework for the IoVT. Specifically, we first propose a collaboration mechanism by introducing the social attributes of IoVT devices and their owners. In this mechanism, D2D collaborative coalitions are automatically formed among IoVT devices and video content is shared in the coalitions through D2D links. In this way, the burden of controlling massive IoVT devices and video data traffic are offloaded. Then, by integrating the scalability of the scalable-high-efficiency-video-coding (SHVC) streams and the flexibility of D2D networking, a collaborative video streaming strategy is developed. It takes advantage of provider set arrangement and transmission scheduling to reduce the impact of network instability on video services. Simulation results verify the effectiveness of the proposed mechanism and strategy.

A Mobility-Based Epidemic Model for IoT Malware Spread

With the rapid advancement of technology, IoT has become inseparable from human lives. IoT is extensively used in transport, healthcare, manufacturing, among other sectors. However, this technology lacks sufficient security defense capabilities, thus becoming a highway for malicious actors. IoT networks use infrastructure-based (INF) and device-to-device (D2D) communications to propagate data. The INF communication utilizes technologies such as WLAN, LTE, GPRS, and GSM to relay information from source to destination. The D2D paradigm, on the other hand, is a close-proximity communication in which sensors exchange data in a multi-hop manner. Since malware can utilize both D2D and INF links to spread out, IoT networks are exceptionally vulnerable to attacks. Therefore, we propose Susceptible-Exposed-Infected-Recovered-Dead (SEIRD) model to examine the dynamics of IoT malware spread via INF and D2D communications. We analyze the impacts of mobility on infection propagation and illustrate that our model adequately captures IoT malware spread behaviors through mathematical analysis and simulations. We also compute the malware transmission threshold, which can be used as a guideline to mitigate and suppress an attack.

Genetic Based Approach for Optimal Power and Channel Allocation to Enhance D2D Underlaied Cellular Network Capacity in 5G

With the obvious throughput shortage in traditional cellular radio networks, Device-to-Device (D2D) communications has gained a lot of attention to improve the utilization, capacity and channel performance of next-generation networks. In this paper, we study a joint consideration of power and channel allocation based on genetic algorithm as a promising direction to expand the overall network capacity for D2D underlaied cellular networks. The genetic based algorithm targets allocating more suitable channels to D2D users and finding the optimal transmit powers for all D2D links and cellular users efficiently, aiming to maximize the overall system throughput of D2D underlaied cellular network with minimum interference level, while satisfying the required quality of service QoS of each user. The simulation results show that our proposed approach has an advantage in terms of maximizing the overall system utilization than fixed, random, BAT algorithm (BA) and Particle Swarm Optimization (PSO) based power allocation schemes.

An Optimal Balanced Energy Harvesting Algorithm for Maximizing Two-Way Relaying D2D Communication Data Rate

Combining energy harvesting (EH) and device-to-device (D2D) communication underlaying 5G cellular networks is a very promising direction to improve both energy and spectral efficiencies. Unlike conventional relay-aided D2D communication that assumes one-way relaying (OWR) protocols, this paper proposes a two-way relaying (TWR) model. It aims to maximize the TWR D2D link rate that shares the uplink (UL) resources of the conventional cellular user (CU) considering the quality of service (QoS) constraints of all users. Besides, the relays are considered to harvest renewable energy (RE) from the ambient environment by relying on an attached solar panel. Also, they can harvest radio frequency (RF) energy from the received signal based on the power splitting (PS) EH protocol. Assuming that the UL resource allocation (RA) is already performed, the paper’s objective is to jointly optimize the transmission power of all users in addition to the PS factor of relays based on the well-known meta-heuristic algorithm particle swarm optimization (PSO). Also, the best relay is selected by relying on the delimited area (DA) mechanism and the balanced residual energy (BRE) leading to TWR D2D link rate maximization and better energy efficiency (EE). The performance of the proposed algorithm is investigated through the results as well as comparing its performance to two of the most recent relay-aided D2D algorithms.

Files Delivery and Share Optimization in LEO Satellite-Terrestrial Integrated Networks: A NOMA Based Coalition Formation Game Approach

Non-orthogonal multiple access (NOMA) in satellite communication (SATCOM) system can bring high spectral efficiency and massive connectivity. In this paper, we investigate files delivery and share optimization in LEO satellite-terrestrial integrated networks (STINs). A NOMA based coalition formation game (CFG) approach is proposed for minimizing total cost, in which satellite transfer files to head users in each group via NOMA, and head users utilize device to device (D2D) communications to share files among users. Firstly, head users selection algorithm is proposed to choose each group's head users receiving files from satellite. Precoding vectors and transmit power optimization with imperfect channel state information (CSI) are derived to achieve successful NOMA transmission and files download cost minimization in each group. A graph theory based algorithm is proposed to find the optimal D2D links and minimize files share cost. Then, we formulate a CFG and propose a preference order (Group-Best order) for user grouping. Furthermore, we have proved that the CFG with Group-Best order is an exact potential game (EPG), which can get stable group partition and achieve global optimization, i.e., minimizing total cost. Finally, a best response algorithm for NOMA based CFG (NCFG) is proposed to find stable group partition. Simulation results verify that our proposed approach is better than other approaches.

Throughput and Age of Information in a Cellular-Based IoT Network

This paper studies the interplay between device-to-device (D2D) communications and real-time monitoring systems in a cellular-based Internet of Things (IoT) network. In particular, besides the possibility that the IoT devices communicate directly with each other in a D2D fashion, we consider that they frequently send time-sensitive information/status updates (about some underlying physical processes observed by them) to their nearest cellular base stations (BSs). Specifically, we model the locations of the IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another independent PPP. For this setup, we characterize the performance of D2D communications using the average network throughput metric whereas the performance of the real-time applications is quantified by the Age of Information (AoI) metric. The IoT devices are considered to employ a distance-proportional fractional power control scheme while sending status updates to their serving BSs. Hence, depending upon the maximum transmission power available, the IoT devices located within a certain distance from the BSs can only send status updates. This association strategy, in turn, forms the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Johnson-Mehl (JM)</i> tessellation, such that the IoT devices located in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">JM cells</i> are allowed to send status updates. The average network throughput is obtained by deriving the mean success probability for the D2D links. On the other hand, the temporal mean AoI of a given status update link can be treated as a random variable over space since its success delivery rate is a function of the interference field seen from its receiver. Thus, in order to capture the spatial disparity in the AoI performance, we characterize the spatial moments of the temporal mean AoI. In particular, we obtain these spatial moments by deriving the moments of both the conditional success probability and the conditional scheduling probability for status update links. Our results provide useful design guidelines on the efficient deployment of future massive IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications.

Sum-Rate Maximization for Multi-Reconfigurable Intelligent Surface-Assisted Device-to-Device Communications

This paper proposes to deploy multiple reconfigurable intelligent surfaces (RISs) in device-to-device (D2D)-underlaid cellular systems. The uplink sum-rate of the system is maximized by jointly optimizing the transmit powers of the users, the pairing of the cellular users (CUs) and D2D links, the receive beamforming of the base station (BS), and the configuration of the RISs, subject to the power limits and quality-of-service (QoS) of the users. To address the non-convexity of this problem, we develop a new block coordinate descent (BCD) framework which decouples the D2D-CU pairing, power allocation and receive beamforming, from the configuration of the RISs. Specifically, we derive closed-form expressions for the power allocation and receive beamforming under any D2D-CU pairing, which facilitates interpreting the D2D-CU pairing as a bipartite graph matching solved using the Hungarian algorithm. We transform the configuration of the RISs into a quadratically constrained quadratic program (QCQP) with multiple quadratic constraints. A low-complexity algorithm, named Riemannian manifold-based alternating direction method of multipliers (RM-ADMM), is developed to decompose the QCQP into simpler QCQPs with a single constraint each, and solve them efficiently in a decentralized manner. Simulations show that the proposed algorithm can significantly improve the sum-rate of the D2D-underlaid system with a reduced complexity, as compared to its alternative based on semidefinite relaxation (SDR).

Reconfigurable Intelligent Surface Empowered Device-to-Device Communication Underlaying Cellular Networks

Reconfigurable intelligent surface (RIS) is a new and revolutionary technology to achieve spectrum-, energy- and cost-efficient wireless networks. This paper studies the resource allocation for RIS-empowered device-to-device (D2D) communication underlaying a cellular network, in which an RIS is employed to enhance desired signals and suppress interference between paired D2D and cellular links. We maximize the overall network’s spectrum efficiency (SE) and energy efficiency (EE), respectively, by jointly optimizing the spectrum reuse indicators, the transmit power, the RIS’s passive beamforming and the BS’s receive beamforming. To solve both mixed-integer non-linear programming problems, we first propose an efficient and low-complexity user-pairing scheme based on relative channel strength to determine the spectrum reuse indicators. Other variables are then optimized to maximize the SE by an iterative algorithm, based on the techniques of alternating optimization, successive convex approximation, Lagrangian dual transform and quadratic transform. The EE-maximization problem is solved by an alternating algorithm integrated with Dinkelbach’s method. Numerical results show that the proposed design achieves significant SE and EE enhancements compared to traditional underlay D2D network without RIS, relay-assisted D2D network and other benchmarks.

Enhancement for Content Caching and Delivery with Energy Consumption Awareness in Wireless Networks

The advanced capabilities of smart mobile devices for mobile network users have led to an explosive increase in the loads of networks, mainly driven by the increasing demand for multimedia services. Content caching in users’ devices can potentially enhance both the quality of service for mobile users and network performance. Upon users’ requests and their mobility models, a network can perform proactive caching to reduce network congestion. Furthermore, implementing an optimal caching method can increase the offloading probability, leading to an overall enhanced performance of the network. In this paper, we present an enhanced energy-based content caching and delivery approach for D2D assisted wireless networks. A cost problem is formulated for optimal caching by considering two important constraints establishing D2D links: Signal-to-Noise Ratio (SNR) and Energy Consumption (EC). An effective optimization algorithm, the Frequency Searching Adaptive Bat Algorithm (FSABA), is used to enhance the cached and delivered content, minimizing the total network cost. The system performance is analyzed and evaluated in terms of total network gain, offloading probability and average transmission delay. To validate the proposed enhancement caching approach, extended simulations are provided.

Transfer Learning-powered Resource Optimization for Green Computing in 5G-Aided Industrial Internet of Things

Objective: Green computing meets the needs of a low-carbon society and it is an important aspect of promoting social sustainable development and technological progress. In the investigation, green computing for resource management and allocation issues is only discussed. Therefore, in the context of the 5G communication network, the investigation of the data classification and resource optimization of the Internet of Things are conducted. Method: The virtualization architecture of the heterogeneous wireless network resource based on 5G technology is designed. The related investigation is conducted based on 5G network and Internet of Things technology. Under the traditional method, the transfer learning is introduced to improve the AdaBoost (Adaptive Boosting) algorithm to classify the data. The investigated complete resource reuse method is used to optimize resources. A method that a sub-channel can be reused by a cellular link and any number of D2D links at the same time is proposed to conduct resource optimization investigation. Results: The investigation indicates that the classification accuracy of the algorithm is excellent for the data classification of the Internet of Things and has different advantages in various aspects compared with other algorithms. The designed algorithm can find a larger set of resource reuse and have a significant increase in spectrum utilization efficiency. Conclusion: The investigation can contribute to the boom in the Internet of Things in terms of data classification and resource optimization based on 5G.

Deep reinforcement learning-based resource allocation for D2D communications in heterogeneous cellular networks

Device-to-Device (D2D) communication-enabled Heterogeneous Cellular Networks (HCNs) have been a promising technology for satisfying the growing demands of smart mobile devices in fifth-generation mobile networks. The introduction of Millimeter Wave (mm-wave) communications into D2D-enabled HCNs allows higher system capacity and user data rates to be achieved. However, interference among cellular and D2D links remains severe due to spectrum sharing. In this paper, to guarantee user Quality of Service (QoS) requirements and effectively manage the interference among users, we focus on investigating the joint optimization problem of mode selection and channel allocation in D2D-enabled HCNs with mm-wave and cellular bands. The optimization problem is formulated as the maximization of the system sum-rate under QoS constraints of both cellular and D2D users in HCNs. To solve it, a distributed multiagent deep Q-network algorithm is proposed, where the reward function is redefined according to the optimization objective. In addition, to reduce signaling overhead, a partial information sharing strategy that does not observe global information is proposed for D2D agents to select the optimal mode and channel through learning. Simulation results illustrate that the proposed joint optimization algorithm possesses good convergence and achieves better system performance compared with other existing schemes.

Enabling Multi-Source Device-to-Device Content Delivery in Cellular Networks

Multi-source device-to-device (D2D) communication allows the base station (BS) to serve the content requests of the users locally through D2D links. As a result, the load on the base stations (BSs) and the consumption of radio resources reduces significantly. Clearly, the success of multi-source content delivery relies on the willingness of the content owners (COs), i.e., sources, to deliver their content to requesting user. Consequently, in this article, we investigate the economic interaction between single BS and multiple COs for content delivery. In view of the fact that participating COs are heterogeneous with respect to the amount of content in their cache and the sensitivity towards energy consumption, there is a need to design a fair incentive mechanism which motivates COs for content delivery. To this end, we model the interaction among the BS and multiple COs as one-to-many bargaining game and design an incentive mechanism based on the Nash bargaining framework. Specifically, we obtain the optimal amount of content delivered by participating COs and their corresponding incentives under two variants of one-to-many bargaining, namely sequential bargaining and concurrent bargaining. For both variants of bargaining, the obtained optimal solutions are capable of minimizing the amount of content delivered by the BS while ensuring fair incentive transfer among the participating COs.

Outage Trade-Offs Between Full/Half-Duplex Relaying for NOMA Aided Multicarrier Cooperative D2D Communications System

ABSTRACT Non-orthogonal multiple access (NOMA) and full-duplex (FD) have recently emerged as a potential candidate for the fifth-generation and beyond (B5G) cellular networks with the target of increasing spectral efficiency and guaranteeing the quality-of-services (QoS). This paper proposes an orthogonal frequency division multiple access (OFDMA) based FD cooperative device-to-device (C-D2D) communications system, wherein a D2D transmitter (DT) acts as an FD relay to support simultaneous bidirectional communication between a base station (BS) and a cellular user (CU). In addition, a power splitting protocol using NOMA is incorporated for the FD C-D2D system to share the resources between the cellular and D2D user. Further, the successive interference cancellation (SIC) is used to decode the signals, while self-interference suppression (SIS) is employed at DT to mitigate self-interference (SI). The analytical expressions for achievable rates of the cellular and D2D links and their outage probabilities for the FD and half-duplex (HD) systems are derived. Outage trade-offs between FD/HD relaying have been analyzed. Results show that the proposed model enhances the overall outage performance of the cellular and D2D users. Moreover, for high cellular outage constraints, DT as an FD relay outperforms the conventional HD C-D2D communications system wherein DT acts as an HD relay.

Heterogeneous Stream Scheduling for Cross-Modal Transmission

Cross-modal communication is playing an increasingly important role in improving receivers' immersive experience. The main challenge lies in ensuring the heterogeneous requirements of the cross-modal stream. Especially, the discontinuity of the received haptic signals caused by the delay should be eliminated. Unfortunately, existing schemes study the cross-modal stream separately, which leads to that the haptic signal is distorted, or the audio-visual quality is reduced. To solve this problem fundamentally, we propose a joint transmission framework combining prediction and device-to-device (D2D) communication by taking advantage of the correlation of the haptic signals and the proximity feature of the receivers. Specifically, on the theoretical end, to completely eliminate the discontinuity, we propose a prediction mechanism by predicting and sending the future signals in advance. To compensate for the reliability loss brought by prediction, D2D links are efficiently established on the receivers' side. On the technical end, we first design a minimum resource (e.g., power) consumption search algorithm based on the binary search to obtain the optimal prediction horizon. Moreover, we develop a simple but efficient transmission mode selection algorithm based on the Hungarian algorithm. Experimental results demonstrate the advantages of our proposed scheme in saving the power consumption.

Statistical QoS guarantees of a device‐to‐device link assisted by a full‐duplex relay

AbstractDevice‐to‐device (D2D) communication is a promising technique to enhance the spectral efficiency for 5G and beyond cellular networks. Traditionally, D2D communication was considered solely as communication directly between two closely‐located devices, without any support from the available network infrastructure; however, this understanding limits the advantages of D2D communication. Relay‐assisted D2D communication was later proposed to allow devices to exchange data and extend coverage via relay strategy for transmission distance extension and capacity improvements. This work aims to determine the statistical quality‐of‐service (QoS) guarantees of a D2D link assisted by a full‐duplex relay by employing a commonly used analytical tool, the effective capacity (EC). We provide a closed‐form expression for the EC of relay‐assisted D2D communication, in which both the transmitter and the relay devices operate under individual QoS constraints. We also provide an analysis of the impact of mode selection on the EC of relay‐assisted D2D. Additionally, for the mode selection, we propose a novel multiple features‐based mechanism by utilizing all the features of a wireless link. We further provide a closed‐form solution for the non‐convex calibration problem of the optimal weights for these underlying features. Finally, simulation results demonstrate how the quality of self‐interference cancellation techniques impacts the EC of the relay‐assisted D2D. The results also show that the performance of our proposed mode selection mechanism with optimal weights outperforms the traditional mode selection mechanism.

Energy Efficient D2D Communications Using Multiple UAV Relays

In this paper, we propose a novel optimization model for multiple Unmanned Aerial Vehicles (UAVs) working simultaneously as relays to help two set of ground users; namely relay users and device-to-device (D2D) users. The relay users are assumed out communication rages from each other and use the UAV link to for their data transmission. While the D2D users are assumed to be nearby users and use the UAV for managing the resources without being involved in the transmission. The goal of the paper is to operate the UAVs in an energy-efficient manner to support the different set of users by i) optimizing the available bandwidth and power allocations of the D2D links, and ii) acting as relays when needed to maintain the communication links between relay users. We formulate an optimization problem that maximizes the throughput-energy utility while respecting the resource availability including the UAVs’ energy consumption, UAV-user association, and trajectory constraints. Due to the non-convexity of the problem, we propose to solve it in three steps using Taylor series approximation. Firstly, we optimize the transmit power of the UAVs and users. Then, we optimize the bandwidth allocation for a given transmit power values. Finally, an efficient heuristic algorithm based on a recursive shrink-and-realign process is proposed to optimize the UAVs’ trajectories. The performance of the proposed method shows advantages in terms of average throughput compared to the fixed power and bandwidth solutions.