D2D Multicast Research Articles

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

Multicast device-to-device (D2D) communication technology is considered as one of the new technologies in the fifth generation (5G) networks that directly addresses the need for content sharing among internet users. In fact, when direct communication is available between devices, the spectral efficiency is improved by reusing the licensed cellular spectrum. The current studies show that D2D communication increases network capacity and reduces latency. In order to achieve the alternate capabilities, coordination is required to implement interference management. We considered subcarrier allocation for the uplink, in addition to the power control that takes place on the underlay network. The completed data rate in single multicast communication is significantly reduced and limited by nodes with lower channel quality. In this paper, we used Shuffled Frog Leaping Algorithm (SFLA) for resource allocation (RA) in D2D multicast communications. We compared the results of the SFLA algorithm with the Firefly Algorithm (FA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO); in terms of D2D user throughput, Cellular User (CU) throughput, network average throughput, network interference and signal interference noise ratio (SINR) target. The simulation results show that SFLA clearly outperforms other algorithms in terms of data rate under the high pressure of infeasibility.

A Joint Cluster Formation Scheme With Multilayer Awareness for Energy-Harvesting Supported D2D Multicast Communication

Recently, Energy Harvesting-supported Device-to-Device (EH-D2D) communication receives extensive concerns due to its excellent properties in Energy Efficiency (EE), offloading capability, etc. Researches on EH-D2D mainly focus on resource allocation schemes of available energy. With the popularity of mobile devices and applications, content sharing becomes anytime, anywhere. Multicast is the foundation to realize the content sharing and can reduce transmission consumption because of multi-user service feature. Thus, Mutlicast can further improve EE of EH-D2D in communication mode rather than in resource allocation. However, the heterogeneity and imbalance of available energy, content requests and social ties on the user side will bring great challenges to the deployment of EH-D2D Multicast (EH-D2MD) communication. This paper aims at EE optimization and establishes a two-layer cluster formation model, which solves grouping, cluster head selection and power control jointly. When facing the non-convex of the sum of fractional functions and the product of binary variables, this paper specifically designs a two-step Convex Approximation Algorithm (CDA). CDA skillfully transforms the modeled non-convex Mixed Integer Non-Linear Programming (MINLP) problem into a convex MINLP one, which is easier to solve. Simulation results reveal that CDA can obtain the joint cluster formation results with approximate optimal EE and lower complexity.

Joint optimization for RIS-assisted multicast D2D communications

Multicast device-to-device (D2D) communication is an important technique to overcome the problem of spectrum shortage. Due to the channel gain difference between users in different locations, receivers in the same multicast D2D group still suffer from the problem of different quality of service (QoS) levels. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted multicast D2D communication system to enhance the QoS of users, in which RIS solves the problem of different QoS between users through intelligent resource optimization. To maximize the achievable sum rate of all multicast D2D users, we formulate a joint optimization problem. This problem combines channel assignment, power allocation, and reflection matrix design by considering the QoS fairness among different D2D users. To solve this problem, we propose a novel joint optimization algorithm, which has three steps. First, we propose a matching algorithm based on swap operations to solve the channel assignment problem. Then, we propose a successive convex approximation (SCA) method to solve the power allocation problem. Finally, we present the optimization of the RIS reflection coefficients with mathematical analysis. The numerical results demonstrate that the proposed algorithm can effectively enhance the QoS of users in the multicast D2D group. With the aid of RIS, the proposed algorithm decreases the service disparities between users in different locations. Furthermore, it reveals that it is helpful to deploy the RIS closer to the receivers or the transmitters.

Towards efficient device-to-device multicast content delivery with social awareness

Frequent content retrieval leads to significant energy consumption in cellular networks. Thereby, device-to-device (D2D) communication is used for proximal content delivery. Be aware that social friends may have common content interests, D2D content delivery is further improved by considering social network. However, how to implement efficient D2D multicast with the aid of social relationship needs further consideration. In this paper, with a proposed architecture on combined networks, the cooperation range of content sharing is investigated for D2D multicast to minimize the average energy consumption of serving a content request. Based on combined networks, a cooperation group formation scheme is proposed. Simulation results verify our analyzes and the advantage of proposed scheme.

Multicast Through Device to Device Communication Using Enhanced Diffie–Hellman Algorithm

The mobile device demand has been increased and it is estimated that in upcoming 10 years, the wireless cellular demand will increase up to 500 times. And it is not possible only with the cellular networks to satisfy this huge demand. To meet the huge requirement from the mobile end user, 5G networks enabled device to device communication can be applied. Device to Device (D2D) connection is the direct link among the two devices without the intervention of main station. Thus, D2D reduces the latency. And 5G are supported for the high data rates. Furthermore, for efficient transmission, multicast device to device communication is used. Multicast distrubutes the messages to multiple users of the restricted geographical area. Trustworthy communication is necessary for multicast D2D communication. Enhanced Diffie–Hellman algorithm is used for the trustworthiness.

Non-Orthogonal Multiple Access for Unicast and Multicast D2D: Channel Assignment, Power Allocation and Energy Efficiency.

Non-orthogonal multiple access (NOMA) techniques have emerged in the past years as a solution to approximate the throughput performance of wireless communications systems to their theoretical capacity region. We consider in this paper an optimization-based model for multicast device-to-device (MD2D) communications where the channels are not orthogonal and may be (partially or fully) shared among the transmitters in each cluster. This setting leads naturally to the introduction of NOMA transmitters and receivers who use successive interference cancellation (SIC) to separate the superposed signals. To analyze the role of NOMA in MD2D, its performance impact, potential performance gains and possible shortcomings, we formulate a model that includes SIC operations in the decoders, so that higher rates can be attained when several sources transmit on the same channel(s). We also investigate the energy efficiency of the network (global and max-min) through a dynamic power control algorithm and present a centralized and a semi-distributed solution to these optimization problems. Through numerical simulations, we show that NOMA is able to improve both the sum-rate and the max-min rate of a MD2D network even from a small degree of resource sharing. Furthermore, these gains also improve the global energy efficiency on the network, but not always the max-min energy efficiency of the devices.

Multicast Through Device to Device Communication using Enhanced Diffie-Hellman Algorithm

The mobile device demand has been increased and it is estimated that in upcoming 10 years, the wireless cellular demand will increase up to 500 times. And it is not possible only with the cellular networks to satisfy this huge demand. To meet the huge requirement from the mobile end user, 5G networks enabled device to device communication can be applied. Device to Device (D2D) connection is the direct link among the two devices without the intervention of main station. Thus, D2D reduces the latency. And 5G are supported for the high data rates. Furthermore, for efficient transmission, multicast device to device communication is used. Multicast distrubutes the messages to multiple users of the restricted geographical area. Trustworthy communication is necessary for multicast D2D communication. Enhanced Diffie-Hellman algorithm is used for the trustworthiness.

Distributed Resource Allocation in Underlay Multicast D2D Communications

Multicast device-to-device communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to nearby receivers. However, due to the critical challenge of having an intelligent interference coordination between multicast groups along with the cellular network, it is necessary to judiciously perform resource allocation for the combined network. In this work, we present a framework for a joint channel and power allocation strategy to maximize the sum rate of the combined network while guaranteeing minimum rate to individual groups and cellular users. The objective function is augmented by an austerity function that penalizes excessive assignment of low rate channels. The formulated problem is a mixed-integer-non-convex program, which requires exponential complexity to obtain the optimal solution. To tackle this, we exploit fractional programming and integer relaxation to obtain a parametric convex approximation. Based on sequential convex approximation approach, we first propose a centralized algorithm that ensures convergence to a limit point. Next, we propose a distributed algorithm in which via dual decomposition, separable sub-problems are formulated to be solved at the respective groups in cooperation with the base station. We provide convergence guarantees of the proposed solutions and demonstrate their merits by simulations, showing improvement in network throughput.

A social-aware K means clustering algorithm for D2D multicast communication under SDN architecture

In order to improve efficiency and reliability of data sharing, this paper investigates D2D multicast users clustering problem based on the social attributes of terminal equipment. A software-defined network (SDN) architecture is introduced to relieve the signaling and calculating pressures of base station. Besides, a social-aware K means clustering algorithm for D2D multicast communication that includes user equipments (UEs) clustering and core user selection is proposed under SDN architecture. Both physical distance and social distance between UEs are taken into account when performing D2D multicast clustering algorithm. In addition, the power of UE, storage space, social attribute and mobility properties are comprehensive considered when selecting core user in D2D multicast groups. The numerical simulation results show that the proposed scheme is effective in improving system utility and throughput performance and has a good robustness for D2D multicast clusters.

Energy-efficient relay selection and power allocation for multi-source multicast network-coded D2D communications

Device-to-device (D2D) communication improves spectrum efficiency (SE) and increases system capacity. Heterogeneous networks comprise macrocell and several small cells, such as picocells and femtocells, where femtocells are more efficient. In cooperative communications, one or several additional nodes help to send data from source to destination to improve the quality of service (QoS). Network coding (NC) in cooperative networks can extend data rate and coverage area of D2D communication. The purpose of this article is to minimize power consumption of transmitting nodes during network-coded cooperative D2D transmission to reduce the interference on co-channel transmissions. In this study, we consider cooperative multicast D2D communication with NC, where femtocell base stations (FBSs) are used as relay. We propose a method based on energy efficiency (EE) maximization for relay selection and power allocation for multi-source network-coded cooperative D2D communication in heterogeneous cellular networks. We present a solution to the proposed method and evaluate its performance. The results show that the introduced method significantly improves the SE and EE compared to the coordinated multi-point (CoMP) transmission, multi-source direct D2D communication (D-D2D), and other works.

Distributed Energy Efficient Channel Allocation in Underlay Multicast D2D Communications

In this paper, we address the optimization of the energy efficiency of underlay multicast device-to-device (D2MD) communications on cellular networks. In particular, we maximize the energy efficiency of both the global network and the individual users considering various fairness factors such as maximum power and minimum rate constraints. For this, we employ a canonical mixed-integer non-linear formulation of the joint power control and resource allocation problem. To cope with its NP-hard nature, we propose a two-stage semi-distributed solution. In the first stage, we find a stable, yet sub-optimal, channel allocation for D2MD groups using a cooperative coalitional game framework that allows co-channel transmission over a set of shared resource blocks and/or transmission over several different channels per D2MD group. In the second stage, a central entity determines the optimal transmission power for each user in the system via fractional programming. We performed extensive simulations to analyze the resulting energy efficiency and attainable transmission rates. The results show that the performance of our semi-distributed approach is very close to that obtained with a pure optimal centralized one.

Relay selection for multi-source network-coded D2D multicast communications in heterogeneous networks

Multicast service is considered as a useful transmission mode for future mobile social services to deliver the traffic to multiple mobile users simultaneously. Device-to-device (D2D) multicast communication enables multiple proximate users to share the contents of their common interests directly. This paper aims to present a relay selection scheme for multi-source network-coded D2D multicast communication underlying heterogeneous cellular network which consists of macrocells and femtocells. We propose two methods for selecting efficient relays from the set of femtocell base stations (FBSs). The first method, named destination-blind relay selection, identifies those idle FBSs that can correctly decode all source messages. Since all identified relays may not be necessary, this method reduces the average transmission rate. Hence, in the second method named destination-aware relay selection, we find the minimum number of required relays by maximizing the spectral efficiency to make sure that all destinations can correctly decode all source messages. In contrast to the first method, the second method requires information about destinations. We present efficient solutions for the proposed methods and obtain their performance in terms of outage probability and spectral efficiency. We also derive analytical expressions for the outage probability to verify simulation results. It is shown that the introduced methods improve the spectral efficiency compared to the direct D2D multicast, random relay selection, and coordinated multi-point transmission. Further, the presented methods achieve lower outage probability than direct D2D multicast and random relay selection methods. The proposed methods reduce the average outage probability about 17 times and increase the average spectral efficiency about 16 times in comparison with direct multi-source D2D multicast, which indicate their efficiency.

Traffic Offloading in Multicast Device-to-Device Cellular Networks: A Combinatorial Auction-Based Matching Algorithm.

In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, there is need for smart spectrum management policies. In this paper, we consider a fractional frequency reuse (FFR) technique which divides the whole spectrum into multiple sections and allows reusing of spectrum resources between the conventional cellular users and multicast D2D users in a non-orthogonal scenario. Since conventional cellular users and multicast D2D users shared same resources simultaneously, they generate severe data traffic and high communication overhead. To overcome these issues, in this paper we propose Lagrange relaxation technique to solve the non-convex problem and combinatorial auction-based matching algorithm to select the most desirable resource reuse partners by fulfilling the quality of service (QoS) requirements for both the conventional cellular users and multicast D2D users. Then, we formulate an optimization problem to maximize the overall system performance with least computational complexity. We demonstrate that our method can exploit a higher data rate, spectrum efficiency, traffic offload rate, coverage probability, and lower computational complexity.

Social-aware resource allocation for multicast device-to-device communications underlying UAV-assisted networks

Unmanned aerial vehicle (UAV)-assisted base stations (BS) can keep the system performance of the cellular networks with device-to-device (D2D) communications in the scenarios that terrestrial BSs are not available. In this paper, the resource allocation is investigated for D2D communications underlaying UAV-assisted cellular networks, where a UAV-assisted BS serves and coordinates multicast D2D clusters and CUs who share common cellular resources. We propose a novel matching-based social-aware resource allocation algorithm (MSARA) for multicast device-to-device (D2D) communications underlaying a UAV-assisted cellular network. The proposed MSARA allows the joint exploitation of wireless characteristics and social ties between users for optimizing the overall utilities and improving the data offloading in UAV-assisted cellular networks. This resource allocation problem is formulated as a matching game, in which D2D social clusters and resource blocks (RB) rationally and selfishly negotiate with each other on basis of utility functions to obtain optimal matching. The social context influences the formation of D2D clusters and the preferences of players in matching game. To solve the peer effects caused by interacting between the D2D social clusters sharing the same RB, we define swap operations between D2D social clusters to exchange their matched RBs. The proposed algorithm is proved to converge to a two-sided exchange stable matching between D2D social clusters and RBs. Simulation results show that the proposed scheme achieves a good performance gain compared with other schemes in the literature. Moreover, D2D clusters of socially connected users allow a substantially larger data offloading than the social-unaware approach.

Dynamic Link Selection for Interference Mitigation in D2D Multicast Content Sharing Networks

In this letter, we study the link selection problem for interference mitigation under dynamic channel conditions in D2D multicast content sharing networks. We firstly transform the problem into a stochastic game, in which the utility of each player (i.e., cluster head) is designed as the maximum expected weighted interference suffered by the receivers in the cluster. Then, we propose a simple and uncoupled link selection algorithm based on stochastic learning algorithm to obtain the Nash Equilibrium points. Importantly, the proposed algorithm only requires little intra-cluster information feedback. Theoretical analyses and numerical results validate the effectiveness of our proposed algorithm under dynamic channel conditions.

Learning-Based User Clustering and Link Allocation for Content Recommendation Based on D2D Multicast Communications

Content recommendation based on device-to-device (D2D) multicast communications is expected to become a promising approach to improve local area services. Importantly, two main challenges should be considered: i) user clustering—in order to be tailored to recommend contents, the members in the same cluster should have great similarity in multiple characteristics, and ii) link allocation—we should use as little resource consumption and information exchange as possible while keeping the recommendation accuracy. In this paper, we firstly quantify the degree of the similarity between two target users with regard to multiple characteristics. Guided by such similarity, we define a clustering validity index in terms of between-within proportion (BWP) to characterize the clustering performance. Then, the issue of user clustering is modeled as a sum BWP maximum problem, and a user clustering algorithm based on modified K-means algorithm is designed to solve it in a fast-operating and low-complexity way. After user clustering, we model the issue of link allocation as a weighted aggregate interference minimization problem, and then transform it to an exact potential game. As such, a link allocation algorithm based on stochastic learning algorithm is proposed which helps to obtain the result of this game in a distributed way without complete information. Also, we analyze its convergence and optimality performance. Simulation results demonstrate the effectiveness of our proposed algorithms.

Energy efficient resource allocation for D2D multicast communications

The resource allocation for device-to-device (D2D) multicast communications is investigated. To achieve fair energy efficiency (EE) among different multicast groups, the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function. The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate, which is modeled as a non-convex and mixed-integer fractional programming problem. Here, suboptimal resource allocation algorithms are proposed to solve this problem. First, channel assignment scheme is performed to assign channel to D2D multicast groups. Second, for a given channel assignment, iterative power allocation schemes with and without loss of cellular users’ rate are completed, respectively. Simulation results corroborate the convergence performance of the proposed algorithms. In addition, compared with the traditional throughput maximization algorithm, the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.

Improving Energy Efficiency of Multimedia Content Dissemination by Adaptive Clustering and D2D Multicast

While achieving desired performance, there exist still many challenges in current cellular networks to support the multimedia content dissemination services. The conventional multimedia transmission schemes tend to serve all multicast group members with the data rate supported by the receiving user with the worst channel condition. The recent work discusses how to provide satisfactory quality of service (QoS) for all receiving users with different quality of experience (QoE) requirements, but the energy efficiency improvement of multimedia content dissemination is not its focus. In this paper, we address it based on adaptive clustering and device-to-device (D2D) multicast and propose an energy-efficient multimedia content dissemination scheme under a consistent QoE constraint. Our scheme extends the recent work with the proposed K-means-based D2D clustering method and the proposed game-based incentive mechanism, which can improve energy efficiency of multimedia content dissemination on the premise of ensuring the desired QoE for most multicast group members. In the proposed scheme, we jointly consider the cellular multicast, intracluster D2D multicast, and intercluster D2D multicast for designing the energy-efficient multimedia content dissemination scheme. In particular, we formulate the energy-efficient multicast transmission problem as a Stackelberg game model, where the macro base station (MBS) is the leader and the candidate D2D cluster heads (DCHs) are the followers. Also, the MBS acts as the buyer who buys the power from the candidate DCHs for intracluster and intercluster D2D multicast communications, and the candidate DCHs act as the sellers who earn reward by helping the MBS with D2D multicast communications. Through analyzing the above game model, we derive the Stackelberg equilibrium as the optimal allocation for cellular multicast power, intracluster D2D multicast power, and intercluster D2D multicast power, which can maximize the MBS’s utility function. Finally, the proposed scheme is verified through the simulation experiments designed in this paper.

Hybrid Multicast and Device-to-Device Communications Based on Adaptive Random Network Coding

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.

Resource Allocation for D2D Video Multicast Using Multi-Leader Multi-Follower Stackelberg Game With Personalized Incentives

With the growing demand of mobile multimedia services, device-to-device (D2D) multicast provides an efficacious solution to local content sharing. The radio resource needs to be allocated efficiently to enhance the content sharing via D2D multicast. However, the existing resource allocation schemes for solving D2D multicast usually ignore the attribution problem of mobile users to multiple communities and the problem of social differentiation. Unlike the existing works, this paper performs the D2D video multicast in two steps: community formation and resource allocation. In the process of D2D multicast community formation, the best core users for content distribution in the network are selected in consideration of both physical and social factors, and detect whether there is overlap in the communities to which others belong. Then, we explore mobile users' social features including the historical request file similarity, the QoS request differentiation and the random mobility characteristic to arrange the best attribution schemes targeting at not only ensuring the quality of the video multicast service but also maximizing the content hit ratio. In the resource allocation step, a multi-agent hierarchical learning (MAHL) algorithm with personalized incentives based on multi-leader multi-follower Stackelberg game is proposed to maximize the throughput of the network. Simulations are conducted to reveal that compared with the other three benchmark algorithms, the proposed algorithm can significantly improve the throughput of the network under different scenarios.