D2D Users Research Articles

An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

With the emergence of 5G mobile multimedia services, end users’ demand for high-speed, low-latency mobile communication network access is increasing. Among them, the device-to-device (D2D) communication is one of the considerable technology. In D2D communication, the data does not need to be relayed and forwarded by the base station, but under the control of the base station, a direct local link is allowed between two adjacent mobile devices. This flexible communication mode reduces the processing bottlenecks and coverage blind spots of the base station, and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks. One of the important factors which affects the quality-of-service (QoS) of D2D communications is co-channel interference. In order to solve this problem of co-channel interference, this paper proposes a graph coloring based algorithm. The main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user resource. The proposed algorithm also provides simpler power control. The heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is acquired. Simulation results show that the proposed algorithm effectively reduced the co-channel interference, power consumption and improved the system throughput as compared with existing algorithms.

Double Deep Q-Network Based Distributed Resource Matching Algorithm for D2D Communication

Device-to-Device (D2D) communication with short communication distance is an efficient way to improve spectrum efficiency and mitigate interference. To realize the optimal resource configuration including wireless channel matching and power allocation, a distributed resource matching scheme is proposed based on deep reinforcement learning(DRL). The reward is defined as the difference of achieve rate of D2D users and the consumed power, which is limited by the Signal to Interference plus Noise Ratio (SINR) of the other cellular users on the current channel. The proposed algorithm maximizes the D2D throughput and energy efficiency in a distributed manner, without online coordination and message exchange between users. The considered resource allocation problem is formulated as a random non-cooperative game with multiple players (D2D pairs), where each player is a learning agent, whose task is to learn its best strategy based on locally observed information, multi-user communication resource matching algorithm is proposed based on a Double Deep Q-network (DDQN), where the total cellular throughput and user energy efficiency could converge to the Nash equilibrium (NE) under the mixed strategy. Simulation results show that the proposed algorithm can improve the communication rate and energy efficiency of each user by selecting the optimal strategy, and has better convergence performance compared with existing schemes.

NOMA‐enabled D2D adaptive relaying and transmission in cellular networks

AbstractWith the aim of improving the spectrum efficiency, we propose a two‐phase cellular non‐orthogonal and device‐to‐device (D2D) non‐orthogonal (CNDN) transmission scheme, wherein a base station (BS) can transmit data to a near‐user and a far‐user simultaneously assisted by two D2D users. Either the D2D transmitter or the D2D receiver can adaptively forward the BS data to the far‐user according to their decoding statuses. When the D2D transmitter forwards the cellular data, it also transmits its own data to the D2D receiver simultaneously. The superposition coding technique is adopted for the non‐orthogonal transmission and the opportunistic interference cancelation is performed for the data decoding. Taking into account the adaptive operations of cellular and D2D users, we analyze the throughput of both cellular and D2D systems. Extensive simulations show that the proposed CNDN scheme greatly outperforms the cellular orthogonal and D2D non‐orthogonal (CODN) transmission scheme in terms of throughput.

Efficient radio resource allocation scheme for 5G networks with device-to-device communication

<p>A vital technology in the next-generation cellular network is device-to-device (D2D) communication. Cellular user enabled with D2D communication provides high spectral efficiency and further increases the coverage area of the cell, especially for the end-cell users and blind spot areas. However, the implementation of D2D communication increases interference among the cellular and D2D users. In this paper, we proposed a radio resource allocation (RRA) algorithm to manage the interference using fractional frequency reuse (FFR) scheme and Hungarian algorithm. The proposed algorithm is divided into three parts. First, the FFR scheme allocates different frequency bands among the cell (inner and outer region) for both the cellular and the D2D users to reduce the interference. Second, the Hungarian weighted bipartite matching algorithm is used to allocate the resources to D2D users with the minimum total system interference, while maintaining the total system sum rate. The cellular users share the resources with more than one D2D pair. Lastly, the local search technique of swapping is used for further allocation to minimize the interference. We implemented two types of assignments, fair multiple assignment, and restricted multiple assignment. We compared our results with existing algorithms which verified that our proposed algorithm provides outstanding results in aspects like interference reduction and system sum rate. For restricted multiple assignment, 60-70% of the D2D users are allocated in average cases.</p>

Power-Spectrum Trading for Full-Duplex D2D Communications in Cellular Networks

Device-to-device (D2D) communications allows two adjacent mobile terminals transmit signal directly without going through base stations, which has been considered as one of the key technologies for future mobile networks. As full-duplex (FD) communications can improve the performance (i.e., throughput, energy efficiency (EE)) of communications systems, it is commonly used in practical D2D communications scenarios. However, FD-enabled D2D communications also results in self-interference. To fully realize the potential benefits of FD-enabled D2D communications, an effective resource allocation mechanism is critical to avoid not only the self-interference of FD-enabled D2D communications but also the interference between D2D users (DUs) and cellular users (CUs). In this paper, we investigate the resource allocation issue for FD-enabled DUs and traditional CUs. Considering the asymmetry of energy and spectrum resources of DUs and CUs, we propose a power-spectrum trading mechanism to achieve mutual benefits for both types of users. A concave-convex procedure algorithm is employed to solve the optimization problem of power allocation, and then a maximum weighted bipartite matching based method is proposed to select proper D2D pairs to maximize the overall system throughput. Numerical results show that the proposed scheme can remarkably improve the overall throughput and EE of FD-enabled D2D communications system.

Joint Uplink-Downlink Resource Allocation for D2D Underlaying Cellular Network

Device-to-device (D2D) communication is an emerging 5G technology to improve spectrum efficiency and reduce data traffic load in wireless networks. In this paper, a joint uplink-downlink resource allocation (JUDRA) approach is presented to maximize the network throughput while guaranteeing the quality of service (QoS) for both cellular users (CUs) and D2D users (DUs). In particular, the proposed JUDRA approach allows DUs to reuse either uplink or downlink CUs’ resources. Besides, an optimization problem is formulated as a mixed-integer nonlinear programming problem (MINLP) which is generally NP-hard. The problem is solved in two stages: sub-channel (SC) assignment stage and power allocation stage by utilizing the characteristics of sequential geometric programming. Moreover, a low computational complexity approach is suggested that converges to the near-optimal solution in polynomial time. Finally, comparative results are presented to validate the effectiveness of the proposed approach.

A caching strategy based on many-to-many matching game in D2D networks

Wireless edge caching has been proposed to reduce data traffic congestion in backhaul links, and it is being envisioned as one of the key components of next-generation wireless networks. This paper focuses on the influences of different caching strategies in Device-to-Device (D2D) networks. We model the D2D User Equipments (DUEs) as the Gauss determinantal point process considering the repulsion between DUEs, as well as the caching replacement process as a many-to-many matching game. By analyzing existing caching placement strategies, a new caching strategy is proposed, which represents the preference list of DUEs as the ratio of content popularity to cached probability. There are two distinct features in the proposed caching strategy. (1) It can cache other contents besides high popularity contents. (2) It can improve the cache hit ratio and reduce the latency compared with three caching placement strategies: Least Recently Used (LRU), Equal Probability Random Cache (EPRC), and the Most Popular Content Cache (MPC). Meanwhile, we analyze the effect of caching on the system performance in terms of different content popularity factors and cache capacity. Simulation results show that our proposed caching strategy is superior to the three other comparison strategies and can significantly improve the cache hit ratio and reduce the latency.

Anchored User Selection for Traffic Offloading Optimization in D2D-Aided Mobile-Edge Computing

Recently, integrated with the advanced communication technologies (e.g., 5G) and artificial intelligence (AI), mobile-edge intelligence (MEI) is regarded as the promising method to deal with the emerging challenges. Specifically, Device-to-Device (D2D) communications have been put forward to reduce the traffic pressure while extending cellular network capacity. However, the stability of the social network is important for the design of efficient and reliable traffic offloading strategy, which is often absent from the related work. Besides, most existing studies merely model the relation between a node pair as a binary or continuous value, neglecting the rich information between users. Moreover, many traditional models are conducted based on small-scale data sets or online Internet services, severely confining their applications in the D2D scenario. Thus, it is necessary to understand the network structure and select the key users to address the aforementioned challenges. In this article, we first propose a network representation model, named MPPT, to regard the multidimensional relations as a probability in a third-order (3-D) tensor space. Then, a mobile D2D social community is derived by integrating an edge base station (BS) and the nearby D2D users, and develop an anchored user selection algorithm to maintain the stability of multiple D2D social communities by choosing and retaining critical users adaptively under the limited network resources. Finally, we devise a probability-based onion layers anchored <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(k,r)$ </tex-math></inline-formula> -core (P-OLAK) algorithm to identify the anchor users. The large-scale data sets-based experimental results show the superiorities of the proposed methods.

Robust Resource Allocation Algorithm for Energy-Harvesting-Based D2D Communication Underlaying UAV-Assisted Networks

Energy efficiency (EE) is a significant performance indicator in unmanned aerial vehicle (UAV)-assisted communication networks for providing a balance between power consumption minimization and transmission rate maximization. However, most of the current works focus on the transmission rate maximization under perfect channel state information (CSI) and exact coordinate information, which is too ideal in practical systems due to channel estimation errors and coordinate estimation errors. Thus, robust resource allocation algorithms with imperfect CSI and coordinate information are critically important to reduce users’ outages and improve system robustness. In this article, a robust EE maximization problem with channel uncertainties and coordinate uncertainties is formulated for an energy harvesting-based device-to-device (D2D) communication underlaying UAV-assisted network under some necessary constraints, which involve the outage probability constraints of ground terminals, the flight altitude constraint of the UAV, the minimum harvested energy constraints of D2D users, and the transmission time constraint. Both radio resource allocation and the flight altitude are jointly optimized based on the worst case approach. The considered nonconvex problem is transformed into a convex one by exploiting variable relaxation and variable substitution approaches. The Lagrange dual theory is used to derive the closed-form expressions of robust resource allocation. Simulation results demonstrate the effectiveness of the proposed algorithm by comparing it with the benchmark algorithms in terms of EE and robustness.

Exploiting outage performance in device-to-device for user grouping

The spectrum efficiency and massive connections are joint designed in new form of device-to-device for user grouping. A pair of users is implemented with nonorthogonal multiple access (NOMA) systems. Although NOMA benefits to such system in term of the serving users, device to device (D2D) faces the interference from normal cellular users (CUE). In particular, we derive exact formulas of outage probability to show system performance. In this article, we compare two schemes to find relevant scheme to implement in practice. The frame structure is designed with two timeslot related to uplink and downlink between the base station and D2D users. We confirm the better scheme in numerical result by considering the impacts of many parameters on outage performance.

Robust Transmit Power Control With Imperfect CSI Using a Deep Neural Network

In this paper, a robust transmit power control scheme is proposed for multi-channel underlay device-to-device (D2D) communications with imperfect channel state information (CSI). The transmit power of the D2D user equipment (DUE) on each channel is optimized to maximize the average spectral efficiency (SE) whilst maintaining the quality-of-service (QoS) of the cellular user equipment (CUE) in the presence of errors in the CSI. To this end, we propose a novel deep neural network (DNN) structure and training methodology, in which artificially distorted CSI is used to compensate for the effect of imperfect CSI, such that a robust transmit power control strategy against channel error can be derived. Our simulation results show that even when the CSI is inaccurate, in our proposed scheme the degradation of the average SE can be kept low whilst maintaining negligible QoS violation, thereby confirming its effectiveness and robustness.

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.

DRL-Based Sum-Rate Maximization in D2D Communication Underlaid Uplink Cellular Networks

Device-to-device (D2D) communication affords the benefits of improved network spectral efficiency, throughput, energy efficiency, and delay performance. However, D2D communication underlaid cellular uplinks are degraded by interference between D2D user equipment (DUE) and cellular user equipment (CUE) that use the same frequency band. In this context, researchers have proposed the optimization of the transmit power of the devices to mitigate interference. This power allocation problem is generally modeled as a NP-hard combinatorial optimization problem with linear constraints; therefore, traditional optimization methods are ineffective in addressing the problem. In this paper, we apply a deep reinforcement learning (DRL) algorithm to optimize the transmit power for both DUEs and CUEs in the context of D2D communication underlaid uplink cellular networks, which can solve the optimization problem via optimal decision-making together with efficient deep network training. Our simulation results show that the proposed algorithm affords a near-global-optimum solution with lower computation complexity than exhaustive search, and outperforms the fixed-power, weighted minimum mean square error (WMMSE), gradient descent method, and Newton-Raphson approaches.

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.

DM-COM: Combining Device-to-Device and MU-MIMO Communications for Cellular Networks

In cellular networks, multiuser multiple-input multiple-output (MU-MIMO) is a key technology and has already been deployed in many real systems. Recently, device-to-device (D2D) communication has emerged as another promising technology as it offers several advantages, such as traffic offloading, low-latency transmissions, and enhanced spectral efficiency. Although there are many results of these two technologies, most of them are limited to their respective domains and there is a lack of practical design to combine both technologies for cellular networks. In this article, we present DM-COM, a practical scheme for enabling the coexistence of D2D and MU-MIMO subsystems in cellular networks. The enabler of DM-COM is a new approach for managing the mutual interference between the two subsystems, which does not require channel state information and is, therefore, amenable to practical implementation. We have built a prototype of DM-COM on a wireless testbed and evaluated its performance in a real-world wireless environment. Our experimental results show that, using DM-COM in a small cellular network, D2D users achieve 1.9 bit/s/Hz spectral efficiency, while MU-MIMO users have less than 8% throughput degradation compared to the case without D2D users.

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.

Robust Energy-Efficient Transmission for Wireless-Powered D2D Communication Networks

This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large.

Lyapunov optimization machine learning resource allocation approach for uplink underlaid D2D communication in 5G networks

AbstractDevice‐to‐device (D2D) communication plays a vital role in communication technologies with resource management and power control, which are the major issues for researchers in the era of 2020. In 5G networks, machine learning algorithms play a crucial role to manage these issues. In the proposed work, the problem is formulated for the uplink underlaid model of D2D communication. The Lyapunov optimization technique is utilized with a combination of machine learning techniques for resource allocation in D2D communication. First, the maximization of uplink and overall system capacity is formulated with resource management, which guarantees the signal to interference noise ratio for the D2D users. The optimization is a mixed integer non‐linear problem which uses the Lyapunov optimization method to optimize the bit error rate value and iterative algorithm to optimize the power value with different constraints. After attaining the optimized value, the support vector machine technique is utilized to ensure the spectral efficiency of an overall system in autonomous mode. Simulation results show that the proposed method provides higher reliability and power efficiency with higher system capacity in comparison to prevailing technologies.

Formal Verification of Authentication and Service Authorization Protocols in 5G-Enabled Device-to-Device Communications Using ProVerif

Device-to-Device (D2D) communications will be used as an underlay technology in the Fifth Generation mobile network (5G), which will make network services of multiple Service Providers (SP) available anywhere. The end users will be allowed to access and share services using their User Equipments (UEs), and thus they will require seamless and secured connectivity. At the same time, Mobile Network Operators (MNOs) will use the UE to offload traffic and push contents closer to users relying on D2D communications network. This raises security concerns at different levels of the system architecture and highlights the need for robust authentication and authorization mechanisms to provide secure services access and sharing between D2D users. Therefore, this paper proposes a D2D level security solution that comprises two security protocols, namely, the D2D Service security (DDSec) and the D2D Attributes and Capability security (DDACap) protocols, to provide security for access, caching and sharing data in network-assisted and non-network-assisted D2D communications scenarios. The proposed solution applies Identity-based Encryption (IBE), Elliptic Curve Integrated Encryption Scheme (ECIES) and access control mechanisms for authentication and authorization procedures. We formally verified the proposed protocols using ProVerif and applied pi calculus. We also conducted a security analysis of the proposed protocols.

A New Power Control Algorithm in MMSE Receiver for D2D Underlying Massive MIMO System

Device to device (D2D) underlying massive MIMO cellular network is a robust deployment which enables network to enhance its throughput. It also improves services and applications for the proximity-based wireless communication. However, an important challenge in such deployment is mutual interference. Interference, in the uplink spectrum, reusing the same resource with cellular user, is caused by D2D users. In this paper, we study a distributed power control (DPC) algorithm, using minimum mean square error (MMSE) filter in receiver, to mitigate the produced interference in this deployment scenario. For the DPC algorithm, employing the coverage probability of D2D links, an optimal power control approach is proposed, which maximizes the spectral efficiency of D2D links. Using this modeling approach, it is possible to derive closed-form analytical expressions for the coverage probabilities and ergodic spectral efficiency, which give insight into how the various network parameters interact and affect the link.‎ Also, the DPC algorithm is modeled by stochastic geometry and receiver filter is designed by estimation theory that a new structure in this robust network is an approach to improve spectral efficiency. Simulation results illustrate enhancing coverage probability performance of D2D links in term of the target (signal to interference ratio) SIR with respect to different receiver filter and other parameters which are existing in D2D links.