D2D Users Research Articles

Joint resource allocation and power control for D2D communication with deep reinforcement learning in MCC

Mission-critical communication (MCC) is one of the main goals in 5G, which can leverage multiple device-to-device (D2D) connections to enhance reliability for mission-critical communication. In MCC, D2D users can reuses the non-orthogonal wireless resources of cellular users without a base station (BS). Meanwhile, the D2D users will generate co-channel interference to cellular users and hence affect their quality-of-service (QoS). To comprehensively improve the user experience, we proposed a novel approach, which embraces resource allocation and power control along with Deep Reinforcement Learning (DRL). In this paper, multiple procedures are carefully designed to assist in developing our proposal. As a starter, a scenario with multiple D2D pairs and cellular users in a cell will be modeled; followed by the analysis of issues pertaining to resource allocation and power control as well as the formulation of our optimization goal; and finally, a DRL method based on spectrum allocation strategy will be created, which can ensure D2D users to obtain the sufficient resource for their QoS improvement. With the resource data provided, which D2D users capture by interacting with surroundings, the DRL method can help the D2D users autonomously selecting an available channel and power to maximize system capacity and spectrum efficiency while minimizing interference to cellular users. Experimental results show that our learning method performs well to improve resource allocation and power control significantly.

Real-Time Energy Harvesting Aided Scheduling in UAV-Assisted D2D Networks Relying on Deep Reinforcement Learning

Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported by UAVs, both the user experience and network performance can be substantially enhanced at public events. However, the continuous moving of D2D users, limited energy and flying time of UAVs are impediments to their applications in real-time. To tackle this issue, we propose a novel model based on deep reinforcement learning in order to find the optimal solution for the energy-harvesting time scheduling in UAV-assisted D2D communications. To make the system model more realistic, we assume that the UAV flies around a central point, the D2D users move continuously with random walk model and the channel state information encountered during each time slot is randomly time-variant. Our numerical results demonstrate that the proposed schemes outperform the existing solutions. The associated energy efficiency game can be solved in less than one millisecond by an off-the-shelf processor using trained neural networks. Hence our deep reinforcement learning techniques are capable of solving real-time resource allocation problems in UAV-assisted wireless networks.

Quality of service optimisation of device‐to‐device communications underlaying cellular networks

Device-to-device (D2D) communications play a significant role in increasing the capacity of cellular networks. D2D enables direct interaction between mobile users without traversing the base station (BS) and at the same time causes interference between D2D and cellular links which can affect the system performance. To ensure efficient and reliable D2D operations within a cellular network, the related quality of service (QoS) requirement needs to be met in the design of such a system involving both D2D and cellular users in a same network. In this study, we have investigated three different transmission schemes (cellular, D2D and cooperative D2D modes) by deriving the closed-form expressions of key QoS performance such as system reliability, achievable data rate, and energy efficiency for these three transmission schemes. Based on this establishment, we have also examined the effects of using different numbers of parallel relay branches and different number of relays within each branch on the network performance to reveal how effectively the cooperative D2D can enhance QoS in comparison with other transmission methods. The proposed approach can be optimised through adaptively selecting appropriate transmission schemes and, as a result, good trade-offs between system reliability and efficiency can also be achieved under various environmental conditions.

Resource Allocation Scheme for Guarantee of QoS in D2D Communications Using Deep Neural Network

In this letter, we propose a hybrid resource allocation scheme for multi-channel underlay device-to-device (D2D) communications. In our proposed scheme, the transmit power of D2D user equipment (DUE) allocated to each channel is controlled in order to maximize the sum rate of the DUEs for a given Quality of Service (QoS) constraints. We consider two QoS constraints such that the interference caused on cellular user equipment (CUE) is kept to be less than a predefined level and the rate of individual DUE is managed to be larger than a predefined threshold. In order to solve the drawbacks associated with previous deep neural network (DNN)-based approaches in which QoS constraints could be violated with high probability, a heuristic equally reduced power (ERP) scheme, is utilized together with a DNN-based scheme. By means of simulations under various environments, we verify that the proposed scheme provides a near-optimal sum rate while guaranteeing the QoS constraints with a low computation time.

Integrating device to device network with internet of health things: Towards minimum power allocation

Among the crucial invention of the 5G is the device to device (D2D) system, whereby cellular gadgets correspond via immediate transfer or by multihop transfer excluding the ground-terminal. It is probable that D2D users are concurrent with human body network. Due to this, we suggested an internet of health things (IoHT) system which enables collaboration work among D2D users and human body indicators. We may regard the power as the most unique source in the wireless body area network (WBAN). The least needed transferring capacity may accomplish a particular degree of function, and minimum capacity for transfer holds a crucial responsibility in decreasing power usage. In this study, we discovered the needed transfer energy of four transferring modes: the straight transferring system, the double-hop transferring system, as well as double increasing coordinated transferring system with Rayleigh medium vanishing in its layout. Besides that, we suggested an energy-competent system named as efficient-power transmission mode selection-based (EPTMS) system. The suggested system chooses suitable transferring system whereby it offers the least needed transferring energy that assures a particular transfer duration. The statistical as well as simulation results shows that the two-master node cooperative protocols (TMNCP), EPTMS may enhance system conduction within the main criteria.

Energy efficiency maximisation based on RALA protocol for underlay D2D communication

Energy efficiency (EE) maximisation is one of the most important necessities of the fifth-generation (5G) wireless networks. In this study, both power allocation and channel assignment issues for the cellular network underlaid with device to device (D2D) communication are introduced. The main objective is to assign the users’ resources in order to maximise the total EE of the network. The distributions of both the D2D users (DUs) and the cellular users (CUs) are based on the stochastic geometry theory. Hence, the mathematical formula for the optimisation problem is demonstrated. Additionally, the resource allocation link admission (RALA) algorithm is proposed to achieve the maximum EE. Moreover, it assigns the optimal powers for each CU and DU in the system. Furthermore, the best channel assignment for each new D2D link request is determined. The optimisation problem in the RALA scheme is solved by utilising a proposed modified Cuckoo search algorithm. Furthermore, the complexity analysis for the proposed scheme is introduced. From simulation results, the proposed RALA scheme is efficient to assign the resource allocation for the users to achieve the near-optimal EE.

Age-of-Information Aware Scheduling for Edge-Assisted Industrial Wireless Networks

Industrial wireless networks (IWNs) have attracted significant attention for providing time-critical delivery services, which can benefit from device-to-device (D2D) communication for low transmission delay. In this article, a distributed scheduling problem is investigated for D2D-enabled IWNs, where D2D links have various age-of-information (AoI) constraints for information freshness. This problem is formulated as a constrained optimization problem to optimize D2D packet delivery over limited spectrum resources, which is intractable since D2D users have no prior knowledge of the operating environment. To tackle this problem, in this article, an AoI-aware scheduling scheme is proposed based on primal-dual optimization and actor--critic reinforcement learning. In specific, multiple local actors for D2D devices learn AoI-aware scheduling policies to make on-site decisions with their stochastic AoI constraints addressed in the dual domain. An edge-based critic estimates the performance of all actors' decision-making policies from a global view, which can effectively address the nonstationary environment caused by concurrent learning of multiple local actors. Theoretical analysis on the convergence of learning is provided and simulation results demonstrate the effectiveness of the proposed scheme.

Power Control for Full-Duplex Device-to-Device Underlaid Cellular Networks: A Stackelberg Game Approach

In spectrum sharing cognitive radio networks, unauthorized users (secondary users) are allowed to use the spectrum of authorized users (primary users) to improve spectrum utilization. Due to limited spectrum resources, how to formulate a reasonable spectrum allocation scheme is very important. As a mathematical analysis tool, game theory can solve the problem of resource allocation well. In recent years, it has been applied to the research of resource allocation in spectrum sharing networks by some literatures. In a cellular network consisting of multiple cellular users and full-duplex end-to-end communication users D2D (device-to-device), the self-interference caused by full-duplex communication and the interference caused by the D2D users to the cellular users will significantly reduce system throughput. In order to reduce the interference in the network, this paper introduces a power control algorithm based on Stackelberg game, which sets the cellular users and D2D users as the leaders and followers, respectively. The cellular users and the D2D users compete with each other to minimize the cost, and we propose new utility functions. We build an optimization problem under the outage probability constraint and power constraint and the transmission power of the users is obtained by using the Lagrangian dual decomposition method. The simulation results show that the proposed game algorithm improves network performance compared with other existing schemes.

Deep Learning Based Power Allocation for Workload Driven Full-Duplex D2D-Aided Underlaying Networks

Both Device-to-device (D2D) and full-duplex (FD) have been widely recognized as spectrum efficient techniques in the fifth-generation (5G) networks. By combining them, the FD-D2D aided underlaying networks (FN) has exhibited considerable technical advantages in terms of both spectral efficiency (SE) and energy efficiency (EE). Considering the fact that the performance of FN may be severely affected by users’ workload, the workload-driven FN (WFN) must be investigated. In this paper, a deep learning based transmit power allocation (TPA) method is proposed for automatically determining the optimal transmit powers of co-spectrum cellular users (CUs) and D2D users (DUs) relying on a deep neural network. Unlike the conventional transmit-power-control schemes, in which complex optimization problems must be addressed in an iterative manner (it usually requires a relative longer computational time), the proposed scheme enables each DU to determine its transmit power with a relatively shorter time. Furthermore, an improved iterative subspace-pursuit algorithm, as the performance benchmark, is formulated for WFN. In addition, to reflect the influence imposed by the workload, the penalty-based statistical sum-date-rate (PSS) can be employed as the performance metric of WFN. Numerical results show that the proposed scheme is capable of achieving a PSS comparable with that of the traditional iterative-based algorithms even under heavy-workload scenarios, but the computational complexity of the former can be significantly reduced.

Power Distribution of Device-to-Device Communications Under Nakagami Fading Channel

Recently, device-to-device (D2D) communication was designated to work in cellular networks as a new standard to boost their performances. This kind of communication can occur either underlay to the existing cellular infrastructure, or overlay in the industrial scientific and medical (ISM) unused band. In this paper, we consider the case that D2D transmissions take place concurrently with the usual cellular players in the same spectrum band, and thus, controlling the interference caused by the cellular user equipments (UEs) is of crucial importance. In this work, we consider the uplink transmission of a tier of D2D users. Using network model based on stochastic geometry, we express the general cumulative distribution function (CDF) of the D2D transmit power under Nakagami fading channel. Special cases as CDF of the transmit power for Rayleigh fading and noiseless environment were derived. Besides, an upper bound of the general CDF expression is proposed. To deal with D2D lifetime, first, we express the expectation of the transmit power, then we determine the expectation of the devices lifetime and finally, we give an upper bound of this lifetime expectation. Through simulation, the obtained numerical results confirm the effectiveness of the proposed analytic schemes.

Radio Resource Management Approaches for Reliable Device-to-Device (D2D) Communication in Wireless Industrial Applications

The manufacturing industry is regarded as one of the most demanding verticals with respect to URLLC requirements. A Device-to-Device (D2D) communication system is a key enabler that has been introduced to support URLLC. To enhance spectrum utilisation, D2D links can share access of radio spectrum resources occupied by cellular users. Spectrum reuse may result in performance degradation due to mutual co-channel interference of co-existing cellular and D2D users. Resource sharing in a D2D-based cellular network for wireless industrial applications is investigated in this article. The presented schemes aim to maximise the overall system throughput while maintaining the Quality of Service/Experience (QoS/QoE) requirements. Interference is managed by jointly considering admission and power control based on the relative distance between devices. A priced-Deferred Acceptance (p-DA) scheme with an incentive-based stability is developed to match D2D users to cellular resources. Numerical simulations show that the p-DA scheme is able to achieve better performance than the conventional DA algorithm and is close in performance to the presented centralised optimisation approach.

Secure Communication for Uplink Cellular Networks Assisted with Full-Duplex Device-to-Device User

In this paper, the secure communication based on the full-duplex (FD) device-to-device (D2D) in cellular networks is proposed. For the proposed scheme, the novel model is established, in which a D2D user is played as a relay operating in FD mode to assist in the secure transmission of uplink information. Considering that the D2D user as a relay is untrusted, D2D link rate maximization is formulated with the constraint of secrecy rate, which ensures the security of uplink cellular networks. To cope with the optimization problem, the optimal power allocation factors of the cellular user (CU) and the D2D user are jointly optimized. Firstly, by using the monotonicity of the objective function, the optimal solution of the power allocation factor at the D2D user can be obtained. Subsequently, the closed-form expression of the optimal power allocation factor at the CU is derived and verified that the solution is the global minimum point. Simulation results verify that the proposed scheme has better output performance than the conventional scheme.

Two policies for wireless non‐orthogonal multiple access systems: System model and performance enhancement of far user

SummaryDevice‐to‐device (D2D)‐enabled non‐orthogonal multiple access (NOMA) network is introduced as promising candidate to support massive connectivity and satisfy the high‐throughput demand. In this paper, two schemes are investigated for situations associated with D2D users and interference from traditional cellular users. The base station equipped multiple antennas is designed to exploit higher diversity gain and make full use of spectrum resources with NOMA technique. Especially, performance improvement can be achieved as enabling D2D link and signal reception of the far user (FU) on cell‐edge area. We study two policies related to downlink and uplink of such D2D NOMA system. The simulation results indicate that the improved ergodic capacity and outage performance can be achieved at FU with more antennas at the base station and higher allocated power factor. In addition, the D2D‐enabled NOMA can significantly improve the spectral efficiency.

Throughput Maximization for Multiple D2D Group Communications Underlaying Cellular Networks

In the scenario where multiple device-to-device (D2D) users and cellular users coexist, the large number of D2D users not only results in the shortage of spectrum resources but also brings interference to the communication of cellular users. In this paper, we establish a clustering model centered on cellular users and propose a resource allocation algorithm based on a D2D clustering model. On the basis of ensuring the throughput requirements of cellular users, this algorithm reconstructs various matrices in the graph theory through probability models, sets the priority of D2D pairs, and maximizes the number of user accesses under interference tolerance. Besides, on the optimization of the number of users, we adopt the Rubinstein game model to adjust the game order according to the priority and optimize the bandwidth allocation mode, so as to improve the overall throughput of the network. Simulation results show that the proposed algorithm can increase the number of users and the network throughput, while shortening the spectrum allocation time by more than half.

Proactive discovery protocol with security enhancement for D2D communication system

With the increase in mobile traffic and the band-width demand, Device-to-Device (D2D) communication has gained tremendous interest by the researchers, cellular operators and equipment manufacturers. However, D2D communication has been limited to study the converge services at cell edge. D2D users that located outside the cellular network coverage haven’t received enough attention. Some of the problems faced in this case are discovering process of neighbor user equipment (UE) and services, as well as designing suitable and secure protocols for D2D communication. Toward these problems, in this paper, we propose security enhancement for D2D communication based on modified elliptic curve cryptography (MECC), which provides greater efficiency in computational overhead, key sizes and bandwidths for user’s authentication applied on proactive routing protocol for neighbor and service discovery. We study Diffie–Hellman, ElGamal and MECC techniques to improve service of D2D users at cell edge. Results show that the proposed scheme can strength the secrecy with less control overhead and can increase the robustness in a wide range of scenarios for service discovery in D2D networks.

Integrating D2D network with IoHT : toward minimum power allocation

One of the important technologies of the 5G is the device to device (D2D) network, where mobile device will communication together either over direct transmission or over multihop transmission without need for the base-station. And it is quite possible that D2D users overlap with human body network. For this reason, we proposed an internet of health things (IoHT) design that make cooperation possible between D2D users and human body sensors. Because we can consider the energy as the rarest provider in the Wireless Body Area Network (WBAN). Finding optimal and minimum required transmission power that can achieve a certain level of utility while utilizing as little power for transmission as possible plays an important role in reducing energy consumption. In this paper, we found the required transmission power of four transmission modes: the direct transmission mode, the dual-hop transmission mode, and two incremental cooperative transmission modes with Rayleigh channel fading in proposed IoHT design. In addition, we propose a power-efficient algorithm that we have termed efficient-power transmission mode selection-based (EPTMS) algorithm. The proposed algorithm is select appropriate transmission mode that it provides a minimum required transmission power which ensures a certain transmission rate. The numerical and simulation results exhibit that, when compared with the two master node cooperative protocol (TMNCP), EPTMS can improve network performance under general circumstances.

Probabilistic Cache Placement in UAV-Assisted Networks With D2D Connections: Performance Analysis and Trajectory Optimization

With the exponential growth of data traffic, caching is regarded as a promising solution to combine with unmanned aerial vehicle (UAV)-assisted networks, which can offload cellular traffic and improve the system performance. Moreover, the cache capacity at user side can be leveraged, e.g., through local data storage or device-to-device (D2D) sharing. In this paper, we focus on the performance analysis and trajectory optimization of cache-enabled UAV-assisted networks with underlaid D2D communications. We consider both static and dynamic UAV deployments. For static UAV deployment, we first formulate an optimization problem to design the cache placement in order to maximize the cache hit probability. Then, the successful transfer probability (STP) and sum-rate are analyzed by using stochastic geometry, and their closed-form expressions are derived. For dynamic UAV deployment, the UAV moves over the cell and stops at several path points to serve users. To shorten the time required for the UAV to cover all users, a spiral algorithm is proposed to optimize the UAV trajectory, aiming at minimizing the number of UAV path points. Moreover, since at different locations, the UAV communication will incur different interference on D2D users, we derive the outage probability for the D2D users. Simulation results show the significant performance gain of our proposed probabilistic cache placement over existing strategies. For a given user density, we show that the optimal values for the UAV height which lead to the maximum UAV-STP and sum-rate exist.

Bayesian Coalition Game for Overlay D2D Spectrum Sharing in Cellular Networks

In this paper, we present a Bayesian coalition game model for spectrum sharing in order to increase the overall spectrum resource capacity in a cellular network with device-to-device (D2D) communication capabilities. The coalition game is used to enforce cooperation among D2Ds competing for the use of the limited spectrum resource. In this paper, D2D users can access the network in an overlaid mode and share their allocated sub-bands with other D2D users using the proposed game which enables coalition formation that guarantee an optimal rate for all users in the network. The paper establishes sufficient conditions needed for stability in the coalitions formed and present simulation results to show that it is possible to increase the overall capacity of the D2D-enabled cellular network without reducing the performance of the licensed cellular users using the proposed game. D2D spectrum sharing using our proposed algorithm was compared with spectrum sharing using the random pairing method to validate our game model. Results show that there was a 44% and 36% increase in cell sum-rate, when our proposed sub-band allocation game was deployed compared to the random pairing method, for the 50 and 100 cells respectively. The proposed sub-band allocation game also performed better with an increased number of users in the network because of the effective coordination offered by the algorithm.Keywords— Cellular, coalition game, D2D, overlay, spectrum.

Resource Allocation for GBR Services in D2D-Enabled Communication

In device-to-device (D2D)-enabled communications, a channel can be matched to a cellular user (CU)–D2D user (DU) pair, where either two separate resources can be allocated in a dedicated mode, or a single resource is shared in a shared mode. It affects the channel capacity and the channel bandwidth needed to guarantee the target data rate. The transmit power also needs to be adaptively controlled according to channel bandwidth, as well as channel quality. In this paper, we propose a resource allocation scheme to minimize the total channel bandwidth while guaranteeing the target data rate of guaranteed bit rate services. The original problem is separated into two sub-problems; (1) the channel bandwidth calculation and channel sharing mode selection for arbitrary matching (2) the 3-D matching problem of the CU, DU and channel. To mitigate computational complexity, the 3-D matching problem is transformed into two 2-D matching problems, and a sub-optimal solution is derived using a sub-gradient algorithm. From numerical results, it was confirmed that the average channel bandwidth was almost the same even though the computational complexity of the sub-optimal algorithm was very low compared to the original algorithm. We also investigate the impact of various parameters, such as maximum D2D distance and the number of users.

A Novel Weighted Clustering Algorithm Supported by a Distributed Architecture for D2D Enabled Content-Centric Networks.

Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms.