D2D Underlaid Cellular Networks Research Articles

Effectiveness of Guard Zone in Mitigating Interference in D2D Underlaid Cellular Networks

Device-to-device (D2D) communications, a promising technology, let cellular devices that are close to each other connect with one another directly, by passing the network infrastructure. As such, the technology provides the cellular network with many benefits, such as higher sum data rates, lower latency, extended coverage, higher energy efficiency, added security, higher spectral efficiency, and more useful services like inexpensive file caching. However, it comes with a cost—the interference underlaid D2D users inflict on cellular users at the base station when both use the same uplink frequency, which is the prefered reuse mode. This cost can, however, be reduced using a guard zone—a disk around the base station (BS) where D2D communications are not allowed. This simple and easy to implement approach, which has unfortunately not received enough research attention, is demonstrated in the present article to be highly effective in reducing the interference to cellular users. We leverage for this demonstration the mathematical tool of stochastic geometry. With the assumption that the BSs are distributed as a Poisson point process (PPP), we construct an analytical model to characterize the coverage probability of the cellular user in the presence of D2D nodes. The numerical results obtained, which are validated by rigorous Monte Carlo simulations, confirm that the guard zone approach is highly effective in protecting the cellular user from potential D2D interference.

Energy-efficient full-duplex D2D for SWIPT-empowered underlay cellular networks using a deep neural network

Close-packed small-cell networks enables a device-to-device (D2D) user equipment (DUE) to harvest energy from an ambient source, which increases the energy efficiency of the devices. Conceptually, full-duplex (FD) communications can double spectral efficiency compared to traditional half-duplex (HD) communications. However, the use of FD mode increases self-interference, which impacts spectral capacity and energy efficiency. In this paper, we study the performance of full-duplex D2D underlay cellular networks, where D2D devices decipher information and harvest energy simultaneously using SWIPT technology. Specifically, we build an optimization model to maximize the energy efficiency of the system, and obtain global- and sub-optimal solutions from the exhaustive search (ES) algorithm and the gradient search (GS) with barrier algorithm, respectively. In addition, we design deep neural networks (DNN) algorithm for the optimization model and evaluate the performance of the proposed DNN algorithm compared to the ES and GS algorithms. From the results, we confirm that the FD mode outperforms the HD mode in terms of energy efficiency and sum-rate and the proposed DNN algorithm can achieve near-global-optimal solution.

Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach

Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network’s spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes.

Learning-Based Robust Resource Allocation for D2D Underlaying Cellular Network

In this paper, we study the resource allocation in D2D underlaying cellular network with uncertain channel state information (CSI). For satisfying the minimum rate requirement for cellular user and the reliability requirement for D2D user, we attempt to maximize the cellular user’s throughput whilst ensuring a chance constraint for D2D. Then, a robust resource allocation framework is proposed for solving the highly intractable chance constraint, where the CSI uncertainties are represented as a deterministic set and the reliability requirement is enforced to hold for any CSI within it. Then, a symmetrical-geometry-based learning approach is developed to model the uncertain CSI into polytope, ellipsoidal and box. After that, the chance constraint under these uncertainty sets is transformed into computation convenient convex constraints. To overcome the conservatism of symmetrical-geometry-based approach, we develop a support vector clustering (SVC)-based approach to model uncertain CSI as a compact convex uncertainty set. Based on that, the chance constraint is converted into a linear convex set. Then, we develop a bisection search-based power allocation algorithm for solving the resource allocation in D2D underlaying cellular network with the obtained convex constraints. Finally, we conduct the simulation to compare the proposed robust optimization approaches with the non-robust one.

Energy Efficiency Optimization for SWIPT-Based D2D-Underlaid Cellular Networks Using Multiagent Deep Reinforcement Learning

In this article, we study the optimization of energy efficiency in wireless device-to-device (D2D-underlaid cellular networks where multiple D2D pairs adopt simultaneous wireless information and power transfer functionality. We formulate the optimization problem, which is a NP-hard combinatorial problem with nonlinear constraints. First, we use optimization-based-iterative techniques such as exhaustive search (ES) and gradient search (GS) with barrier, which are generally used to obtain the global optimum and local optimum of the nonconvex optimization problem, respectively. Considering that these techniques require a centralized unit to share information with each other, we propose multiagent deep reinforcement learning to solve this optimization problem in a distributed manner, which provides optimal decision making together with efficient deep network training under inequality constraints including transmit power, power splitting ratio, and minimum requirement data rate for D2D and cellular users. In this proposed method, we consider the virtual environment in which each agent can train their model according to shared information, and then, we deploy the trained model into the actual environment where each agent can only know their channel gain, interference power, and required minimum throughput. Simulation results show that the proposed algorithm can afford a near-global-optimum solution with much lower computation complexity than ES and outperforms the GS.

On the Performance of Underlay Device-to-Device Communications

This paper comprehensively investigates the performance of the D2D underlaying cellular networks where D2D communications are operated concurrently with cellular networks provided that the aggregate interference measured on licensed users is strictly guaranteed. In particular, we derive the outage probability (OP), the average rate, and the amount of fading (AoF) of the D2D networks in closed-form expressions under three distinct power allocation schemes, i.e., the path-loss-based, equal, and random allocation schemes. It is noted that the considered networks take into consideration the impact of the intra-D2D networks, the inter-interference from the cellular networks and background noise, thus involving many random variables and leading to a complicated mathematical framework. Moreover, we also reveal the behavior of the OP with respect to the transmit power based on the rigorous mathematical frameworks rather than the computer-based simulation results. The derived framework shows that increasing the transmit power is beneficial for the OP of the D2D users. Regarding the cellular networks, the coverage probability (Pcov) of the cellular users is computed in closed-form expression too. Monte Carlo simulations are given to verify the accuracy of the proposed mathematical frameworks. Our findings illustrate that the power allocation method based on prior path-loss information outperforms the other methods in the average sum rate.

Coverage Probability Analysis for Device-to-Device Communication Underlaying Cellular Networks

As one of the most promising techniques in wireless communication systems, device-to-device (D2D) has drawn much attention due to its superior performance. Meanwhile, the interference between cellular users and D2D users is still a challenging problem and needs to be mitigated effectively. A large number of simulation experiments for D2D communications have been studied to reduce the impact of the interference in the existing literature. However, theoretical research is still lacking. Thus, in this paper, we use stochastic geometry to evaluate the uplink performance of users by considering the impact of the previous moment on the next moment, which captures the effect of temporal and the spatial correlation of the interference in D2D communication underlaying cellular networks. Using a Poisson point process to model locations of D2D users, we derive an analytic expression for conditional probability and unconditional probability of link success, and prove that the probability of link success is temporally correlated. Moreover, we provide a theoretical framework for interference mitigation in D2D underlaying cellular networks.

A GNN-Based Supervised Learning Framework for Resource Allocation in Wireless IoT Networks

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.

Covert Communication With Truncated Channel Inversion Power Control in D2D Underlaid Cellular Networks

Device-to-device (D2D) underlaid cellular networks are regarded as an emerging network structure in the 5G systems. We study the covert communication of such networks consisting of a base station BS, a cellular user Alice, a D2D pair with a transmitter Dt and a receiver Dr , and a warden W illie. To conduct the covert communication, Alice adopts the truncated channel inversion power control (CIPC) to hide itself from W illie, where the received signal power at BS is fixed avoiding channel estimation for decoding Alice’s signal. Meanwhile, the interference generated by the D2D pair reusing the spectrum of Alice is exploited to confuse W illie. To understand the fundamental covert rate performance in the considered network, we first provide a theoretical model for characterizing the detection error probability of W illie as well as the effective covert rate at BS under the truncated CIPC scheme. Based on the main results, we then explore the effective covert rate maximization under the constraint of the detection error probability to identify the optimal truncated CIPC scheme designing and interference management. Finally, extensive numerical results are presented to illustrate the impacts of the truncated CIPC and interference from Dt on the covert performance.

Physical Layer Security in D2D Underlay Cellular Networks With Poisson Cluster Process

Device-to-device (D2D) communication is a promising solution to meet rapidly growing demands for data services via spectrum reuse. This paper studies the physical layer security in a D2D underlay cellular network from a network-wide perspective, where the locations of D2D and cellular users are modeled as Poisson cluster processes (PCPs) to characterize the clustering feature of D2D users, the locations of eavesdroppers (Eves) and base stations (BSs) are modeled as a PCP and Poisson point process (PPP), respectively. We establish an analytical framework to assess the coverage and security performance of the network. Two scenarios are considered, i.e., one D2D pair scenario and multiple D2D pairs scenario, where in each cell there is one or multiple D2D users (DUs) sharing the frequency spectrum with the cellular users (CUs) in each time slot of the TDMA scheme adopted by BSs. In each considered scenario, we derive exact expressions for the coverage outage probabilities (COPs) and secrecy outage probabilities (SOPs), respectively, for both the CUs and DUs. Furthermore, the exact expression for the network-wide secrecy throughput (ST) is derived. Numerical results are presented to verify our theoretical derivations and reveal some insights into the impact of various parameters on the system performance.

Interference graph construction for D2D underlaying cellular networks and missing rate analysis

This paper studies the interference graph construction problem for device-to-device (D2D) communications underlaying cellular networks. Firstly, an improved interference graph construction method compared to the previous work in Zhang et al. (IEEE Trans Vehicular Technol 66(4):3293–3305, 2017) is proposed. The difference is mainly that, in this work the BS allocates resources for transmitting probe packets for links in a centralized manner; while in the previous work the links select resources for transmitting probe packets in a random and autonomous manner. With this “BS-allocation” method, the BS can obtain more useful information about the interference graph than the previous “random allocating” method. Secondly, this work proposes a new theoretical analysis metric, i.e., the missing rate; while previous work analyzed the traditional convergence time. This difference is caused by that this work considers the dynamic scenario in which cellular and D2D links arrive to and leave the cell dynamically, while the previous work considered the static scenario. When considering dynamic scenario, it is possible that the interference graph has changed before the BS completes the graph construction. Hence, we must evaluate the accuracy of the constructed interference graph, i.e., the missing rate, for dynamic scenario. Simulation results validate the theoretical analysis and show that the proposed method outperforms existing methods. The impact of parameters on the missing rate is also investigated.

A near-optimal content placement in D2D underlaid cellular networks

The rapid growth of mobile data traffic, especially video streaming traffic, places a serious burden on cellular networks. D2D caching has emerged as a promising paradigm to alleviate network congestions, in which contents are cached at user terminals proactively and then shared among neighbor requesting users via D2D communications. In this paper, we study the content placement problem to maximize cache hit probability (i.e., the probability that contents requested by users are successfully served by neighbor helpers through D2D communications) in D2D underlaid cellular networks. To decide where to cache and which contents to be pushed, we formulate our problem considering D2D communication probability of helpers and preference probability of requesting users. Then our problem is proved to be a submodular function maximization problem under a matroid constraint. To solve this problem, we present an improved greedy algorithm which can achieve an approximation guarantee of ${\min \limits } (1, \linebreak \frac {1}{v_{0}+\frac {1}{t_{min}}} )$ , based on the classic $\frac {1}{2}$ -approximation algorithm. Simulation results show that our proposed scheme achieves higher cache hit probability and lower energy consumption compared with existing caching schemes.

Resource and Power Allocation in SWIPT-Enabled Device-to-Device Communications Based on a Nonlinear Energy Harvesting Model

Due to the limited battery capacity in mobile devices, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve the energy efficiency (EE) in Internet-of-Things (IoT) networks, i.e., device-to-device (D2D) networks, by allowing mobile devices to harvest energy from ambient radio-frequency (RF) signals. However, the nonlinear behavior of RF energy harvesters has largely been ignored in the existing works on SWIPT. In this article, we propose to maximize the sum EE of all D2D links in a D2D underlaid cellular network by optimizing the resource and power allocation based on a nonlinear energy harvesting (EH) model. Toward this end, we first propose a prematching algorithm to divide the D2D links into a SWIPT-enabled group and a non-EH group that cannot meet the EH circuit sensitivity. We then develop a two-layer iterative algorithm to jointly optimize the D2D transmission power and the power splitting ratio to maximize the EE for each SWIPT-enabled D2D link. On this basis, we build the preference lists for both SWIPT-enabled D2D links and cellular user equipment (CUE), and propose a one-to-one constraint stable matching algorithm to maximize the sum EE of all SWIPT-enabled D2D links by optimizing the spectrum resource sharing between D2D links and CUEs. The sum EE of non-EH D2D links is maximized through an iterative power control algorithm and a one-to-one stable matching algorithm. Simulation results show that our proposed algorithms achieve a much higher sum EE than the existing matching-based energy-efficient resource allocation scheme for SWIPT-enabled D2D networks.

Resource Allocation with a Rate Guarantee Constraint in Device-to-Device Underlaid Cellular Networks

Device-to-device (D2D) communication is a crucial technique for various proximity services. In addition to high-rate transmission and high spectral efficiency, a minimum data rate is increasingly required in various applications, such as gaming and real-time audio/video transmission. In this paper, we consider D2D underlaid cellular networks and aim to minimize the total channel bandwidth while every user equipment (UE) needs to achieve a pre-determined target data rate. The optimization problem is jointly involved with matching a cellular UE (CU) to a D2D UE (DU), and with channel assignment and power control. The optimization problem is decoupled into two suboptimization problems to solve power control and channel assignment problems separately. For arbitrary matching of CU, DU, and channel, the minimum channel bandwidth of the shared channel is derived based on signal-to-interference-plus-noise ratio (SINR)-based power control. The channel assignment is a three-dimensional (3-D) integer programming problem (IPP) with a triple (CU, DU, channel). We apply Lagrangian relaxation, and then decompose the 3-D IPP into two two-dimensional (2-D) linear programming problems (LPPs). From intensive numerical results, the proposed resource allocation scheme outperforms the random selection and greedy schemes in terms of average channel bandwidth. We investigate the impact of various parameters, such as maximum D2D distance and the number of channels.

Covert Communications in D2D Underlaying Cellular Networks With Antenna Array Assisted Artificial Noise Transmission

Due to its high speed and low latency, D2D communication plays an important role in providing proximity data services in 5G systems, which may contain privacy sensitive data. Covert communication is useful to protect the privacy of user data against adversaries. In a D2D underlaying cellular network, an antenna array can be used at a base station (BS) to transmit artificial noise to confuse adversaries that try to detect D2D covert signals. Based on the numbers of antennas at BS, two covert schemes are presented in this work and their performances are evaluated in terms of D2D covert throughput, i.e., maximum achievable D2D data rate with the given covertness requirements. As only channel distribution information (CDI) of adversary links is known to the BS, the average minimum error probability (AMEP) is used as a metric to measure the covertness performance. In the proposed schemes, closed-form expressions of the minimum AMEP and achievable D2D data rate are derived. It is shown that the derivation of minimum AMEP can be simplified if either one or massive antennas are allocated for sending artificial noise. The analytical results are compared to Monte-Carlo simulation results to verify the feasibility of the proposed schemes. It is also revealed that covert throughput can be further improved by setting system parameters properly, e.g., the number of antennas at BS and artificial noise transmit power.

Deep learning based physical layer security of D2D underlay cellular network

In order to improve the physical layer security of the device-to-device (D2D) cellular network, we propose a collaborative scheme for the transmit antenna selection and the optimal D2D pair establishment based on deep learning. Due to the mobility of users, using the current channel state information to select a transmit antenna or establish a D2D pair for the next time slot cannot ensure secure communication. Therefore, in this paper, we utilize the Echo State Network (ESN) to select the transmit antenna and the Long Short-Term Memory (LSTM) to establish the D2D pair. The simulation results show that the LSTM-based and ESN-based collaboration scheme can effectively improve the security capacity of the cellular network with D2D and increase the life of the base station.

Joint mode selection and power control for D2D underlaid cellular networks

Device-to-Device (D2D) communication is a new paradigm proposed in cellular networks, which promises several benefits to the network providers as well as end users. Firstly, it improves spectral efficiency (SE) of cellular networks by re-using the same frequency resources occupied by cellular users. In fact, mobile users cellular networks use high data rate services (e.g., video sharing, gaming, proximity aware social networking etc.), in which they could potentially be in range for direct communications (i.e., D2D). Secondly, D2D can enhance energy efficiency (EE), since the mobile terminals use less transmission power when communicating directly between each other. Thirdly, D2D communication can also reduce communication delay and increase network throughput, due to its short distance communication. This paper investigates the future challenges in order to improve the network throughput by proposing a joint mode selection (MS) and centralized power control (PC), which maximizes the sum rate of D2D links. To keep the interference under control, coverage probabilities are studied in this paper for both cellular and D2D users, while a signal-to-interference-plus-noise ratio (SINR) threshold is maintained for more than one CUE. Based on the latter assumption, this paper provides two sufficient conditions to ensure the existence of the Pareto optimal power, that should be verified by both Cellular User Equipment (CUE) and D2D User Equipment (DUE) during the MS process. The mathematical expressions of these two sufficient conditions are proved. Then, the PC approach is deduced based on two derived sufficient conditions. Two joint MS and PC algorithms are proposed in this paper and compared in terms of coverage probability and total power consumption with a conventional algorithm for both CUE and DUE.

Game-Theoretic Learning Approaches for Secure D2D Communications Against Full-Duplex Active Eavesdropper

In this paper, we analyze the anti-eavesdropping and anti-jamming performance of D2D communications with a full-duplex active eavesdropper (FAE). We consider the scenario that when the FAE intrudes the D2D underlaying cellular networks, it can passively wiretap confidential messages in D2D communications and actively jam all legitimate links. A hierarchical and heterogeneous power control mechanism with multiple D2D user equipments (DUEs) and one cellular user equipment (CUE) is proposed to combat the intelligent FAE. Moreover, a multi-tier Stackelberg game is formulated to model the complex interaction among them and the existence of Stackelberg equilibrium (SE) is proved. The best response (BR)-based hierarchical power control algorithm with perfect information and a robust learning method with imperfect information are proposed to obtain SE. The numerical results illustrate the convergence of the two proposed hierarchical power control algorithms, which are also compared with the random selection algorithm (RSA).

Relay-Assisted D2D Underlay Cellular Network Analysis Using Stochastic Geometry: Overview and Future Directions

Device-to-Device (D2D) communication is one of the enabling technologies for meeting the capacity requirements of the fifth-generation wireless systems (5G). It has diverse applications in traffic offloading, disaster management and content sharing, to mention a few. The network coverage and capacity further improve when relays are introduced to D2D communication. However, the interference becomes more severe since these devices also share resources with the traditional cellular users in the underlay. To take the benefits and avert the drawbacks of this spectrum sharing scenario, analytical tools capable of revealing the mathematical relationships among pertinent network design parameters are needed. This brings stochastic geometry (SG) into the picture. With SG-based analyses, designers can model concepts to understand, provide insights, and address the problems of spectrum sharing in relay-assisted D2D communication. Some of the key metrics of particular interest to network designers are the transmission capacity and spectral efficiency of D2D communication, as they reveal the performance gains and quantify the level of interference within the network. These enable them to properly correlate relevant cause-and-effect relationships before wealth and time are invested in network implementation. Despite the studies on the analysis of relay-assisted D2D underlay cellular networks using SG in recent years, there is no available survey material where researchers can find models, assumptions, key results and derived lessons to further comprehend this area and open up new research lines. This motivates the presentation of this paper which in addition to the aforementioned, gives elaborate discussions on promising areas for future research with respect to the recent advancements in D2D communication and SG research.

Incentive Design for Cache-Enabled D2D Underlaid Cellular Networks Using Stackelberg Game

Caching in wireless device-to-device (D2D) networks can bring contents closer to users and reduce backhaul traffic. However, the major obstacle to realize cache-enabled D2D communications is the selfish nature of users. In this paper, we propose an incentive mechanism for pricing the contributions of D2D transmitters in cache-enabled D2D-underlaid cellular networks modeled by stochastic geometry. A Stackelberg game is employed to resolve the conflict of interests between the operator and D2D transmitters. The main objective of this game is to reach the Stackelberg equilibrium, in which both the operator and D2D transmitters achieve the maximum profit by designing the optimal incentive price and the optimal caching strategy. We show the existence of the equilibrium and propose a low-complexity algorithm to obtain the near-optimal caching strategy and incentive price. Numerical results verify the existence of the SE and the advantages of the proposed scheme in terms of the utilities for the operator and D2D transmitters over existing caching schemes with fixed incentive.