D2D Communication Underlaying Cellular Networks Research Articles

Energy-efficient resource allocation for D2D communication underlaying cellular networks with incomplete CSI

Device-to-device (D2D) communication supports direct communications between nearby devices, which has potential to improve network capacity, spectrum efficiency and energy efficiency. Considering the high overhead to obtain the complete channel state information (CSI), we investigate resource allocation problems of D2D communication underlaying cellular networks with incomplete CSI to minimize the total power consumption. To deal with the challenge brought by incomplete CSI in estimating the instantaneous rates of D2D pairs (DPs), we consider two QoS metrics in terms of the expected rate and outage probability using statistical CSI. Based on that, we first investigate the energy-efficient power control problem for single cellular user (CU) and single DP sharing the same spectrum. With rigorous theoretical analysis of the intrinsic properties of CU rate and two QoS metrics, we design an optimal energy-efficient power control (EPO) algorithm for single CU and single DP. Using EPO as a building block, we then propose an energy-efficient resource allocation algorithm for multiple CUs and multiple DPs with incomplete CSI. Simulation results show that our algorithms consume the lowest powers compared with two baseline algorithms.

Joint Resource Allocation Algorithm for Energy Harvest-Based D2D Communication Underlaying Cellular Networks Considering Fairness

In this paper, we aim to maximize the minimum data volume for the energy harvest (EH)-based D2D communication underlaying cellular network. The formulated problem is a mixed integer nonlinear programming (MINLP), which we split into two sub-problems to solve separately. We first employ the Successive Convex Approximation (SCA) method to solve the time and power allocation, while putting the spectrum multiplexed pairs as fixed. Then, we develop a fair allocation mechanism to allocate spectrum resources. Numerical results show that our developed algorithm outperforms other benchmark schemes in terms of the minimum and overall data volumes.

Deep Reinforcement Learning Based Resource Allocation for D2D Communications Underlay Cellular Networks

In this paper, a resource allocation (RA) scheme based on deep reinforcement learning (DRL) is designed for device-to-device (D2D) communications underlay cellular networks. The goal of RA is to determine the transmission power and spectrum channel of D2D links to maximize the sum of the average effective throughput of all cellular and D2D links in a cell accumulated over multiple time steps, where a cellular channel can be allocated to multiple D2D links. Allowing a cellular channel to be shared by multiple D2D links and considering performance over multiple time steps require a high level of system overhead and computational complexity so that optimal RA is practically infeasible in this scenario, especially when a large number of D2D links are involved. To mitigate the complexity, we propose a sub-optimal RA scheme based on a multi-agent DRL, which operates with shared information in participating devices, such as locations and allocated resources. Each agent corresponds to each D2D link and multiple agents perform learning in a staggered and cyclic manner. The proposed DRL-based RA scheme allocates resources to D2D devices promptly according to dynamically varying network set-ups, including device locations. The proposed sub-optimal RA scheme outperforms other schemes, where the performance gain becomes significant when the densities of devices in a cell are high.

Survey on the state-of-the-art in device-to-device communication: A resource allocation perspective

Device to Device (D2D) communication takes advantage of the proximity between the communicating devices in order to achieve efficient resource utilization, improved throughput and energy efficiency, simultaneous serviceability and reduced latency. One of the main characteristics of D2D communication is reuse of the frequency resource in order to improve spectral efficiency of the system. Nevertheless, frequency reuse introduces significantly high interference levels thus necessitating efficient resource allocation algorithms that can enable simultaneous communication sessions through effective channel and/or power allocation. This survey paper presents a comprehensive investigation of the state-of-the-art resource allocation algorithms in D2D communication underlaying cellular networks. The surveyed algorithms are evaluated based on heterogeneous parameters which constitute the elementary features of a resource allocation algorithm in D2D paradigm. Additionally, in order to familiarize the readers with the basic design of the surveyed resource allocation algorithms, brief description of the mode of operation of each algorithm is presented. The surveyed algorithms are divided into four categories based on their technical doctrine i.e., conventional optimization based, Non-Orthogonal-Multiple-Access (NOMA) based, game theory based and machine learning based techniques. Towards the end, several open challenges are remarked as the future research directions in resource allocation for D2D communication.

Spectrum efficiency maximization for multi-hop D2D communication underlaying cellular networks: Machine learning-based methods

Multi-hop D2D communication has been proposed with the purpose of improving the coverage, quality of service (QoS), and flexibility and adaptability of single-hop D2D communication. However, multi-hop D2D communication often experiences obstacles caused by interference from the shared channel, which makes spectrum efficiency in multi-hop D2D networks an important issue to tackle. In this paper, we study the optimization of spectrum efficiency in multi-hop D2D communication underlaying cellular networks. First, we use iteration-based optimization techniques such as exhaustive search (ES) and gradient search (GS) with barrier function to find the global and local optimal solutions, respectively. More importantly, we propose two machine learning (ML) techniques, the unsupervised deep neural network (DNN) and deep Q-learning (DQL) algorithms and evaluate the performances of both algorithms compared to iteration-based optimization methods. The simulation results verify that both algorithms achieve near-global optimums compared to GS. Moreover, it is verified that the DQL outperforms the unsupervised DNN in terms of optimal spectrum efficiency, while the DQL algorithm has higher time complexity than the unsupervised DNN.

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.

Optimal Interference for Device to Device Communication Underlaying Cellular Network

Device to Device (D2D) communication can effectively improve the spectrum eficiency and reduce the load of Base Station in cellular networks. However, when D2D users and cellular users share the wireless channels, it will bring signal interference for the cellular networks. In order to achieve mutual interference between users and base stations, and increase the spectrumutilization efficiency and energy utilization efficiency, this paper proposes a method control algorithm based on bearnforrning and interference alignment in D2D communication underlaying cellular networks. Simulation results show that the proposed algorithm can effectively reduce the interference of the BS transrnitting signal for D2D pais and significantly improve system capacity. Furthermore, D2D communication is more applicable to short-range links.

Energy Efficiency Maximization of Two-Time-Slot and Three-Time-Slot Two-Way Relay-Assisted Device-to-Device Underlaying Cellular Networks

The continuous development of fifth generation (5G) communication and Internet of Thing (IoT) inevitably necessitates more advanced systems that can satisfy the growing wireless data rate demand of future equipment. Device-to-Device (D2D) communication, whose performance is evaluated in terms of the overall throughput, energy efficiency (EE) and spectral efficiency (SE), is considered a promising solution for the aforementioned problem. Thereby, this paper aims at improving the performance of the D2D communication underlaying cellular networks operating on multiple bands by maximizing the EE in its uplink. Thanks to the stochastic geometry theory, it is possible to derive the closed-form expressions for the successful transmission probability (STP), the total average transmission rate (TATR), and the total average energy efficiency (TAEE) of cellular and D2D users in different time slot setting. Particularly investigated and compared in this study, there are one-hop, direct, D2D communication in two time slots (2TS), and multi-hop, indirect, D2D communication in three time slots (3TS) with an additional D2D user acting as a two-way relay to assist the communication. Moreover, an optimization problem is formulated to calculate the maximum TAEE of D2D users and the optimum transmission power of both the cellular and D2D users. Herein this optimization study, which is proven to be non-convex, the Quality of Service (QoS) is ensured as the STP on every link is considered. The herein approach is referred to as relay-assisted D2D communication which is capable of delivering a notably better QoS and lower transmission power for communication among distant D2D users.

Power allocation and resource assignment for secure D2D communication underlaying cellular networks: A Tabu search approach

The primary goal of introducing device-to-device (D2D) communication underlaying cellular systems is to increase spectral efficiency (ES). However, it can lead to performance degradation caused by severe interference between cellular and D2D links. In addition, due to the complexity of control and management, direct connections between nearby devices are vulnerable. Thus, D2D communication is not robust against security threats and eavesdropping. However, if the physical layer security (PLS) concept is taken into account, the interference can help reduce eavesdropping. In this work, we address power control and resource block (RB) assignment problem to improve security of D2D communication and cellular network while satisfying the minimum rate requirements and power budget of the users. To solve this problem, we employ a decomposition method and individually address the power allocation and the RB assignment problem with lower complexity. However, RB assignment problem reduces to the three-dimensional matching problem, which is under NP-hard category and an optimal solution can be obtained through a complicated method such as Brute-force search or branch-and-bound with exponential time complexity. We, therefore, propose an approach based on tabu search (TS) meta-heuristic algorithm with reduced time complexity to globally find the near-optimal RB assignment solution. Moreover, we evaluate the performance of proposed scheme with a genetic algorithm (GA) based approach and other baseline methods. Simulation results show that the applied TS algorithm outperforms the GA since it employs both exploitation and exploration through a memory-based local search method and a perturbation mechanism, respectively. However, the GA, as a population-based method, focuses only on exploration by searching the entire search space without concentration on the current solution.

Interference-aware channel assignment and power allocation for device-to-device communication underlaying cellular network

Device-to-device (D2D) communication is a promising technology in 5G new radio (NR) interface to improve the system performance in terms of high data rate, low latency, coverage extension, traffic offloading, and higher spectrum and energy efficiency. In this study, channel assignment and power allocation techniques for D2D communication underlaying cellular network are proposed, those in turn, lead to a spectrum-efficient system. Firstly, the channel assignment problem is solved by employing channel-characteristics of target link and interference links. Then the power allocation problem is solved by allocating the optimal power to the D2D transmitters with allowable interference. This optimal power allocation approach allows multiple D2D communications to reuse the available cellular uplink resources. Numerical results demonstrate significant throughput gains for D2D communication for different transmission and interference power settings. Network throughput comparison with other existing approaches is provided to confirm the efficacy of the proposed approach. In addition, energy efficiency analysis is carried out by considering D2D transmission power, circuit power consumption, D2D link distance, and D2D user density.

Learning to Tradeoff Between Energy Efficiency and Delay in Energy Harvesting-Powered D2D Communication: A Distributed Experience-Sharing Algorithm

Efficient energy management, especially in terms of energy efficiency (EE) optimization, is the cornerstone to realize the energy harvesting (EH)-powered device-to-device (D2D) communication underlaying cellular network. However, too much focus on energy management is likely to negatively impact other quality-of-service, such as transmission delay. Consequently, this paper aims to investigate an EE and delay tradeoff (EDT) optimization scheme in EH-powered D2D communication underlaying cellular network (EH-DCCN). A more practical scenario, one cellular user and multiple EH-powered D2D pairs matching model and limited energy and data storage space, is considered. In view of the characteristic of the available transmission energy, a weighted EE and delay utility problem is modeled to investigate the EDT optimization scheme. Due to the complexity of the modeled problem, this paper proposes a modified distributed ${Q}$ -learning (QL) algorithm, namely experience-sharing distributed cooperation learning (EDCL) algorithm, to tackle the EDT optimization issue and enhance the convergence speed. Numerical results discuss convergence speed of EDCL performance, proper experience-sharing interval setting, as well as EDT performance. With the proper experience-sharing interval, EDCL can obtain the near-optimal performance as the classical centralized QL mechanism with more rapid convergence rate by sacrificing the appropriate tolerable additional signaling overhead.

Enabling Relay-Assisted D2D Communication for Cellular Networks: Algorithm and Protocols

Recently, there is a growing emphasis on device-to-device (D2D) communication, which is the key component of the Internet-of-Things ecosystem. D2D communication can operate on the licensed spectrum of cellular networks so as to improve the spectrum utilization. In this paper, we focus on the resource allocation problem for general multihop D2D communication and introduce users’ mobility into D2D communication underlaying cellular networks. Maximizing the total end-to-end data rate involves complex tasks, such as resource allocation and routing. By leveraging on the square tessellation technique, we propose an efficient square-division-based resource allocation scheme . Furthermore, we design a relay-assisted D2D communication protocol that addresses the challenges in enabling multihop D2D communications, namely, spectrum resource allocation, users’ mobility, and relay incentive. Through extensive simulations, we show that our relay-assisted D2D communication protocol improves the system throughput up to 55% and the user access rate up to four times in typical scenarios, as compared with state-of-the-art schemes.

Joint Subcarrier and Power Allocation in the Energy-Harvesting-Aided D2D Communication

Device-to-device (D2D)-enabled Internet-of-things promises higher spectral efficiency and system capacity by sharing the cellular spectrum and offloading the cellular traffic. However, it poses new challenges in terms of resource allocation because of interference from D2D to base station and vice versa in the downlink. Moreover, energy efficient operation of the network becomes a major challenge because of unattended operation of devices. In this study, we investigate resource allocation for the energy-harvesting-aided D2D communication underlaying cellular network. We formulate a joint subcarrier assignment and power allocation problem for multiple cellular and energy harvesting direct D2D links to maximize the overall sum rate subject to quality of service, subcarrier reuse, power, and energy harvesting constraints. The problem is formulated as a mixed integer nonlinear programming and is difficult to be solved in polynomial time. Therefore, a low complexity algorithm named energy harvesting and gain-based resource allocation (EHGRA) is proposed, which determines reuse partners considering interference among D2D and cellular links, and then, allocates power optimally to them such that the sum rate is maximized. Our performance evaluation demonstrates that energy harvesting can boost up the system performance and at the same time can achieve higher sum rate over short frame duration. Comparison shows that EHGRA performs better than the existing algorithms in terms of overall sum rate.

Relax online resource allocation algorithms for D2D communication

SummaryMaximizing the system sumrate by sharing the resource blocks among the cellular user equipments and the D2D (device to device) pairs while maintaining the quality of service is an important research question in a D2D communication underlaying cellular networks. The problem can be optimally solved in offline by using the weighted bipartite matching algorithm. However, in long‐term evolution and beyond (4G and 5G) systems, scheduling algorithms should be very efficient where the optimal algorithm is quite complex to implement. Hence, a low complexity algorithm that returns almost the optimal solution can be an alternative to this research problem. In this paper, we propose 2 less complex stable matching–based relax online algorithms those exhibit very close to the optimal solution. Our proposed algorithms deal with fixed number of cellular user equipments and a variable number of D2D pairs those arrive in the system online. Unlike online matching algorithms, we consider that an assignment can be revoked if it improves the objective function (total system sumrate). However, we want to minimize the number of revocation (ie, the number of changes in the assignments) as a large number of changes can be expensive for the networks too. We consider various offline algorithms proposed for the same research problem as relaxed online algorithms. Through extensive simulations, we find that our proposed algorithms outperform all of the algorithms in terms of the number of changes in assignment between 2 successive allocations while maintaining the total system sumrate very close to the optimal algorithm.

Social aware joint link and power allocation for D2D communication underlaying cellular networks

Joint link and power allocation for device-to-device (D2D) communication underlaying cellular networks are necessary to coordinate the mutual interference. Most of the works consider the sum interference from D2D pairs. However, mobile devices are carried by human beings who are connected with social ties. For one cellular user, the strengths of the social ties with D2D pairs are different. Thus, his interference tolerance temperatures (ITTs) are various. In this study, we distinguish D2D pairs through the social ties, and propose a social aware pricing-based joint link and power allocation scheme. Specifically, the base station (BS) prices the interference on every link and updates the prices according to the ITTs of cellular users. Subsequently, with the prices, the competition of D2D pairs for the links is modelled as a non-cooperative game. The authors prove the existence and the uniqueness of the Nash equilibrium (NE), and demonstrate that the NE is Pareto optimal. Moreover, an iterative decentralised algorithm is designed to solve the game. Especially, if the number of the price on one link for one D2D pair being updated is up to an upper bound, the power is forced to be 0. Numerical simulations verify the effectiveness of the authors' proposed scheme.

A minimum knapsack-based resource allocation for underlaying device-to-device communication

As the numbers of smart devices increases, proximity-based services have enabled device-to-device (D2D) communication to be regarded as one of the major communication paradigms. In underlaying D2D, the devices communicating with each other directly use shared cellular resources. In this paper we propose a minimum knapsack-based interference aware resource allocation algorithm (MIKIRA) for D2D communication underlaying cellular networks. We compare the system sum rates, interference and signal-to-interference-and-noise-ratios (SINR) of MIKIRA with a graph-based resource allocation (GRA) algorithm and random allocation. In our three different sets of experiments with different percentages of D2D pairs in the total number of cellular users in the network, we observe that MIKIRA performs better than the other algorithms in terms of interference and SINR, and obtains a similar system sum rate. MIKIRA (O(n2 log(n))) is also computationally more efficient when compared with the GRA (O(n3)), which makes it suitable for use in the LTE scheduling period of 1 ms.

Cooperative Full Duplex Device to Device Communication Underlaying Cellular Networks

Recently, device-to-device (D2D) communication and full duplex communication are both considered key technologies for 5G networks. In this paper, two novel cooperative modes are developed for full duplex D2D communication underlaying cellular networks: the network MU-MIMO based mode (N-mode) and the sequential forwarding mode (S-mode). In the N-mode, two D2D users work as network MIMO to forward the data to cellular users and thus leverage the channel diversity. In the S-mode, the spatial distribution of D2D users and cellular users is explored to improve the transmission rate. Based on these modes, two D2D users share the downlink resources with two nearby cellular users simultaneously to achieve both proximity gain and reuse gain. Moreover, these modes are well suited for edge cellular users. To optimize the performance of the two modes, optimal power allocation is conducted by considering the influence of residual self-interference at full duplex radios and the requirements of the minimum transmission rate for both cellular users and D2D users. Simulation results show that, compared with the dedicated mode, the sum rate of cooperative modes in the perfect self-interference cancellation case can achieve 32% overall improvement and about 70% improvement in the scenario with non-edge D2D users and edge cellular users. With residual self-interference at a full duplex radio, the overall performance improvement is reduced to 15%. However, the cooperative modes can still achieve 40% improvement in the scenario with non-edge D2D users and edge cellular users.

Ergodic rate analysis on applying antenna selection in D2D communication underlaying cellular networks

By reusing the spectrum of a cellular network, device-to-device (D2D) communications is known to greatly improve the spectral efficiency bypassing the base station (BS) of the cellular network. Antenna selection is the most cost efficient scheme for interference management, which is crucial to D2D systems. This paper investigates the achievable rate performance of the D2D communication underlaying the cellular network where a multiple-antenna base station with antenna selection scheme is deployed. We derive an exact closed-form expression of the ergodic achievable rate. Also, using Jensen's inequality, two pairs of upper and lower bounds of the rate are derived and we validate the tightness of the two sets of bounds. Based on the bounds obtained, we analyze the ergodic achievable rate in noise-limited scenario, interference-limited high SNR scenario and larger-scale antenna systems. Our analysis shows that the presence of D2D users could be counter-productive if the SNR at cellular UE is high. Further analysis shows that the relationship between the ergodic rate and the number of antennas it positive, but keeps decreasing as the antenna number increasing. These show the inefficiency of antenna selection in D2D interference management.

FRESH: Push the Limit of D2D Communication Underlaying Cellular Networks

Device-to-device (D2D) communication has been recently proposed to mitigate the burden of base stations by leveraging the underutilized cellular spectrum resources, where high overall network throughput and D2D access rate are critical for its service performance and availability. In this paper, we study the resource allocation problem to push the limit of D2D communication underlaying cellular networks by allowing multiple D2D links to share resource with multiple cellular links. We propose FRESH, a f ull re source sh aring scheme where each subchannel can be shared by a cellular link and an arbitrary number of D2D links. In particular, FRESH first divides the communication links into so-called full resource sharing sets such that, within each set, all D2D link members are able to reuse the whole allocated resources. Thereafter, it allocates a sum of spectrum resources to each obtained full resource sharing set. As compared with state-of-the-art schemes, FRESH provides fine-grained resource allocation, resulting in throughput improvements of up to one order of magnitude, and D2D access rate improvements of up to 5 times with a moderate node density (e.g., on the order of 1 user per 400 square meters).

Joint channel selection and optimal power allocation for multi‐cell D2D communications underlaying cellular networks

Device-to-device (D2D) communication underlaying cellular networks can improve the spectrum efficiency as a result of sharing the radio resources allocated to cellular user equipments (CUEs). However, the severe interference between D2D and CUEs communications may lead to performance degradation of cellular system if not coordinated properly. In this study, a joint channel assignment and power allocation algorithm is proposed, which addresses the intra-cell and inter-cell interference management problems for D2D communication underlaying cellular network. The case of multiple D2D-user equipments (DUEs) sharing the same channel while each DUE can reuse multiple channels is considered. The proposed algorithm is designed with two complementary steps in such a way that its computational complexity can be adapted according to the network condition. The preliminary set of CUEs candidate channels that can be reused by each DUE is adaptively decided in the first step. In the second step, the optimal power allocation for each DUE is determined using Lagrangian dual decomposition to maximise the network sum-rate. Simulation results show that the proposed algorithm outperforms the current comparable algorithms especially in terms of achievable throughput. Moreover, the effect of various system parameters on the performance of the proposed technique is also investigated.