D2D User Equipments Research Articles

A modified LSTM with QoS aware hybrid AVO algorithm to enhance resource allocation in D2D communication

In communication technologies, device-to-device (D2D) communication is essential for resource management and power control, which are major research concerns nowadays. D2D resource allocation involves dividing vital resources, such as time, power, and spectrum, among several devices. Each device can connect to other devices via one or more frequency channels. D2D communication shares the cellular user resources, while signal power transmission causes interference to the users who share the same channel. So, there is a need to control the power of the D2D device to prevent interference. For proper power control and optimization of multi-channel D2D communication, which is a challenging task, we proposed a deep learning approach incorporating a hybrid resource allocation framework. This framework aims to increase the sum rate of D2D user equipment (DUE) while considering quality of service (QoS) factors like limiting interference to cellular user equipment (CUE) and guaranteeing individual DUE rates above a certain threshold. The proposed resource allocation scheme combines two methods, namely a metaheuristic hybrid particle swarm Cauchy approach to African vulture optimization (HPSCAV) and a modified long short-term memory (MLSTM) based approach. The HPSCAV scheme helps to ensure that the QoS constraints are met, while the MLSTM-based approach is utilized for efficient resource allocation by optimizing the power and improving it with HPSCAV. Simulation results validate that the proposed model achieved better performance in various metrics such as system capacity, power consumption, spectral efficiency (SE), and energy efficiency (EE).

Cluster Head Selection in Device to Device (D2D) Communication Based on Weighted Network Performance Factor

In this study, an unsupervised machine learning algorithm, Self Organizing Map was utilized to cluster D2D User Equipment using their network values as inputs. A weighting factor referred to as Hardware Sensing Factor (HSF) was formulated to take into account the device’s channel quality and the status of its underlying hardware circuitry. The values of the HSF were used as inputs to cluster the devices and to select cluster head for each cluster. The performance of SOM when HSF was input was compared with the performance when RSSI, RSRP or RSRQ was used as input. The comparison showed that the use of HSF as input to SOM cluster algorithm gave better cluster performance than the use of respective network values such as RSSI, RSRP or RSRQ. In addition, the use of HSF as input data to both SOM and K-Means algorithms showed that SOM cluster formation has better performance than K-Means algorithm.

Performance Analysis of Radio Resource Allocation to Maximize Power Efficiency in Device-To-Device Communication


 
 
 Technological developments in the field of telecommunications have progressed. However, the more cellular network users, the more traffic on the Base Station (BS) will be. One way to overcome this is to implement a Device-To-device (D2D) communication system. However, when D2D User Equipment (DUE) reuses Cellular User channel resources Equipment (CUE), then interference will occur. This research is conducted to address interference problems and maximize energy efficiency for CUEs and DUEs by implementing the Greedy algorithm with additional power control. The aim is to block unnecessary resources and optimize resource allocation. The Optimal Power Control (OPC) scheme will utilize the transmit power threshold. The simulation scheme for the D2D communication system used is the uplink and underlay mode single-cell system model. Where the DUE pair will use the same resources as the CUE that has been given by the BS. Resources in CUE can only be reused by one pair of DUEs, and vice versa. The greedy algorithm using power control gets superior performance at a sum rate of 1.79 × 107 bps with an increase of 36.03%. Spectral efficiency of 2.49 bps/Hz with an increase of 36.03%. The power efficiency of 2.08 × 103 bps/mw with an increase of 118.47%. Based on the result the greedy algorithm without power control gets superior performance at CU Fairness of 1 with an increase of 50.14%.
 
 

Performance Analysis of Resource Allocation Algorithm Based on Simple Particle Swarm Optimization for Device to Device Communication System

The development of Device-to-Device (D2D) communication as an alternative communication scheme continues to grow. D2D communication enables each device to communicate directly with other devices without going through the Evolved Node B (eNB). These days, D2D communication can be implemented in conventional cellular communication, and use the same spectrum as the conventional cellular user (underlay). This underlay scheme can improve the cell’s spectrum efficiency, but the interference level that happens in the cell is increased. Because D2D communication and cellular communication use the same spectrum, there will be interference between D2D user equipment (DUE) and Cellular user Equipment (CUE). A well-designed radio resource allocation is needed to reduce the interference level, while maintaining the overall performance of the cell. In this research, Simplified Particle Swarm Optimization (SPSO) is proposed to overcome this problem. SPSO is a PSO-based algorithm with a limited number of iterations designed to halt calculations when the PSO algorithm cannot find a solution. If SPSO exceeds the limit iteration, a greedy algorithm is executed to do the allocation process. From the simulation, the SPSO algorithm can achieve 1.3310 × 108 bps, 12.3239 bps/Hz, 2.1328 × 103 bps/Watt and 92% on total sumrate, spectral efficiency, power efficiency, and system fairness respectively. These number is better if compared with the conventional greedy allocation algorithm. The total sumrate, spectral efficiency, and power efficiency are increased by 0.9%, 0.74%, and 0.95% in average datarate, spectral efficiency, and power efficiency respectively. Meanwhile the SPSO’s system fairness is decreased by 1.65% compared with the conventional greedy algorithm.

Downlink Achievable Rate of D2D Underlaid Cell-Free Massive MIMO Systems With Low-Resolution DACs

Device-to-device (D2D) communication coexisted with a cell-free massive multiple-input multiple-output (MIMO) system will be one of the pieces of the B5G/6G jigsaw puzzle. In this article, a D2D underlaid cell-free massive MIMO system with low-resolution digital-to-analog converters is studied, where multiantenna D2D user equipments (DUEs) communicate pairwise with each other in the same time-frequency resources as cell-free with equipments (CFUEs). Assuming imperfect channel state information is available at access points (APs), the downlink closed-form achievable rates for both CFUEs and multiantenna DUEs are derived, which enable us to give insights into the impacts of the key design parameters (e.g., the AP number, the DUE density, and its antenna number) on achievable rate. After that, a power control scheme is developed to maximize the average sum rate of CFUEs and DUEs, and we show that the proposed power control problem can be effectively solved by using the path-following algorithm. Finally, numerical simulations are provided to highlight the system performance and verify the effectiveness of the proposed algorithm.

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.

OCHSA: Designing Energy-Efficient Lifetime-Aware Leisure Degree Adaptive Routing Protocol with Optimal Cluster Head Selection for 5G Communication Network Disaster Management

As an underlayment to cellular 5G communication network, device-to-device (D2D) communications will not only boost capacity utilization and power efficiency but also provide public health and public safety services. One of the most important requirements for these businesses is to have alternate access to cellular networks in the event that they are partially or completely disrupted as a result of a natural disaster. Despite limited communication coverage and bandwidth scarcity, the 3rd Generation Partnership Project (3GPP) must have developed a new device-to-device (D2D) communication method fundamental enhanced mobile that can strengthen spectral efficiencies besides allowing direct communication of gadgets in close propinquity devoid of transitory by elevated-node B (eNB). Unfortunately, enabling data transmission on a cellular connection offers a challenge in terms of two-way radio source administration, because D2D associates recycle cellular users’ uplink radio resources, which might create interference to D2D user equipment’s (DUE) receiving channels. In this study, we concentrate on optimal cluster head selection using the binary flower pollination optimization algorithm by designing an energy-efficient lifetime-aware leisure degree adaptive routing protocol named OptCH_L-LDAR. This topology is constructed with a multi-hop obliging communication system, instructed on the way to wrap an extensive remoteness connecting source and destination. The proposed OptCH_L-LDAR is compared with three state-of-art methods such as binary flower pollination (BFP) algorithm, time division multiple access (TDMA), and data-driven technique (DDT). As a result, the proposed OptCH_L-LDAR achieves 96% of energy efficiency, 89% of lifetime, 97% of outage probability, and 98% of spectral efficiency.

Power control scheme using particle swarm optimization method in resource allocation process on D2D underlaying communication

Fast growing number of cellular telecommunication technology resulting on the increasing number of the user equipment. This condition increased the eNodeB load. To overcome this problem, the device-to-device (D2D) underlaying communication is introduced. In underlaying scheme, the D2D user equipment (DUE) will do the communication process using the same radio resources with the conventional cellular user equipment (CUE). To avoid a severe interference between these two types of user in the system, a good resource allocation is needed. In this work, a power control scheme using particle swarm optimization (PSO) is proposed, to manage the transmit power on each user on the system. The power control scheme take place after the greedy scheduling algorithm, after all user is given a resource block (RB) to do the communication process. The power transmit for each user is managed to reach a better system capacity, and to reduce the power consumed in one communication process. From the simulation, the PSO power control can improve the sumrate and spectral efficiency up to 12.97% and 3.38% respectively. The PSO power control also can reduce the power consumed by the system up to 8.84%. The fairness happens among the CUEs also can be maintained, despite of the decreasing fairness among DUEs.

Power and delay optimization based uplink resource allocation for wireless networks with device-to-device communications

In this paper, the resource allocation problem for all devices is investigated in a multi-sharing uplink scenario of mmWave cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) based wireless networks with device-to-device (D2D) communications. More specifically, a power and delay optimization based uplink resource allocation (PDO-URA) algorithm is proposed divided into two steps. At first, network resources are allocated to cellular user equipments (CUEs) in terms of power and transmission rate through a proposed approach that intends to maximize throughput. Next, idle resources are allocated by considering delay minimization. To this end, we propose to apply an algorithm based on delay optimization where idle resources are shared with D2D user equipments (DUEs) in the network considering the formed conflicts graphs and the delay information estimated through Network Calculus concepts such as envelope process and service curve. Computational simulations are carried out considering a communication scenario with 5G characteristics such as millimeter waves at frequencies above 6 GHz, comparing also the performance of other algorithms from the literature in terms of quality of service (QoS) parameters such as throughput and delay. The results confirm that the proposed algorithm presents superior performance in terms of throughput and delay for the mmWave CP-OFDM wireless networks with D2D communications than the other considered algorithms.

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.

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.

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.

Delay minimization based uplink resource allocation for device-to-device communications considering mmWave propagation.

This paper addresses the resource allocation problem in multi-sharing uplink for device-to-device (D2D) communication, one aspect of 5G communication networks. The main advantage and motivation in relation to the use of D2D communication is the significant improvement in the spectral efficiency of the system when exploiting the proximity of communication pairs and reusing idle resources of the network, mainly in the uplink mode, where there are more idle available resources. An approach is proposed for allocating resources to D2D and cellular user equipments (CUE) users in the uplink of a 5G based network which considers the estimation of delay bound value. The proposed algorithm considers minimization of total delay for users in the uplink and solves the problem by forming conflict graph and by finding the maximal weight independent set. For the user delay estimation, an approach is proposed that considers the multifractal traffic envelope process and service curve for the uplink. The performance of the algorithm is evaluated through computer simulations in comparison with those of other algorithms in the literature in terms of throughput, delay, fairness and computational complexity in a scenario with channel modeling that describes the propagation of millimeter waves at frequencies above 6 GHz. Simulation results show that the proposed allocation algorithm outperforms other algorithms in the literature, being highly efficient to 5G systems.

Cooperative content caching and power allocation strategy for fog radio access networks

Fog radio access networks (F-RANs) architecture is regarded as a prominent solution to deploy the caching and computing functions at the edge nodes of the network. However, the ever-increasing data traffic demand and the latency requirements of emerging applications pose new challenges to F-RANs. To minimize content fetching latency, we propose a cooperative content caching and power allocation scheme, which proactively caches the content at the network edge and enables users to dynamically obtain the desired content either from Fog-Access points (F-APs) or proximate user equipments (UEs) through Device-to-Device (D2D) communication. Furthermore, the proposed scheme allocates appropriate transmit power to D2D user equipments (DUEs) so that the transmission rate can be maximized. Specifically, the cooperative content caching issue is initially formulated as a probability-triggered combinatorial multi-armed bandit (CMAB) framework. By considering the user preference and content popularity prediction, an enhanced multi-agent reinforcement learning algorithm is proposed to obtain an optimal caching strategy. Besides, to minimize the content fetching latency and guarantee that each UE can retrieve the desired content, the power allocation problem is then modeled as maximizing the sum data rates of users. Finally, a Q-learning based power allocation strategy is derived. The simulation results based on the dataset from MovieLens reveal that compared with baseline methods, our proposed cooperative caching and power allocation scheme can not only reduce the content fetching latency but also increase the cache hit rate.

User Clustering and Optimized Power Allocation for D2D Communications at mmWave Underlaying MIMO-NOMA Cellular Networks

Fifth-generation (5G) cellular networks are being developed to meet the ever-growing data traffic across mobile devices and their applications. The core of 5G cellular networks is leveraging wider and higher frequencies available at millimeter wave frequency (mmWave) bands, thus providing very high data rates for mobile devices. Multi-input multi-output (MIMO) is an essential technology for overcoming the high propagation loss at mmWave communications. In non-orthogonal multiple access (NOMA), multiple cellular user equipments (CUEs) communicate over the same time-frequency resources using a multiplexed power domain. In device-to-device (D2D) communications, two D2D user equipments (DUEs) communicate without passing through the base station. In the underlaying scenario, DUEs reuse the frequency resources allocated to CUEs for spectrum utilization but DUEs cause interferences for cellular and D2D communications. Integrating D2D communications with other 5G technologies has great potential for spectral efficiency improvement. Unfortunately, interference management and resource allocation are becoming increasingly challenging due to aggressive frequency reuse. In this paper, D2D communications at mmWave underlaying MIMO-NOMA cellular network system model is developed. Consequently, a novel resource allocation for D2D communications underlaying MIMO-NOMA cellular network is proposed. A resource allocation optimization problem is formulated for spectral efficiency maximization. To solve this NP-hard problem, the problem is decomposed into three subproblems: interference-aware graph-based user clustering, MIMO-NOMA beamforming design, and optimized power allocation based on particle swarm optimization. Simulation results demonstrate that the proposed algorithm for D2D communications at mmWave underlaying MIMO-NOMA cellular network delivers a greater spectral efficiency compared to the conventional D2D communications that operate underlay MIMO-orthogonal multiple access cellular networks.

SWIPT-Enabled D2D Communication Underlaying NOMA-Based Cellular Networks in Imperfect CSI

Device-to-Device (D2D) communication is an emerging paradigm which enhances the coverage, capacity, and spectral efficiency of the network using cooperative communication in an underlay of cellular networks. In spite of these advantages, it suffers from large bandwidth and energy loss due to usage of half-duplex relaying and limited energy storage devices. To solve these issues, we integrate the full-duplex (FD) relaying and time splitting simultaneous wireless information and power transfer (SWIPT) technique at the D2D transmitters (DDTs). Moreover, to support the ubiquitous connectivity and reduce the latency, non-orthogonal multiple access is used at the base station. The SWIPT-FD-based DDTs and SWIPT-based cell user equipment (CUE) harvest the energy from the base station, after that the DDTs and CUEs decode the desired signal from the multiplexed signal using successive interference cancellation technique, and forward the desired signal to the D2D user equipment (DUE) to improve their QoS. In this paper, ergodic capacity at the DDTs and DUEs & closed-form expressions of the outage probabilities are derived with imperfect CSI. The results demonstrate that the proposed scheme achieves better performance as compared to that of the existing SWIPT-FD-OMA based scheme.

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.

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.

Energy- and Spectral-Efficiency Tradeoff With $\alpha$-Fairness in Energy Harvesting D2D Communication

Energy Harvesting (EH) technology enables Device-to-Device (D2D) User Equipments (DUEs) to harvest energy from ambient energy, making contributions to green communications and breaking the single battery-powered and the regional limitations of device deployment. The joint problem of energy harvesting and resource allocation in EH-based D2D Communication Networks (EH-DCNs) is a challenge issue. In this paper, we investigate the joint problem of resource allocation and EH time slot allocation of DUEs in EH-DCNs, where the DUEs harvest energy and multiplex Cellular User Equipments (CUEs) uplink resources. A channel assignment, power allocation and EH time slot allocation problem in EH-DCNs is formulated. The goal is to maximize energy-and spectral-efficiency with α-fairness while guaranteeing the EH constraints of DUEs and the quality of service of CUEs. The formulated problem is a non-convex mixed integer multi-objective optimization problem. In order to solve the formulated problem, the multi-objective optimization problem is transformed into a single-objective optimization problem based on the weight sum method. We propose a joint iterative algorithm based on Lagrangian dual decomposition for α > 0 and α = 0, respectively. Numerical results illustrate that the proposed algorithm achieves higher energy efficiency and spectral efficiency for different network parameter settings.

Competition Analysis of Diverse Request-Aware Packet Caching Policy for D2D Communication

Device-to-device (D2D) based packet caching technologies recently attract increasing attention, thanks to their great potentials to facilitate network traffic offloading. Despite the many popular issues arising in the D2D caching area, one of the new perspectives, namely the competition due to the packet request diversity originating from various D2D user equipment (UE) groups is not sufficiently investigated. In this work, we analyze several key aspects of the competition for packet allocation among diverse packet requests. Firstly, we study the impact from diverse group proportions on the system throughput and the packet allocation fairness. Particularly, the novel group separation index (GSI) is introduced, which helps to reflect the packet allocation fairness. We derive and analyze both the upper and lower bounds of GSI. Secondly, we investigate how the concentration levels of diverse packet requests may affect the system performance, such as the impact from the caching size limit on packet allocation. Thirdly, we derive the average energy consumption metric using the binomial point process based network model, which facilitates a comprehensive evaluation of the competition among UE groups. Finally, simulations validate our proposed analysis method, which may provide important design hints for improving existing D2D caching schemes.