Cellular User Equipments Research Articles

Fast Globally Optimal Transmit Antenna Selection and Resource Allocation Scheme in mmWave D2D Networks

Transmit antenna selection (TAS) at base station has been widely studied and employed in many communication networks (such as those using massive multiple-input multiple-output (MIMO) systems). Thanks to the small size of microstrip antenna elements applicable to millimeter-wave (mmWave) frequencies, the implementation of TAS in small user equipments (UEs) employing switchable directional antennas is recently becoming popular. In this paper, we consider device-to-device (D2D) communications underlaying a cellular network wherein each D2D UE is equipped with switchable transmit antennas. By employing Generalized Bender’s Decomposition (GBD) algorithm, we obtained the solution to the problem of globally optimal transmit antenna selection and channel allocation to D2D UEs together with transmit powers of cellular and D2D UEs. Although we reformulated the non-convex primal subproblem of the proposed GBD-based method into convex form, we further managed to obtain the corresponding closed-form solution through analytical manipulations; this extensively increased (at least 60 times) the execution speed of the proposed method as shown in the simulation results.

Energy Efficiency Optimization for Distributed Antenna Systems With D2D Communications Under Channel Uncertainty

In this paper, we investigate the energy efficiency (EE) maximization problem in distributed antenna systems (DAS) with underlaid device-to-device (D2D) communications. The EE optimization relies on the channel state information (CSI), and the interference from D2D pairs to the cellular user equipment (CUE) also closely depends on CSI. In this paper, we consider the case that the CSI in the systems is uncertain, which is more realistic and reasonable. We aim to obtain the robust power allocation (PA) solution that can achieve the maximum EE of the D2D systems. The optimization problem can be formulated as a non-convex and non-linear problem with infinite interference constraint. In order to solve it, the interference constraint is treated as chance constraint and handled by Bernstein approximation. Moreover, an equivalent objective function with a sub-tractive form is transformed by exploiting fractional programming theory. After that, the non-convex objective function can be transformed by using the difference of convex (D.C.) programming. The concave convex procedure (CCCP) algorithm is used to tackle the problem and obtain the optimal power allocation for D2D users. Simulation results show the robustness of the system and the effectiveness of the proposed algorithm.

Joint resource block and power allocation through distributed learning for energy efficient underlay D2D communication with rate guarantee

In this paper, we propose a novel distributed energy efficient joint resource block (RB) and discrete transmit power allocation scheme for an underlay device-to-device (D2D) network. In addition to minimizing the total transmit power of the D2D tier, the proposed scheme also ensures the rate constraints for all the cellular user equipments (CUEs) and D2D pairs. D2D pairs learn the RB and transmit power to be used by playing a game in Satisfaction Form (SF). The Efficient Satisfaction Equilibrium (ESE) of the game is shown to be the RB and discrete transmit power allocation with the lowest aggregate transmit power, in the case when the rate requirements of all the D2D pairs can be satisfied with non-zero transmit powers; else, the game is shown to converge to the K-Person Satisfaction Point (K-PSP), where the largest subset of D2D pairs are satisfied. The base station (BS) then decides to reject or accommodate the D2D pairs to ensure the rate constraints of the CUEs. The proposed scheme is established to be equivalent to the deferred acceptance (DA) algorithm applied to a many-to-one matching game. We exploit this equivalence to investigate the stability and optimality of the solution. The performance of the proposed scheme is evaluated and compared with existing schemes in the literature.

Deployment Model and Performance Analysis of Clustered D2D Caching Networks Under Cluster-Centric Caching Strategy

Device-to-Device (D2D) communication has become a promising candidate in future cellular networks to improve spectrum efficiency and energy efficiency, while reducing the latency. As the capacity of D2D user equipments (DUEs) increases, it makes DUEs caching possible, and it can offload traffic from macro base stations, perform computation-intensive and latency-critical tasks. In this paper, in-band communication is considered, and the Poisson cluster process is utilized to model and analyze the clustered D2D networks under cluster-centric caching strategy. Firstly, we use the Thomas cluster process to model cellular user equipments (CUEs) and DUEs, and give a deployment scheme of clustered D2D caching networks. Secondly, the aggregated interference of the typical D2D receiver is analyzed in the clustered D2D networks. Then the Laplace transform of the aggregated interference is analyzed, and the expressions of coverage probability, average achievable rate and cache hit probability of the typical D2D receiver are deduced. The simulation results show that we can adjust the path loss exponent, densities of DUEs and CUEs, transmitting power of CUEs, mean of simultaneously active transmitters in each cluster and Zipf exponent to improve the performance of clustered D2D caching networks.

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.

D2D Resource Allocation with Power Control Based on Multi-player Multi-armed Bandit

Device-to-device (D2D) communication is defined as the direct communication between two D2D user equipments (DUEs) without traversing the evolved NodeB of 5G networks. In the underlay mode of resource reuse, DUEs and cellular user equipments share resource blocks to improve system throughput by reusing the spectrum. In order to further enhance the performance, an extended version of reinforcement learning algorithm, Multi-Player Multi-Armed Bandit, is employed to control the transmission power of the DUEs to reduce the interference induced by resource sharing. Three learning strategies, namely Epsilon-first, Epsilon-greedy, Upper-Confidence-Bound, are applied. Simulation results show that the proposed method improves performance in terms of the average transmission power of D2D pairs, the ratio of unallocated D2D pairs, energy efficiency, and total throughput.

Resource Allocation for D2D-Based V2X Communication With Imperfect CSI

Different from the traditional device-to-device (D2D) communication, vehicle-to-everything (V2X) communication is characterized by the high mobility of vehicles, which introduces the fast variations of the channel state information (CSI) such that the accurate CSI is difficult to be obtained. Unlike other previous work which focuses on the perfect CSI, in this article, we investigate the joint power control and resource allocation problem for the D2D-based V2X communication in a more practical case where the CSI is imperfect. We aim at maximizing the sum ergodic capacity of all vehicular user equipments (VUEs) with imperfect CSI under the minimum signal-to-interference-plus-noise-ratio (SINR) requirements of cellular user equipments (CUEs) and the outage probability constraints of VUEs. We derive an approximate closed-form expression of VUE's ergodic capacity based on Jensen's inequality and propose a simple and efficient lower bound-based power control approach to solve the power control problem with low computational complexity. Based on the optimal power allocation results, we further propose an enhanced Gale-Shapley algorithm to solve the spectrum resource allocation problem, which is tailored to the scenario where each VUE can reuse spectrum resources of multiple CUEs but the resource of each CUE can be shared with one VUE at most and the maximum number of reuse CUEs for each VUE is limited by the maximum transmit power of the VUE. The numerical results verify the tightness of the proposed algorithm and demonstrate the proposed algorithm can enhance the sum ergodic capacity of VUEs efficiently and effectively.

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.

Energy Efficiency of Distributed Antenna Systems with D2D Communications under Imperfect CSI

In this paper, we investigate the distributed antenna systems (DAS) based on device to device (DAS-DID) communications under the imperfect channel state information (CSI). Our aim is to maximize the energy efficiency (EE) of the D2D users equipment (DUE) under the constraints of the maximum transmission power of D2D pairs and the quality of service (QoS) requirements of the cellular user equipment (CUE). The worst-case design is considered so that the QoS of the CUE can be guaranteed for every realization of the CSI error in the ellipsoid region. The EE objective function of the optimization problem is non-convex and non-linear, and thus this problem cannot be solved by the traditional optimization methods. To solve this problem, first we transform it to an EE maximization problem without uncertain parameters by exploiting the Markov and Cauchy-Schwartz inequality. Then using the fractional programming theory and difference of convex functions optimization method, the robust EE maximization algorithms based on the hard and soft protection method are developed to maximize the system's EE performance, respectively. However, these two algorithms are designed at the cost of the reduced EE of the DUE. Therefore, in order to further improve the EE performance and make a trade-off between the EE performance and the robustness, the iterative update algorithms for the total power constraint and average interference constraint are developed to maximize the system's EE performance, respectively. Simulation results demonstrate the effectiveness of the four proposed EE algorithms and illustrate the trade-off between the EE performance and robustness for the iterative update algorithms.

Efficient Resource Allocation and Power Control for LTE-A D2D Communication With Pure D2D Model

To support the rising number of user equipments (UEs), LTE-A allows some UEs directly talking with each other to facilitate spectrum reuse, which is known as device-to-device (D2D) communication. Since D2D UEs (DUEs) consume resources and bring out interference, how to allocate resources and power is important. Existing studies seek to make more DUEs reuse resources of cellular UEs (CUEs, which are the UEs talking with the eNB) to increase throughput. However, it is inefficient for some CUEs (e.g., those near cell edge) to share resources with others due to high interference. Thus, a new sharing paradigm, called the pure D2D model, is proposed to allow DUEs sharing resources without involving CUEs for flexibility. This new model is helpful to IoT (Internet of Things) applications, where the overwhelming majority of devices are usually DUEs. The paper defines an optimization problem to maximize links supported in the network, and proposes a D2D resource allocation and power control (DRAPC) framework. By vertex coloring, DRAPC gives a preliminary grouping of UEs for resource allocation. Then, each group is carefully reformed by exchanging members and adding new ones, so as to increase signal quality and degree of resource sharing. Simulation results show that DRAPC not only improves network performance but also guarantees fairness among links.

A novel collision aware network assisted device discovery scheme empowering massive D2D communications in 3GPP LTE-A networks

With the proliferation of proximity based services/applications, Device-to-Device (D2D) communication has become an emerging technology in facilitating data transfer among proximal cellular User Equipments (UEs). However one of the most challenging issues in D2D communication is the need for a robust device discovery scheme that effectively discovers UEs without any performance bottlenecks. In a densely populated cellular network, the conventional device discovery schemes proposed in the literature suffer from one or more adversities. High signaling overhead, large number of collisions and inefficient use of resources are some of the major issues that severely limit the performance of a device discovery scheme. Based on the characteristics of the network-assisted and autonomous device discovery schemes, we propose a novel device discovery scheme that discovers proximal UEs without causing any significant performance degradation. The cellular UEs are first divided into clusters based on their social relationship status and common interests. Every cluster will then execute a device discovery algorithm which is composed of two stages. In the first stage, UEs discover their neighbors using the conventional beacon based discovery scheme. The second stage comprises of successful UEs reporting to BS by initiating the LTE random access procedure. In this way, we not only reduce the signaling overhead but also enable substantial number of device discoveries to happen simultaneously at any instant of time. Further, we present a stochastic geometry based framework to analyze the collision probability and link discovery probability of the proposed scheme. Through extensive simulations we find that our proposed device discovery scheme effectively results in higher discovery probability compared to a recently proposed discovery scheme, by reducing the amount of collisions.

Interference management for D2D‐enabled HetNets with imperfect channel estimation

Device-to-device (D2D) and heterogeneous networks (HetNets) are two promising technologies to be introduced in the upcoming 5G network, for improving spectral efficiency and decreasing latency. However, sharing the same spectrum resources among different tiers of HetNet raised the interference challenge. In this paper, cross-tier interference management for underlay D2D users is studied in a heterogeneous macro-small cell network assuming imperfect channel state information (CSI). The network consists of one macro-cell with a centered macro-base Station (MBS) and several small-cell base stations (SCBS). Two types cellular users are considered; macro-cell cellular user equipment (MUE) associated with the MBS, and small-cell cellular user equipment (SUE) linked to SCBS. While two types of D2D users are considered; macrocell D2D (MD) located outside the range of the small cells and cross-tier D2D user equipment (CD), where each user of the D2D pair belongs to different network tier. Closed form expressions for the D2D optimal power that aim to maximize the coverage probability and the signal-to-interference-noise ratio (SINR) of both links are derived, while guaranteeing the quality of service (QoS) requirements for the cellular user. The effect of channel estimation error on the performance of both D2D and cellular users is evaluated.

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.

A Joint Power and Channel Scheduling Scheme for Underlay D2D Communications in the Cellular Network.

Device-to-device (D2D) communication, as one of the promising candidates for the fifth generation mobile network, can afford effective service of new mobile applications and business models. In this paper, we study the resource management strategies for D2D communication underlying the cellular networks. To cater for green communications, our design goal is to the maximize ergodic energy efficiency (EE) of all D2D links taking into account the fact that it may be tricky for the base station (BS) to receive all the real-time channel state information (CSI) while guaranteeing the stability and the power requirements for D2D links. We formulate the optimization problem which is difficult to resolve directly because of its non-convex nature. Then a novel maximum weighted ergodic energy efficiency (MWEEE) algorithm is proposed to solve the formulated optimization problem which consists of two sub-problems: the power control (PC) sub-problem which can be solved by employing convex optimization theory for both cellular user equipment (CUE) and D2D user equipment (DUE) and the channel allocation (CA) sub-problem which can be solved by obtaining the weighted allocation matrix. In particular, we shed light into the impact on EE metric of D2D communication by revealing the nonlinear power relationship between CUE and DUE and taking the QoS of CUEs into account. Furthermore, simulation results show that our proposed algorithm is superior to the existing algorithms.

Uplink Resource Sharing and Power Management Scheme for an Underlay D2D Communication

With the implementation of device-to-device (D2D) communication in primary cellular networks, there will be notable benefits such as increase in cellular capacity, energy efficiency and spectral efficiency. The interference to the cellular user equipment (CUE), due to D2D pairs is reduced with proper strategies on resource allocation and transmit power assignment to the D2D pairs. This leads to healthier sum rate and battery life of D2D pairs. In this paper, we propose a distance-based interference reduction and power management scheme to reduce the interference on CUEs and evolved NodeB (eNB) and thus improve the sum rate of D2D pairs. The power allocation is based on the D2D pairs distance to the CUEs and eNB, which is estimated based on Haversine formula. The transmit power of D2D transmitter is effectively manipulated with respect to the transmit power of CUEs. The system sum rate, total transmit power of D2D pairs and the battery life of D2D user equipment are analyzed and validated through simulation.

Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication

In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted sum rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved in an iterative manner, which possibly requires long computation time, in our proposed scheme the transmit power can be determined with a relatively low computation time. Through simulations, we confirm that the proposed scheme achieves a sufficiently high WSR with a sufficiently low computation time.

Three-Dimensional Resource Allocation in D2D-Based V2V Communication

Device-to-Device (D2D) communication is the major enabler of Vehicle-to-Everything communication in 3rd Generation Partnership Project (3GPP) Release 14. The user equipment/device can engage either in direct communication with the infrastructure, use a relay node, or it can communicate directly with another device with or without infrastructure support. The user equipment can be either a hand-held cellular device or a moving vehicle. The coexistence of cellular user equipment with the vehicular user equipment imposes different Quality of Service (QOS) requirements due to the rapid mobility of the vehicles and interference. Resource allocation is an important task by which the user equipment is allocated the required resources based on different QOS parameters. In this paper, we introduced the case of three types of users which share uplink resources: two types of vehicular users, and a third user that acts as a handheld cellular phone, which is nearly static. By keeping in mind, the differential QOS requirements for the three types of users, we have calculated the optimum power and then applied a 3-dimensional graph-based matching and hypergraph coloring based resource block (RB) allocation.

Mode selection map‐based vertical handover in D2D enabled 5G networks

One of the promising features of 5G networks is device-to-device (D2D) communication that enables direct transmission between D2D user equipments (UEs). Besides the traditional cellular transmission mode, UEs can select between the reuse and dedicated modes. In this study the authors consider a scenario where a communicating D2D pair and a cellular UE that communicates with an evolved Node-B can use the same spectrum. It is assumed that the cellular UE can move in the network while the D2D UEs are static. The movement of the cellular UE can affect the quality of the communication between the D2D pair. Therefore, the transmission mode between the D2D UEs might change to keep the best quality. In this study the authors propose a new mobility management and vertical handover algorithm that handles the transmission mode transition during the D2D connection to maximise the overall throughput. The algorithm uses distance from the border and critical direction set as mobility variables that are analytically determined. These variables are calculated using a mode selection map that is derived analytically when pathloss and fading models are used. Finally, in order to analyse the performance of the proposed handover algorithm, the authors analytically calculate handover rate and sojourn time metrics.

Energy‐Efficient resource allocation for device‐to‐device underlay communications in cellular networks

In device-to-device (D2D) communication underlaying cellular networks, the D2D user equipments (DUEs) can communicate by sharing the radio resources assigned to cellular user equipments. D2D communication is expected to improve the network throughput and save more energy for the user equipments. However, D2D communication causes severe interference to both the cellular users and other DUEs which is a challenge to radio resource allocation. The authors propose joint channel and power allocation algorithm to maximise the network achievable throughput, control the interference from the DUEs transmissions to cellular users and improve the energy efficiency of the user equipments. The authors formulate the problem and solve it using convex optimisation methods to select DUEs and set their powers in each channel. Simulation results show that the proposed algorithm can offer near-optimal performance and outperforms the comparable algorithms especially in terms of achievable throughput even with markedly reduced complexity levels.

An online resource allocation algorithm to minimize system interference for inband underlay D2D communications

SummaryThis paper addresses the research question of total system interference minimization while maintaining a target system sum rate gain in an inband underlay device‐to‐device (D2D) communication. To the best of our knowledge, most of the state of the art research works exploit offline resource allocation algorithms to address the research problem. However, in Long‐Term Evolution (LTE) and beyond systems (4G, 5G, or 5G+), offline resource allocation algorithms do not comply with the fast scheduling requirements because of the high data rate demand. In this paper, we propose a bi‐phase online resource allocation algorithm to minimize the total system interference for inband underlay D2D communication. Our proposed algorithm assumes D2D pairs as a set of variable elements whereas takes the cellular user equipment (UEs) as a set of constant elements. The novelty of our proposed online resource allocation algorithm is that it incurs a minimum number of changes in radio resource assignment between two successive allocations among the cellular UEs and the D2D pairs. Graphical representation of the simulation results suggests that our proposed algorithm outperforms the existing offline algorithm considering number of changes in successive allocation for a certain percentage of sum rate gain maintaining the total system interference and total system sum rate very similar.