Cellular User Equipments Research Articles

Navigating urban congestion: A Comprehensive strategy based on an efficient smart IoT wireless communication for PV powered smart traffic management system.

Egypt faces extreme traffic congestion in its cities, which results in long travel times, large lines of parked cars, and increased safety hazards. Our study suggests a multi-modal approach that combines critical infrastructure improvements with cutting-edge technologies to address the ubiquitous problem of traffic congestion. Assuring vehicles owners of their timely arrival, cutting down on fuel usage, and improving communication using deep learning approach and optimization algorithm within the potential of IoT enabled 5G framework are the main goals. The traffic management system incorporates detection cameras, Raspberry Pi 3 microcontroller, an Android application, cloud connectivity, and traditional traffic lights that are powered using PV modules and batteries to secure the traffic controllers operation in case of grid outage and assure service continuity. The model examines the difficulties associated with Internet of Things (IoT) communication, highlighting possible interference from device-to-device (D2D) devices and cellular user equipment. This all-encompassing strategy aims to reduce fuel consumption, increase road safety and improve traffic efficiency. The model predicts a significant increase in Egypt's urban mobility by utilizing the possibilities of IoT and 5G technologies, which would improve Egypt's towns' livability and efficiency. The goal of this paper is to modernize Egypt's traffic management system and bring it into compliance with global guidelines for intelligent transportation networks.

Resource allocation for device-to-device communications underlaying uplink cellular networks

Underlying cellular networks, device-to-device (D2D) communications are a practical network technology that can increase power efficiency and spectrum usage for close-proximity wireless services and applications. However, D2D link interference, when sharing resources with cellular users (CUs), poses a major challenge in such distribution situations. In this research, we primarily utilize wireless channel data that exhibits slowly shifting large-scale fading to conduct spectrum sharing and power allocation. The overall ergodic capacity of all cellular user equipment (CUE) links is initially considered as the optimization target in order to maximize the overall throughput of CUE links while ensuring the reliability of each D2D link. Then, the expansion of the minimum ergodic capacity is measured to ensure a more consistent capacity performance across all CUE links. We utilized algorithms that are resilient to channel fluctuations and produce optimal resource allocation. We use MATLAB, and the computer simulation validates its intended performance.

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).

Genetic Algorithm for Mode Selection in Device-to-Device (D2D) Communication for 5G Cellular Networks

The widespread use of smart devices and mobile applications is leading to a massive growth of wireless data traffic. With the rapidly growing of the customers’ data traffic demand, improving the system capacity and increasing the user throughput have become essential concerns for the future fifth-generation (5G) wireless communication network. In a conventional cellular system, devices are not allowed to directly communicate with each other in the licensed cellular spectrum and all communications take place through the base stations (BS) and core network. Device-to-Device (D2D) communication refers to a technology that enables devices to communicate directly with each other, without sending data to the base station and the core network. This technology has the potential to improve system performance, enhance the user experience, increase spectral efficiency, reduce the terminal transmitting power, reduce the burden of the cellular network, and reduce end to end latency. In D2D communication user equipment’s (UEs) are enabled to select among different modes of communication which are defined based on the frequency resource sharing. Dedicated mode where D2D devices directly transmit by using dedicated resources. Reuse mode where D2D devices reuse some resources of the cellular network. Outband mode where D2D communication uses unlicensed spectrum (e.g. the free 2.4 GHz Industrial Scientific and Medical (ISM) band or the 38 GHz millimetre wave band) where cellular communication does not take place. Cellular mode where the D2D communication is relayed via gNode B (gNB) and it is treated as cellular users. In this work, the target was to reach the optimal mode selection policy and genetic algorithm method was used with the objective of maximizing the total fitness function. Optimal mode selection policy was presented and analysed amongst cellular, dedicated, reused and outband mode. In the present study of mode selection issues in D2D enabled networks, genetic algorithm was proposed for the case when the cellular user equipment (UE) moves in the network. Quality of service (QoS) parameters, mobility parameters and Analytic Hierarchy Process (AHP) method were used to define the mode selection algorithm. To evaluate the performance of the proposed genetic algorithm, a study of the convergence of the algorithm and the signal-to-interference plus noise ratio (SINR) was done.

Optimizing V2X Communication: Spectrum Resource Allocation and Power Control Strategies for Next-Generation Wireless Technologies

The upcoming wireless technology developments in the next generations are expected to substantially transform the vehicle-to-everything (V2X) communication network. The challenge of limited spectrum resources in V2X communication, caused by the need for high data rates, necessitates a thorough analysis of spectrum resource allocation and power control. This complex problem falls under the domain of mixed-integer nonlinear programming; a strategic approach is implemented to overcome these issues, which divides the main challenge into two sub-problems. The issue of resource allocation is addressed by implementing a multiaccess spectrum allocation method, which is deliberately designed to optimize the utilization of the spectrum resources that are currently accessible. Concurrently, the power control issue is resolved by employing a continuous convex approximation technique, which effectively converts non-convex power-allocation issues into convex equivalents. This approach helps to alleviate interference between users. Finally, the simulation results prove that the proposed approaches can improve vehicle performance. The algorithms proposed in this article significantly improve the system throughput and access rate of vehicular user equipment (VUEs) while ensuring the data rate of cellular user equipment (CUEs).

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.

Fairness-aware resource allocation for D2D-enabled IoT in NOMA-based cellular networks with mutual successive interference cancellation

Device-to-device (D2D) communications and non-orthogonal multiple access (NOMA) are promising technologies to meet the growing demand for IoT-connected devices. However, they bring about new challenges including the co-channel interference, that can limit the performance improvement. To manage the co-channel interference, we address the problem of joint power allocation and sub-channel assignment for D2D-enabled IoT devices (IoTDs) underlaying a NOMA-based cellular network, in which the successive interference cancellation (SIC) decoding is enabled at the level of IoTDs and cellular user equipment (CUE)to increase the number of connected devices and the capacity. This problem is modeled as a mixed-integer nonconvex optimization problem which includes the concept of fairness with respect to the data rates of IoTDs. To solve the problem, a semi-distributed algorithm is developed, which is of polynomial time complexity. The proposed algorithm leverages the successive convex approximation and a heuristic approach. Evaluation results demonstrate the efficiency of the proposed scheme with respect to the sum rate, fairness, access rate and computational complexity.

Joint Resource and Power Management for D2D Communication Across Multiple Service Providers

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Device-to-device (D2D)</i> communication broadens the applicability of cellular networks, which permits <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user equipments (UEs)</i> to converse with each other without the relay by a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base station (BS)</i> . D2D pairs can reuse the spectrum resources allocated to cellular UEs but they would cause interference. How to well manage resources and transmitted power for UEs is critical. This article considers a radio access network (RAN) sharing scenario, where multiple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">service providers (SPs)</i> collocate in the BS and have dedicated resources. To serve the maximum UEs, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">joint resource allocation and power control with RAN sharing (JAPS)</i> scheme. Each SP allocates the dedicated resource to UEs and decides initial power to meet their signal-to-interference-plus-noise ratio (SINR) demands and restrain interference. Then, SPs can borrow resources from each other (i.e., inter-SP loan) to serve more UEs and better resource utilization. Finally, the power of senders is carefully adjusted to improve the signal quality. Simulation results show that JAPS can achieve a high service ratio, improve D2D throughput, and raise energy efficiency.

Joint Resource and Power Allocation for Clustered Cognitive M2M Communications Underlaying Cellular Networks

The massive number of Machine-Type Communication Devices (MTCDs) coexisting with Cellular User Equipment (CUE), in addition to the diverse Quality-of-Service (QoS) requirements of M2M communications and cellular communications, present significant implementation challenges due to interference, congestion, and spectrum scarcity. This makes resource allocation an important but challenging problem. In this article, clustered Cognitive Machine-to-Machine (CM2M) communications underlaying cellular networks is proposed to solve this problem. In this system, MTCDs are grouped in clusters based on their spatial locations and communicate with the Base Station (BS) via a Machine-Type Communication Gateway (MTCG). Underlay Cognitive Radio (CR) is employed so that MTCDs within a cluster can share the spectrum of neighbouring CUE. A joint resource-power allocation problem is formulated and solved using a two-phase resource and power allocation scheme. The goal is to maximize the uplink sum-rate of the neighbouring CUE and clustered MTCDs while satisfying interference, power, and minimum data rate constraints. Simulation results are presented which show that the proposed scheme significantly improves the sum-rate of the network compared to other schemes while satisfying the constraints.

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.

Modeling D2D Underlaying Cellular Network for Hotspot Communications with Poisson Cluster and Hole Processes

This paper develops a new approach to the modeling and analysis of device-to-device (D2D) underlaying multi-tier cellular network for dense hotspot communications, which consists of macro base stations (MBSs), pico BSs (PBSs), femto BSs (FBSs). A typicl user equipment (UE) can work either in D2D mode or cellular mode. Considering the dense hotspot communications, this work employs Poisson point process (PPP) to model the locations of MBSs and PBSs, and uses Poisson cluster process (PCP) to model the ones of UEs and FBSs. The locations of PBSs are also modeled as the centers of hotspots, referred to as the centers of PCPs. UEs and FBSs cluster around the common parent process PBSs. To guard the cluster-edge UEs, the clustered-UE classification and modified fractional frequency reuse (FFR) are jointly used, by which both the UEs and FBSs are classified two sets, cluster-center UEs and cluster-edge UEs, cluster-center FBSs and cluster-edge FBSs, respectively. The total frequency band is divided into two orthogonal segments, one of which is shared by D2D devices, cluster-edge FBSs, and PBSs, and the other segment of which is shared by cluster-center FBSs and MBSs. For such clustered multi-tier network, by using the methods from PPP, PCP, and PHP, this paper presents a tractable approach for modeling and analyzing the performance of cellular and D2D networks and gives the statistical descriptions of the experienced interferences at a typical D2D or cellular receiver by using the approximated Poisson hole processes (PHP) theory. This yields the derivations of the coverage probabilities of both the D2D receivers and cellular destinations. In additon, during the analysis of cellular UEs, to derive the coverage probabilities, this paper specially constructs one UE association criterion as well as the derivations of both the association probabilities and the statistical descriptions of association distances for cluster-center and cluster-edge UEs. The simulations results exploit the effect of various network parameters on the network performance and give the insights in terms of the proposed schemes as well as the comparison between cluster-center and cluster-edge UEs.

Energy optimized resource and power allocation in an uplink‐based underlay device‐to‐device communication for 5G network

SummaryFifth generation (5G) and beyond fifth generation (B5G) networks are expected to provide more energy efficiency, spectral efficiency, and very high data rate with minimum latency to an ultradense environment. To meet the requirements of 5G and B5G standards, the network infrastructure can be network‐centric approach where the network is modified with respect to new radio signals and waveforms. The other approach is device centric where the existing network infrastructure can be efficiently utilized. Device‐to‐Device (D2D) communication is a key technique in device centric approach that supports 5G and B5G networks. We propose a Sequential Best Throughput Seek Algorithm (SBTSA), to compute the interference threshold for each Cellular User Equipment (CUE), and optimum resource blocks (RBs) are assigned to D2D pairs which provide best throughput to D2D pairs without affecting the Quality of Service (QoS) of the CUEs. The transmit power is optimized using genetic algorithm (GA) to effectively handle the interference between cellular users and D2D pairs. Proposed algorithm is compared with existing methods, and best results are obtained in terms of system throughput, battery utilization and energy efficiency.

Spatial-Reuse-Based Efficient Coexistence for Cellular and WiFi Systems in the Unlicensed Band

With the increasing data traffic in the fifth-generation (5G) communication system, the 5G new radio extended to unlicensed bands (5G NR-U) has become a promising approach to relieve the heavy pressure on the cellular system. To achieve the efficient coexistence with the WiFi system and improve the efficiency of temporal, spectral and spatial resource utilization, we first divide the transmission space into two subspaces by leveraging spatial reuse, where the data transmitted by cellular user equipments (UEs) falls into one subspace and the data transmitted by Internet of Things (IoT) devices coexisting with WiFi users through power control is in the other subspace. Then, the coexistence among cellular UEs, IoT devices, and WiFi users is formulated as an optimization model with the aim of maximizing the cellular system throughput via the joint power and subchannel allocation under the interference constraint. Although the resulting optimization problem is a mixed-integer nonlinear programmming, we decompose it into two subproblems and develop an alternating iterative approach to effectively solve them. Also, the closed-form allocations of the power and subchannels are obtained. Simulation results confirm that the proposed scheme can improve the cellular system performance and guarantee the coexistence in the unlicensed band.

Energy-Efficient Interference-Aware Cognitive Machine-to-Machine Communications Underlaying Cellular Networks

Machine-to-machine (M2M) communications can effectively utilize cognitive radio (CR) to coexist with cellular users in what is known as cognitive M2M (CM2M) communications. In this system, underlay CR is used to manage spectrum sharing among machine type communication devices (MTCDs) and cellular user equipment (CUE) where the CUE are considered to be primary users (PUs) and the MTCDs are secondary users (SUs). Moreover, due to the limited battery capacity of MTCDs and diverse quality-of-service (QoS) requirements of both MTCDs and CUE, energy efficiency (EE) is critical to prolonging network lifetime. This paper investigates the power allocation problem for energy-efficient CM2M communications underlaying cellular networks. Underlay CR is employed to manage the coexistence of MTCDs and CUE and exploit spatial spectrum opportunities to improve spectrum utilization. Two power allocation problems are proposed where the first targets MTCD power consumption minimization while the second considers MTCD EE maximization subject to MTCD transmit power constraints, MTCD minimum data rate requirements, and CUE interference limits. The proposed power consumption minimization problem is transformed into a geometric programming (GP) problem and solved iteratively. The proposed EE maximization problem is a nonconvex fractional programming problem. Hence, a parametric transformation is used to convert it into an equivalent convex form and this is solved using an iterative approach. Simulation results are presented which show that the proposed algorithms provide MTCD power allocation with lower power consumption and higher EE than the equal power allocation (EPA) scheme while satisfying the constraints.

Testing 5G User Equipment: Review, Challenges, and Gaps from the Medical Device Perspective.

5G technology can enable novel healthcare applications and augment existing medical device use of wireless technology to improve healthcare delivery. Having the dual role of medical device and cellular user equipment (UE), 5G-enabled devices navigate a plethora of rules and tests before they can operate on a 5G network to deliver their intended medical function. In this paper, we review the state-of-the-art of 5G UE certification programs and testing specifications. We then identify test challenges and discuss the relevance of existing test practices to support the 5G-enabled medical device safety and effectiveness. This information is useful to medical device developers planning to incorporate 5G technology in their products and wireless engineers working to expand mobile services into the healthcare vertical.

Practical Papers, Articles and Application Notes

5G technology has led to many novel healthcare delivery applications. The 5G-enabled medical devices also double up as cellular user equipment (UE), and therefore must comply with EMC regulations before they can operate on a 5G network. The first paper, &#x201C;Testing 5G User Equipment: Review, Challenges, and Gaps from the Medical Device Perspective&#x201D;, reviews the state-of-the-art of 5G UE certification programs and testing specifications. It also identifies test challenges and discusses the relevance of existing test practices to support the 5G-enabled medical devices. This paper is informative and useful to medical device developers planning to incorporate 5G technology in their products.

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.

Interference-graph and fuzzy C-means based resource allocation scheme in LTE-V2V communication networks

In this paper, a framework is proposed to improve the effectiveness of the long-term-evolution vehicle-to-vehicle (LTE-V2V) communication system, where the uplink channel of the cellular user equipment (CUE) is reused by multiple V2V links. Since the orthogonal channels occupied by each CUEs are multiplexed by multiple V2V links, the serious interference is generated in the co-tier layer and cross-tier layer. In order to minimize the co-channel interference between CUE link and V2V links, an optimized resource sharing scheme is proposed. Firstly, Fuzzy C-means (FCM) algorithm is proposed to perform the clustering based on the geographical location characteristics of V2V links. In the FCM algorithm, the adjacent V2V links are grouped into a cluster to facilitate the solution of the channel allocation problem. Assuring the V2V links in the same cluster do not share the same channel, the adjacent V2V links are avoided to reuse the same channel. On this basis, the use of reasonable channel allocation scheme can effectively reduce the co-channel interference. Considering that the channel state information (CSI) of mobile links is slowly faded, the total system capacity is maximized by leveraging the CSI while the reliability can be guaranteed for all V2V links. The outage probability constraint is used to ensure the reliability of all V2V links. The channel assignment problem of V2V links is formulated as a weighted two-dimensional matching problem, and the interference bipartite graph is constructed. Finally, Kuhn Munkras (KM) algorithm is introduced to allocate the uplink orthogonal channel of CUEs for the V2V links in the same class in order to maximize the total system capacity. Numerical simulation results show that the algorithm can improve the total system capacity.

Radio Resource Allocation in D2D Underlay Communication Using Two Phased Auction Based Fair and Interference Resource Allocation

The high demands of the mobile user will affect the workload of eNodeB, which results in the decreasingperformance system of eNodeB. Device-to-Device (D2D) underlaying communication system is a solution inreducing the workload of eNodeB and increasing the system data rate. This communication system consistsof two users, namely Cellular User Equipment (CUE) and D2D pair, where CUE shares its resources withthe D2D pair. This sharing of resources also causes interference and should be managed using the resourceallocation algorithm. This research used the TAFIRA D2D algorithm and compared it with the greedyalgorithm and the TAFIRA CUE algorithm. The research calculates parameter performance of the system,such as spectral efficiency, power efficiency, and fairness among D2D pairs. The simulation results showthat Greedy algorithm has a better performance compared with TAFIRA algorithm. TAFIRA D2D onlycan achieve 19.94 bps/Hz in spectral efficiency, 23.88 Kbps/watt in power efficiency, and 89% fairnessamong D2D pairs.