Channel Quality Indicator Research Articles

AI-based resource allocation techniques in D2D communication: Open issues and future directions

The fifth-generation (5G) mobile network enhances network connectivity between many mobile devices by utilizing higher bandwidth with lower communication delay. This massive connectivity generates huge network traffic while burdening the base station (BS). Hence, it is required to offload the BS and enhance the system’s overall performance. Device-to-device (D2D) communication is the prominent solution to offload the network traffic and enhance spectrum efficiency. However, D2D communication significantly disadvantages resource allocation while operating in underlay mode. A prominent solution is to embrace artificial intelligence (AI) in the D2D environment to tackle resource allocation issues. This paper exhaustively surveys all AI-based resource allocation schemes researchers have proposed globally. We found that no exhaustive literature surveys discuss the role of AI in resource allocation in a D2D environment. Thus, this motivated us to prepare a comprehensive survey and propose an AI-based resource allocation taxonomy comprising channel, power, and spectrum allocation. We also discuss the various real-time applications of AI-driven schemes for efficient resource allocation in D2D communication. Further, we presented a case study highlighting AI’s role in efficient resource allocation in D2D communication. Here, we used a novel channel quality indicator (QI) that uses signal-to-noise ratio (SINR) and signal-to-noise-and-distortion Ratio (SNDR) to decide the best D2D users for the resource allocation process. Then, we discussed the open research challenges that point to the future research direction in AI-based efficient resource allocation in D2D communication.

Modified Best Channel Quality Indicator Scheduler for Heterogeneous LTE-A Network

In this paper, a Long-Term Evolution-Advanced (LTE-A) Modified Best Channel Quality Indicator (M-BCQI) Scheduler is proposed for enhanced cell edge throughput performance. The scheduling algorithm utilizes two time slots, each of 0.5 ms in length, with the First Time Slot (FTS) implemented with the Best Channel Quality Indicator (BCQI) algorithm has a CQI-focused Resource Block (RB) allocation mechanism, while the Robin (RR) algorithm is the mechanism used in the Second Time Slot (STS) for RB allocation. Vienna LTE system level simulator was used for the proposed scheduling algorithm assessment. The results obtained showed significant improvement, indicating 44% superior cell edge performance and 56 % better fairness index in comparison to BCQI. However, M-BCQI slightly underperformed by 4.2% in terms of average throughput in comparison to BCQI and by 7% in comparison to RR in terms of fairness index.

The drift handling framework for open radio access networks: An experimental evaluation

The Open Radio Access Network (i.e., Open RAN) aims to transform the inflexible, proprietary RAN into one that is flexible, programmable, and disaggregated to provide intelligent solutions at the network edge for delivering the highly dynamic service demands of Beyond 5G (B5G) networks. Open RAN Working Group-2 (WG2) focuses on the architecture and standards of Artificial Intelligence/Machine Learning (AI/ML) to meet the Service Level Agreement (SLA) requirements for emerging use cases. The inherent dynamic nature of B5G networks and their applications often leads to AI/ML model performance degradation (i.e., drift), resulting in violations of SLAs, over- or under-provisioning of resources, etc.This paper proposes a drift handling framework which includes drift detection, analysis, and its adaptation to minimize the SLA violations and efficient resource utilization. The proposed drift handling framework is evaluated across three diverse use cases: (i) Quality of Service prediction, (ii) Channel Quality Indicator prediction, and (iii) User traffic prediction. The experimental evaluations are performed by using the Open RAN Software Community, and OpenAirInterface FlexRIC platforms with a real-time dataset. Results demonstrate that the proposed drift handling framework outperforms all other considered state-of-the art frameworks for all the scenarios in terms of the drift detection time, accuracy, precision, F1-score, etc.

Underlay Loosely Coupled Model for Public Safety Networks Based on Device-to-Device Communication

In several emergency situations, during natural or human-caused disasters, frontline responders need to be able to communicate and collaborate to properly carry out relief missions. Some countries build their national Public Safety Mobile Broadband based on cellular LTE technology to provide fast, safe, and secure emergency services. However, in several emergency situations, cellular antennas can be overloaded or partially damaged in a manner that affects group communication services. In the last few years, direct device-to-device (D2D) communications have been proposed by the 3GPP as an underlay of long-term evolution (LTE) networks based on proximity, reuse, and hop gains. This paper focuses on a loosely coupled model based on direct D2D communication in a public safety context. Many scenarios related to user membership and network management are detailed. Both the “less cost” and “optimized tree” approaches are proposed and implemented, and their performance is evaluated in terms of the network update number and the resulting average Channel Quality Indicator (CQI). Other optimization approaches, with different CQI thresholds and optimization interval parameters, are simulated to compare their performance with the “optimized tree” approach. By conducting simulations that combine a CQI threshold = 1 and optimization interval = 2 s, it becomes possible to keep an average CQI level close to the “optimized tree” approach, while the costs related to network updates significantly decrease by almost 35%. Other simulations are also carried out to measure the bandwidth required by the control messages between the server and active users. It was found that both inbound and outbound traffic on the server side can be well supported with LTE and 5G networks.

Joint MCS Adaptation and RB Allocation in Cellular Networks Based on Deep Reinforcement Learning With Stable Matching

Joint modulation-coding scheme (MCS) adaptation and resource block (RB) allocation is an effective approach to guarantee different quality of service (QoS) requirements of all UEs under dynamic network environments. In this paper, we consider a fifth generation (5 G) cellular network with time-varying wireless channels, in which the BS serves multiple user equipments (UEs) under limited available RBs. We aim to minimize the total RB consumption subject to the rigorous constraints of each UE's QoS requirement. To attain this objective, this paper puts forth an online learning technique, referred to as integrated Deep Reinforcement learning and stable Matching (DeepRM), in the sense that the MCS adaptation and RB allocation decisions are conducted without acquiring the real-time channel quality indicator (CQI) feedback. DeepRM is a closed-loop framework, where the output of deep reinforcement learning (DRL) is imported into the stable matching to guide optimal RB allocation whilst the output of stable matching is fed into the DRL framework to assist efficient MCS decision-making. Specifically, in DeepRM, we first develop a powerful DRL algorithm, termed as Action-and-Reward Branching Deep Q-network (ARBDQ), by incorporating the action branch architecture into conventional DRL and modifying the traditional deep neural network training mechanism, to perform judicious MCS decisions on different links in parallel. Then, a new many-to-one stable matching algorithm, called adaptive deferred acceptance, is exploited to dynamically adjust the RB quota of each UE in a computationally efficient fashion. Simulation results demonstrate that compared with ACO-HM, OLLA-ADA, and ARBDQ-Random algorithms, DeepRM induces much less RB consumption while guaranteeing the QoS requirements of all UEs in various network scenarios. Furthermore, under miscellaneous QoS requirement, number of UEs, and CQI reporting period setups, DeepRM is more robust than other baselines.

Dynamic LSTM-Based Channel Quality Indicators Prediction for LEO Satellite Communication

Dynamic LSTM-Based Channel Quality Indicators Prediction for LEO Satellite Communication

Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing

Fog computing is a key component of future intelligent transportation systems (ITSs) that can support the high computation and large storage requirements needed for autonomous driving applications. A major challenge in such fog-enabled ITS networks is the design of algorithms that can reduce the computation times of different tasks by efficiently utilizing available computational resources. In this paper, we propose a data-enabled cooperative technique that offloads some parts of a task to the nearest fog roadside unit (RSU), depending on the current channel quality indicator (CQI). The rest of the task is offloaded to a nearby cooperative computing vehicle with available computing resources. We developed a cooperative computing vehicle selection technique using an artificial neural network (ANN)-based prediction model that predicts both the computing availability once the task is offloaded to the potential computing vehicle and the link connectivity when the task result is to be transmitted back to the source vehicle. Using detailed simulation results in MATLAB 2020a software, we show the accuracy of our proposed prediction model. Furthermore, we also show that the proposed technique reduces total task delay by 37% compared to other techniques reported in the literature.

Delphi: Computing the Maximum Achievable Throughput in SD-RAN Environments

Software-Defined Radio Access Networks (SD-RANs) foster the concepts of programmability and flexibility, which are vital for next generation cellular networks. However, SD-RANs render network management and orchestration very challenging. Indeed, related works indicate that when thousands of connected devices are spread across the underlying network, SD-RAN approaches with a single controller become deficient and exhibit undesired behavior. Despite this, state-of-the-art research papers lack concrete solutions and evaluations with respect to throughput predictability, where the latter is jeopardized by irregularities in the SD-RAN control plane, specifically in realistic testbeds. In order to overcome the aforementioned issues, in this work, we present : a novel platform that provides both analytical and experimental methods to achieve our goal, which is computing the maximum achievable throughput in SD-RAN environments. Analyzing the results provided by, we can capture the impact of the SD-RAN control plane on throughput. Moreover, we can design important guidelines as to which policy to choose given objectives such as throughput maximization or robustness. Providing a platform for SD-RAN evaluations based on open-source components, enables new avenues for research in the mobile network community. Focusing on FlexRAN SD-RAN controller for our initial results, overall, our findings show that when the number of Base Stations (BSs) and User Equipment (UEs) in the network increases beyond 5000, due to non-timely received control packets for the maximum Channel Quality Indicator (maxCQI) policy the overall throughput decreases by more than 20%.

CSI Sensing From Heterogeneous User Feedbacks: A Constrained Phase Retrieval Approach

This paper investigates the downlink channel state information (CSI) sensing in 5G heterogeneous networks composed of user equipments (UEs) with different feedback capabilities. We aim to enhance the CSI accuracy of UEs only affording the low-resolution Type-I codebook. While existing works have demonstrated that the task can be accomplished by solving a phase retrieval (PR) formulation based on the feedback of precoding matrix indicator (PMI) and channel quality indicator (CQI), they need many feedback rounds. In this paper, we propose a novel CSI sensing scheme that can significantly reduce the feedback overhead. Our scheme involves a novel parameter dimension reduction design by exploiting the spatial consistency of wireless channels among nearby UEs, and a constrained PR (CPR) formulation that characterizes the feasible region of CSI by the PMI information. To address the computational challenge due to the non-convexity and the large number of constraints of CPR, we develop a two-stage algorithm that firstly identifies and removes inactive constraints, followed by a fast first-order algorithm. The study is further extended to multi-carrier systems. Extensive tests over DeepMIMO and QuaDriGa datasets showcase that our designs greatly outperform existing methods and achieve the high-resolution Type-II codebook performance with a few rounds of feedback.

Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication

SummaryDevice‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.

Deep Reinforcement Learning Based Link Adaptation Technique for LTE/NR Systems

Outdated channel quality indicator (CQI) feedback causes severe performance degradation of traditional link adaptation (LA) techniques in long term evolution (LTE) and new radio (NR) systems. This paper puts forth a deep reinforcement learning (DRL) based link adaptation (LA) technique, referred to as deep reinforcement learning link adaptation (DRLLA), to select efficient modulation and coding scheme (MCS) in the presence of the outdated CQI feedback. The goal of DRLLA is to maximize the link throughput while achieving a low block error rate (BLER). We first give explicit definitions of state, action, and reward in DRL paradigms, thereby realizing DRLLA. Then, to trade off the throughput against the BLER, we further develop a new experience replay mechanism called classified experience replay (CER) as the underpinning technique in DRLLA. In CER, experiences are separated into two buckets, one for successful experiences and the other for failed experiences, and then a fixed proportion from each is sampled to replay. The essence of CER is to obtain different trade-offs via adjusting the proportion among different training experiences. Furthermore, to reduce the signaling overhead and the system reconfiguration cost caused by frequent MCS switching, we propose a new action selection strategy termed as switching-controlled <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy (SC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy) for DRLLA. Simulation results demonstrate that compared with the state-of-the-art OLLA, LTSLA, and DRLLA with other experience replay mechanisms, DRLLA with CER can achieve higher throughput and lower BLER in various time-varying scenarios, and be more robust to different CQI feedback delays and CQI reporting periods. Furthermore, with the SC- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -greedy policy, DRLLA can capture better trade-offs between the link transmission quality and the MCS switching overhead compared with other baselines.

Evaluating Latency in Fifth-Generation Vehicle-to-Everything Communications using Adaptive Neuro-Fuzzy Inference System Model

The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks ,and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks ,andswitchmodes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90%for UE in VoIP.

Lightweight LSTM-Based Adaptive CQI Feedback Scheme for IoT Devices

As the number of Internet of things (IoT) devices increases exponentially, scheduling and managing the radio resources for IoT devices has become more important. To efficiently allocate radio resources, the base station (BS) needs the channel state information (CSI) of devices every time. Hence, each device needs to periodically (or aperiodically) report its channel quality indicator (CQI) to the BS. The BS determines the modulation and coding scheme (MCS) based on the CQI reported by the IoT device. However, the more a device reports its CQI, the more the feedback overhead increases. In this paper, we propose a long short-term memory (LSTM)-based CQI feedback scheme, where the IoT device aperiodically reports its CQI relying on an LSTM-based channel prediction. Additionally, because the memory capacity of IoT devices is generally small, the complexity of the machine learning model must be reduced. Hence, we propose a lightweight LSTM model to reduce the complexity. The simulation results show that the proposed lightweight LSTM-based CSI scheme dramatically reduces the feedback overhead compared with that of the existing periodic feedback scheme. Moreover, the proposed lightweight LSTM model significantly reduces the complexity without sacrificing performance.

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

SummaryFor the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles.

Deep learning-based channel quality indicators prediction for vehicular communication

Vehicular communication shares essential information for safety and convenience. Vehicular communication must guarantee a high transmission rate with stable communication. In vehicular communication environments, the channel characteristic frequently varies due to the high-speed movement of the vehicles. Understanding the channel conditions is essential to maintain stable communication. We propose an optimal channel quality indicator (CQI) prediction model for expecting channel characteristics. Our prediction model defines the CQI from the received signal strength indication (RSSI) and is applied to the IEEE 802.11p wireless access in vehicular environments (WAVE) standard. The prediction part applies robust long-short term memory (LSTM) network to sequential data. The CQI prediction model is trained and evaluated using vehicular communication data collected by an IEEE 802.11p WAVE device. We compare the prediction performance of the proposed model with the auto-regressive integrated moving average, support vector regression, and multilayer perception models.

Performance Analysis of Support Vector Machine Learning Based Carrier Aggregation Resource scheduling in 5G Mobile Communication

The 5G cellular system will enable heterogeneous networks that incorporate 5G, 4G, Wi-Fi, and other telecommunication connectivity. This will result in huge changes in existing infrastructure and architecture network. Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine Type Communication (mMTC) are some of the 5G's basic application cases. The variety of heterogeneous networks makes it challenging to manage, network traffic, inter-cell, and intra-cell interfaces. Due to diverse interfaces and data transmission rates, network traffic in heterogeneous networks can be difficult to manage and control. Advanced techniques including Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) are used in the 5G Network to overcome such issues. In this paper, a Support Vector Machine (SVM) learning-based resource allocation approach for heterogeneous networks is designed. A machine learning method is proposed to enhance the performance of 5G NR mobile networks. This method is used to upgrade the throughput of the overall networks. The SVM learning function detects by splitting network data of channel state information (CSI). The considered channel state data of two variables as labels with X1 and X2 and trained using SVM-LINEAR, SVM-RBF, and SVM-SIGMOID. The proposed SVM classifier by splitting data for training 80% and testing 20%. A Static Error rate is set as a threshold limit at 10% (0.10) of BLER (Block Error Rate). If the error rate passed the threshold, then update Carrier Aggregation with aggregated component else set carrier aggregation with ordinary carrier component values to Zero. The proposed approach further compares with the round robin (RR), best channel quality indicator (BCQI), fractional frequency reuse (FFR), MU-MIMO, and beamforming resource scheduling techniques. The innovative results indicated that the proposed approach enhanced spectral efficiency, average cell throughput, edge throughput peak throughput, and fairness index.

A Next-Generation Codebook Evolution Strategy for Massive Arrays Using Deep Neurals Networks

The discrete Fourier transform (DFT)-based codebook is currently among the mostly commonly adopted codebooks for beamforming using arrays of different shapes and sizes, including the large-scale two-dimensional uniform planar array (UPA). DFT-based codevectors can be easily generated in arbitrary angle resolutions and apply well to millimeter-wave (mmWave) channels due to their directive nature of resulting beams. However, a fixed set of codevectors is applied regardless of the user distributions and the propagation environment, which may exhibit limited beamforming performance under certain transmission scenarios. In this paper, we propose a new way of generating a set of beamforming vectors for multiple-input multiple-output (MIMO) transmission using massive arrays under the limited feedback of the channel state information (CSI). Precoder matrix indicator (PMI) and channel quality indicator (CQI) reports from the users have become the sources for the generation of a new set of codevectors, which are autonomously determined by the deep learning (DL) module at the base station (BS). The process is operated in an iterative fashion to produce updated versions of the codebook with the reduced return of the loss function at the deep neural network (DNN). The time-varying codebook for each BS automatically reflects the characteristics of a given wireless environment to adapt to its channel and traffic conditions. The reference signal (RS) at the BS is periodically transmitted in the form of beamformed CSI-RS, thus the operation is transparent to the users of the system and no significant specification changes are necessary. A simple plug-and-play type of BS installation suffices to achieve the potential gain of the proposal, which is demonstrated by the implementation details of the DL engine and the corresponding performance simulation results.

Design and Performance Analysis of 3-D Markov-Chain-Model-Based Fair Spectrum-Sharing Access for IoT Services

The spectrum-sharing access technology using unlicensed spectrum bands is considered as a promising solution to solve the spectrum deficiency problem for various Internet of Things services. But, the efficient and fairness-guaranteed spectrum-sharing access among different systems is challenging by using the listen-before-talk (LBT) technology due to the uncertain channel quality and the channel access collision issues in unlicensed spectrum bands. To solve these problems, a novel three dimension-based Markov chain model is designed to formulate the collision probability of the spectrum-sharing access process using the contention window (CW) back-off algorithm based on the channel quality indicator feedback information. The key reasons for the packet transmission failure are comprehensively analyzed by considering both the channel collision and the channel quality deterioration conditions. A fairness-based spectrum-sharing access algorithm is proposed by assigning the optimal CW for the LBT system to minimize the collision probability and guarantee the fairness among LBT and wireless fidelity coexisted systems. Hardware platform-based evaluation results prove that our proposed algorithms can improve the system throughput and guarantee the fairness among different systems in the unlicensed spectrum band.

Physical Layer Security Design for Polar Code Construction

In contrast to the network security that relies on upper-layer encryption for the confidentiality and authenticity of communications, physical layer security (PLS) exploits the uniqueness and randomness of the physical channel to encrypt information and enhance the security of the system. In this paper, we study the PLS of a polar-coded wireless communication system. To be more specific, we leverage the unique properties in polar code construction and propose a channel quality indicator (CQI)-based frozen-bit pattern generation scheme. The transmitter employs the Gaussian approximation algorithm to generate the corresponding frozen bit pattern according to the instantaneous CQI of the legitimate link. At the receiver, by leveraging the full channel reciprocity in the time-division duplex (TDD) mode, we can map the CQI to the corresponding frozen bit pattern and correctly decode the received bits. By contrast, the eavesdropper was unable to have the knowledge of the legal channel, and hence cannot determine the frozen bit pattern of the polar-coded bit sequence. Our simulation results demonstrate that by adopting the proposed PLS key generation scheme, Eve was hardly able to correctly decode a complete frame, leading to a high block error rate (BLER), while Bob was able to attain a 10−5 BLER.

Devising a traffic control method for unmanned aerial vehicles with the use of gNB-IOT in 5G

UAVs or drones as an alternative solution to providing high-quality Internet service in difficult terrain are environmentally friendly and do not consume electricity during the day as is the case with communication towers. But the developers of the network face difficulties in the drone communication system associated with the need to take into consideration unpredictable weather conditions and terrain, as well as the short life of the drone's batteries. Therefore, the object of this study is the process of managing UAV traffic through the use of gNB-IoT in 5G. The possibility of using a mobile UAV repeater during traffic management using radio resources (RR), radio access network (RAN), the infrastructure with broadcasting tools and dynamic connection using MU-MIMO modulation is shown. The use of these tools makes it possible to connect the drone to the wired base network from the provider and then restore the radio frequency signal and broadcast to another coverage area where this subscriber does not have network coverage, use the channel quality indicator (CQI) representation as a QoE function. Undoubtedly, traffic management is the process of obtaining information about traffic control from one endpoint to another, which confirms the reliability and management of data transmission. Meanwhile, drone traffic management can be used to reduce time delays and remove network interference by relying on Internet of Things programs that use NB-5G technology. The UAV's traffic management improvement process uses a proposed algorithm to generate dynamic flow data management to enhance traffic processing of flow control in the IoT