Physical Resource Block Research Articles

Reinforcement Learning Approach for Adaptive C-V2X Resource Management

The modulation coding scheme (MCS) index is the essential configuration parameter in cellular vehicle-to-everything (C-V2X) communication. As referenced by the 3rd Generation Partnership Project (3GPP), the MCS index will dictate the transport block size (TBS) index, which will affect the size of transport blocks and the number of physical resource blocks. These numbers are crucial in the C-V2X resource management since it is also bound to the transmission power used in the system. To the authors’ knowledge, this particular area of research has not been previously investigated. Ultimately, this research establishes the fundamental principles for future studies seeking to use the MCS adaptability in many contexts. In this work, we proposed the application of the reinforcement learning (RL) algorithm, as we used the Q-learning approach to adaptively change the MCS index according to the current environmental states. The simulation results showed that our proposed RL approach outperformed the static MCS index and was able to attain stability in a short number of events.

Enabling Grant-Free URLLC for AoI Minimization in RAN-Coordinated 5G Health Monitoring System

Age of information (AoI) is used to evaluate the performance of 5G health monitoring systems because stale data can be fatal for patients with serious illness. Recently, grant-free ultra-reliable and low latency communications (URLLC) have shown greater potential of minimizing AoI than conventional grant-based approaches; however, existing grant-free schedulers cannot provide guaranteed performance in 5G health monitoring systems because they involve two fundamental problems in time and frequency domains, namely the joint scheduling problem and physical resource block (PRB) allocation. In this study, we investigate two resource allocation problems for the first time, aiming to enable grant-free URLLC to minimize AoI in 5G health monitoring systems. Specifically, we propose two adaptive solutions based on an open radio access network-coordinated wireless system: 1) a joint scheduling algorithm and 2) an adaptive PRB allocation algorithm. To verify the effectiveness of the proposed solutions, we built a simulation environment similar to a real health monitoring system and captured the performance variations under realistic deployment scenarios.

Prediction of Channel Stability to Optimize Signalling Overhead in 5G Networks using ML Algorithm

As we are leaving in 5G era which promises us to give high throughput rate, better efficiency rate, and high bandwidth etc as its feature make headlines to give advanced and enhanced services in various sector whether it could be IOT, infrastructure, agriculture, and health care [1]. Giving well-grounded communication technology possesses a fundamental challenge for 5G system in both Core network and Radio Access Network (RAN) levels [2]. To get a better credibility at RAN level, base station (EnodeB or gNodeB in 4G and 5G respectively) should allocate proper amount of resource as per UE and select appropriate Modulation and scheme (MCS) to give better user experience. The amount of Physical Resource Blocks is directly proportional to channel condition at the User Equipment (UE) end [5]. The Base Station would generally know the real time channel strength of each UE, which then later provides accordingly resource to the UE, as UE would be sending periodically CQI to Base station. However, the periodic transmission of CQI would lead to signaling overhead which hampers the performance of RAN. Henceforth its foremost thing to average it or to optimize the CQI value to give a better user experience. Therefore, will be exploring four such machine learning algorithms (ANN, reinforcement learning, KNN and SVM) to optimize the CQI and would be analyzing which Algorithm will give better performance. Key Words: CQI, Machine Algorithm, RAN, Offloading, Handover, Base Station

Congestion Analysis of Transport Layer in a Multicell 5G DL Communication System.

The fifth generation (5G) marks an important advance in mobile network capabilities. When it comes to high data rates, capacity, spectrum efficiency, and availability, 5G mobile broadband goes above and beyond what was previously possible with standard mobile broadband. The construction of 5G networks is still in the planning stages. These 5G networks will create intelligent networked communication environments by connecting people, things, data, applications, and transport networks. Mobile networks have made it possible for customers' mobile devices (such as smartphones, tablets, laptops, and so on) to connect to the internet. A variety of distinct protocols will be required to take into consideration the various aspects that 5G possess. One of these is the transport protocol, which is intended to deliver extremely high data transfer rates up to 400 Gbps. The transmission control protocol (TCP) is one of the numerous protocols that are necessary for supporting 5G's many capabilities. Our work focuses on the detection and analysis, on the downlink (DL) side, of the congestion of the transport layer in single- and multicell environments. For the purpose of the analysis, the following metrics were analyzed: physical resource blocks (PRBs), user throughput, cell throughput, cell edge user throughput, and delay. The work emphasizes the activation of the TCP slow-start algorithm using file transfer protocol (FTP) model two according to 3GPP standards.

Load balancing in 5G heterogeneous networks based on automatic weight function

Load balancing is a major challenge in heterogeneous networks (HetNets) consisting of 5G and 6G ultra-dense small cells with long-term evaluation advanced (LTE-A) networks. A key factor in achieving efficient load balancing during user mobility is creating appropriate optimisation for handover control parameters (HCP). This paper proposes a coordinated load balancing algorithm for LTE-A/fifth generation (5G) HetNets. The algorithm automatically optimises HCP settings for a given user based on three bounded functions (the signal-to-interference-plus-noise ratio (SINR) of the user equipment (UE), the number of physical resource blocks (PRBs) per UE and the UE’s speed) as well as their automatic weight levels. A two-step target cell determination strategy is implemented according to the cell load level and RSRP criteria, ensuring that users are handed over to low-loaded target cells. A new HO procedure that considers the pilot signal power is also proposed, which includes the number of PRBs per UE and the RSRP. Cells with freer PRBs are prioritised in user association to provide load balance and enhanced throughput. The proposed load balancing algorithm is compared with five other load balancing algorithms selected from the literature. The simulation results reveal that under various mobile speed scenarios, the proposed load balancing scheme enhances network performance in terms of load level, throughput, spectral efficiency and call dropping ratio (CDR).

Resource Allocation in an Open RAN System Using Network Slicing

The next radio access network (RAN) generation, open RAN (O-RAN), aims to enable more flexibility and openness, including efficient service slicing, and to lower the operational costs in 5G and beyond wireless networks. Nevertheless, strictly satisfying quality-of-service requirements while establishing priorities and promoting balance between the significantly heterogeneous services remains a key research problem. In this paper, we use network slicing to study the service-aware baseband resource allocation and virtual network function (VNF) activation in O-RAN systems. The limited fronthaul capacity and end-to-end delay constraints are simultaneously considered. Optimizing baseband resources includes O-RAN radio unit (O-RU), physical resource block (PRB) assignment, and power allocation. The main problem is a mixed-integer non-linear programming problem that is non-trivial to solve. Consequently, we break it down into two different steps and propose an iterative algorithm that finds a near-optimal solution. In the first step, we reformulate and simplify the problem to find the power allocation, PRB assignment, and the number of VNFs. In the second step, the O-RU association is resolved. The proposed method is validated via simulations, which achieve a higher data rate and lower end-to-end delay than existing methods.

Time Domain Analysis of NB-IoT Signals

The NB-IoT (NarrowBand-Internet of Things) radio technology is now widely implemented by mobile phone network operators to support the communication of IoT devices such as smart meters, insurance black boxes for cars, network connected waste bins, smart bicycles. In the present work, some LTE800 cells of different mobile phone network operators implementing the NB-IoT technology in the guard band mode have been investigated. The signals, consisting of a PRB (Physical Resource Block) 180 kHz wide, have been analyzed and characterized in the time domain by means of a narrow band instrumental chain equipped with a Rohde & Schwarz FSH8 spectrum analyzer. Time domain analysis allows us to identify, within the transmission frame, the position of the NB-IoT signaling channels such as the Narrowband Reference Signal (NRS), the primary (NPSS) and secondary (NSSS) synchronization signals and the broadcast channel (NPBCH), but, above all, to measure the power received during the transmission of the NRS. This value has been compared with that measured by the NB-IoT decoding module supplied on the same analyzer, in order to verify the equivalence of these measurement methods. This would allow use of a more diffuse and cheaper instrumentation rather than more expensive vector analyzers, currently required to assess electric fields due to the NB-IoT signals through the extrapolation techniques set by Italian CEI 211-7/E technical standard.

Deep Learning Based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization concerning with traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research focuses on extensive 6.2 million real network time series Long-Term Evolution (LTE) data traffic and other associate parameters, including eNodeB-wise Physical Resource Block (PRB) utilization, which mainly focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. A state-of-the-art Deep Learning algorithm-based fusion model is proposed. The combination of different deep learning algorithms, namely Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM) and Gated Recurrent Unit (GRU), enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.

Dynamically Resource Allocation in Beyond 5G (B5G) Network RAN Slicing Using Deep Deterministic Policy Gradient

Network slicing makes it possible for future applications with a variety of adaptability requirements and performance requirements by spliting the physical network into several logical networks. Radio access network (RAN) slicing’s main goal is to assign physical resource blocks (RBs) to mMTC, eMBB, and uRLLC services while ensuring the Quality of service (QoS). Consequently, it is challenging to determine the optimal strategies for 5G radio access network (5G-RAN) slicing because of dynamically changes in slice needs and environmental data, and conventional approaches have difficulty addressing resource allocation issues. In this paper, we present an energy-efficient deep deterministic policy gradient resource allocation (EE-DDPG-RA) method for RAN slicing in 5G networks to choose the resource allocation policy that increases long-term throughput while satisfying the requirements of B5G systems for quality of service. This method’s main goal is to remove unnecessary actions in order to lower the amount of available action space. The numerical outcomes demonstrate that the proposed approach outperforms boundaries by enhancing deep-rooted throughput and effectively managing resources.

Resource Slicing for eMBB and URLLC Services in Radio Access Network Using Hierarchical Deep Learning

Network slicing is a promising technique for wireless service providers to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services in a shared radio access network (RAN) infrastructure. In this paper, we apply numerology, mini-slot based transmission, and punctured scheduling techniques to support eMBB and URLLC network slices. For efficient allocation of radio resources (e.g., physical resource blocks, transmit power) to the users, we formulate RAN slicing problem as a multi-timescale problem. To solve this problem and address the dynamics of the traffic, we propose a hierarchical deep learning framework. Specifically, in each long time slot, the service provider employs a deep reinforcement learning (DRL) algorithm to determine the slice configuration parameters. The eMBB and URLLC schedulers use their own attention-based deep neural network (DNN) algorithm to allocate radio resources to their corresponding users in each short and mini time slot, respectively. Simulation results show that the proposed framework can achieve a higher aggregate throughput and a higher service level agreement (SLA) satisfaction ratio compared to some other RAN slicing approaches, including the resource proportional placement algorithm, decomposition and relaxation based resource allocation algorithm, and distributed bandwidth optimization algorithm.

Reconfigurable Physical Resource Block Using Novel – Beamforming Filter Circuit for LTE-Based Cell-Edge Terminals

This article presents a novel reconfigurable physical resource block (PRB), where long-term evolution (LTE) and mobile cell-edge terminals are interfaced using low-power tunable second-order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G_{m}-C$ </tex-math></inline-formula> filter beamforming circuit, hence achieving an uplink–downlink (UL-DL) cellular system. A total of ten wireless multipoint (MP) terminals were structured and investigated. The BS-to-UAV-enabled terminals are propagated by line-of-sight (LoS) and nonline-of-sight (NLoS) beamforming. The subordinate terminals are handled using the derived Rayleigh signal distribution expressions. To guarantee higher frequency shift, the embedded control logic (CL) circuit is modeled upon the NMOS low dropout (LDO) regulator configurations with center frequency of 9.2 MHz and pass band of 1.4 MHz. The quality factors for both the first and second poles remain stable at 4.1 and 6.5 respectively. Stochastic analysis of the anticipated signal impacts with further estimation of its inherent variation challenges within the confined spectrum-sharing zone was corroborated. Our work is validated using an introduced direct and relay network density variations. The model’s performance evaluation compliments a ≥ 45.3-dB gain, an optimal transmission power and robust network sum rates in both LoS and NLoS conditions.

Mathematical Model of Short Circuit Fault Diagnosis in management and collaborative System Based On Improved SOM Neural Network

The existing mathematical model for short-circuit fault diagnosis of management and collaborative system has the problem that the session characteristics of communication link are not clear, resulting in the slow upload speed of fault data. In order to solve this problem, a short-circuit fault diagnosis mathematical model of communication system based on improved SOM neural network and the innovative strategies also management and collaborative system is describe in this paper. Firstly, the early warning information of the system is collected, combined with the repeated information, the session characteristics of the communication link are extracted by using the quasi Newton algorithm, the mutually orthogonal physical resource blocks are transformed, the short-circuit fault diagnosis mode is optimized, the parameters of the components to be detected are estimated, the feature space vector is truncated, and the mathematical model is established by using the improved SOM neural network. Experimental results: the average value of the management and collaborative system short-circuit fault diagnosis mathematical model designed in this paper is 8.244 Mbps, and the average values of the other two management and collaborative system short-circuit fault diagnosis mathematical models are 5.756 Mbps and 5.863 Mbps respectively. The results show that the designed mathematical model for short circuit fault diagnosis of management and collaborative system has high application value.

SmartCon: Deep Probabilistic Learning-Based Intelligent Link-Configuration in Narrowband-IoT Toward 5G and B5G

To enhance the coverage and transmission reliability, repetitions adopted by Narrowband Internet of Things (NB-IoT) allow repeating transmissions several times. However, this results in a waste of radio resources when the signal strength is high. In addition, in low signal quality, the selection of a higher modulation and coding scheme (MCS) level leads to a huge packet loss in the network. Moreover, the number of physical resource blocks (PRBs) per-user needs to be chosen dynamically, such that the utilization of radio resources can be improved on per-user basis. Therefore, in NB-IoT systems, dynamic adaptation of repetitions, MCS, and radio resources, known as auto link-configuration, is crucial. Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN)-based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet loss rate is significantly reduced in NB-IoT networks. For the training purpose of the GAN, we use a Multi-Armed Bandit (MAB)-based reinforcement learning mechanism that intelligently tunes its output depending on the present network condition. The performance of SmartCon is thoroughly evaluated through simulations where it is shown to significantly improve the performance of NB-IoT systems compared to baseline schemes.

Joint Radio Resource Allocation and Beamforming Optimization for Industrial Internet of Things in Software-Defined Networking-Based Virtual Fog-Radio Access Network 5G-and-Beyond Wireless Environments

Fog computing-based radio access network (Fog-RAN) leveraging the software-defined networking (SDN) and network function virtualization (NFV) is the most promising solution to offer real-time support for the massive number of connected devices in the industrial Internet of Things (IIoT) networks. However, designing an optimal dynamic radio resource allocation to handle the fluctuating traffic loads is critical. In this article, a novel architectural design of an SDN-based virtual Fog-RAN is proposed, in which we jointly study radio resource allocation and transmit beamforming to improve resource utilization and IIoT users’ satisfaction, by minimizing the network power consumption (NPC) and maximizing the achievable sum-rate (ASR), simultaneously. To this end, we first formulate a mixed-integer nonlinear problem to optimize the physical resource block allocation, the assignment of user equipments, and radio unit, and the downlink transmit beamforming, by considering imperfect channel state information. To solve the ntractable MINLP, we exploit the successive convex approximation approach. Then, we formulate a multiple knapsack problem (MKP) to optimize the assignment between RUs and virtual baseband units, by exploiting the set of active RUs minimized in the previous problem. We solve the formulated MKP by decomposing the dual problems and solving them through the dual descent method. Through performance analysis, we show the proposed approach provides a high users’ satisfaction rate, maximizes the ASR and minimizes the NPC, and provides better savings, in terms of the number of radio and baseband resources utilized, than its counterparts.

A QoS-aware and channel-aware Radio Resource Management framework for multi-numerology systems

To support the diversified 5G usage scenarios, the 5G New Radio (NR) physical layer is enriched with the flexible multi-numerology feature, where multiple frame structures with different sub-carrier spacings coexist in one frequency band. However, this flexibility brings with it a further challenge in Radio Resource Management (RRM), how to best allocate the available band spectrum among the different non-orthogonal numerologies. This work presents a novel Quality of Service (QoS)-aware and channel-aware RRM framework that manages diversified type of services, i.e., Best Effort (BE) services and Guaranteed Bit Rate (GBR) services with different priorities. The goal is to maximize the number of satisfied GBR services, according to the priority, and the throughput of the BE services. To provide a feasible solution, the proposed framework consists of two control levels. The first one appropriately splits the available bandwidth among the various numerologies, with the aim of achieving the above goal, trying to assign continuous portion of band to the same Numerology, in order to mitigate the Inter-Numerology Interference (INI) phenomenon. Moreover, it includes an advanced dropping strategy to support overload conditions. The second level distributes the Physical Resource Blocks (PRBs) to the User Equipments (UEs) belonging to the same numerology, by exploiting scheduling algorithms available in literature. Through an extensive simulation campaign, under different traffic load and channel conditions, we compare the proposed RRM framework with other reference schemes available in literature. The results show that the proposed framework outperforms them in any simulation setup.

Enhanced performance of long-term evolution small-cell networks using improved mobility algorithms

With an ever-increasing number of user equipment (UE) and bandwidth demands of new applications, the deployment of dense heterogeneous cellular networks has been adopted in many network scenarios. The small cells are unable to unload the traffic due to the random UEs mobility and cells deployment. This degrades the network performance such as handover success, throughput, and load distribution. To address such a problem, we propose an enhanced mobility load-balancing algorithm for small cells. The conventional mobility load balancing (MLB) algorithms study only the fixed or adaptive thresholds of the network separately to consider the load balance process, while other MLB algorithms consider the neighboring cells of the network those experience unnecessary MLB actions. The proposed load balancing algorithms study overloaded cells and neighbors using the proposed efficiency parameter, B. To identify the overloaded cells, we propose B which compares between a pre-defined threshold and the network threshold to categorize medium and overloaded cells, based on B, the algorithm is triggered. Then, to control the shifted load to a target neighbor cell, we propose the Rescue factor, f. the f ensures the load of the target cell after handover is equal or less than Ɓ. The simulation results showed a lower standard deviation and higher throughput and physical resource block utilization than surveyed algorithms.

Machine Learning for Hidden Nodes Detection in Unlicensed LTE Networks

This paper analyzes and evaluates the use of Machine Learning (ML) techniques as an alternative to heuristic methods to identify UEs in Licensed-Assisted Access (LAA) networks that are being affected by collisions from hidden nodes sources. The paper analyzes different models based on supervised ML methods to classify UEs into two possible classes: UEs affected by collisions and UEs free of collisions. To implement them, we employ a set of typical parameters of the LTE standard, e.g., Channel Quality Indicator (CQI), Reference Signal Received Quality (RSRQ) and number of Physical Resource Blocks (PRB). These parameters are fed into different supervised ML models which, once optimized, obtain the best models to predict when an UE is facing interference from hidden nodes in a dynamic channel environment. Results show that classification methods based on ML techniques are capable of predicting with a high level of accuracy when a UE is affected by collisions and, consequently, if this node is located in a hidden area. Nevertheless, and despite the growing interest in applying ML techniques in an increasing number of fields, we can conclude that, in this case, the performance of the ML approach is not necessarily better than heuristic methods. For instance, it does not outperform the Dynamic Collision Detection (DCD) previously presented by the authors. In addition, the ML algorithms exhibit a higher prediction time and computational complexity, with respect to those obtained with DCD algorithm. This should be considered a drawback in some LTE-Advanced and 5G wireless network scenarios that prioritize lower latencies.

Predictive Capacity Planning for Mobile Networks—ML Supported Prediction of Network Performance and User Experience Evolution

Network performance prediction is crucial for enabling agile capacity planning in mobile networks. One of the key problems is predicting evolution of spectral efficiency in growing network load conditions. The main factor driving network performance and spectral efficiency is reportedly the Channel Quality Indicator (CQI). In this paper, the performance of different Machine Learning (ML) models were examined, and XGBoost was selected as the best performing model. Furthermore, to improve modeling accuracy, several features were introduced (operating frequency band, Physical Resource Block (PRB) utilization in surrounding cells, number of surrounding cells within a radius, heavy data factor and higher order modulation usage). The impact of these features on CQI prediction were examined.

Service Provisioning in Sliced Cloud Radio Access Networks

Network slicing- (NS-) based cloud radio access networks (C-RANs) have emerged as a key paradigm to support various novel applications in 5G and beyond networks. However, it is still a challenge to allocate resources efficiently due to heterogeneous quality of service (QoS) requirements of diverse services as well as competition among different network slices. In this paper, we consider a service provisioning allocation framework to guarantee resource utilization while ensuring the QoS of users. Specifically, an inter/intraslice bandwidth optimization strategy is developed to maximize the revenue of the system with multiple network slices. The proposed strategy is hierarchically structured, which decomposes into network-level slicing and packet scheduling level slicing. At the network level, resources are allocated to each slice. At the packet scheduling level, each slice allocates physical resource blocks (PRBs) among users associated with the slice. Numerical results show that the proposed strategy can effectively improve the revenue of the system while guaranteeing heterogeneous QoS requirements. For example, the revenue of the proposed strategy is 21% higher than that of the average allocation strategy.

Comparative Analysis of TDD Frame Structure Technology LTE-Advanced (A Case Study in Cibitung Area Indonesia)

Based on the results of the drive-test, it showed that 72.22% of the Gramapuri Tamansari Cibitung area had downlink throughput quality below the standard of Key Performance Indicator (KPI) parameter, which was less than 8 Mbps. In addition, based on the Operation and Support System (OSS) data, in this area, the value of Physical Resource Block (PRB) utilization reached 83.93%; thus, an additional capacity from the downlink was needed. This study aims to compare Time Division Duplexing (TDD) frame structure and Inter-Band Carrier Aggregation FDD-TDD method in the Cibitung area, with band 3 FDD 1800 MHz and band 40 TDD 2300 MHz by simulation using Atoll 3.3 software. The parameters measured are downlink throughput, uplink throughput, RSRP, SINR, and connected user. The simulation results showed improving downlink throughput by 63%, uplink throughput by 65%, and connected users by 4%.