Resource Block Research Articles

Actor-Critic Learning Based QoS-Aware Scheduler for Reconfigurable Wireless Networks

The flexibility offered by reconfigurable wireless networks, provide new opportunities for various applications such as online AR/VR gaming, high-quality video streaming and autonomous vehicles, that desire high-bandwidth, reliable and low-latency communications. These applications come with very stringent Quality of Service (QoS) requirements and increase the burden over mobile networks. Currently, there is a huge spectrum scarcity due to the massive data explosion and this problem can be solved by helps of Reconfigurable Wireless Networks (RWNs) where nodes have reconfiguration and perception capabilities. Therefore, a necessity of AI-assisted algorithms for resource block allocation is observed. To tackle this challenge, in this paper, we propose an actor-critic learning-based scheduler for allocating resource blocks in a RWN. Various traffic types with different QoS levels are assigned to our agents to provide more realistic results. We also include mobility in our simulations to increase the dynamicity of networks. The proposed model is compared with another actor-critic model and with other traditional schedulers; proportional fair (PF) and Channel and QoS Aware (CQA) techniques. The proposed models are evaluated by considering the delay experienced by user equipment (UEs), successful transmissions and head-of-the-line delays. The results show that the proposed model noticeably outperforms other techniques in different aspects.

Personal resources providing stress resistance of hospice medical workers

IntroductionThe functional approach to the study of stress resistance allows us to distinguish two blocks of resources – the “active” one, which includes the analysis of the content, conditions, subject and means of labor, and the “personal” one, which considers values, motivation and the expressiveness of personal qualities that contribute to the stress resistance (Granek, Buchman, 2020; Cross, 2019; Powell et al., 2020; Hernández-Marrero, Fradique, 2019).ObjectivesThe objective of our work was to study the relationship between the personal and motivational characteristics of hospice employees with the different symptoms of professional burnout as an indicator of a reduced stress resistance.Methods62 hospice medical employees with an average work experience of 4,5 years took part in the survey. They were asked to fill out questionnaires to diagnose the burnout symptoms, a motivational personality profile and to assess themselves by the personal semantic differential.ResultsBy the means of the procedure for determining the extreme groups (M ± σ), two groups of respondents were identified, which are characterized by different degrees of burnout symptoms. The results of the comparative analysis showed that the less advantaged respondents from the burnout perspective are focused on the life support, comfort, social status, which indicates a certain rationality in the choice of this job.ConclusionsThe portrait of a professionally successful hospice employee includes an orientation towards communication, social and creative activity, which is complemented with independence, confidence and decisiveness – the features that allow carrying out their work in stressful conditions and mainly in uncertain situations.DisclosureNo significant relationships.

Performance Analysis of Filtered OFDM Based Downlink and Uplink NOMA System over Nakagami-m Fading Channel

Efficient consumption of available resources and fulfillment of increasing demands are the two main challenges which are addressed by exploring advanced multiple access schemes along with efficient modulation techniques. To this end, non-orthogonal multiple access (NOMA) is discussed as a promising scheme for future 5G traffic. NOMA enables the users to share same resource block, permitting certain level of interference. In this paper, we propose filtered OFDM (F-OFDM) as a transmission waveform for NOMA systems, as it offers all the advantages of OFDM with the additional provision of sub-band filtering to satisfy the diverse services of the users. We examine F-OFDM in both downlink and uplink NOMA systems. Error-related performances of both downlink and uplink F-OFDM NOMA systems are analyzed and compared with conventional OFDM NOMA system over Nakagami-m fading channel. The results show that the error performance of F-OFDM NOMA is better than that of OFDM NOMA. An improvement of about 2 dB and 1 dB in bit error rate is achieved in downlink and uplink F-OFDM NOMA, respectively. Monte Carlo simulations are conducted for different values of fading parameter m, supporting the obtained analytical results

Joint uplink and downlink resource allocation for device to device communications

With the ever-increasing number of intelligent devices, extensive research efforts have been spent towards developing efficient resource allocation in underlying device to device communications. Centralised and distributed approaches have been proposed to improve the network performances through uplink and downlink schemes. However, the full-duplex resource allocation remains challenging. Here, a joint uplink–downlink resource block assignment for device to device communications is proposed. The device's geometric distribution via the Poisson point process is formulated and the optimization problem as a mixed strategy non-cooperative game is modelled. The proposed resource allocation scheme allows many device to device communication pairs to share the same cellular resource block but does not restrict their number in advance; it is determined dynamically according to the channel's capacity and interference condition. Inversely, a device to device communication transmitter can simultaneously be allocated many resource blocks according to its bandwidth requirement and the number of its antennas. Furthermore, the optimal amount of data and power transmitted by each device is calculated. The efficiency of this algorithm is evaluated through computer simulations, and the results demonstrate good performances in the spectral and energy efficiency with relatively low complexity and convergence time.

Massive IoT Access With NOMA in 5G Networks and Beyond Using Online Competitiveness and Learning

This paper studies the problem of online user grouping, scheduling and power allocation in beyond 5G cellular-based Internet of things networks. Due to the massive number of devices trying to be granted to the network, non-orthogonal multiple access method is adopted in order to accommodate multiple devices in the same radio resource block. Different from most previous works, the objective is to maximize the number of served devices while allocating their transmission powers such that their real-time requirements as well as their limited operating energy are respected. First, we formulate the general problem as a mixed integer non-linear program (MINLP) that can be transformed easily to MILP for some special cases. Second, we study its computational complexity by characterizing the NP-hardness of different special cases. Then, by dividing the problem into multiple NOMA grouping and scheduling subproblems, efficient online competitive algorithms are proposed. Further, we show how to use these online algorithms and combine their solutions in a reinforcement learning setting to obtain the power allocation and hence the global solution to the problem. Our analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms with comparison to optimal and state-of-the-art methods.

An Efficient Spectrum Utilization Scheme for Energy-Constrained IoT Devices in Cellular Networks

The cellular Internet of Things envisions the connection of billions of devices with the Internet through cellular networks. These Internet-of-Things Devices (IoDs) are normally energy constrained. In order to provide network services to these energy-constrained IoDs, this article proposes a cellular architecture, whereby IoDs utilize resources of the cellular network for energy harvesting and information transmission. The radio resources of the cellular network are divided by time and frequency, and each time-frequency block is called a cellular resource block (CRB). At the beginning of each time slot, IoDs select CRBs randomly. IoDs receive the control information from the base station (BS) and extract the resource allocation information about the selected CRB. When selected CRBs are busy, IoDs harvest energy from the radio-frequency signals of the BS, and then they use that stored energy to transmit their information to the cluster head (CH) when selected CRBs are idle. In order to obtain the average harvested energy, the random distance between cellular BS and randomly deployed IoD has been statistically calculated. Using the finite-state Markov chain model, the transmission probability of each IoD is calculated. Analytical expressions for the collision probability and average throughput of IoDs are also derived. To verify the derived analytical expressions, system-level simulations are carried out, and it is shown that the analytical results match the simulation results. Moreover, the overall performance of the considered architecture is studied for different system parameters.

Cross-Layer Resource Allocation for UAV-Assisted Wireless Caching Networks With NOMA

Unmanned aerial vehicle (UAV) assisted wireless caching networks (WCN) have been recognized as a promising way to reduce the network load and improve the energy efficiency in the sixth generation (6 G) communication systems. Aiming to improve spectrum efficiency and system capacity, we apply non-orthogonal multiple access (NOMA) in UAV-assisted WCN to serve multiple users on the same spectrum simultaneously and propose the cross-layer resource allocation strategy including the scheduling of UAVs, the grouping of users, and the allocation of power. First, the ρ-K-means algorithm is proposed to assign users to multiple clusters and deploy UAVs according to the distance from UAVs to the base station in the UAV deployment layer. Then, the base station broadcasts the popular files to UAVs via NOMA in the content placement layer. Based on the existing fixed power allocation strategy, we propose a statistic quality of service (QoS) based fixed (SQF) power allocation method to take the statistic QoS of the popular files into consideration and improve the energy efficiency through introducing the discount factor. On the basis of SQF, an instantaneous QoS based adaptive (IQA) strategy allocates power according to the instantaneous QoS of the popular files to reduce the file outage probability. Furthermore, we propose an improved method that is a cross-layer based optimal (CLO) power allocation strategy to maximize the system hit probability. Finally, in the content delivery layer, users in each cluster are grouped according to the channel gain from users to UAVs. In addition, each UAV serves two users on the same time-frequency resource block based on the cognitive radio inspired power allocation for the NOMA user pairs. Simulation results confirm that the proposed ρ-K-means algorithm and CLO strategy reduce the file outage probability and improve the hit probability.

Resource Allocation in Uplink NOMA-IoT Networks: A Reinforcement-Learning Approach

Non-orthogonal multiple access (NOMA) exploits the potential of the power domain to enhance the connectivity for the Internet of Things (IoT). Due to time-varying communication channels, dynamic user clustering is a promising method to increase the throughput of NOMA-IoT networks. This article develops an intelligent resource allocation scheme for uplink NOMA-IoT communications. To maximise the average performance of sum rates, this work designs an efficient optimization approach based on two reinforcement learning algorithms, namely deep reinforcement learning (DRL) and SARSA-learning. For light traffic, SARSA-learning is used to explore the safest resource allocation policy with low cost. For heavy traffic, DRL is used to handle traffic-introduced huge variables. With the aid of the considered approach, this work addresses two main problems of fair resource allocation in NOMA techniques: 1) allocating users dynamically and 2) balancing resource blocks and network traffic. We analytically demonstrate that the rate of convergence is inversely proportional to network sizes. Numerical results show that: 1) Compared with the optimal benchmark scheme, the proposed DRL and SARSA-learning algorithms have lower complexity with acceptable accuracy and 2) NOMA-enabled IoT networks outperform the conventional orthogonal multiple access based IoT networks in terms of system throughput.

A Non Linear PUF Circuit Design for Two Factor Authentication in IoT Cryptography

Internet-of-Things (IoT) is growing network paradigm which enables mutual communication between the user and smart devices using the internet. The IoT devices are susceptible to the security threats, due to placement of restricted computational capabilities of the computing devices in IoT. The conventional encryption algorithm utilizes the high amount of resource block in it which increases the area and power. Moreover, Two Factor Authentication (TFA) scheme based authentication protocols does not have the efficiency to secure the data. Because the random number generated by the TFA is ideal for all IoT devices which are easy to hack by the unauthorized persons. In this Research paper, the Linear Feedback Shift Register (LFSR) based Reconfigurable Physical Unclonable Function (RPUF) is proposed to overcome the security issues caused in the IoT communication. The RPUF is designed based on the LFSR to generate the random number for every clock cycle. Normally, reconfigurable process helps to generate the different output values for every clock cycle. But, it failed to generate different outputs for same input values. Here, LFSR based RPUF helps to generated the different response values even the same challenge is given to the input side. The Lightweight TFA scheme is presented for IoT, where PUF has been considered as one of the major authentication factors. At last, Spartan 6 and Virtex 6 Field Programmable Gate Array (FPGA) performances are calculated for proposed TFA-RPUF-IoT and existing TFA-PUF-IoT protocols. In Spartan 6, TFA-RPUF-IoT protocol occupied 11 slices, 31 LUTs, 42 flip flops which are less compared to conventional TFA-PUF-IoT.

Resource Allocation for Open-Loop Ultra-Reliable and Low-Latency Uplink Communications in Vehicular Networks

To support ultra-reliable and low-latency communication (URLLC) in vehicular networks, the virtual cells, where multiple access points (APs) cooperatively serve one mobile node, have been proposed to reduce the end-to-end latency in the downlink. The latency can be further reduced by eliminating the need for retransmission and feedback control, i.e., open-loop communications. However, it is difficult to achieve a high reliability of the uplink via virtual cells, because of multiple access interference and collisions from other virtual cells nearby. In this paper, we formulate the proactive radio resource allocation in the open-loop uplink of vehicular networks as a stochastic optimization problem with the objective to maximize the uplink reliability while ensuring the network stability. The optimal resource allocation policies are obtained solving the optimization problem using the Lyapunov optimization technique in a distributed manner. To reduce the computational and to improve the challenges of the Lyapunov optimization, we propose a virtual resource slicing algorithm that maps radio resource units to virtual resource blocks. Simulation results exhibit that both ultra-low latency and ultra high reliability are guaranteed in the open-loop uplink of vehicular networks. Based on the theoretical performance analysis and simulations, the uplink radio access procedure is summarized for the URLLC in vehicular networks.

NOMA and 5G emerging technologies: A survey on issues and solution techniques

Power Domain Non-Orthogonal Multiple Access (PD-NOMA) is a potential technology for the next generation of cellular networks. Compared to classical orthogonal multiple access (OMA) techniques, PD-NOMA leverages the distinct channel gains of users for multiplexing different signals in a single resource block (time, frequency, code) in power domain. This results in higher spectral efficiency, improved user fairness, better cell-edge throughput, increased reliability and connectivity and low-latency. The flexible combination of PD-NOMA with existing and emerging technologies such as heterogeneous networks (HetNets), multiple-input multiple-output (MIMO), massive MIMO, cooperative communication, cognitive radios (CRs), millimeter wave communication, simultaneous wireless information and power transfer (SWIPT), visible light communication (VLC), mobile edge computing (MEC), intelligent reflecting surfaces (IRS), unmanned aerial vehicles (UAVs), underwater communication etc., is expected to cause further enhancements in performance. Existing survey papers on NOMA mainly focus on its concept, comparison, issues and analysis without any categorization of different techniques to solve the issues related to it. This survey paper highlights the main issues and constraints of resource allocation, signaling, practical implementation and security aspects of NOMA and its integration with 5G and upcoming wireless technologies. Various solutions have been proposed in the literature that involve optimization, analytical, game theory, matching theory, graph theory and machine learning (ML) techniques. We present an in-depth analysis and comparison of these solutions with key insights emphasizing the feasibility and applicability for a qualitative analysis. We finally identify promising future research directions and challenges in the context of PD-NOMA’s application to the existing 5G and next generation wireless networks.

How Should I Orchestrate Resources of My Slices for Bursty URLLC Service Provision?

Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure slicing systems for bursty ultra-reliable and low latency communications (URLLC) service provision due to its stringent requirements on low packet blocking probability and low codeword decoding error probability. In this paper, we propose to orchestrate network resources for a slicing system to guarantee more reliable bursty URLLC transmission. We re-cut physical resource blocks and derive the minimum upper bound of bandwidth for URLLC transmission with a low packet blocking probability. We correlate coordinated multipoint beamforming with channel uses and derive the minimum upper bound of channel uses for URLLC transmission with a low codeword decoding error probability. Considering the agreement on converging diverse services onto shared infrastructures, we further investigate the network slicing for URLLC and enhanced mobile broadband (eMBB) service multiplexing. Particularly, we formulate the service multiplexing as an optimization problem, which is challenging to be mitigated due to requirements of future channel information and of tackling a two timescale issue. To address the challenges, we develop a resource optimization algorithm based on a sample average approximate technique and a distributed optimization method with provable performance guarantees.

A 660 MHz–5 GHz 6-Phase/3-Phase Transmitter With Cancellation of Counter-Intermodulation Distortion and Improved Image Rejection

In the transmit signal path of a cellular terminal that supports the Long-Term Evolution (LTE) standard, the third-order counter-intermodulation (CIM3) product can be a significant contributor to spurious emissions in protected bands, primarily in cases where all the transmit power is concentrated in one resource block at the edge of the allocated bandwidth. This problem can be addressed with a harmonic rejection architecture for the up-conversion mixer in the transceiver. Prior work in this area retains the four-phase baseband inputs of a conventional quadrature mixer, utilizes various techniques in the local oscillator (LO) path involving overlapping LO waveforms to suppress harmonics and requires the digitally-controlled oscillator (DCO) to be operated at two or more times the LO frequency. The harmonic rejection architecture presented in this paper has six baseband phases and six non-overlapping LO phases and, for higher-frequency bands, allows the DCO to run at 1.5 times the LO frequency. It also provides an inherently higher uncalibrated image rejection than architectures with quadrature baseband inputs, which is advantageous for any wireless communication standard that supports higher-order quadrature amplitude modulation (QAM) schemes such as 1024-QAM and 4096-QAM.

5G network slices resource orchestration using Machine Learning techniques

To efficiently serve heterogeneous demands in terms of data rate, reliability, latency and mobility, network operators must optimize the utilization of their infrastructure resources. In this context, we propose a framework to orchestrate resources for 5G networks by leveraging Machine Learning (ML) techniques. We start by classifying the demands for resources into groups in order to adequately serve them by dedicated logical virtual networks or Network Slices (NSs). To optimally implement these heterogeneous NSs that share the same infrastructure, we develop a new dynamic slicing approach of Physical Resource Blocks (PRBs). On first hand, we propose a predictive approach to achieve optimal slicing decisions of the PRBs from a limited resource pool. On second hand, we design an admission controller and a slice scheduler and formalize them as Knapsack problems. Finally, we design an adaptive resource manager by leveraging Deep Reinforcement Learning (DRL). Using our 5G experimental prototype based on OpenAirInterface (OAI), we generate a realistic dataset for evaluating ML based approaches as well as two baselines solutions (i.e. static slicing and uninformed random slicing-decisions). Simulation results show that using regression trees for both classification and prediction, coupled with the DRL-based adaptive resource manager, outperform alternative approaches in terms of prediction accuracy, resource smoothing, system utilization and network throughput.

Graph-Based Resource Allocation for Air-Ground Integrated Networks

With the combined advantages of satellite communications, aerial networks and terrestrial systems, a space-air-ground integrated network has gradually become a promising architecture for the next generation wireless communication. Due to heterogeneous characteristics of different layers, it is necessary to perform efficient resource allocation. Motivated by this fact, we propose a novel architecture of air-ground integrated networks (AGIN), which leverages civil aircrafts and ground base stations to support terrestrial users’ service. Aiming at maximizing the overall capacity of downlink transmission in an AGIN, we formulate the resource allocation problem as an optimization problem subject to both quality of service (QoS) and fairness requirements. To address the formulated problem, we propose a graph-based joint optimization algorithm for resource block (RB) and power allocation. Specifically, an improved Kuhn-Munkras (KM) algorithm based on graph theory is proposed for RB allocation, which guarantees the fairness. Meanwhile, a multi-level water-filling method is proposed for power allocation. By leveraging an alternating descent approach, a joint optimal solution can be obtained after a finite number of iterations. It is demonstrated through simulation results that the proposed joint optimization algorithm is converges fast and shows significant improvement in terms of the sum-rate, fairness, access latency, and system capacity.

A QoS-based joint user association and resource allocation scheme in ultra-dense networks

Ultra-dense networks (UDNs) have become an important architecture for the fifth generation (5G) networks. A large number of small base stations (SBSs) are deployed to provide high-speed and seamless connections for users in the network. However, the advantage of increasing the system capacity brought by the dense distribution of SBSs comes at the cost of severe inter-cell interference. Although the user-centric virtual cell method has been proposed to solve the interference problem, some challenges have been encountered in practical applications. For example, inter-cell interference still exists to a certain extent, and the cell load may be imbalance. Hence, under the virtual cell architecture, we propose a quality of service (QoS)-based joint user association and resource allocation scheme in UDNs. In order to mitigate the interference, balance cell load and improve the system throughput, a non-convex NP-hard problem is formulated. To effectively solve this problem, we decouple the formulated problem into three sub-problems: user association, physical resource block (PRB) allocation and power allocation. First, we consider the QoS requirements of user equipment (UE) and perform user association based on the PRB estimation method. Then, based on the overlapped virtual cells constructed, we propose a graph-based PRB allocation scheme for reducing virtual inter-cell interference. Moreover, we solve power allocation sub-problem by using the difference of concave (DC) programing method. The simulation results show that our proposed scheme is superior to other schemes in terms of user rates, cell load and system throughput.

A Rapid Updating Method to Predict Grade Heterogeneity at Smaller Scales

Measures of grade heterogeneity, or the spatial distribution of grades, depend on the scale of sampling. At the resource modelling scale, heterogeneity measures are limited to the scale of the data used to estimate the model. As denser sampling becomes available (e.g., from blast holes immediately prior to mining), it is, in principle, possible to provide measures of heterogeneity at smaller scales to allow selective mining of large resource blocks. However, this can only be done if the local resource model can be updated rapidly with the newly acquired data in time for selectivity decisions to be made (e.g., selective blasting and loading from a resource block). The economic value of quantifying small-scale grade heterogeneity is significant in terms of mining selectivity and recoverability. This study proposes an approach, based on the Kalman filter, for near real-time resource model downscaling and updating by integrating additional data from production blast holes. In this approach, the model assimilates newly acquired data and generates measures of small-scale grade heterogeneity to provide a basis on which better selective mining and loading decisions can be made. A synthetic dataset is used to demonstrate and validate the algorithm. The results show that the proposed algorithm is capable of updating a resource model in near real time and identifying 68% of the small-scale grade variability within a mining block.

Radio Resource Allocation and Power Control Scheme in V2V Communications Network

Reasonable resource management scheme is the premise to ensure system reliability and practicability of vehicle-to-vehicle (V2V) communications network. In this paper, spectrum resource allocation and power control scheme is studied. Firstly, the problem of resource block (RB) sharing is considered. Assuming that each V2V link occupies at most one resource block while multiple links can share the same RB. In this work, a factor graph model is proposed corresponding to the RB allocation problem through function mapping. Therefore, the RB allocation problem is equivalent to studying the message update and belief inference problem on the factor graph model. We propose a Belief Propagation based on Real-time Update of Messages (BPRUM) algorithm, in which the message of each node is calculated based on the new messages of other nodes rather than the messages calculated in the last iteration. Secondly, after the RB allocation is completed, a power control scheme under the maximum power limit is proposed by utilizing the Lagrange dual decomposition. And the total throughput of the system is maximized by using the proposed power control scheme. Simulation results show that the resource allocation and power control scheme can achieve a significant increment in spectrum utilization and throughput of the V2V communications network.

Performance Analysis of Grant-Free Random-Access NOMA in URLL IoT Networks

Internet-of-Things (IoT) networks have recently emerged to provide massive connectivity for many application scenarios and services. Additionally, developing spectrum-access strategies for a large number of nodes with sporadic data traffic behaviors in IoT networks has attracted much attention recently. However, developing such strategies becomes more challenging when ultra-reliable low-latency (URLL) transmissions are required. As IoT networks entail spectrum-efficient transmission schemes, non-orthogonal multiple-access (NOMA) has emerged as a key enabler for such networks. On the other hand, grant-free random-access (RA) techniques are particularly promising for high spectral-efficiency and massive connectivity, since they reduce signaling overhead, and packet latency. Therefore, in this paper, uplink RA-NOMA IoT networks with clustered IoT devices is studied, where short packet and diversity transmissions are adopted to meet the URLL requirements. To reduce the negative effect of diversity transmission on packet latency, multiple replicas of packets are accommodated within different resource blocks (RBs) in the same transmission time interval (TTI). The analytical expressions of network metrics, namely, average packet latency, reliability, and GoodPut are derived. Furthermore, the effect of the number of packet replicas, blocklength, and cluster size on the network metrics is evaluated. Finally, the analytical derivations are utilized to find the optimal values for the number of packet replicas, blocklength, and power control parameters, such that the network GoodPut is maximized, subject to URLL constraints.

Preventing RLC Buffer Sojourn Delays in 5G

The 3rd Generation Partnership Project (3GPP) is investing a notable effort to mitigate the endogenous stack and protocol delays (e.g., introducing new numerology, through preemptive scheduling or providing uplink granted free transmission) to attain to the heterogeneous Quality of Service (QoS) latency requirements for which the fifth generation technology standard for broadband cellular networks (5G) is envisioned. However, 3GPP’s goals may become futile if exogenous delays generated by the transport layer (e.g., bufferbloat) and the Radio Link Control (RLC) sublayer segmentation/reassembly procedure are not targeted. On the one hand, the bufferbloat specifically occurs at the Radio Access Network (RAN) since the data path bottleneck is located at the radio link, and contemporary RANs are deployed with large buffers to avoid squandering scarce wireless resources. On the other hand, a Resource Block (RB) scheduling that dismisses 5G’s packet-switched network nature, unnecessarily triggers the segmentation procedure at sender’s RLC sublayer, which adds extra delay as receiver’s RLC sublayer cannot forward the packets to higher sublayers until they are reassembled. Consequently, the exogenously generated queuing delays can surpass 5G’s stack and protocol endogenous delays, neutralizing 3GPP’s attempt to reduce the latency. We address RLC’s related buffer delays and present two solutions: (i) we enhance the 3GPP standard and propose a bufferbloat avoidance algorithm, and (ii) we propose a RB scheduler for circumventing the added sojourn time caused by the packet segmentation/reassembly procedure. Both solutions are implemented and extensively evaluated along with other state-of-the-art proposals in a testbed to verify their suitability and effectiveness under realistic conditions of use (i.e., by considering Modulation and Coding Scheme (MCS) variations, slices, different traffic patterns and off-the-shelf equipment). The results reveal current 3GPP deficits in its QoS model to address the bufferbloat and the contribution of the segmentation/reassembly procedure to the total delay.