Bandwidth Capacity Research Articles

Analysis of ICONNET Fiber Optic Network Improvement in Cluster Rayon Panam at Strategic Business Unit Central Sumatra PT PLN ICON PLUS

Optical fiber is a telecommunications transmission medium that has a large bandwidth and bit rate so that it can meet the needs of today's data services with great reliability and efficiency. Optical fiber applications continue to be broad and have included seabed networks, terrestrial networks, metropolitan and regional scope networks, and small-scale networks. Optical fiber communication systems have 2 factors that affect the quality of network performance, namely internal aspects and external aspects. These internal and external aspects can degrade the performance quality of the optical fiber used and can cause attenuation and other transmission losses. As an effort to prevent sudden and significant deterioration in the quality of a network, it is necessary to try regular maintenance activities such as scheduled fiber optic cable network service quality measurements. These maintenance activities can help ensure network capacity increase decisions. One of the quality of service parameters that is often measured is transmission attenuation and received signal energy (received power). This research examines increasing the bandwidth capacity of PT PLN ICON PLUS in the Central Sumatra region during a network anomaly, namely a slow internet connection in the Rayon Panam Cluster. The sample taken from one of the customers shows the results of measuring the internet speed of 4-5 Mbps only, while the service taken is 10 Mbps. The results of checking on the up-link side to OLT Rayon Panam found that the data output was close to its capacity, which was 940,919,000 bits/sec or 0.9 Gb/sec. Increasing bandwidth capacity is done by moving the OLT port on the up-link side from the gigabit ethernet port to the tengigabit ethernet port, then replacing the SR type SFP with SFP ER and adding fiber optic attenuators so that the speed test results on the customer side have returned to their original appearance., 10 Mbps.

Exploring the potential of dynamic quality of service cloud-based network slicing in 5G and next generation virtualized networks: A simulation-based study

With global mobile traffic doubling every 18 months along with the massive increase in unique data users across digital platforms during the Covid-19 pandemic, the world's demand for high-speed connectivity continues to increase every second. The Covid-19 pandemic has transformed the global workforce with remote working arrangements, embracing new platforms, subsequently increasing the requirement for reliable, quality, ubiquitous high speed data connectedness everywhere. 5th Generation (5G) mobile communication technology provides an effective solution, modernizing network infrastructure, efficiently providing reliable high bandwidth capacity for massive data growth and ultra-low latency, making delays virtually impossible to perceive. This means elevating reality even further with fiber-like speeds for everyone, everywhere. 5G network slicing makes it possible for services to own dedicated portions of the 5G network with guaranteed performance for their particular need. These virtual networks allow for a service like IoT, at industrial scale, making society more sustainable and increasing resource efficiency. Our paper proposes a model for 5G network slice creation that meets industry verticals service QoS requirements which are critical to service adoption. A software emulation is utilized to guide the design and evaluation of our proposed model. Simulated results show QoS guarantees are met for the reviewed service requirements in varying network conditions.

Sgap: towards efficient sparse tensor algebra compilation for GPU

Sparse compiler is a promising solution for sparse tensor algebra optimization. In compiler implementation, reduction in sparse-dense hybrid algebra plays a key role in performance. Though GPU provides various reduction semantics that can better utilize the parallel computing and memory bandwidth capacity, the central question is: how to elevate the flexible reduction semantics to sparse compilation theory that assumes serial execution. Specifically, we have to tackle two main challenges: (1) there are wasted parallelism by adopting static synchronization granularity (2) static reduction strategy limits optimization space exploration. We propose Sgap: s egment g roup and a tomic p arallelism to solve these problems. Atomic parallelism captures the flexible reduction semantics to systematically analyze the optimization space of sparse-dense hybrid algebra on GPU. It is a new optimization technique beyond current compiler-based and open-source runtime libraries. Segment group elevates the flexible reduction semantics to suitable levels of abstraction in the sparse compilation theory. It adopts changeable group size and user-defined reduction strategy to solve challenge (1) and (2), respectively. Finally, we use GPU sparse matrix-matrix multiplication (SpMM) on the TACO compiler as a use case to demonstrate the effectiveness of segment group in reduction semantics elevation. We achieve up to $$1.2\,\times$$ speedup over the original TACO’s SpMM kernels. We also apply new optimization techniques found by atomic parallelism to an open-source state-of-the-art SpMM library dgSPARSE. We achieve $$1.6 \, \times \sim 2.3 \, \times$$ speedup on the algorithm tuned with atomic parallelism.

Enhanced electromagnetic wave absorption of nitrogen-doped reduced graphene oxide aerogels with LaFeO3 cluster modifications

Today, electromagnetic wave (EMW) absorbing materials with strong absorption capacity alone are hardly enough to meet the growing problem of EMW pollution. This work is the first designs and synthesizes LaFeO3-decorated nitrogen (N)-doped reduced graphene oxide aerogel (LFO/N-rGO) novel composites by a simple hydrothermal method. When the mass ratio of the LaFeO3 (LFO) nanoparticles: graphene oxide (GO): urea is 1:5:7.5, LFO/N-rGO achieves broadband absorption while possessing strong electromagnetic wave absorption capability. A strong reflection loss (RL) of −64.5 dB is obtained at 9.2 GHz. In addition, the maximum effective absorption bandwidth with a 2.83 mm thickness can reach up to 6.72 GHz. The EMW absorption mechanism analysis shows that the excellent microwave absorption capability is mainly attributed to the enhanced multiple reflections and multi-scattering, enhanced interfacial polarization process, proper conduction loss, and optimized impedance matching characteristics. Herein, this work proposes a new approach to improve the microwave absorption capability of N-doped reduced graphene oxide-based composites and provides a novel strategy to design EMW absorbing materials with wide effective absorption bandwidth and strong absorption capacity.

Phase and Polarization Digitally Modulated Array Using Reconfigurable DR Element: Proposal, Design, and Verification

The proposal, design, and verification of a phase and polarization digitally reconfigurable / modulated array using the reconfigurable dielectric resonator (DR) elements are developed in this paper. Different from the conventional phased arrays and reconfigurable antennas, the radiated electromagnetic (EM) wave can be flexibly controlled by simultaneously modulating its phase and polarization. To realize phase switching, a clever 1-bit phase shifter (0° / 180°) is introduced in the element design. The polarization states are controlled by integrating the switch-loaded feeding structure. And a compact 2-bit polarization tuner (0°-LP, 90°-LP, RHCP, and LHCP) is finally realized. Since every element can realize 1-bit phase and 2-bit polarization switching, the far-field beams and polarization types of the array format can be arbitrarily modulated by designing the coding sequence. To verify the proposed working principle, a compact reconfigurable metasurface-loaded DR element, and a four-element digitally reconfigurable linear array are implemented, respectively. The overlapped -10 dB impedance and 3 dB axial ratio bandwidths are 2.35-2.55 GHz (12.5 %) and 2.42-2.48 GHz (2.4 %), respectively (Covering 2.40 GHz WIFI / BT band). More importantly, it can achieve 16 different beam & polarization modes. The proposed antennas exhibit a low-cost, compact size, good bandwidth, and flexible radiation modulation capacity. They are promising candidates for future smart antennas to manipulate and control electromagnetic (EM) waves.

Teletraffic analysis and simulation on FSO systems: the traffic generators approach

Free Space Optical Communications (FSO) has become an interesting topic to researchers in recent years since the amount of data generated by devices is growing, and it is necessary to use data links that support high bandwidth to transmit them from one device to another. This technology is used to establish not only terrestrial links but also space links.A CubeSat satellite is generally deployed in the Low Earth Orbit (LEO) region. To maintain the altitude, the satellite must have a high orbital speed. For that reason, the time to transmit data between a particular ground station and the CubeSat satellite is limited. On the other hand, the volume, mass, and energy storage capacity are restricted in a CubeSat. The greater the bandwidth capacity of radio frequency links, the greater the demand for volume, mass, and energy they require. For that reason, to transmit a significant amount of data, traditional radio frequency links are not suitable and are becoming replaced by FSO as technology improves.Despite research in physical layer technologies on FSO (modulation schemes, error mitigation techniques, pointing and tracking systems), there is very little research in the literature about data link layer protocols for FSO. Secondly, there is little research to measure the data traffic demand on CubeSat satellites so that a certain data link layer protocol can be selected or adapted to be implemented in an FSO system.This paper presents research to address the issue of teletraffic through the use of traffic generators. The result is the design and development of a traffic generator for a discrete event simulator that will later be used to observe the behavior and to measure the performance of the Selective Repeat ARQ protocol in a simulated satellite FSO link, in order to propose improvements to adapt the protocol to this scenario.

Towards a cognitive engineering of transactional services in IoT based systems

Cognitive computing is the capability of a system to mimic the ability of human brain to learn and adapt from the surroundings. Cognitive systems have decision-making capabilities based on new information, actions and outcomes. Similarly, Internet of Things (IoT) aims at making things smart, and enabling them to perform complex tasks. Reliability and flexibility are persistent challenges in the IoT context where the promise is managing a multitude of devices and delivering real-time responses for critical smart applications. A limited number of studies examine these challenges while considering cognitive capabilities of things and have failed to handle thing’s specificities in terms of communication bandwidth, power availability and storage capacity. Following the service oriented architecture (SOA), the functionality can be encapsulated as services. Thus, automating the management of transactional services in smart IoT ecosystem can be fulfilled through the coupling of transactional properties to cognitive things. This paper provides a comprehensive approach to alleviate reliability restrictions in cognitive IoT service compositions. The concept of cognitive faculty (CF) is introduced to leverage transactional properties of services and can be customized to specific requirements of IoT applications. A proof-of-concept is included in this paper based on a self-monitoring IoT application for diabetic patients.

Bottleneck-Stationary Compact Model Accelerator With Reduced Requirement on Memory Bandwidth for Edge Applications

State-of-the-art compact models such as MobileNets and EfficientNets are structured using a linear bottleneck and inverted residuals. Hardware architecture using a single dataflow strategy fails to balance the required memory bandwidth with the given computational resources. This work presents a heterogeneous dual-core accelerator that performs a block-wise pipelined process as a unit using a bottleneck-stationary (BS) dataflow. The BS greatly relieves the requirement on DRAM bandwidth and on-chip SRAM capacity. A look-behind-only attention is also proposed as a co-optimized algorithm. Compared to the state-of-the-art hardware scheme, the proposed accelerator demonstrates a reduction of 1.8- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.9\times $ </tex-math></inline-formula> in latency and 2.2- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> in energy consumption, respectively.For verification, the accelerator with a 16-bit integer precision was implemented using 28nm CMOS process. Measurements show energy efficiencies of 0.5-to-3.75 TOPS/W in a supply voltage range of 0.55-to-1.15V.

Synthesis and characterization of Tb doped Ni–Zn nano ferrites as substrate material for dual band MIMO antenna

Recently, ferrite materials with outstanding characteristics, like reflection and absorption ability, lightweight, and thinness in size, are essential to resolve electromagnetic interference challenges. In the present work, Terbium doped Nickel-Zink nano-ferrite material with a composition of Ni0.5Zn0.5Tb0.02Fe1.98O4 was prepared and used as a substrate in a dual-band yagi-Uda-shaped 4×4 multi input multi output antenna. X-ray diffraction (XRD) measurements confirmed the cubic spinel ferrite sample. A vibrating sample magnetometer and Field Emission Scanning Electron Microscope analysis are also performed to evaluate its magnetic and morphological properties. Dielectric constant (εr) and permeability (μr) are determined using a vector network analyzer. The Multi Input Multi Output (MIMO) antenna operates in dual frequency bands of 3.5 and 5.8 GHz, which is suitable for fifth-generation smartphone applications. The performance of a yagi-Uda-shaped 4×4 MIMO antenna with terbium-doped Nickel-Zink ferrite used as a substrate is explored regarding return loss, radiation efficiency, bandwidth, gain, inter-terminal isolation, envelope correlation coefficient, and Channel Capacity Loss. It is found that the size of the MIMO antenna is reduced with improved bandwidth, isolation, and Channel Capacity Loss performance using a nano-ferrite substrate as compared to those conventional Flame retardant (FR4) substrates.

Mathematical Modeling of Flow Control Mechanism in Wireless Network-on-Chip

Network-on-chip (NoC) is an effective interconnection solution of multicore chips. In recent years, wireless interfaces (WIs) are used in NoCs to reduce the delay and power consumption between long-distance cores. This new communication structure is called wireless network-on-chip (WiNoC). Compared to the wired links, demand to use the shared wireless links leads to congestion in WiNoCs. This problem increases the average packet latency as well as the network latency. However, using an efficient control mechanism will have a great impact on the efficiency and performance of the WiNoCs. In this paper, a mathematical modeling-based flow control mechanism in WiNoCs has been investigated. At first, the flow control problem has been modeled as a utility-based optimization problem with the wireless bandwidth capacity constraints and flow rate of processing cores. Next, the initial problem has been transformed into a dual problem without limitations and the best solution of the dual problem is obtained by the gradient projection method. Finally, an iterative algorithm is proposed in a WiNoC to control the flow rate of each core. The simulation results of synthetic traffic patterns show that the proposed algorithm can control and regulate the flow rate of each core with an acceptable convergence. Hence, the network throughput will be significantly improved.

A globally shared resource paradigm for encoded storage systems in the public cloud

Public clouds favor sharing of storage resources, in which many tenants acquire bandwidth and storage capacity from a shared storage pool. To provide high availability, data are often encoded to provide fault tolerance with low storage costs. Regarding this, efficiently organizing an encoded storage system for shared I/Os is critical for application performance. This is usually hard to achieve as different applications have different stripe configurations and fault tolerance levels. In this paper, we first study the block trace from the Alibaba cloud, and find that I/O patterns of modern applications prefer the resource sharing scheme. Based on this, we propose a globally shared resource paradigm for encoded storage system in the public cloud. The globally shared resource paradigm can provide balanced load and fault tolerance for numerous disk pool sizes and arbitrary application stripe configurations. Furthermore, we demonstrate with two case studies that our theory can help address the device-specific problems of HDD and SSD RAID arrays with slight modifications: comparing the existing resource partition and resource sharing methods, our theory can promote the rebuild speed of the HDD RAID arrays by 2.5×, and reduce the P99 tail latency of the SSD arrays by up to two orders of magnitude.

An Adaptive Routing Algorithm for Inter-Satellite Networks Based on the Combination of Multipath Transmission and Q-Learning

In a satellite network, the inter-satellite link can facilitate the information transmission and exchange between satellites, and the packet routing of the inter-satellite link is the key development direction of satellite communication systems. Aiming at the complex topology and dynamic change in LEO satellite networks, the traditional single shortest path algorithm can no longer meet the optimal path requirement. Therefore, this paper proposes a multi-path routing algorithm based on an improved breadth-first search. First, according to the inter-satellite network topology information, the improved breadth-first search algorithm is used to obtain all the front hop node information of the destination node. Second, all the shortest paths are obtained by backtracking the path through the front hop node. Finally, according to the inter-satellite network, the bandwidth capacity of the traffic and nodes determines the optimal path from multiple shortest paths. However, due to the high dynamics of low-orbit satellite networks, the topology changes rapidly, and the global topology of the network is often not available. At this time, in order to enhance the adaptability of the algorithm, this paper proposes an inter-satellite network dynamic routing algorithm based on reinforcement learning. Verified by simulation experiments, the proposed algorithm can improve the throughput of the inter-satellite network, reduce the time delay, and the packet loss rate.

Adaptive Partial Task Offloading and Virtual Resource Placement in SDN/NFV-Based Network Softwarization

Edge intelligence brings the deployment of applied deep learning (DL) models in edge computing systems to alleviate the core backbone network congestions. The setup of programmable software-defined networking (SDN) control and elastic virtual computing resources within network functions virtualization (NFV) are cooperative for enhancing the applicability of intelligent edge softwarization. To offer advancement for multi-dimensional model task offloading in edge networks with SDN/NFV-based control softwarization, this study proposes a DL mechanism to recommend the optimal edge node selection with primary features of congestion windows, link delays, and allocatable bandwidth capacities. Adaptive partial task offloading policy considered the DL-based recommendation to modify efficient virtual resource placement for minimizing the completion time and termination drop ratio. The optimization problem of resource placement is tackled by a deep reinforcement learning (DRL)-based policy following the Markov decision process (MDP). The agent observes the state spaces and applies value-maximized action of available computation resources and adjustable resource allocation steps. The reward formulation primarily considers task-required computing resources and action-applied allocation properties. With defined policies of resource determination, the orchestration procedure is configured within each virtual network function (VNF) descriptor using topology and orchestration specification for cloud applications (TOSCA) by specifying the allocated properties. The simulation for the control rule installation is conducted using Mininet and Ryu SDN controller. Average delay and task delivery/drop ratios are used as the key performance metrics.

Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach

The smart grid integrates Information and Communication Technologies (ICT) into the traditional power grid to manage the generation, distribution, and consumption of electrical energy. Despite its many advantages, it faces significant challenges, such as detecting abnormal behaviours in the grid. Identifying anomalous behaviours helps to discover unusual user power consumption, faulty infrastructure, power outages, equipment failures, energy thefts, or cyberattacks. Machine learning (ML)-based techniques on smart meter data has shown remarkable results in anomaly detection. However, traditional ML-based anomaly detection requires smart meters to share local data with a central server, which raises concerns regarding data security and user privacy. Server-based model training faces additional challenges, such as the requirement of centralised computing power, reliable network communication, large bandwidth capacity, and latency issues, all of which affect the real-time anomaly detection performance. Motivated by these concerns, we propose a Federated Learning (FL)-based smart grid anomaly detection scheme where ML models are trained locally in smart meters without sharing data with a central server, thus ensuring user privacy. In the proposed approach, a global model is downloaded from the server to smart meters for on-device training. After local training, local model parameters are sent to the server to improve the global model. We secure the model parameter updates from adversaries using the SSL/TLS protocol. Using standard datasets, we investigate the anomaly detection performance of federated learning and observe that FL models achieve anomaly detection performance comparable to centralised ML models while ensuring user privacy. Further, our study shows that the proposed FL-based models perform efficiently in terms of memory, CPU usage, bandwidth and power consumption at edge devices and are suitable for implementation in resource-constrained environments, such as smart meters, for anomaly detection.

Hybrid load-balancing algorithm for distributed fog computing in internet of things environment

Fog computing is a novel idea created by Cisco that provides the same capabilities as cloud computing but close to objects to improve performance, such as by minimizing latency and reaction time. Packet failure can happen on a single fog server across a large number of messages from internet of things (IoT) sensors due to several variables, including inadequate bandwidth and server queue capacity. In this paper, a fog-to-server architecture based on the IoT is proposed to solve the problem of packet loss in fog and servers using hybrid load balancing and a distributed environment. The proposed methodology is based on hybrid load balancing with least connection and weighted round robin algorithms combined together in fog nodes to take into consideration the load and time to distribute requests to the active servers. The results show the proposed system improved network evaluation parameters such as total response time of 131.48 ms, total packet loss rate of 15.670%, average total channel idle of 99.55%, total channel utilization of 77.44%, average file transfer protocol (FTP) file transfer speed (256 KB to 15 MB files) of 260.77 KB/sec, and average time (256 KB to 15 MB) of 19.27 sec.

Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints

Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy compression can be an effective method for addressing these issues: not only can it significantly reduce data size, but it can also control the data distortion based on user-defined error bounds. In practice, many scientific applications have specific requirements or constraints for lossy compression, in order to guarantee that the reconstructed data are valid for post hoc analysis. For example, some datasets contain irrelevant data that should be isolated in particular and users often have intuition regarding value ranges, geospatial regions, and other data subsets that are crucial for subsequent analysis. Existing state-of-the-art error-bounded lossy compressors, however, do not consider these constraints during compression, resulting in inferior compression ratios with respect to user's post hoc analysis, due to the fact that the data itself provides little or no value for post hoc analysis. In this work we address this issue by proposing an optimized framework that can preserve diverse constraints during the error-bounded lossy compression, e.g., cleaning the irrelevant data, efficiently preserving different precision for multiple value intervals, and allowing users to set diverse precision over both regular and irregular regions. We perform our evaluation on a supercomputer with up to 2,100 cores. Experiments with six real-world applications show that our proposed diverse constraints based error-bounded lossy compressor can obtain a higher visual quality or data fidelity on reconstructed data with the same or even higher compression ratios compared with the traditional state-of-the-art compressor SZ. Our experiments also demonstrate very good scalability in compression performance compared with the I/O throughput of the parallel file system.

Analysis of Computer Network Performance on Communication and Informatics Office of West Sumbawa Regency Using Quality of Service Method

This study aims to determine and analyze network performance with quality of service parameters in realizing good and optimal network performance quality so that it can be adjusted between the efficiency of network needs and the effectiveness of user access at the Communication and Informatics Office of West Sumbawa Regency. The process of accumulating data is obtained by measuring the quality of service parameters using the quality of service method and measuring bandwidth load distribution with the load set at 30 Mbps. The quality of service method on the network, the influence between the variables of network performance quality on the internet connection, and the bandwidth capacity of each user are known. The study results from the quality of service analysis at the Communication and Informatics Service of West Sumbawa Regency entered the excellent category with an average throughput value ranging from 10,320-1,026,454 kbps from the fourth index being in the excellent category. The average packet loss value of 0-1.5% of the third index is in a good category, with the highest value occurring on the fifth day, 1.5%. The average delay value ranges from 7-150 ms, with the highest delay value occurring on the first day due to the user's distance from the remote network access source and queues in dense packet transmission. The average jitter value of 1-12 ms from the third index is in a good category, and the maximum result of the bandwidth value set in size is 9,092,407 bps

Using a hybrid encoding method based on the hexagonal resonators to increase the coding capacity of chipless RFID tags

A chipless RFID tag was designed using two hexagonal resonators. By defining a relation for the phases of S-parameters and rotating the resonators around their axes, a senary coding scheme was obtained at the resonant frequency. The designed resonator was able to generate six different codes using six different phase parameters at 3.03 GHz. While the conventional binary (“0” and “1”) coding of ordinary tags makes minimal use of bandwidth capacity, the senary coding in the proposed design allowed the use of a larger bandwidth capacity. Requiring much fewer resonators for generating more codes, the proposed technique leads to significantly smaller tags. Using the proposed tag with two resonators, 62 = 36 different codes can be generated; more generally, an n-resonator tag of the proposed type can create 6n different codes.

Proof of Concept Project: Temperature Measurement Using Lora Sensors on the Bridge Over the Danube River Near Smederevo Kovin

Abstract: On the road network in the Republic of Serbia, is establish monitoring of parameters at certain points of road network for the needs of the organization that deals with road maintenance and implementation of PBMC (Performance based Maintenance Contract), which has a need for mass collection of data from roads and road facilities. One of the parameter is the temperature, whose measurement on the bridge structure over the Danube near Kovin/Smederevo will be the primary focus of this paper. New technology Lo Ra sensors were used to collect temperature data. This type of sensor has low energy consumption so it does not require a specially implemented power supply. The service life of such sensors is estimated at a minimum of 5 years in environmental conditions. Communication is achieved using Lo Ra WAN technology that requires an access communication point at a distance of up to 8 km from the sensor with a bandwidth capacity of 3 kbps to 50 kbps. This sensor allows the measurement and collection of temperature data as a primary function and is also able to measure local acceleration in three axes on the structural element on which it is mounted. This way of collecting data using wireless sensors with low power consumption significantly improves efficiency and economy in the field of road maintenance and monitoring.

Effective Energy Efficiency and Statistical QoS Provisioning Under Markovian Arrivals and Finite Blocklength Regime

In this article, we evaluate the effective energy efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited Internet of Things (IoT) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model, measured by the ratio of effective capacity to the total consumed power. To maximize the EEE, there is a need to optimize transmission parameters, such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power, or rate alone, the complexity is aggravated by the simultaneous consideration of both variables. Hence, we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore, we investigate the performance of the EEE under constant and random arrivals, where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories, we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that, we compare the performance of different iterative algorithms, such as Dinkelbach’s and cross entropy, which guarantee the convergence for the optimal solution. By numerical analysis, the influence of source characteristics, fixed transmission rate, error probability, coding blocklength, and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally, the results illustrate that modified Dinkelbach’s algorithm has high performance and low complexity compared to others.