User Throughput Research Articles

Reconfigurable Intelligent Surfaces-Enhanced Uplink User-Centric Networks on Energy Efficiency Optimization

User-centric network (UCN) has been considered as a promising technology to improve the throughput of users, especially users at the cell edge, where each user is connected to and served by a group of access points. With the rapid growth of user’s uplink traffic demands, energy consumption has become an important issue in uplink UCN as user’s battery capacity is limited. To address this issue, in this research, we develop a reconfigurable intelligent surface (RIS)-enhanced uplink UCN, where RIS is exploited to improve the signal quality of uplink transmission for users with virtually no extra energy consumption. To approach an optimal energy efficiency, we jointly perform reflect beamforming at RISs and uplink power control at users. This is formulated into a non-convex and intractable energy efficiency maximization problem. To solve it effectively, we decompose it into two subproblems and carry out the optimization alternately. That is, we design the reflect beamforming matrices at RISs through fractional programming and an uplink power control at users through constructing a surrogate function via majorization-minimization algorithm. Numerical results demonstrate that the proposed algorithm could achieve significant gain both on energy efficiency and spectral efficiency compared to the benchmark algorithm.

Business Sensitivity Options of Carrier Aggregation Strategies: A Simulation Model for 4G Deployment in Emerging Countries

Indonesia’s growing population increasingly relies on 4G networks, presenting challenges for the Ministry managing mobile broadband adoption. There are severe data network congestion problems in metropolitan areas, particularly Jakarta, Indonesia’s capital city. Urgent action is required to address infrastructure and capacity limitations and maintain reliable connectivity. Carrier aggregation (CA) may alleviate bottlenecks by giving users higher bandwidth, throughput, and data rates. This research examined the techno-economic aspects of using 2.1 GHz CA versus non-CA in an urban region of North Jakarta. CA required fewer eNodeB and performed better on key performance indicators like received signal power, signal-to-noise ratio, and throughput. Cellular operators also saw faster investment returns with CA. CA’s performance and economic benefits make it a promising solution for Indonesia’s 4G network congestion.

A multi-agent federated reinforcement learning-based optimization of quality of service in various LoRa network slices

The innovations heralded through the implementation of next-generation 5G (Fifth-Generation) networks provide an opportunity for the efficient coexistence of heterogeneous services distributed by a single physical virtualized infrastructure. Indeed, thanks to the possibility of virtualization of network functions implemented in 5G networks, the management of physical resources will be able to become more flexible and users will be able to benefit from a service customization to satisfy their demand in terms of energy efficiency, throughput and communication reliability. However, these advances are not without constraints. The management of physical and virtual resources will become more complex given the number of connected objects which will increase, generating a large volume of data to manage. This therefore requires the implementation of much more intelligent systems in the network controllers in order to guarantee the QoS (Quality of Service) of the communications. Thus, an important axis of research is now oriented towards artificial intelligence techniques, more precisely reinforcement learning to overcome this problem. Driven by this context, we are directing our research towards improving the QoS offered to users of connected objects by proposing an optimization model based on network slicing and federated reinforcement learning in order to minimize energy consumption, maximize user throughputs and reduce latency during communications between LoRa (Long Range) devices. The results obtained by the simulations carried out in a realistic framework clearly demonstrate that our proposal optimizes the traffic in each network slice and also for the individual user.

Energy-Efficient Resource Allocation in Aerial Base Stations

Drones, or unmanned aerial vehicles, can be used as air base stations (UAV-BSs) for telecommunications. They prove useful in situations where the network is overloaded or unavailable due to natural disasters or maintenance work. UAV-BSs grant access to user/IoTs sensors on the ground, but their electromagnetic signals may suffer losses because of their dynamic capacity to provide access at different altitudes. These losses lead to transmission impairments, such as attenuation, fading, and distortion. To overcome these issues and improve signal quality, the UAV-BS position must be optimized. However, finding the optimal placement is a challenge, and a wide range of strategies employing different approaches have been adopted. In this study, we proposed a 3D positioning strategy for UAV-BSs that serves the maximum number of users with the smallest number of UAV-BSs. Results showed that the proposed heuristic could find the best position and altitude for the UAV-BSs, provide network access for mobile user/IoTs (Internet of things) sensors, maximize the number of devices connected to the UAV-BSs, and guarantee a minimum throughput for users. The proposed heuristic not only performs well in terms of coverage and performance, but is also more energy-efficient than other algorithms found in the literature.

A Low-Cost High-Throughput C-Band Communication System for CubeSats

This paper presents the physical layer of a proprietary broadband communication system for CubeSats. The system operates in the C band (5.8 GHz), delivering at least 10 Mbps of the net user throughput. Operation at low elevation angles (and therefore low SNRs) and high Doppler shifts is made possible thanks to a sophisticated synchronization subsystem. The system can be adapted to propagation conditions experienced during a given visibility window by changing the signal bandwidth and coding rate. It is implemented using Software Defined Radio (SDR) technology. The system will be used in two missions that are scheduled for 2023 and 2024 and are planned in cooperation with the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” and SatRev S.A.

A chaotic whale optimization algorithm (CWOA) based multiobjective conjoint spectrum utilization in a cognitive radio (CR) network

SummaryIn this manuscript, a novel energy detection‐based chaotic whale optimization algorithm (ED‐CWOA) is proposed to maximize the spectrum utilization, duration, and threshold in cognitive radio network (CRN). Here, the spectrum utilization is designed taking into account sensing duration and threshold in multi‐objective function is formulated by considering its primary user (PU) protection restrictions and secondary user (SU) throughput. To get the global optimal solution, the CWOA and the multi‐objective optimization problem are finally carried out on a single objective. The execution is performed on NS2 simulator. The simulation outcome demonstrates that the proposed ED‐CWOA system has lower delay 33.878%, 26.206%; higher delivery ratio 35.536%, 22.65%; lower Fairness Index 46.66%, 58.332%; lower energy consumption 25.462%, 35.524%; and higher throughput 96.82%, 49.28% for different nodes analyzed with the existing methods, like energy detection‐based bivariate Levy‐stable bat algorithm (ED‐BLSBA) and genetic algorithm‐based energy detection (ED‐GA), respectively. The simulation outcome indicates that the proposed system may be able to get the optimal global solutions efficiently and accurately.

Fairness guarantee algorithm of high-density WLAN based on neural network and genetic algorithm

In order to meet the growing requirements of users for wireless services in various scenarios, highly densely deployed wireless local area network(WLAN) will be developed. However, due to the limited frequency resources, there must be a large number of wireless access points(APs) in the same channel. But APs which are located in the same channel will interfere with each other, resulting in a decline in the fairness of throughput in the network. And it can not provide users with good quality of service. In order to improve the fairness of throughput and experience of users, it is necessary to formulate the reasonable network parameter adjusting methods. A method based on neural network and genetic algorithm is proposed. The neural network is used to build the relationship between the parameters of WLAN and the fairness of throughput. The trained model is used as the fitness evaluation function of the genetic algorithm. And the genetic algorithm is used to solve the optimization parameter combination configuration to improve the fairness of throughput in WLAN. Simulation results show that the present algorithm can improve the fairness of throughput in the whole network.

Dynamic multiple access based on deep reinforcement learning for Internet of Things

With the development of wireless communication technology, the rational use of spectrum resources utilizing efficient multiple access technology has become a research hotspot. The channel access strategy in dynamic spectrum access technology will directly affect the system’s spectrum utilization. Deep reinforcement learning is proposed to train users in the network to learn optimal channel access strategy to achieve reasonable allocation of spectrum resources on the channel. At the same time, the multiple access scheme adopted by users who have access to the channel will also affect the throughput of the system. Traditional multiple access schemes include Orthogonal Multiple Access scheme and Non-orthogonal Multiple Access (NOMA) scheme. In a dynamic environment, the multiple access scheme suitable for users on the channel is also dynamic. Therefore, DRL is introduced to help users learn the multiple access scheme selection strategy to maximize user throughput. Finally, we propose a dynamic multiple access algorithm based on deep reinforcement learning, which optimizes the channel access strategy and scheme selection strategy respectively, and improves the performance. The simulation results show that the proposed scheme is superior to other schemes in various scenarios, especially, the average throughput of the proposed scheme is about 0.8 Mbps and 0.5 Mbps higher than that of the FDMA scheme and NOMA scheme, respectively.

Adaptive real-time spectrum selection framework and handover decision algorithm (RSSF-HDA) for heterogeneous networks

Carrier aggregation (CA) is a technology introduced by the Long-Term Evolution-Advanced (LTE-A) system to increase user throughput by combining a selected number of component carriers (CCs). Its integration with heterogeneous networks enables the mobile user equipment to take advantage of increased throughput and radio coverage of various access technologies. However, due to the mobility of user equipment, these innovations have increased the likelihood of handoff scenarios, resulting in a high outage probability and low throughput. Handover is a crucial part of mobility management since it enables users to move from one cell to another while maintaining connections. However, no single access technique can provide seamless communication without interruption or delay. As a result, creating a suitable handover decision algorithm is necessary for ensuring high-quality service continuity and dependable user equipment access to the network at all times. An Adaptive Real-Time Spectrum Selection Framework and Handover Decision Algorithm (RSSF-HDA) with optimal resource allocation to the deployed system model is presented in this paper. This effort is geared toward preventing communication failure and improving system performance. It further positions the 4G LTE-A framework for inclusive coexistence with the current 5G New Radio enhanced Mobile Broadband (eMBB) use case scenario. The results of the simulation reveal that this method increases system performance in terms of Cell edge spectral efficiency and Handover success rate above 10% and 13%, respectively, when compared to Conventional Handover Decision Algorithm (Conv-HDA) and Multi-Influence Factor Handover Decision Algorithms (MIF-HDA).

A Multiarmed Bandit Approach for LTE-U/Wi-Fi Coexistence in a Multicell Scenario.

Recent studies and literature reviews have shown promising results for 3GPP system solutions in unlicensed bands when coexisting with Wi-Fi, either by using the duty cycle (DC) approach or licensed-assisted access (LAA). However, it is widely known that general performance in these coexistence scenarios is dependent on traffic and how the duty cycle is adjusted. Most DC solutions configure their parameters statically, which can result in performance losses when the scenario experiences changes on the offered data. In our previous works, we demonstrated that reinforcement learning (RL) techniques can be used to adjust DC parameters. We showed that a Q-learning (QL) solution that adapts the LTE DC ratio to the transmitted data rate can maximize the Wi-Fi/LTE-Unlicensed (LTE-U) aggregated throughput. In this paper, we extend our previous solution by implementing a simpler and more efficient algorithm based on multiarmed bandit (MAB) theory. We evaluate its performance and compare it with the previous one in different traffic scenarios. The results demonstrate that our new solution offers improved balance in throughput, providing similar results for LTE and Wi-Fi, while still showing a substantial system gain. Moreover, in one of the scenarios, our solution outperforms the previous approach by 6% in system throughput. In terms of user throughput, it achieves more than 100% gain for the users at the 10th percentile of performance, while the old solution only achieves a 10% gain.

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.

Investigation of a HAP-UAV Collaboration Scheme for Throughput Maximization via Joint User Association and 3D UAV Placement.

In this paper, a collaboration scheme between a high-altitude platform (HAP) and several unmanned aerial vehicles (UAVs) for wireless communication networks is investigated. The main objective of this study is to maximize the total downlink throughput of the ground users by optimizing the UAVs' three-dimensional (3D) placements and user associations. An optimization problem is formulated and a separate genetic-algorithm-based approach is proposed to solve the problem. The K-means algorithm is also utilized to find the initial UAV placement to reduce the convergence time of the proposed genetic-algorithm-based allocation. The performance of the proposed algorithm is analyzed in terms of convergence time, complexity, and fairness. Finally, the simulation results show that the proposed HAP-UAV integrated network achieves a higher total throughput through joint user association and UAV placement schemes compared to a scheme with a single HAP serving all users.

Empowering communication networks with MMR scheduler: A novel approach to balancing user throughput and fairness

In the field of communication networks, the management of resource allocation is a crucial research area. To achieve efficient radio resource management, mobile telecommunications operators utilize opportunistic scheduling algorithms. This study presents a novel opportunistic scheduler, called MMR (Min–Max Rate), which aims to ensure equal treatment of users, good quality of service, and maximum overall system throughput simultaneously and improve network throughput and fairness by up to 70% compared to traditional and classical scheduling algorithms. To evaluate the performance of MMR, we compared it with other reference algorithms such as Round Robin, MaxSNR, and Proportional Fair, known for their scheduling efficiency and robustness. The simulation results obtained by the MATLAB software and analysis focused on fairness criteria and system throughput for different user groups, and we found that our proposed algorithm outperforms the existing algorithms.

Outage and Throughput Analysis of UAV-Assisted NOMA Relay Systems With Indoor and Outdoor Users

Non-orthogonal multiple access (NOMA) and unmanned aerial vehicle (UAV) are two promising technologies for the fifth generation (5G) and beyond networks. On the other hand, the transition environment from outdoor to indoor is an important problem in practical wireless communication. In this paper, we mathematically study a multi-antenna UAV-assisted communication system where a multi-antenna base station (BS) communicates with indoor and outdoor users through a multiantenna UAV using the power-domain NOMA technique. The metric used to evaluate the system performance are outage probability (OP) and throughput of indoor and outdoor users under different communication urban environments. Moreover, the impacts of the UAV's altitude and location and the light-of-sight (LoS) probability on system performance are also considered. We give the exact and asymptotic closed-form OP expressions and then optimize the transmission rate to maximize the throughput of each user and the overall system throughput. Numerical results show that better system performance can be achieved by controlling the altitudes and location of the UAV, selecting an appropriate transmission rate, or equipping BS and UAV with more antennas. Monte-Carlo simulation results are provided to demonstrate the performance of the investigated system and the accuracy of the developed analytical results.

Performance Analysis and Simulation of Millimeter Wave Cell-Free mMIMO Networks

Towards the 5Gnetworks and beyond, there are a lot of emerging technologies. These technologies include but not limited to; multiple input multiple output (MIMO), cell-Free networks, and millimeter wave bands. The cellular MIMO can provide a satisfied performance for users except the shadowed and the cell-edge ones. In order to overcome this disadvantage, the cell-Free networks are deployed. Through applications of distributed access points (APs), the cell-Free networks can provide a ubiquitous coverage for users as whole. Therefore, they can provide a high throughput for users. Moreover, the applications of millimeter wave bands can provide a high bandwidth and hence, a high throughput for users. In other words, the application of the cell-Free technology combined with the millimeter wave bands can extremely enlarge the users’ throughput. This is the motivation of our manuscript. The purpose of this manuscript is to provide mathematical model and simulation for the cell-Free mMIMO network, operating in the millimeter wave bands. The performance metrics can include; the spectral efficiency (SE), bit error rate (BER), and energy efficiency (EE). It is observed that the centralized cooperation among the APs can let users have a satisfied throughput even the system employs the maximal ratio combining (MRC). Furthermore, all cooperation levels, among APs, can perform well even for non-light of sight (NLOS) environment.

Model-based Bayesian reinforcement learning for enhancing primary user performance under jamming attack

Due to the broadcast nature of wireless networks, jamming attacks are inevitable. Therefore, this paper considers enhancing the throughput of a primary user (PU) suffering from a random jamming attack near the receiver in a cognitive radio network. A cooperative spectrum-sharing scheme is proposed between the PU and an energy-constrained secondary user (SU) harvesting non-RF energy. The SU location is optimized to maximize the PU throughput. Then, a decision problem is formulated to adaptively optimize the PU decision regarding the cooperation with SU and maximize the PU throughput in the long run, considering the uncertainties about the jammer activity and the SU’s available energy. Unlike the previous work, our scheme assumes no prior knowledge about the parameters controlling the jammer’s random behavior, the energy harvesting process, and the sensing system. Bayesian reinforcement learning (BRL) is adopted to learn the unknown parameters and optimize the PU decisions simultaneously. The Bayes-adaptive mixed-observable Markov decision process (BAMOMDP) model is proposed to extend the current BRL models to handle the state’s mixed observability. Simulation results show the efficiency and robustness of the proposed scheme in enhancing the PU/SU throughputs and its ability to adapt to non-stationary environments. Compared with previous work, the proposed scheme can improve the PU average throughput by 15% in non-stationary environments and up to 40% in stationary environments with low jamming sensing capabilities.

Stability region characterization of a two-user age of information restricted cognitive radio model

In this paper, we consider stability and latency analysis of a two-user cognitive radio network while considering Age of Information requirements imposed on the data transmissions of the users. We provide two different formulations. In the first formulation, our objective is to maximize the stable throughput of the secondary user subject to Age of Information constraints imposed on the data transmission of the users. While, in the second formulation, we introduce a multi-objective programming approach that investigates the trade-off between the secondary user stable throughput and its Age of Information. Our numerical results characterize the effect of imposing different Age of Information restrictions on the stable throughput region of the network; we show that the stability region, in certain cases, is reduced by only 11% with strict Age of Information restrictions. Our results also investigate the trade-off between the secondary user throughput and the Age of information. Finally, our results show the potential accuracy of the algorithm adopted in this paper to solve the formulated non-linear optimization problems.

Is proportional fair scheduling suitable for age-sensitive traffic?

Proportional Fair (PF) scheduling with successful deployments in various cellular wireless networks and wireless LANs, aims at maximizing the sum of the logarithms of user throughputs. PF scheduling is known to strike an appropriate balance between fairness and throughput, for conventional data traffic. On the other hand, there has recently been a surge of interest in status update networks carrying age-sensitive traffic for which information freshness is crucial and therefore network performance metrics driven by Age of Information (AoI) are instrumental, as opposed to conventional performance metrics such as delay, loss, or throughput, used for conventional data traffic. This paper studies the scheduling problem for the downlink of a cellular wireless network with a transmitter sending age-sensitive status update packets from multiple information sources to users with the goal of keeping the information as fresh as possible for the users. For this purpose, under the generate-at-will scenario, an age-agnostic model-free scheduler is proposed with the goal of minimizing the weighted sum peak AoI of the network, which is the performance metric used in this paper for quantifying information freshness. With numerical examples, the proposed scheduler is compared and contrasted with weighted PF scheduling in terms of implementation and performance, in both non-opportunistic and opportunistic scenarios.

Research on Network Resource Collection and Distribution Strategies in Big Data-Driven Multimedia Teaching

The multimedia teaching information data generated in the era of big data are increasing at a huge scale, but the computing environment used before is difficult to meet users’ requirements for data integration. Multimedia information has the characteristics of diversity and structure diversity, which lead to the situation that the storage and processing methods are too complicated and time-consuming to varying degrees. This brings great challenges to big data analysis. Therefore, the fusion of this part of the data information requires a large-scale data parallel framework environment to ensure that multiple data calculation instructions can be executed each time, improve the range of problem solving, and essentially improve the complexity of multimedia teaching information. The main index of large-scale data fusion is computing speed. Aiming at the performance defects of the open source computing framework Hadoop, this paper intends to establish a new computing model. This model uses distributed data sets to replace the previous data structures and solve some problems in the process of data integration, thereby significantly improving the computation efficiency and reducing resource usage. Starting from the network’s perception of energy resources and considering that the randomness of energy available to nodes in the energy harvesting cognitive relay different systems will lead to different data volumes such as throughput and resource utilization, this paper takes the network’s throughput as the optimization network object for resource collection and allocation research. Experiment description: compared with the previous resource management, this model can reduce the total consumption of the system; compared with the single base station mode, the proposed method can extend the network life cycle to a greater extent; meanwhile, it ensures the use of users. Based on the stability analysis of the data, an optimal solution is obtained that can maximize the stable throughput of cognitive users and effectively improve resource collection and allocation.

Joint Maneuver and Beamforming Design for UAV-Enabled Integrated Sensing and Communication

This paper studies the unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC), in which UAVs are dispatched as aerial dual-functional access points (APs) that can exploit the UAV maneuver control and strong line-of-sight (LoS) air-to-ground (A2G) links for efficient communication and sensing. In particular, we consider that one UAV-AP, equipped with a vertically placed uniform linear array (ULA), sends combined information and sensing signals to communicate with multiple users and at the same time sense potential targets at interested areas on the ground. Under this setup, we consider two scenarios with quasi-stationary and fully mobile UAVs, in which the UAV is deployed at an optimizable location over the whole ISAC mission period and can fly over different locations during the ISAC mission period, respectively. For the two scenarios, our objective is to jointly design the UAV maneuver (deployment location or flight trajectory) and the transmit beamforming, for maximizing the weighted sum-rate throughput of communication users, while ensuring the sensing beampattern gain requirements, subject to the transmit power and flight constraints. However, due to the ULA consideration at the UAV, the two formulated problems are highly non-convex and very difficult to be optimally solved, as the UAV’s location/trajectory variables are involved on the exponent parts of each entry in the steering vectors, and are closely coupled with the transmit beamforming vectors. To tackle this issue, we propose efficient algorithms to find their suboptimal but high-quality solutions, by using various techniques from convex and non-convex optimization. Finally, numerical results are provided to validate the superiority of our proposed designs as compared to various benchmark schemes with heuristic maneuver designs. It is shown that the joint maneuver and transmit beamforming design efficiently balances the inherent tradeoff between sensing and communication with regards to different beampattern gain thresholds.