Proportional Fair Algorithm Research Articles

Comparative Analysis of Max-Throughput and Proportional Fair Scheduling Algorithms in 5G Networks

Mobile network service use is growing, especially after the Covid-19 pandemic. To improve the quality of mobile network services, 5G comes as a network service ecosystem with low latency, which is 1ms, or about 10 times lower than 4G, making 5G able to provide more efficient access, especially in real-time network utilization. Maximum speed can be obtained by sharing limited bandwidth. The limited amount of available bandwidth causes the need for a Packet Scheduler, which aims to improve the efficiency and fairness of bandwidth usage. This research uses two packet schedulers comparing the Proportional Fair algorithm and the Max-Throughput algorithm using test scenarios for changes in the number of users and user speed. The resulting output value analyzes resource limits such as frequency, power, speed, and time in each scenario to allocate resources so that their use remains efficient with a Quality of Service that remains stable and maintained. In simulation testing using the 5G-air-simulator, the average value obtained in the delay is 1,394 ms, throughput is 0,636, and fairness index is 0,967.

Resource allocation based on differentiated user services for indoor hybrid multi-color VLC/WiFi networks

Multi-color visible light communication (VLC) and wireless fidelity (WiFi) networks provide a promising solution for the indoor wireless communication, while the hybrid networks face the challenge of imbalanced access point (AP) load and resource allocation. A resource allocation method based on user service differentiation (RAMUSD) is proposed for multi-color VLC/WiFi hybrid networks. In our design, the user’s premium level is defined according to the user’s channel conditions and requested data rate. The heterogeneous network selects the service AP for the users according to its premium level, user with high premium level is served by one of VLC access points (APs), while user with low premium level is served by WiFi AP. Then, the average satisfaction is used as the load index to adjust the number of users served by the WiFi AP or the VLC AP to achieve load balance among APs. Furthermore, the RAMUSD uses the potential game theory to optimize user’s migration and resource allocation among APs for improving the load balancing index. In the potential game, user can improve its return value by changing access AP. So, it makes user who pursue more resources give up the congested APs. Simulation results show that the proposed RAMUSD can achieve better system throughput and user satisfaction than an enhanced proportional fairness algorithm based on evolutionary game theory (EPF-EGT).

Multiuser-scheduling and resource allocation using max-min technique in wireless powered communication networks

Wireless <span>powered communication network (WPCN) is a promising research area for improving network security and speed. The transmission power of the source during the uplink functions as a random variable in WPCN due to the intrinsic power transfer process, whereas it is a constant in typical cooperative networks, culminating in the signal to noise ratio of the source-access point and all the source-relay-access point being mutually correlated. As a result of the massive increase in communication devices powered by battery, the goal of prolonging their life is critical. To get the most throughput in the shortest amount of time, the best uplink and downlink time allocations were calculated. For high throughput and secrecy performance, the proportional max-min fairness algorithm was used in this paper for secure communication in hybrid relays integrated with WPCN. This method allows for multi-user scheduling with optimum targets to provide reliable hybrid outage probability, secrecy outage probability, and energy outage probability. Efficacy of the proposed system was demonstrated regarding throughput, outage probability, and confidentiality. The performance of the model was compared to that of various energy harvesting models like random user scheduling, best user scheduling, and several others, and was found to outperform them for secure transmission.</span>

Location-Based LTE-M Uplink Power Control and Radio Resource Scheduling.

Long-Term Evolution for Metro (LTE-M) is adopted as the data communication system in urban rail transit to exchange bio-direction train-wayside information. Reliable data communication is essential in LTE-M systems for ensuring trains’ operation safety and efficiency. However, the inter-cell inference problem exists in LTE results in throughput reduction, especially when trains are in the edge area of adjacent cells, and has negative effects on train operation. The uplink power control and radio resource scheduling scheme is studied in LTE-M system which differentiates from public cellular networks in user numbers and the availability of the trains’ locations. Since the locations of the trains are available, the interferences from the neighbouring cells can be calculated, and a location based algorithm together with soft frequency reuse is designed. In addition, a proportional fair algorithm is taken to improve uplink radio resource scheduling considering the fairness to different train-wayside communication service requirements. Through simulation, the practicability of the proposed schemes in communication system of urban rail transit is verified in aspects of radio power control and data communication throughput.

The Optimization of Automated Container Terminal Scheduling Based on Proportional Fair Priority

With the goal of maximizing the efficiency of automated container terminals, taking the loading and unloading time as the research object, the integrated optimization of automated container terminal scheduling with proportional fair priority is established by setting the priority parameters under the premise of balancing the operation efficiency of each container ship at the berth. The model considers separate assignments of general containers and special containers in the actual operation at the automated container terminal, which is never considered in other literatures. A proportional fairness algorithm (PFA) is designed to solve the problem, taking the maximum of the ratio of quantities of remaining loading and unloading containers of each container ship to the distance of empty AGVs/YTs as the proportional fair priority. The priority of all current tasks is ranked, and the highest priority task is fulfilled first. Different from the published literature, the study of this paper can be presented in two aspects. One is in the modeling. This includes (i) formalizing the description of the purpose of the model and (ii) conducting a real-world coordination of four types of equipment that incorporate at automated container terminals which considers different operations between general goods and special goods in the actual automated terminal operations. The other is that PFA is applied to deal with the integrated scheduling whose results are better with the balance of efficiency and fairness.

5G Ultra-Reliable and Low Latency Communication Resource Scheduling for Power Business Quality Assurance

The resource scheduling method of 5G URLLC (Ultra-Reliable and Low Latency Communication) is studied in this paper to assure the Quality of Service (QoS) of various power business, which utilizes the limited spectrum and power in low-band cellular communication system to meet the requirement of power terminal about transmission rate, scheduling delay and fairness in an efficient manner. Firstly, based on the high reliability and low latency characteristics of URLLC, a multi-cell downlink system model is built. Then, a resource allocation problem model for maximizing downlink throughput is proposed and solved step by step. Power allocation algorithm bases on pricing mechanism and non-cooperative game and an Delay-based Proportional Fair algorithm (DPF）is designed to schedule channel resource dynamicly..Simulation results show that the proposed resource scheduling method can reduce the scheduling delay of power terminals under the constraints of transmission reliability and fairness while meeting the different QoS requirements. The proposed method outperforms some known algorithms.

An Improved Proportional Fair Scheduling Algorithm for Downlink LTE Cellular Network

Abstract: Network providers of LTE networks can achieve maximum gain and Quality of Service (QoS) requirement of their users by employing a radio resource management technique that has the ability to allocate resource blocks to users in a fair manner without compromising the capacity of the network. This implies that for a better performing LTE network, a fair scheduling and balanced QoS delivery for various forms of traffic are needed. In this paper an improved proportional fair scheduling algorithm for downlink LTE cellular network has been developed. This algorithm was implemented using a MATLAB-based System Level simulator by Vienna University. The developed algorithm was compared to other scheduling algorithms such as the Proportional Fair (PF) algorithm, Best Channel Quality Indicator (CQI), and Round Robin (RR) scheduling methods. The system performance was also analyzed under different scenarios using different performance metrics. The achieved results showed that the developed algorithm had a better throughput performance than the Round Robin and Proportional fair scheduling. The developed algorithm shows improved cell edge throughputs of about 19.2% (as at 20 users) and 9.1% higher for cell edge users without and with mobility impact respectively. The Best CQI algorithm had higher peak throughput values but the fairness was highly compromised. The developed algorithm outperforms the Best CQI by 136.6% without the impact of mobility. Finally, in dense conditions, the developed algorithm still outperforms the other algorithms with a QoS metric of 4.6% increment when compared to the PF algorithm which was the closest competitor. Keywords: UE, eNodeB, Scheduling, Proportional Fair, LTE,

Link Scheduling Algorithm to Mitigate Worst-Case Interference Problems for LTE-A

As the number of apps increases, the traffic of multimedia services in wireless networks grows exponentially. Because of the proportional fair algorithm's shortcomings in terms of achieving Quality of Service (QoS) requirements such as delay and packet loss, it is not extensively employed in modern wireless communication. This study provides a scheduling method based on the QoS utility function, with a parameter added to improve the priority mechanism in order to overcome such flaws. Our scheduling algorithm can match the QoS criteria of real-time traffic, according to simulation results, and the overall system transmission performance has increased significantly.

A flexible scheduling algorithm for the 5th-generation networks

At present, the 5th-Generation (5G) wireless mobile communication standard has been released. 5G networks efficiently support enhanced mobile broadband traffic, ultra-reliable low-latency communication traffic, and massive machine-type communication. However, a major challenge for 5G networks is to achieve effective Radio Resource Management (RRM) strategies and scheduling algorithms to meet quality of service requirements. The Proportional Fair (PF) algorithm is widely used in the existing 5G scheduling technology. In the PF algorithm, RRC assigns a priority to each user which is served by gNodeB. The existing metrics of priority mainly focus on the flow rate. The purpose of this study is to explore how to improve the throughput of 5G networks and propose new scheduling schemes. In this study, the package delay of the data flow is included ic the metrics of priority. The Vienna 5G System-Level (SL) simulated is a iCATLAB-based SL simulation platform which is used to facilitate the research and development of 5G and beyond mobile communications. This paper presents a new scheduling algorithm based on the analysis of different scheduling schemes for radio resources using the Vienna 5G SL simulator.

Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques

In this paper, a deep learning approach is used to conduct an in‐depth study and analysis of intelligent resource allocation in wireless communication networks. Firstly, the concepts related to CSCN architecture are discussed and the throughput of small base stations (SBS) in CSCN architecture is analyzed; then, the long short‐term memory network (LSTM) model is used to predict the mobile location of users, and the transmission conditions of users are scored based on two conditions, namely, the mobile location of users and whether the small base stations to which users are connected have their desired cache states, and the small base stations select the transmission. The small base station selects several users with optimal transmission conditions based on the scores; then, the concept of game theory is introduced to model the problem of maximizing network throughput as a multi‐intelligent noncooperative game problem; finally, a deep augmented learning‐based wireless resource allocation algorithm is proposed to enable the small base station to learn autonomously and select channel resources based on the network environment to maximize the network throughput. Simulation results show that the algorithm proposed in this paper leads to a significant improvement in network throughput compared to the traditional random‐access algorithm and the algorithm proposed in the literature. In this paper, we apply it to the fine‐grained resource control problem of user traffic allocation and find that the resource control technique based on the AC framework can obtain a performance very close to the local optimal solution of a matching‐based proportional fair user dual connection algorithm with polynomial‐level computational complexity. The resource allocation and task unloading decision policy optimization is implemented, and at the end of the training process, each intelligent body independently performs resource allocation and task unloading according to the current system state and policy. Finally, the simulation results show that the algorithm can effectively improve the quality of user experience and reduce latency and energy consumption.

RAPTAP: a socio-inspired approach to resource allocation and interference management in dense small cells

Femto Cells offer higher data rates to users within closed spaces. Dense deployment of small cells is a characteristic of pre-5G/LTE-Advanced Pro (LTE-A Pro) networks and is a precursor to the future 5G cellular networks. Combined with a Frequency Reuse Factor (FRF) of one, the dense small cell systems result in high co-tier interference which is undesirable. High interference Optimized scheduling decisions and interference management techniques are required to guarantee certain minimum data rates to the users at the cell-edge and increase the overall throughput of the system. This work presents a centralized scheduling approach that mitigates the detrimental impact of interference, thereby maximizing the overall throughput of the system. In doing so, we make use of concepts from Social theory and incorporate these ideas in the solution design. The centralized approach uses optimized scheduling algorithm (RAPTA) which takes feedback from the indoor users of all Femtos as the input and formulates a Mixed Integer Non-Linear Programming (MINLP) problem. Thereafter, the MINLP problem is relaxed and its solution carries out the Resource Block allocation and ensures optimal power transmission for every allocated resource block in all Femtos. We implement the proposed OPT algorithm on top of Proportional Fair (PF) in the Vienna Simulator. Since the RAPTA is NP-Hard and takes a considerably long time to solve the MINLP problem, we derive from it a polynomial time Heuristic algorithm (RAPTAP) which performs Resource Block allocation and sub-optimal Power transmission quite close to the RAPTA algorithm in terms of performance. RAPTAP is a two-stage approach each of which is inspired by ideas from two different strands of Sociological theory. We demonstrate through experiments that the proposed RAPTA + PF achieves 60.67% improvement in the services offered to the mobile users when compared to the classic PF algorithm with Soft Fractional Frequency Reuse (SFFR) interference management technique in the built environment. The Socio-inspired RAPTAP performs almost as well as the RAPTA + PF algorithm, with only a marginal 4% drop in the overall system throughput as compared to the latter. Further, we evaluate the RAPTAP heuristic in scenarios involving mobile users and demonstrate 14.52% improvement when compared to classic PF algorithm with Fractional Frequency Reuse-Full Isolation (FFR-FI) interference management. Finally, we compare the proposed Socio-inspired RAPTAP + PF with two state-of-the-art algorithms, viz., a Genetic Algorithm approach to resource allocation (NSGA-UDN) and a recent work on LTE-Unlicensed (LTE-U) + PF. Proposed Socio-inspired solution outperforms the LTE-U + PF and NSGA-UDN by 28.26% and 31.41%, respectively, in terms of throughput.

Cooperative Scheduling for Pilot Reuse in Massive MIMO Systems

To be differentiated from the literature, in this paper, we aim to alleviate the impact of pilot contamination (PC) on the massive MIMO systems by cooperatively scheduling users (rather than just assigning) among neighboring cells to share the limited orthogonal pilots. To this end, we develop the multi-cell cooperative scheduling (MCCS) algorithm together with the cooperative scheduling indexes (COSIs) for maximizing the data rate, maximizing the Jain's fairness index and reaching a better tradeoff in-between. For convenience, they are denoted by the COSIs for the CMDR, CMMF and CPF schedulers, respectively. However, its high computational complexity may somehow resists its from practical applications. Thus, a low-complexity cooperative proportional fairness (LC-CPF) algorithm is designed to well approach the MCCS algorithm using the CPF COSI; and at the meantime, the order of computational complexity can be significantly reduced. Moreover, its ability to reach a better tradeoff can still be maintained under the impact of high spectrum-sharing interference. In addition, it is interesting to find that using the proposed cooperative scheduling methods, the open-loop power control mechanism is no longer required for compensating the differences of received signal quality between users.

Modified Proportional Fair Scheduling Algorithm for Heterogeneous LTE-A Networks

Growing of time-sensitive applications such as streaming multimedia, voice over IP and online gaming required strongly support from mobile communication technology. So, the persistent need for wireless broadband technologies such as LTE-A is essential. LTE-A can achieve QoS in an efficient manner by using a reliable packet scheduling algorithm. It also supports good cell coverage by using heterogeneous capability. In this paper, modifications of proportional fair (PF) algorithm are proposed with different methods to compute the average throughput, which is the main and important parameter in the PF cost function. These methods are geometric mean, root mean square and arithmetic mean. Vienna simulator is used to study the performance of the proposed algorithms. A comparison between PF modifications and the most famous algorithms (the original PF and Best CQI algorithms) with various UE velocities is introduced. Average UE throughput, spectral efficiency, energy per bit, cell throughput and fairness are used as performance indicators. The results expose that QPF has best improved values for spectral efficiency, energy per bit and fairness by 8.4%, 14%, and 9.3%, respectively than original PF. However, Best CQI has a better value of average UE and cell throughput than all algorithms of 2% and 1.8% in low and medium UE velocity, respectively, but the best value of all types of throughput at high speed is gained by QPF. QPF and GMPF has the same performance in fairness with all UEs speeds.

Queue-aware scheduling in full-duplex wireless networks

Contemporary progress in telecommunication technologies have made full-duplex wireless communications feasible. The latter promise to double the capacity of wireless networks by allowing devices to concurrently transmit and receive on the same radio resources. In this paper, we devise mathematical optimal formulations for scheduling in full-duplex and hybrid full-duplex/half-duplex orthogonal frequency division multiple access networks. Our optimal models are queue-aware and address the new interferences that arise from working with full-duplex wireless networks: self-interference and intra-cell co-channel interference. We apply these models with different scheduling objectives, tackling issues such as signal-to-interference-plus-noise ratio (SINR) maximization and user fairness. Accordingly, we first propose an optimal full-duplex Max-SINR algorithm and an optimal full-duplex Proportional Fair algorithm. Additionally, and since full-duplex communications may not always be profitable, we introduce an optimal hybrid Max-SINR algorithm and an optimal hybrid Proportional Fair algorithm. These algorithms switch between full-duplex and half-duplex transmissions, so as to enhance network performance. Moreover, to avoid possible intractability with the optimization problems, we propose heuristic versions of our algorithms. We simulate these proposals, showing that they achieve near optimal performances, and asserting the different gains they attain with respect to their half-duplex counterparts: more than a 50% increase in user equipment throughput values alongside a three fold decrease in the average user equipment waiting delay.

Improved Proportional Fair Algorithm for Transportation of 5G Signals in Internet of Medical Things

The Internet of medical things (IoMT) is a hybrid network inwhich numerous technologies like Bluetooth, Wi-Fi, and Cellular technology are integrated on a single platform. The internet of things applied to the medical healthcare necessitates enormous data rate and tremendous bandwidth along with better battery life with reliable and versatile connectivity. The use of 5G network satisfies these prerequisite with its tremendous data rate capabilities and assists human health services diagnosis and treatment. In this paper, improved proportional fair algorithm is introduced and is compared with existing scheduling algorithm for developing revolutionary changes in the medical healthcare.5G networks represent a contemporary approach which encounter a hybrid digital network for developing Internet of things. Performance metrics considered for simulation studies are throughput, path-loss and SNR.

Low-Latency Communications for Community Resilience Microgrids: A Reinforcement Learning Approach

Machine learning and artificial intelligence (AI) techniques can play a key role in resource allocation and scheduler design in wireless networks that target applications with stringent QoS requirements, such as near real-time control of community resilience microgrids (CRMs). Specifically, for integrated control and communication of multiple CRMs, a large number of microgrid devices need to coexist with traditional mobile user equipments (UEs), which are usually served with self-organized and densified wireless networks with many small cell base stations (SBSs). In such cases, rapid propagation of messages becomes challenging. This calls for a design of efficient resource allocation and user scheduling for delay minimization. In this paper, we introduce a resource allocation algorithm, namely, delay minimization Q-learning (DMQ) scheme, which learns the efficient resource allocation for both the macro cell base stations (eNB) and the SBSs using reinforcement learning at each time-to-transmit interval (TTI). Comparison with the traditional proportional fairness (PF) algorithm and an optimization-based algorithm, namely distributed iterative resource allocation (DIRA) reveals that our scheme can achieve 66% and 33% less latency, respectively. Moreover, DMQ outperforms DIRA, and PF in terms of throughput while achieving the highest fairness.

Performance Evaluation and Comparison of Scheduling Algorithms on 5G Networks using Network Simulator

In this research, we compared the Round Robin (RR) and the Proportional Fair (PF) algorithms for different user equipment density scenarios using voice and video traffic, to evaluate the key impact on performance of 5G mmwave network. This research simulated on NS3.27 with an integrated mmwave module. Based on the result, we found that the RR is a good choice for voice traffic. It has a throughput of 3.65% better than PF with similar fairness index. On the other hand, we found that the PF is the right choice for video traffic due to has better result for throughput. It has a throughput of 1.24% better than RR. For fairness index round robin has better result for voice and video traffic.

Secrecy Throughput Maximization for Full-Duplex Wireless Powered IoT Networks Under Fairness Constraints

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max–min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.

Joint Energy and QoS-Aware Memetic-Based Scheduling for M2M Communications in LTE-M

As a result of the increasing number of machine-type devices connected through the Internet, several challenges remain to date to support machine-to-machine (M2M) communications over long term evolution (LTE) cellular networks. In this paper, we tackle one important challenge of scheduling M2M traffic in uplink over LTE-M mobile networks. We propose a novel cross-layer scheme that considers packet scheduling in time and frequency domain using a memetic-based algorithm and aims to optimize resource allocation of M2M devices by considering their quality of service (QoS) needs while minimizing at the same time their energy consumption. After integrating an energy module for LTE in NS3 simulator, we perform simulations in a realistic M2M scenario and we evaluate the results which show how our proposed scheduling scheme outperforms other existing scheduling methods from the literature such as round robin and proportional-fair algorithms in terms of throughput, energy consumption and the percentage of satisfied devices with regard to their delay and throughput requirements.

Research on anti-conflict extraction method of multimedia video information based on machine learning

In order to solve the problems of long time, poor denoising performance, low quality of multimedia video information and low efficiency of information extraction in current methods of extracting multimedia video information. An anti-collision extraction method of multimedia video information based on machine learning is proposed, which combines K-SVD algorithm and Batch-OMP algorithm to remove noise in multimedia video information. Considering the error concealment method used by decoder, channel condition, importance and lifetime of multimedia video information package, C uses improved proportional fair scheduling algorithm to complete the scheduling of multimedia video information. It calculates the time curve of multimedia video information, gets the local extremum points in the time curve, determines the length and location of the extracted video clips according to the local extremum points, calculates the importance of the video clips, and completes the extraction of multimedia video information according to the importance. In order to verify the effectiveness of the proposed method, a simulation experiment is carried out. The experimental results show that compared with the traditional methods, the proposed method has better denoising performance and higher information extraction efficiency. The above results show that the proposed method has good application prospects.