Proportional Fairness Research Articles

Fairness-Based Multiuser Scheduling for Ambient Backscatter Communication Systems

This letter investigates and analyzes multiuser scheduling for ambient backscatter (AmBack) communication systems by taking the fairness issue into account. In contrast to traditional absolute signal-to-noise ratio (SNR)-based scheduling, proportional fairness (PF)-based scheduling is proposed to achieve the PF among AmBack users. Since it is not amendable to a closed-form expression for the outage performance, the asymptotic performance is derived to analyze the multiuser diversity order. For comparison, the cumulative distribution function (CDF)-based scheduling is also considered, where the fair channel access ratio (CAR) for AmBack users can be guaranteed. Simulation results confirm the correctness of analytical results, and demonstrate the effectiveness of the proposed scheduling schemes.

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.

A Perceptual Spectrum-Hole Proportional Fairness Scheduling Algorithm Integrated with CR

In order to improve the utilization of spectrum resources and solve the problem of unscheduled users in the downlink transmission process in LTE system, we propose a new proportional Fairness scheduling algorithm. The algorithm integrates cognitive radio (CR) technology into proportional Fairness scheduling algorithm, and derives the expression of system throughput under the new algorithm, so that unscheduled users can perceive “spectral-holes” and use the idle spectrum that is not used by the primary user (PU) for data transmission. To achieve maximum resource utilization efficiency. In addition, two secondary user (SU) management schemes for continuing to wait and finding new subcarriers are given, and the delays under the two schemes are calculated. A new decision method is proposed to solve the problem of SU reconstruction. Numerical analysis shows that the new algorithm can ensure the fairness of users, effectively reduce system delay and improve system efficiency.

Mobility analysis of Modified Proportional Fair scheduler in LTE/LTE-Advanced system

Packet scheduling is one of the key schemes used in Radio Resource Management (RRM) for Long Term Evolution (LTE) cellular network. It serves as the resource allocation for each time and frequency dimensions in LTE baseband processing. In previous research, a new scheduling algorithm was developed, namely the Modified-Proportional Fair (PF) scheduler that splits a single subframe into multiple time slots and allocates the resource block (RB) to the identified User Equipment (UE) in some chosen time slots and it is done continuously for each subframe depending on the instantaneous Channel Quality Indicator (CQI) feedback received from UEs. Results have shown that the Modified-PF scheduler produces the most outstanding overall performance in terms of spectral efficiency and throughput with comparable fairness as compared to other state-of-the-art schedulers, namely, Round Robin and Proportional Fair schedulers. However, the performance of the new scheduler is not analysed in UE mobility scenario. Hence, this research will determine the type of response by the scheduler towards different mobility condition by simulating the throughput and the spectral efficiency of the three schedulers for various UE velocities. It is observed that the Modified-PF again shows significant performance improvement for various mobility condition in terms of average UE throughput and average UE spectral efficiency in both single cell omnidirectional and tri-sector eNodeB. This shows that Modified-PF can be considered as one of the packet scheduling options for the LTE cellular network.

Joint Optimization for User Association and Inter-Cell Interference Coordination Based on Proportional Fair Criteria in Small Cell Deployments

Joint Optimization for User Association and Inter-Cell Interference Coordination Based on Proportional Fair Criteria in Small Cell Deployments

Predicted Decoupling for Coexistence Between WiFi and LTE in Unlicensed Band

Wireless fidelity (WiFi) and long term evolution (LTE) coexistence system has attracted significant attention recently. However, most studies on coexistence only consider the issues about downlink performance and fairness between WiFi network and LTE network. In this paper, we take a comprehensive consideration of both the performance of WiFi and LTE, to propose a downlink/uplink decoupling (DUDe) scheme for the fairness of the coexistence system. Through the process of modeling construction, problem formulation with reformulation, solution and simulations, we illustrate that DUDe can achieve proportional fairness in terms of sum throughput and load balancing between uplink and downlink. In our decoupling model for coexistence between WiFi and LTE, resource allocation is under adaptive spectrum partitioning and users can access each network of WiFi and LTE. Moreover, to make our proposal more realistic, we adopt support vector machine (SVM) to predict the total number of users in WiFi and LTE coexistence system, where the predictive simulation shows its high prediction accuracy with low complexity. We further apply the prediction results to the optimization problem of DUDe. Simulation results manifest that the proposed DUDe scheme has better performance in terms of sum throughput. The sum throughput of DUDe scheme can come up to almost twice more than that of coupled association scheme. The result shows that DUDe can also achieve load balance between uplink and downlink in the coexistence system.

Fairness and Transmission-Aware Caching and Delivery Policies in OFDMA-Based HetNets

Recently, wireless edge caching has emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)-based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, in the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27 percent compared to the conventional baseline popular CPSs.

LTE Scheduler Algorithms for VANET Traffic in Smart City

A new concept such as smart city was introduced in the last years where the Intelligent Transportation system (ITS ) plays a critical role to provide road safety and manage Vehicular Ad Hoc Networks (VANETs) traffic. Nevertheless, VANETs have significant constraints like nodes high mobility, intermittent connectivity, variable network density and heterogeneity. However, the different types of traffic, the different Quality of Service requirements, the need to exchange mobile data, multi-services and data diversity leads mainly to load and time constraints in this specific and stringent type of networks. The main characteristic of this kind of networks is the very changing topology that poses supplementary constraints and makes achieving QoS constraints a very challenging task. In VANET network the vehicle generated traffic will be transferred to the data center from road side unit to the base station by using Long Term Evolution (LTE) in an urban area. Despite LTE has a larger system capacity and it provides a higher transmission speed, the network performance is affected by the implemented scheduling algorithm. In this context, we study the efficiency of LTE scheduler algorithms such as Proportional Fairness, Round Robin, Priority Set Scheduler, Maximum Throughput Scheduler and Throughput to Average Scheduler and Blind Equal Throughput mainly at the road side unit using Network Simulator 3(NS3) to determinate the most suitable scheduler for VANET traffic. Results demonstrate that the round robin algorithm is more effective for volumetric VANET traffic in terms of throughput, delay, packet loss rate and fairness.

Channel aware optimized proportional fair scheduler for LTE downlink

Long Term Evolution (LTE) mobile networks, time-frequency radio resources are allotted with respect to Quality of Service (QoS) requirements using scheduling algorithms. Buffer Occupancy (BO), Average Cellular Throughput (ACT), Average User Throughput (AUT), Blocking Probability (BP) and Fairness Index (FI) are the parameters to be considered when designing a scheduler. A new scheduling policy is proposed in this paper, called “Channel Aware Optimized Proportional Fair (CAOPF)” to optimize the channel behavior based on Channel Quality Indicator (CQI). The performance of the introduced scheduler is investigated and compared with the other schedulers such as Round Robin (RR) and Proportional Fair (PF). Simulation results indicate that the new scheduler gives the best level of QoS performance in terms of the above said parameters when compared with the other schedulers taken into account. It provides no pending data of users in good channel condition, higher ACT, higher improvement in user throughput in good and average channel conditions. In addition, very low blocking of user data in good channel condition and optimized FI and resource block (RB) allocated to the users in all channel conditions are seen.

Patient-Centric HetNets Powered by Machine Learning and Big Data Analytics for 6G Networks

Having a cognitive and self-optimizing network that proactively adapts not only to channel conditions, but also according to its users' needs can be one of the highest forthcoming priorities of future 6G Heterogeneous Networks (HetNets). In this paper, we introduce an interdisciplinary approach linking the concepts of e-healthcare, priority, big data analytics (BDA) and radio resource optimization in a multi-tier 5G network. We employ three machine learning (ML) algorithms, namely, naive Bayesian (NB) classifier, logistic regression (LR), and decision tree (DT), working as an ensemble system to analyze historical medical records of stroke out-patients (OPs) and readings from body-attached internet-of-things (IoT) sensors to predict the likelihood of an imminent stroke. We convert the stroke likelihood into a risk factor functioning as a priority in a mixed integer linear programming (MILP) optimization model. Hence, the task is to optimally allocate physical resource blocks (PRBs) to HetNet users while prioritizing OPs by granting them high gain PRBs according to the severity of their medical state. Thus, empowering the OPs to send their critical data to their healthcare provider with minimized delay. To that end, two optimization approaches are proposed, a weighted sum rate maximization (WSRMax) approach and a proportional fairness (PF) approach. The proposed approaches increased the OPs' average signal to interference plus noise (SINR) by 57% and 95%, respectively. The WSRMax approach increased the system's total SINR to a level higher than that of the PF approach, nevertheless, the PF approach yielded higher SINRs for the OPs, better fairness and a lower margin of error.

LTE Scheduler Algorithms for VANET Traffic in Smart City

A new concept such as smart city was introduced in the last years where the Intelligent Transportation system (ITS ) plays a critical role to provide road safety and manage Vehicular Ad Hoc Networks (VANETs) traffic. Nevertheless, VANETs have significant constraints like nodes high mobility, intermittent connectivity, variable network density and heterogeneity. However, the different types of traffic, the different Quality of Service requirements, the need to exchange mobile data, multi-services and data diversity leads mainly to load and time constraints in this specific and stringent type of networks. The main characteristic of this kind of networks is the very changing topology that poses supplementary constraints and makes achieving QoS constraints a very challenging task. In VANET network the vehicle generated traffic will be transferred to the data center from road side unit to the base station by using Long Term Evolution (LTE) in an urban area. Despite LTE has a larger system capacity and it provides a higher transmission speed, the network performance is affected by the implemented scheduling algorithm. In this context, we study the efficiency of LTE scheduler algorithms such as Proportional Fairness, Round Robin, Priority Set Scheduler, Maximum Throughput Scheduler and Throughput to Average Scheduler and Blind Equal Throughput mainly at the road side unit using Network Simulator 3(NS3) to determinate the most suitable scheduler for VANET traffic. Results demonstrate that the round robin algorithm is more effective for volumetric VANET traffic in terms of throughput, delay, packet loss rate and fairness.

Fairness-Driven Energy Efficient Resource Allocation in Uplink MIMO Enabled HetNets

Heterogeneous network (HetNet) is a promising solution to meeting the unprecedented demand for higher data rate in the next generation mobile networks. Compared with the traditional single-layer cellular networks, the resource management (e.g., user association and power control problems) is more challenging in the multi-tier HetNets. In addition, due to the new application scenarios and battery-limited nature of the user equipment (UE), improving energy efficiency (EE) of individual while ensuring EE fairness among UEs is one of the key design issues in the uplink HetNets. In this paper, by considering the fairness for EE, joint user association and power control problems are studied in the uplink multiple input multiple output (MIMO) enabled HetNets. Under the quality of service (QoS) and transmit power constraints of UE, two kinds of fairness, namely, proportional fairness and max-min fairness, are investigated. The optimization problems of maximizing the EE are formulated by considering the fairness among UEs. Considering the non-convex characteristic of the problem, the formulated problem is divided into two subproblems, i.e., user association and power control subproblems, then an iterative algorithm is proposed to achieve the fair EE resource allocation for each fairness criterion until convergence. For the proportional fairness problem, the dual decomposition and Newton methods are applied. Then, by using successive convex approximation (SCA) and dual decomposition, the problem of max-min fairness is solved. Through both theoretical analysis and simulations, the convergence and the effectiveness of the proposed algorithms are proved and evaluated. Simulation results show that the proposed algorithms can significantly improve the average EE of users.

Random Space–Time Line Code With Proportional Fairness Scheduling

As the demand for and services of the Internet of Things (IoT) increase, the dense deployment of a massive number of devices is expected in future IoT networks. Accordingly, an appropriate scheduling method will be required to fairly support the overwhelmingly large number of devices or users operating in practical environments. In this paper, we consider a single-cell downlink network in which one transmitter, such as an access point and base station, supports the massive number of users by using a space–time line code (STLC) scheme. Here, to resolve a prohibitively large amount of channel state information (CSI) feedback from the users to the transmitter, we propose a new STLC scheme called random STLC that uses a random vector for the STLC encoding. Furthermore, a proportional fairness (PF) scheduler is modified with a priority factor that is proportional to the devised alternative signal-to-interference-plus-noise ratio. By a comparison with an optimal STLC system that uses full CSI with a PF scheduler, we justify the benefit of the proposed random STLC with a PF scheduler using partial CSI for massive-user networks. Numerical results obtained from a rigorous simulation confirm that the proposed scheme can provide a comparable to achievable rate and fairness with massive number of users together with affordable feedback overhead in massive networks.

Resource Allocation for D2D Underlay Communications With Proportional Fairness Using Iterative-Based Approach

In this paper, we develop a novel resource allocation scheme that aims to improve the system fairness, with minimum SINR (Signal to Interference and Noise Ratio) constraints and power limits for the Device-to-Device (D2D) underlay communications. Since the evaluation of the system fairness is different for t=1 and t≥2 , where t is the scheduling period, we divide our joint optimization problem into two cases: t=1 and t≥2 . For each case, we decompose the joint optimization problem into two sub-problems: power and channel allocations. We then propose the corresponding power allocation algorithm for each case. By introducing virtual D2D links, we model the channel allocation as a 3-D (3-Dimensional) assignment problem, which is effectively solved by our proposed 2-D (2-Dimensional) iterative method. Simulation results show that our proposed iterative method can produce the close-to-optimal performance with low computational complexity. Moreover, comparing with existing schemes, our proposed scheme can not only enhance the system fairness but also improve the overall throughput.

Delay threshold scheduling algorithm for LTE downlink systems

This paper proposes a delay threshold scheduling (DTS) algorithm for the scheduling of requests in real-time multimedia applications using LTE downlink systems, which takes into account channel conditions as well as the packet waiting time. Delay thresholds were adopted to enable the differentiation of packets in conjunction with proportional weighting between channel conditions and packet waiting time for the prioritisation of real-time services. Simulation results demonstrate that compared to the results obtained using Modified Largest Weighted Delay First (M-LWDF), Exponential Proportional Fair (EXP/PF), and Channel Dependent Earliest Due Date (CD-EDD), the proposed DTS algorithm is able to increase video traffic throughput and decrease packet delays and the packet loss ratio for real-time services.

Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks

Due to the growing demand for spectral resources with the emergence of IoT networks and applications, cooperative cognitive radio IoT networks (CCR-IoTN) are expected to play an essential role in the world of smart technology. In this paradigm, the benefits of cooperative communication and cognitive radio networks (CRN) are merged to meet IoT networks' needs. In this work, we employ the hybrid overlay-underlay CRN to guarantee both the secondary user (SU) stability and provide acceptable total throughput. In our CCR-IoTN, SUs may act as a relay to help the primary transmission. If the primary user (PU) is active, the secondary non-relay nodes transmit concurrently with the PU in underlay mode. In return for their cooperation, SUs are allowed to access the spectrum to transmit their data in overlay mode. In this context, we propose several fair resource allocation frameworks and compare well-known problems such as spectrum efficiency, energy efficiency as an objective function for CCR-IoTN. This kind of problem is inherently nonconvex. To solve the problems, therefore, we reformulate them as a convex problem using quadratic transform. Finally, we use simulation to evaluate the performance of the proposed hybrid resource allocation method. The resource allocation framework based on proportional fairness policy is shown to have acceptable performance in terms of throughput, fairness, and energy efficiency.

Multiplexing URLLC Traffic within eMBB Services in 5G NR: Fair Scheduling

5G New Radio (NR) is envisioned to efficiently support both enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Due to the severe constraint of URLLC traffic, the time resources used by eMBB are further divided into mini-slots. From the media access control (MAC) layer scheduling perspective, the resource allocation occurs on different timescales, and URLLC traffic is pre-emptively overlapped at the mini-slot timescale to obtain the strict requirements of URLLC (achieve 99.999% reliability within 1 ms latency). In this article, we analyze the multiplexing of eMBB and URLLC traffic in 5G downlink transmission, with the dual objectives of maximizing eMBB utility like proportional fairness for eMBB users while satisfying URLLC constraints. We formulate the resource allocation problem in each mini-slot as the integer programming (IP) problem with two solutions: 1) convex relaxation; 2) greedy algorithm. The simulation results show that our algorithms have a higher utility of eMBB users while satisfying the URLLC users' latency and reliability requirements than using basic round robin. The performance comparison between the two algorithms is extensively studied, and the choice of two algorithms in practice can be decided by the trade-off analysis investigated in this article.

Delay threshold scheduling algorithm for LTE downlink systems

This paper proposes a delay threshold scheduling (DTS) algorithm for the scheduling of requests in real-time multimedia applications using LTE downlink systems, which takes into account channel conditions as well as the packet waiting time. Delay thresholds were adopted to enable the differentiation of packets in conjunction with proportional weighting between channel conditions and packet waiting time for the prioritisation of real-time services. Simulation results demonstrate that compared to the results obtained using Modified Largest Weighted Delay First (M-LWDF), Exponential Proportional Fair (EXP/PF), and Channel Dependent Earliest Due Date (CD-EDD), the proposed DTS algorithm is able to increase video traffic throughput and decrease packet delays and the packet loss ratio for real-time services.

New strategy for resource allocation using PSO-PFS hybrid

In this paper, the problem of resource allocation in a tri-sectorial cell taking into account the OFDM transmission technique used in wireless network, such as LTE, and the upcoming 5G, is studied. A new strategy for resource allocation scheme based on PSO-PFS hybrid (Particle Swarm Optimisation and Proportional Fair Scheduling) is proposed to help the users who are in a critical location and to have an optimal distribution of resources. The scheme leverages the PFS algorithm taking into consideration the channel state conditions of the users, where the PSO algorithm provides an optimal solution to the allocation problem and improves the performances of users according to their channel state. The simulation results show that the allocation of resources by the PSO-PFS hybrid guarantees a high throughput of the system by ensuring fairness through all users.

Emergency water supply decision-making of transboundary river basin considering government–public perceived satisfaction

The emergency supply of transboundary water resources is a prominent problem affecting the social and economic development of basin countries. However, current water supply decisions on transboundary water resources may ignore the psychological perception of multi-stakeholders, and the evolution of emergencies increases the uncertainty of decision making. Both factors would lead to the low acceptance of water-related decisions. Utility satisfaction, perceived losses, and quantity satisfaction were selected in this paper to identify the perceived satisfaction of upstream governments, downstream governments, and the public, respectively, over multiple decision-making stages. A modeling framework combining prospect theory and the multi-stage multi-objective programming methodology was then developed to measure the perceived satisfaction of different stakeholders in a watershed under emergency. A two-stage NSGA-II and TOPSIS based approach was adopted to find the optimal compromise solution to solve the model. The framework was applied in the Lancang–Mekong River basin to provide suggestions to decision makers. Upstream decision makers must choose a moderate proportional fairness degree when making emergency decisions to maximize the perceived satisfaction of all stakeholders. Meanwhile, the perceived loss of downstream countries with low water demand should be considered first in the formulation of emergency water supply plans. Furthermore, although water supply from upstream countries can improve perceived water quantity satisfaction of downstream publics, additional actions must still be taken to change the traditional concepts of the public.