Proportional Fairness Research Articles

A self-organized metaheuristic approach towards inter-cell interference management for LTE-Advanced

Abstract In order to meet the high throughput demand set by international mobile telecommunication union, carrier aggregation is exploited for expanding the bandwidth of up to 100 MHz in Long Term Evolution-Advanced (LTE-A). For achieving the aforementioned bandwidth, a maximum of five component carriers (CCs) can be aggregated as per Release 10 LTE-A. Improper CC selection and scheduling will result in hazardous throughput, fairness, and interference problems. Therefore, a proper CC selection and scheduling algorithm is most important for the overall performance of the network. On the other hand, due to an increased information exchange, centralized network planning is not a suitable choice here. In this study, we are employing a self-organized particle swarm optimization (PSO)-based joint component carrier selection and scheduling (JCCS) algorithm for the downlink. More precisely, the concern of the proposed algorithm is the autonomous distribution of resource blocks (RBs) from the pool of CCs by the base station, with the concern of minimizing the impact of inter-cell interference. Our proposed PSO-based JCCS algorithm results in the maximization of the min-max throughput by managing the inter-cell interference in an appropriate manner. Moreover, the proposed algorithm is compared with the traditional CC selection and scheduling algorithms, i.e., random, round robin, and proportional fair. The comparison is carried out in terms of throughput and fairness, and the calculated percentage gain in the end elaborates the performance improvement by exploiting PSO-based JCCS.

Teleoperation Scheduling Algorithm for Smart Grid Communications in LTE Network

This paper investigates the performance of three well-known algorithms namely Proportional Fairness, Exponential Proportional Fairness and Modified-Largest Weighted Delay First in LTE network. This study is based on their performance in three smart grid applications (Sub-Station Automation, Advance Metering Infrastructure and Wide Area Situational Awareness). In addition, a new mathematical model has been developed and proposed to target certain weakness of the above mentioned algorithms.

Exploiting Cell Dormancy and Load Balancing in LTE HetNets: Optimizing the Proportional Fairness Utility

We consider the problem of maximizing the proportional fairness (PF) system utility over heterogeneous wireless networks (HetNets) by jointly exploiting cell dormancy (cell ON–OFF)—wherein some transmission points from a set of interest can be made inactive—and load balancing (user association)—wherein users are associated to the active transmission points in that set, with each user being associated with only one point. We establish that this joint optimization problem, which is a discrete optimization problem, is NP-hard. Nevertheless, we prove that the load balancing subproblem for any given set of active transmission points is not NP-hard but instead can be reformulated as an asymmetric assignment problem and hence can be optimally solved in an efficient manner. In addition, we show that some generalized load balancing problems that incorporate multiuser diversity gains can also be optimally and efficiently solved. We propose another lower complexity greedy algorithm for the load balancing subproblem that offers a worst-case performance guarantee and describe a simple way to approximately realize a given input user association via biasing factors. We then derive low-complexity algorithms for the joint optimization problem, including one based on a successive approximation method that has hitherto been used for continuous nonconvex optimization problems. Simulations over an example Long-Term Evolution HetNet topology reveal the superior performance of the proposed algorithms and underscore the significant benefits of jointly exploiting cell dormancy and load balancing.

An Efficient Downlink Radio Resource Allocation with Carrier Aggregation in LTE-Advanced Networks

In long term evolution-advanced (LTE-A) networks, the carrier aggregation technique is incorporated for user equipments (UEs) to simultaneously aggregate multiple component carriers (CCs) for achieving higher transmission rate. Many research works for LTE-A systems with carrier aggregation configuration have concentrated on the radio resource management problem for downlink transmission, including mainly CC assignment and packet scheduling. Most previous studies have not considered that the assigned CCs in each UE can be changed. Furthermore, they also have not considered the modulation and coding scheme constraint, as specified in LTE-A standards. Therefore, their proposed schemes may limit the radio resource usage and are not compatible with LTE-A systems. In this paper, we assume that the scheduler can reassign CCs to each UE at each transmission time interval and formulate the downlink radio resource scheduling problem under the modulation and coding scheme constraint, which is proved to be NP-hard. Then, a novel greedy-based scheme is proposed to maximize the system throughput while maintaining proportional fairness of radio resource allocation among all UEs. We show that this scheme can guarantee at least half of the performance of the optimal solution. Simulation results show that our proposed scheme outperforms the schemes in previous studies.

A Suboptimal Multiuser-Pairing Algorithm With Low Complexity for Virtual MIMO Systems

In virtual multiple-input-multiple-output (V-MIMO) systems, employing a user-pairing algorithm can significantly improve system capacity and spectral efficiency using multiuser diversity. The optimal pairing algorithm can achieve optimal system performance, but it is not feasible to implement in practice due to its huge complexity. To solve the problem, an efficient suboptimal pairing algorithm with lower complexity is proposed, which maximizes the system throughput by selecting users one by one. Furthermore, to achieve a good balance between throughput and user fairness, a novel pairing algorithm is derived, which combines the advantages of the suboptimal pairing algorithm and proportional fair (PF) criteria. Simulation results show that the proposed algorithm achieves better fairness and higher system throughput than the double PF (D-PF) algorithm, and has much lower complexity than the optimal pairing algorithm.

Multiuser Successive Maximum Ratio Transmission (MS-MRT) for Video Quality Maximization in Unicast and Broadcast MIMO OFDMA-Based 4G Wireless Networks

In this paper, we propose a novel low-complexity beamforming algorithm for multiuser successive maximum ratio transmission (MS-MRT) toward space-division multiplexing to achieve video quality maximization in the downlink of a multiuser (MU) multiple-input-multiple-output (MIMO) orthogonal frequency-division multiple-access (OFDMA)-based fourth-generation (4G) wireless network. We compare the performance of this algorithm in terms of system throughput and video quality with the two popular precoding techniques, namely, block diagonalization (BD) and successive optimization (SO). We also compare its performance with the generalized versions of these two algorithms termed coordinated transmit-receive processing (CTR)-BD and CTR-SO algorithms, respectively. Further, we propose an extension of the MS-MRT scheme to broadcast scenarios, termed broadcast successive maximum ratio transmission (BS-MRT), which computes the optimal broadcast beamforming vector to maximize the video quality at each of the broadcast group members while maintaining orthogonality to the previously scheduled user groups. We also demonstrate that the proposed MS-MRT and BS-MRT schemes can be naturally adapted to the context of various MU OFDMA scheduling algorithms such as proportional fairness (PF) and round robin (RR), to name a few, to maximize video quality through optimal scheduling in the presence of a large number of users. We discuss implementations of the proposed MS-MRT scheme in association with these scheduling algorithms for MU unicast and broadcast video transmission scenarios. The simulation results presented in this work rely on the rate models derived using the Joint Scalable Video Model (JSVM) software developed by the Joint Video Team (JVT) and are, thus, readily applicable in practice. The presented results clearly demonstrate the ability of the proposed algorithm to maximize the video quality in comparison with the other competing MU MIMO precoding techniques. Further, employing the proportional fair scheduling algorithm in conjunction with the MS-MRT results in an overall enhancement in the received video quality compared with the RR and max-rate algorithms.

Multiuser Heterogeneous-SNR MIMO Systems

Previous studies on multiuser multiple-input multiple-output (MIMO) mostly assume a homogeneous signal-to-noise ratio (SNR), where each user has the same average SNR. However, real networks are more likely to feature heterogeneous SNRs (a random-valued average SNR). Motivated by this fact, we analyze a multiuser MIMO downlink with a zero-forcing (ZF) receiver in a heterogeneous SNR environment. A transmitter with Mantennas constructs M orthonormal beams and performs the SNR-based proportional fairness (S-PF) scheduling where data are transmitted to users each with the highest ratio of the SNR to the average SNR per beam. We develop a new analytical expression for the sum throughput of the multiuser MIMO system. Furthermore, simply modifying the expression provides the sum throughput for important special cases such as homogeneous SNR, max-rate scheduling, or high SNR. From the analysis, we obtain new insights (lemmas): i) S-PF scheduling maximizes the sum throughput in the homogeneous SNR and ii) under high SNR and a large number of users, S-PF scheduling yields the same multiuser diversity for both heterogeneous SNRs and homogeneous SNRs. Numerical simulation shows the interesting result that the sum throughput is not always proportional to M for a small number of users.

SCHEDULING SCHEMES FOR CARRIER AGGREGATION IN LTE-ADVANCED SYSTEMS

Considering the needs of higher data rates and hence wider bandwidths for advanced mobile communication systems, 3GPP standardized carrier aggregation (CA) as one of the important technological components for fourth generation (4G) Long term evolution (LTE) advanced systems. Here the user equipments are assigned carrier component (CC) belonging to different (Interband contiguous/noncontiguous) or same spectrum bands (Intra-band contiguous/non-contiguous). The users share the radio resources as per different scheduling schemes like round robin(RR), proportional fair (PF), best channel quality indicator (CQI), max-min, edge prioritized, channel traffic aware etc. Every scheduler has its own characteristic and provides improvement in terms of throughput or fairness. This paper discusses the simulations carried out to study the performance of RR, PF and best CQI schedulers in an urban, CLSM (closed loop spatial multiplexing) environment using an LTE system level simulator.

CAPACITY IMPROVEMENT OF MIMO-OFDMA SYSTEM USING ADAPTIVE RESOURCE ALLOCATION ALGORITHM

The user demands for wireless communication are increasing but the existing networks are not able to fulfill the high data rate of future communication services. More bandwidth can support high data rate wireless access. Due to spectral limitations, it is very expensive to increase. Therefore, the available bandwidth is needed to use efficiently. Orthogonal Frequency Division Multiplexing (OFDM) has been explored as a promising air interface technology to utilize the bandwidth effectively. OFDM can be exploited as a multiple access technique, called Orthogonal Frequency Division Multiple Access (OFDMA) that can support both frequency and multiuser diversity. In order to serve the user population, one of the most sophisticated techniques, resource allocation technique, can be integrated into the OFDMA system. Multiple Input Multiple Output (MIMO) technology can increase the spectral efficiency because multiple data streams can be simultaneously transmitted over the channel. The combination of MIMO technology and OFDMA system can achieve both higher spectral efficiency and better Quality of Service (QoS) such as capacity, fairness, and Bit Error Rate (BER). In this paper, a MIMO-OFDMA system is developed for downlink transmission within one single cell. Two optimization methods such as subcarrier allocation and power allocation are used for resource allocation. To decompose the MIMO channel into parallel single channels, Eigen Value Decomposition (EVD) is used. Subcarrier selection is based on the priority of the user by using the dominant eigen channels. Water-filling algorithm is adapted for power distribution among users. The aim is to maximize the system capacity subject to constraints on total power and proportional fairness. The performance of the system is evaluated by comparing the static allocation method (equal subcarrier allocation with equal power) and two adaptive allocation methods (adaptive subcarrier allocation with equal power and adaptive subcarrier allocation with adaptive power).

Proportional Fair Resource Allocation Based on Chance-Constrained Programming for Cognitive OFDM Network

Proportional fair resource allocation plays a critical role to balance the spectrum efficiency and fairness for cognitive orthogonal frequency division multiplexing (OFDM) network. However, due to the lack of cooperation between cognitive radio (CR) network and primary network, channel state information between CR base station (CRBS) and primary user (PU) could not be estimated precisely. Therefore, the interference of CRBS---PU couldn't be computed precisely and chance-constrained programming is adopted to formulate the resource allocation problem. In this work, we study the proportional fair resource allocation problem based on chance-constrained programming for cognitive OFDM network. The objective function maximizes the spectral efficiency of cognitive OFDM network over subcarrier and power allocation. The constraint conditions include the interference constraint of PU with the target probability requirement and the proportional fair rate requirement of CR users. In order to solve the above optimization problem, two steps are taken to develop hybrid immune optimization algorithm (HIOA). In the first step, the probabilistic interference constraint condition is transformed as an uncertain function which is computed by a generalized regression neural network (GRNN). In the second step, we combine immune optimization algorithm and GRNN to develop HIOA. Simulation results demonstrate that HIOA yields higher spectral efficiency while the probabilistic interference constraint condition and the proportional fair rate constraint condition could be satisfied very well.

QoS-aware composite scheduling using fuzzy proactive and reactive controllers

We consider in this paper downlink scheduling for different traffic classes at the MAC layer of wireless systems based on orthogonal frequency division multiple access (OFDMA), such as the recent 3rd Generation Partnership Project (3GPP) long-term evolution (LTE)/LTE-A wireless standard. Our goal is to provide via the scheduling decisions quality of service (QoS), but also to guarantee fairness among the different users and traffic classes (including delay-sensitive and best-effort traffic). QoS-aware scheduling strategies, such as modified largest weighted delay first (M-LWDF), exponential (EXP), exponential proportional fair (EXP-PF), and the log-based scheduling rules, prioritize delay-sensitive traffic by considering rules based on the head-of-line (HoL) packet delay and the tolerated packet loss rate, whereas they serve best-effort traffic by considering the classical proportional fair (PF) rule. These scheduling rules do not prevent resource starvation for best-effort traffic. On the other side, if both traffic types are scheduled according to the PF rule, then delay-sensitive flows suffer from delay bound violations. In order to fairly distribute the resources among different service classes according to their QoS requirements and channel conditions, we employ the concept of fuzzy logic in our scheduling framework. By employing the fuzzy logic concept, we serve all the traffic classes with one priority rule. Simulation results show better intra-class and inter-class fairness than state-of-the-art scheduling rules. The proposed scheduling framework enables to appropriately balance delay requirements of traffic, system throughput, and fairness.

Distributed two‐hop proportional fair resource allocation in Long Term Evolution Advanced networks

AbstractIn Long Term Evolution Advanced networks with Type I in‐band half‐duplex decode‐and‐forward relay nodes, proportional fair (PF) resource allocation is aiming at guaranteeing two‐hop match and optimising global proportional fairness. The two‐hop match is defined as equal data rates in the access links and the corresponding backhaul links. The global proportional fairness is between all the user equipments served by the evolved nodes B and the relay nodes. Existing centralised schemes achieve these targets at the cost of enormous channel state information (CSI) exchange. Existing distributed schemes focus on resource partitioning and employ a traditional single‐hop PF scheduling algorithm in access links, with less CSI exchange. The traditional PF scheduling algorithm maximises single‐hop proportional fairness between the data rates in the access links rather than two‐hop proportional fairness between the end‐to‐end data rates in the two hops. In order to reduce CSI exchange and at the same time to maximise the two‐hop proportional fairness, a distributed two‐hop PF resource allocation scheme is proposed. The proposed scheme includes two‐hop PF resource scheduling algorithms and adaptive resource partitioning algorithms, applied in different two‐hop transmission protocols. Simulation results demonstrate the proposed scheme is better than the existing distributed schemes in obtaining better proportional fairness and larger cell‐edge user equipment throughputs. Copyright © 2014 John Wiley & Sons, Ltd.

Learning Methods for CDF Scheduling in Multiuser Heterogeneous Systems

In modern heterogeneous wireless networks, the task of supporting fairness along with user priorities and concurrently achieving the highest possible system throughput is desirable and challenging. In this work, two classes of practical cumulative distribution function (CDF) based scheduling algorithms are developed to achieve these goals. These algorithms are shown to frequently outperform, and are potential alternatives to, the well known Proportional Fair (PF) scheduling method. The first class of algorithms, Nonparametric CDF based scheduling (NPCS) algorithms, are used when the channel fading model is unknown. Herein, the mapping from channel quality information (CQI) to the real CDF is unknown but is constructed exploiting the order statistics of the CQI sequence. The constructed CDF mapping methods are shown to converge to the actual CDF. When the channel model is known, a class of Parametric CDF based scheduling (PCS) algorithms are developed which learn parameters of the channel statistics for the scheduler to use. In our experiments, this Bayesian learning approach results in better system throughput than the NPCS approach. We also show collecting a moderate number of CQI data is enough to achieve nearly the performance of CDF based scheduling with known channel distribution. Throughout the work, CDF based scheduling algorithms are supported by simulations which show that they can effectively support not only fairness but also user priorities and often outperform PF in terms of system throughput.

Enhanced cluster computing performance through proportional fairness

The performance of cluster computing depends on how concurrent jobs share multiple data center resource types such as CPU, RAM and disk storage. Recent research has discussed efficiency and fairness requirements and identified a number of desirable scheduling objectives including so-called dominant resource fairness (DRF). We argue here that proportional fairness (PF), long recognized as a desirable objective in sharing network bandwidth between ongoing data transfers, is preferable to DRF. The superiority of PF is manifest under the realistic modeling assumption that the population of jobs in progress is a stochastic process. In random traffic the strategy-proof property of DRF proves unimportant while PF is shown by analysis and simulation to offer a significantly better efficiency–fairness tradeoff.

Study on the Resource Scheduling Algorithm for LTE-Advanced Downlink Systems

In the paper, the resource scheduling algorithm in the downlink of LTE-Advanced (LTE-A) assuming equal power allocation among subcarriers which adopted the technology of carrier aggregation (CA) is investigated. When the independent scheduling (INS) scheme is applied, the LTE users will acquire few resources because they cannot support CA technology. And the fairness of the system is disappointing. Focusing on the problem, a novel proportional fair (PF) scheduling algorithm based on INS is proposed. In the proposed method, the system fairness is well improved without bringing high complexity to the system. And also, we design a weigh factor which is related to the number of the carriers and the percentage of LTE users in the method. The simulation results show that the proposed algorithm can effectively increase the throughput of LTE users and improve the system fairness.

Primary user behavior aware spectrum allocation scheme for cognitive radio networks

Cognitive radio (CR) technology can solve the problems of spectrum scarcity and low spectrum utilization. However, random behavior of the primary user (PU) appears to be an enormous challenge. In this paper, we propose a PU behavior aware joint channel selection and allocation scheme. In the first step, the channels are ranked based on statistics of the PU usage whereas in the second phase, a proportional fair oriented channel allocation scheme is employed to allocate channels among CRs. We also introduce the concept of a time-varying framing process (TVFP) that minimizes the overall data transmission time. Simulation results show that the proposed scheme outperforms existing schemes in terms of the transmission-time and the number of collisions with the PUs. In addition, it helps to save a significant amount of transmission power. Moreover, it provides a significantly higher system throughput as compared to the existing schemes.

Efficiency and Fairness Trade-Offs in SC-FDMA Schedulers

In this paper, we address the uplink resource scheduling problem in single carrier frequency-domain multiple access systems. In particular, we focus on the efficiency and fairness trade-offs in scheduling and resource allocation for wireless cellular networks. We present an efficient implementation method that translates these scheduling problems into set partitioning problems that are well-studied in the literature. Then, we discuss a family of utility functions that enable us to investigate the performance of different frequency domain schedulers such as the sum-rate maximization, proportional fair, and max-min fair schedulers. We use the price of fairness as a metric to analytically quantify these trade-offs. Based on the intuition that fairness of resource allocation in cellular radio networks corresponds to the prioritization of cell-edge user rates, we demonstrate that the proportional fair scheduler significantly improves fairness among users, and increases the rates offered to the cell-edge and median users when compared to the sum-rate maximization scheduler. This comes at the cost of reducing the cell-center user rates and the aggregate user rate. We present the steps on how to take into account the practical implementation constraints, in particular, those related with the discrete Fourier transform implementation, in the problem formulation. Simulation results that illustrate these trade-offs are also presented. We conclude that this type of analysis can provide guidelines for the network operators to control the efficiency and fairness trade-off as the data traffic grows.

Distributed Pricing-Based User Association for Downlink Heterogeneous Cellular Networks

This paper considers optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion. We first consider the case where each BS has a single antenna and transmits at fixed power and propose a distributed price update strategy for a pricing-based user association scheme, in which the users are assigned to the BS based on the value of a utility function minus a price. The proposed price update algorithm is based on a coordinate descent method for solving the dual of the network utility maximization problem and it has a rigorous performance guarantee. The main advantage of the proposed algorithm as compared to an existing subgradient method for price update is that the proposed algorithm is independent of parameter choices and can be implemented asynchronously. Further, this paper considers the joint user association and BS power control problem and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches. Finally, this paper considers the joint user association and BS beamforming problem for the case where the BSs are equipped with multiple antennas and spatially multiplex multiple users. We incorporate dual coordinate descent with the weighted minimum mean-squared error (WMMSE) algorithm and show that it achieves nearly the same performance as a computationally more complex benchmark algorithm (which applies the WMMSE algorithm on the entire network for BS association) while avoiding excessive BS handover.

EICIC 가 적용된 이종 셀룰러 망을 위한 부하 분산 기법

기존의 매크로 셀룰러 망 환경은 공간 재사용의 한계로 인해 최근 급증하는 데이터 트래픽을 제대로 지원할 수 없게 되었다. 이 문제를 극복하기 위해 매크로 셀룰러 망위에 스몰 셀이 설치되어 적극적인 공간 재사용이 가능한 이종 셀룰러 망이 등장하게 되었다. 하지만, 매크로 셀과 스몰 셀 사이의 전송전력 차이로 인해 매크로 셀에 집중된 부하를 스몰 셀로 충분히 분산 시킬 수 없었다. 따라서, 프레임의 일정구간에서 매크로 셀의 전송전력을 차단하는 almost blank subframe (ABS) 와 스몰 셀 커버리지를 확장하여 사용자 부하를 스몰 셀로 강제 분산시키는 cell range expansion을 결합한 enhanced inter-cell interference coordination (eICIC) 기법이 제안되었다. 그러나, 이러한 eICIC 기법만으로는 효과적인 부하 분산을 달성하기 어렵다. 본 논문에서는 eICIC 기법이 적용된 이종 셀룰러 망 환경에서 비례공정을 향상시키는 부하 분산 기법을 제안한다. 구체적으로 제안된 기법은 탐욕 알고리즘 기반의 사용자의 소속 기지국 전환과 ABS 구간비율 갱신을 재귀적으로 결합하여 부하 분산을 실행한다. Recently, heterogeneous networks consisting of small-cells on top of traditional macro-cellular network has attracted much attention, because traditional macro-cellular network is not suitable to support more demanding mobile data traffic due to its limitation of spatial reuse. However, due to the transmit power difference between macro- and small-cells, most users are associated with macro-cells rather than small-cells. To solve this problem, enhanced inter-cell interference coordination (eICIC) has been introduced. Particularly, in eICIC, the small-cell coverage is forcibly expanded to associate more users with small-cells. Then, to avoid cross-tier interference from macro-cells, these users are allowed to receive the data during almost blank subframe (ABS) in which macro-cells almost remain silent. However, this approach is not sufficient to balance the load between macro- and small-cells because it only expands the small-cell coverage. In this paper, we propose a load balance scheme improving proportional fairness for heterogeneous networks employing eICIC. In particular, the proposed scheme combines the greedy-based user association and the ABS rate determination in a recursive manner to perform the load balance.

Large Deviations for the Stationary Measure of Networks Under Proportional Fair Allocations

We address a conjecture introduced by Massoulié [Massoulié L (2007) Structural properties of proportional fairness: Stability and insensitivity. Ann. Appl. Probab. 17(3):809–839], concerning the large deviations of the stationary measure of bandwidth-sharing networks functioning under the proportional fair allocation. For Markovian networks, we prove that proportional fair and an associated reversible allocation are geometrically ergodic and have the same large deviations characteristics using Lyapunov functions and martingale arguments. For monotone networks, we give a more direct proof of the same result, relying on stochastic comparisons, that holds for general service time distribution. These results support the intuition that proportional fairness is “close” to allocations of service being insensitive to the service time distribution.