Proportional Fair Scheduling Research Articles

Proactive Scheduling for mmWave Wireless LANs

To cope with growing wireless bandwidth demand, millimeter wave (mmWave) communication has been identified as a promising technology to deliver Gbps throughput. However, due to the susceptibility of mmWave signals to blockage, applications can experience significant performance variability as users move around due to rapid and significant variation in channel conditions. In this context, proactive schedulers that make use of future data rate prediction have potential to bring a significant performance improvement as compared to traditional schedulers. In this work, we explore the possibility of proactive scheduling that uses mobility prediction and some knowledge of the environment to predict future channel conditions. We present both an optimal proactive scheduler, which is based on an integer linear programming formulation and provides an upper bound on proactive scheduling performance, and a greedy heuristic proactive scheduler that is suitable for practical implementation. Extensive simulation results show that proactive scheduling has the potential to increase average user data rate by up to 35% over the classic proportional fair scheduler without any loss of fairness and incurring only a small increase in jitter. The results also show that the efficient proactive heuristic scheduler achieves from 60% to 75% of the performance gains of the optimal proactive scheduler. Finally, the results show that proactive scheduling performance is sensitive to the quality of mobility prediction and, thus, use of state-of-the-art mobility prediction techniques will be necessary to realize its full potential.

Analysis of QoS aware traffic template in n78 band using proportional fair scheduling in 5G NR

Analysis of QoS aware traffic template in n78 band using proportional fair scheduling in 5G NR

Extending Causal Discovery to Live 5G NR Network With Novel Proportional Fair Scheduler Enhancements

Extending Causal Discovery to Live 5G NR Network With Novel Proportional Fair Scheduler Enhancements

A bipartite graph based proportional fair scheduling strategy to improve throughput with multiple resource blocks

<p>The fifth-generation wireless communication is expected to provide a huge amount of capacity to cater to the need of an increasing number of mobile consumers, which can be satisfied by device-to-device (D2D) communication. Reusing the cellular user’s resources in an efficient manner helps to increase the spectrum efficiency of the network but it leads to severe interference. The important point in reusing cellular user resources is that D2D communication should not affect the cellular user’s efficiency. After achieving this requirement, the focus is now turned toward the allocation of resources to D2D communication. This resource allocation strategy is to be designed in such a way that it will not affect communication among the cellular user (CU). This scheme improves various performance objectives. This paper aims at designing a proportional fair resource allocation algorithm based on the bipartite graph which maintains the quality of service (QoS) of CUs while providing D2D communication. This algorithm can be merged with any other scheme of resource allocation for improving QoS and adopting changing channels. In this scheme, a D2D pair can be allocated with one or more than one resource blocks. The MATLAB simulations analyze the performance of the proposed scheme.</p>

UE Set Selection for RR Scheduling in Distributed Antenna Transmission with Reinforcement Learning

In this paper, user set selection in the allocation sequences of round-robin (RR) scheduling for distributed antenna transmission with block diagonalization (BD) pre-coding is proposed. In prior research, the initial phase selection of user equipment allocation sequences in RR scheduling has been investigated. The performance of the proposed RR scheduling is inferior to that of proportional fair (PF) scheduling under severe intra-cell interference. In this paper, the multi-input multi-output technology with BD pre-coding is applied. Furthermore, the user equipment (UE) sets in the allocation sequences are eliminated with reinforcement learning. After the modification of a RR allocation sequence, no estimated throughput calculation for UE set selection is required. Numerical results obtained through computer simulation show that the maximum selection, one of the criteria for initial phase selection, outperforms the weighted PF scheduling in a restricted realm in terms of the computational complexity, fairness, and throughput.

Proportional fair scheduling using water-filling technique for SC-FDMA based D2D communication

The resource allocation in SC-FDMA is constrained by the condition that multiple subchannels should be allocated to a single user only if they are adjacent. Therefore, the scheduling scheme of a D2D-cellular system that uses SC-FDMA must also conform to the so-called adjacency constraint. This paper proposes a heuristic algorithm with low computational complexity that applies proportional fair (PF) scheduling in the D2D-cellular system. The proposed algorithm consists of two main phases: (i) subchannel allocation and (ii) adjustment of data rates, which are executed for both CUEs and DUEs. In the subchannel allocation phase for CUEs (or D2D pairs), the users’ data rates are maximized via optimal power allocation to frequency-contiguous subchannels. In the second phase, a PF scheduling problem is solved to decide the modulation and coding scheme (MCS) of both CUEs and D2D pairs. Both phases of the proposed algorithm benefit from the Water-Filling (WF) technique. The simulation results suggest that the proposed scheme performs similar to optimal PF scheduling from the perspective of users’ data rate and their logarithmic sum. An additional benefit of the proposed scheme is its low computational overhead.

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.

Joint user association and fair scheduling for dual connectivity heterogeneous networks

Heterogeneous small cell network is one of the most effective solutions to overcome spectrum scarcity for the next generation of mobile networks. Dual connectivity (DC) can improve the throughput for each individual user by allowing concurrent access to two heterogeneous radio networks. In this paper, we propose a joint user association and fair scheduling algorithm (JUAFS) to deal with the resource allocation and load balancing issues for DC heterogeneous small cell networks. Considering different coverage sizes, numbers of users, and quality of experience characteristics of heterogeneous cells, we present a proportional fair scheduling for user association among cells and utilize interference graph to minimize the transmission conflict probability. Simulation results show the performance improvement of the proposed algorithm in spectrum efficiency and fairness comparing to the existing schemes.

A Cluster-Driven Adaptive Training Approach for Federated Learning.

Federated learning (FL) is a promising collaborative learning approach in edge computing, reducing communication costs and addressing the data privacy concerns of traditional cloud-based training. Owing to this, diverse studies have been conducted to distribute FL into industry. However, there still remain the practical issues of FL to be solved (e.g., handling non-IID data and stragglers) for an actual implementation of FL. To address these issues, in this paper, we propose a cluster-driven adaptive training approach (CATA-Fed) to enhance the performance of FL training in a practical environment. CATA-Fed employs adaptive training during the local model updates to enhance the efficiency of training, reducing the waste of time and resources due to the presence of the stragglers and also provides a straggler mitigating scheme, which can reduce the workload of straggling clients. In addition to this, CATA-Fed clusters the clients considering the data size and selects the training participants within a cluster to reduce the magnitude differences of local gradients collected in the global model update under a statistical heterogeneous condition (e.g., non-IID data). During this client selection process, a proportional fair scheduling is employed for securing the data diversity as well as balancing the load of clients. We conduct extensive experiments using three benchmark datasets (MNIST, Fashion-MNIST, and CIFAR-10), and the results show that CATA-Fed outperforms the previous FL schemes (FedAVG, FedProx, and TiFL) with regard to the training speed and test accuracy under the diverse FL conditions.

Proportional Fair Scheduling for Downlink mmWave Multi-User MISO-NOMA Systems

In this paper, we study non-orthogonal multiple access (NOMA) user scheduling and resource allocation problem for a generic downlink single-cell multiple input and single output (MISO) millimeter wave (mmWave) system. The larger number of packed antennas and the highly directional property of mmWave communications enable directional beamforming to achieve spatial diversity. Toward this end, we consider two different hybrid precoding schemes which are based on orthogonal matching pursuit (OMP). Users are assigned into different clusters and the base station (BS) transmits superposed signals that share the same precoding vector. Moreover, both fixed number of users per cluster and dynamic number of users per cluster are investigated. We aim to jointly optimize the user clustering, service scheduling, and power allocation strategy, in maximizing the proportional fairness (PF) among the users and exploring the multiuser diversity and multiplexing gain. Since the formulated joint user clustering, scheduling and power allocation problem is a mixed integer non-convex optimization problem, we propose a two-fold methodology. First, we apply a hybrid precoding and user clustering scheme, where the hybrid precoder is constructed by singular vector division (SVD) or minimum mean square error (MMSE). Then, with the obtained result, we approximate the proportional fairness power allocation problem by a sequence of Geometric Programming (GP) problems which are solved iteratively. The proposed scheme strikes a balance between the spectral efficiency and service fairness. Results show that the proposed MISO-NOMA scheme which is based on MMSE hybrid precoder and the proposed user scheduling and power allocation strategy under proportional fairness metric can outperform various conventional MISO schemes. Furthermore, our proposed dynamic number of users per cluster scheme outperforms the fixed scheme and can be considered as an upper bound in several aspects, including spectral efficiency and fairness.

GPF+: A Novel Ultrafast GPU-Based Proportional Fair Scheduler for 5G NR

5G NR is designed to operate over a broad range of frequency bands and support new applications with ultra-low latency requirements. To support its extremely diverse operating conditions, multiple OFDM numerologies have been defined in the 5G standards. Under these numerologies, it is necessary to perform scheduling with a time resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 100 \mathrm {\mu s}$ </tex-math></inline-formula> . This requirement poses a new challenge beyond existing LTE and cannot be satisfied by any existing LTE schedulers. In this paper, we present the design of GPF+, which is a GPU-based proportional fair (PF) scheduler with timing performance under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 \mathrm {\mu s}$ </tex-math></inline-formula> . GPF+ is an improvement over our GPF in Huang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2018). The key ideas include decomposing the original scheduling problem into a large number of small and independent sub-problems and selecting a subset of sub-problems from the most promising search space to fit into a GPU. By implementing GPF+ on an off-the-shelf NVIDIA Tesla V100 GPU, we show that GPF+ is able to achieve near-optimal PF performance with timing performance under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 \mathrm {\mu s}$ </tex-math></inline-formula> . GPF+ represents the fastest GPU-based PF scheduler that can meet the new real-time requirement in 5G NR.

QoS Aware Uplink Scheduling for M2M Communication in LTE/LTE-A Network: A Game Theoretic Approach

M2M communication in the LTE network is gaining attention with a growing number of connected devices and adaptation of new emerging technologies. Usually, the traffic generated by M2M devices is heterogeneous in terms of packet size, intensity, strict delay, and throughput requirements. The M2M traffic generally flows in the uplink direction. It imposes several challenges in designing scheduling for uplink in the LTE network. The research community proposed various solutions regarding the QoS handling in M2M communications. Allocating the resources based on the QoS of machine type communications (MTCs) has a challenge in deciding on devices&#x2019; priority. This work applies game theory to control devices&#x2019; strategic behavior claiming a false priority. In this paper, a Quality-of-Service aware uplink packet scheduling scheme is proposed using a combinatorial game. The packet scheduling problem is modeled as an Auction game that handles the scheduling in both Time-Domain and Frequency-Domain. The packet scheduler uses the QoS requirement as an allocation metric, and a power control mechanism is applied to control the devices&#x2019; strategic behavior. Simulation is performed in MATLAB R2019b. The performance of the proposed schemes is evaluated in terms of the total system utility in QoS satisfaction against standard schedulers like Round Robin and Proportional Fair schedulers and some other schedulers proposed in the literature.

Using channel predictions for improved proportional-fair utility for vehicular users

As the channel conditions experienced by vehicular users in cellular networks vary as they move, we investigate to what extent the quality of channel allocation could be improved by exploiting predictions on future data rates in non-stationary environments. Assuming mean future rates can be computed from Signal-to-Noise Ratio (SNR) maps, we propose an algorithm which predicts future throughputs over a short-term horizon at regular time intervals, and then uses this extra-knowledge for improved online channel allocation. The prediction of future throughputs is obtained by solving a relaxed version of the problem using a projected gradient algorithm. When the transmit powers of the base stations can be varied over time, a straightforward extension of our algorithm can be used for the joint optimization of channel allocation and transmit power control under average and maximum power constraints. Using event-driven simulations, we compare the performance of the proposed algorithms against those of other channel allocation algorithms, including the Proportional Fair (PF) scheduler, which is known to be optimal in stationary environments, and the (PF)2S scheduler, which was devised for mobiles nodes in non-stationary environments. The simulated scenarios, which cover the cases with and without power control, include scenarios with multiple base stations and are based on realistic mobility traces generated using the road traffic simulator SUMO. Simulation results show that the proposed algorithms outperform the other algorithms and that exploiting the knowledge of future radio conditions allows a significantly better channel allocation.

Power Allocation in NOMA-CR for 5G Enabled IoT Networks

In the power domain, non-orthogonal multiple access (NOMA) supports multiple users on the same time-frequency resources, assigns different transmission powers to different users, and differentiates users by user channel gains. Multi-user signals are superimposed and transmitted in the power domain at the transmitting end by actively implementing controllable interference information, and multi-user detection algorithms, such as successive interference cancellation (SIC) is performed at the receiving end to demodulate the necessary user signals. In contrast to the orthogonal transmission method, the non-orthogonal method can achieve higher spectrum utilization. However, it will increase the receiver complexity. With the development of microelectronics technology, chip processing capabilities continue to increase, laying the foundation for the practical application of non-orthogonal transmission technology. In NOMA, different users are differentiated by different power levels. Therefore, the power allocation has a considerable impact on the NOMA system performance. To address this issue, the idea of splitting power into two portions, intra-subbands and inter-subbands, is proposed in this study as a useful algorithm. Then, such optimization problems are solved using proportional fair scheduling and water-filling algorithms. Finally, the error propagation was modeled and analyzed for the residual interference. The proposed technique effectively increased the system throughput and performance under various operating settings according to simulation findings. A comparison is performed with existing algorithms for performance evaluation.

Is Proportional Fair Scheduling Suitable for Age-Sensitive Traffic?

Is Proportional Fair Scheduling Suitable for Age-Sensitive Traffic?

Is Proportional Fair Scheduling Suitable for Age-Sensitive Traffic?

Is Proportional Fair Scheduling Suitable for Age-Sensitive Traffic?

Distributed UAV-BSs Trajectory Optimization for User-Level Fair Communication Service With Multi-Agent Deep Reinforcement Learning

Unmanned Aerial Vehicles (UAVs) have attacted much attention in the field of wireless communication due to its agility and altitude. UAVs can be used as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground devices (GDs) in various scenarios, such as emergency communication and traffic offloading in hotspots. However, due to the limited communication ranges and high prices of commercial UAV-BSs, covering a target area all the time with sufficient UAVs is quite challenging, especially under dynamic environment. We need to design the trajectory of the UAV-BSs to optimize system performance. Most existing works focus on the energy-efficient coverage and throughput maximization but ignore the fairness of communication service, especially the fairness at user-level. Besides, reinforcement learning is suitable for solving decision problems in dynamic environments. However, most existing works use centralized deep reinforcement learning (DRL) approaches. Due to the scalability and low time complexity, a distributed DRL approach is more suitable for multiple UAV-BSs communication system in dynamic environment. Unlike previous works, we characterize the fairness at user-level based on proportional fairness scheduling and formulate a weighted-throughput maximization problem via designing UAV-BSs’ trajectory. Then we model the dynamic deploymentproblem of UAV-BSs as a Markov game and propose a multi-agent deep reinforcement learning-based distributed UAV-BSs control approach named MAUC. MAUC approach adopts the framework of centralized training with distributed execution. Simulation results show that the MAUC can improve fairness of communication service by sacrificing a small amount of throughput.

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,

A proportional fair scheduling strategy using multiobjective gradient‐based African buffalo optimization algorithm for effective resource allocation and interference minimization

SummaryThe increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine‐tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi‐Objective Gradient‐based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device‐to‐device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC‐FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities.

Parameter aware utility proportional fairness scheduling technique in a communication network

Conventionally, real-time (inelastic) users and non-real-time (elastic) users are scheduled separately. Further, some users may be left without getting any radio resources. In real scenario, such as in high network traffic, less network coverage, resource allocation for all types of users are needed. In the proposed technique, a utility function is defined for each elastic traffic and inelastic traffic type users. Then, it maximises the total utilities by network utility maximisation approach with considering scheduling parameters such as queue length, head of the line delay and experienced channel condition. Rate allocation based on queue awareness improves fairness among elastic traffic users, delay awareness improves QoS performance of inelastic traffic users and channel condition awareness improves throughput of all users. Further, the network utility maximisation approach provides minimum throughput to all users.