Adaptive Proportional Fairness Research Articles

Adaptive proportional fair scheduling with global-fairness

In practical communication systems, there are always multiple subscribers competing for limited resources, such as time and frequency, hence effective user scheduling is essential to multi-user communications in achieving good system throughput and fairness performance. The conventional proportional fair (PF) scheduling achieves fairness at the cost of system spectral efficiency (SE) loss. Such fairness is of long-term feature, i.e., all the users’ scheduling probabilities become approximately the same only when the observation time is long enough. Therefore, PF cannot guarantee the fairness for subscribers who enter the system temporarily or stay in the system for a short period of time. In addition, delay requirement of real-time-service users can hardly be met with conventional PF. In order to remedy these deficiencies, we propose adaptive proportional fair (APF) scheduling algorithms. In each time slot, the infrastructure node, e.g., base station, dynamically adjusts the forgetting factor based on the variance of all the subscribers’ scheduling priorities, so that users’ scheduling weights can be adaptively updated. Our in-depth simulation results show that compared to conventional PF, APF can not only achieve both long-term and short-term fairness which we refer to global-fairness, but also obtain high system SE. Moreover, users’ delay performance can be obviously improved.

Spectrum Resource and Power Allocation With Adaptive Proportional Fair User Pairing for NOMA Systems

In this paper, we investigate the spectrum resource and power allocation problem for the tradeoff between maximizing the sum rate and minimum rate requirements of users in non-orthogonal multiple access (NOMA) system. First, we formulate the NOMA techniques, basic principles, and double-objective optimization (DOO) problem. Then, the non-convexity of the DOO problem is converted into a single-objective optimization (SOO) problem by power discretization method. Global optimal search (GOS) algorithm is applied to solve the user-subchannel matching and power allocation problem. Due to its high complexity and unfairness among users, it is only suitable for determining the upper bound of users throughput performance. Finally, yet importantly, a spectrum resource and power allocation algorithm with adaptive proportional fair (APF) user pairing is proposed to convert the original optimization problem into user pairing, sub-channel, and power allocation. The users paired on the sub-channel are determined by the scheduling priority which is based on the equivalent channel gain. The BS dynamically adjusts the forgetting factor in the APF algorithm based on the variance of all the users' scheduling priorities so as to influence the update of users' scheduling weights. The power allocation stage proposes three power allocation schemes to ensure the users' minimum data rate requirements under the condition that effectively guarantees the correct execution of successive interference cancellation (SIC). The simulation results demonstrate that it can not only approach the throughput performance compared with the global optimal search and the classical water-filling (WF) power allocation using matching theory but also can improve the fairness of the users.

Performance Evaluation of Scheduling Algorithm for Wimax Networks

 Abstract—This paper proposes a new scheduling algorithm for IEEE802.16 Broadband wireless Metropolitan Area Networks in TDD mode. The proposed algorithm focuses on an efficient mechanism to serve high priority traffic in congested networks. A detailed simulation study is carried out for the proposed scheduling algorithm and its performance has been compared with some known algorithms such as Proportional Fairness (PF), Adaptive Proportional Fairness (APF) and Round Robin (RR). For performance evaluation and comparison with existing algorithms, we used OPNET 14.5 modeler simulator. The results show that the proposed algorithm is capable of dealing with different users' requirements under congestion conditions.

Adaptive Subcarrier Allocation for OFDM Relay System

This paper proposes two scheduling algorithms for subcarrier allocation of OFDM relay system, enhanced two-hop adaptive proportional fairness (E-THAPF) and user-number-dynamic two-hop adaptive proportional fairness (UND-THAPF), aiming to improve the performance of the traditional two-hop proportional fairness (THPF) scheduling algorithm. By modifying the priority of the two hops in THPF in an adaptive fashion, E-THAPF and UND- THAPF can optimize the subcarrier allocation dynamically despite of the subcarriers fluctuation, and thus can keep the system flexible and stable. Moreover, the factor α in the dynamic index of UND-THAPF is made adaptive to the change of the number of users. Simulation results show that the proposed algorithms achieve a better compromise between system fairness and spectral efficiency. In particular, while E-THAPF is designed to be adaptive to different sub-channel conditions, UND-THAPF performs even better for taking number of users into account in the optimization. 

Adaptive proportional fairness resource allocation for OFDM-based cognitive radio networks

In this paper, we study the resource allocation problem in multiuser Orthogonal Frequency Division Multiplexing (OFDM)-based cognitive radio networks. The interference introduced to Primary Users (...

On the Fairness of the Multiuser Eigenmode Transmission System

The Multiuser Eigenmode Transmission (MET) has generated significant interests in literature due to its optimal performance in linear precoding systems. The MET can simultaneously transmit several spatial multiplexing eigenmodes to multiple users which significantly enhance the system performance. The maximum number of users that can be served simultaneously is limited due to the constraints on the number antennas, and thus an appropriate user selection is critical to the MET system. Various algorithms have been developed in previous works such as the enumerative search algorithm. However, the high complexities of these algorithms impede their applications in practice. In this paper, motivated by the necessity of an efficient and effective user selection algorithm, a low complexity recursive user selection algorithm is proposed for the MET system. In addition, the fairness of the MET system is improved by using the combination of the proposed user selection algorithm and the adaptive Proportional Fair Scheduling (PFS) algorithm. Extensive simulations are implemented to verify the efficiency and effectiveness of the proposed algorithm.

Adaptive scheduling for MIMO wireless networks: cross-layer approach and application to HSDPA

In this paper, we consider the scheduling problem in multiple-input multiple-output (MIMO) wireless networks. The main important characteristic of an optimal scheduler is to maximize throughput while servicing users in a fair manner. Herein, we formulate MIMO scheduling as a generalized assignment problem (GAP) and propose a general solution for the GAP, namely, a cross-layer MIMO scheduler (CMS), which uses a novel adaptive proportional fairness (APF) mapping approach in conjunction with a new fast transmit antenna selection (FTAS) technique, to determine the set of users to transmit to and the antenna over which the data associated to each user should be transmitted. The proposed scheduler is applied for packet transmission in high-speed downlink packet access (HSDPA), taking advantage of the use of adaptive modulation and coding while coping with the constraints on the maximum number of simultaneous codes a user equipment can support, the limited uplink signalling, and the absence of fast power control. Numerical results show that the proposed CMS provides up to 70% increase in total throughput compared to other scheduling schemes