Proportional Fairness Research Articles

Energy-Efficient OFDMA-Based Two-Way Relay

Energy-efficient orthogonal frequency division multiple access (OFDMA) aims to relieve the booming energy consumption in wireless communications networks and has recently received lots of attention. Meanwhile, two-way relay has been extensively studied and proves to be more spectral-efficient than direct transmission and one-way relay in many scenarios. In this paper, we study energy-efficient resource allocation for OFDMA-based two-way relay, maximizing the aggregated energy efficiency (EE) utility while provisioning proportional fairness in EE among different terminal pairs. The energy-efficient joint power and subchannel allocation and active subchannel selection problem is mixed-integer combinatorial and further demonstrates to be nonconvex. To approach the performance limit, we first find an upper-bound solution relying on continuous relaxation and the Lagrange dual method. To reduce the computational complexity, we separate power allocation and subchannel assignment. To this regard, we exploit the hidden concavity and the pseudoconcavity in the subproblems for any fixed subchannel assignment and propose an EE-oriented sequential subchannel assignment policy. Besides, we discover the sufficient condition for early termination of the sequential subchannel assignment without losing the EE optimality. Simulation results demonstrate that the proposed energy-efficient OFDMA-based two-way relay can achieve much larger EE utility while provisioning proportional fairness in EE among different terminal pairs compared to its spectral-efficient counterpart.

코드북 기반 SIR 향상 빔 형성 기법

We propose a beam selection algorithm that improves inter sector SIR using a code-book of a circular array antenna in multi-sector wireless mesh network environments. The proposed method improves SIR using a combination of fed back code-book and guarantees QoS of all nodes. Computer simulation exhibits the proposed scheme demonstrates 4.42dB higher SIR than that of the conventional code-book method, QoS with proportional fair is improved by 1.70dB and fact that all nodes are satisfied Qos is also shown.

Energy-efficient Resource Allocation Based on Proportional Fairness in OFDM Multi-user Systems

Energy-efficient Resource Allocation Based on Proportional Fairness in OFDM Multi-user Systems

Robust Rate Adaptation and Proportional Fair Scheduling With Imperfect CSI

In wireless fading channels, multi-user scheduling has the potential to boost the spectral efficiency by exploiting diversity gains. In this regard, proportional fair (PF) scheduling provides a solution for increasing the users' quality of experience by finding a balance between system throughput maximization and user fairness. For this purpose, precise instantaneous channel state information (CSI) needs to be available at the transmitter side to perform rate adaptation and scheduling. However, in practical setups, CSI is impaired by, e.g., channel estimation errors, quantization and feedback delays. Especially in centralized cloud based communication systems, where main parts of the lower layer processing is shifted to a central entity, high backhaul latency can cause substantial CSI imperfections, resulting in significant performance degradations. In this work, robust rate adaptation as well as robust PF scheduling are presented, which account for CSI impairments. The proposed rate adaptation solution guarantees a fixed target outage probability, which is of interest for delay critical and data intensive applications, such as, video conference systems. In addition to CSI imperfections, the proposed scheduler is able to account for delayed decoding acknowledgements from the receiver.

QoS-Aware Power-Efficient Scheduler for LTE Uplink

The continuous increase of mobile data traffic has created a substantial demand for high data rate transmission over mobile networks. However, mobile devices are provided with small batteries that can be drained quickly by high data rate transmission. Motivated by the fundamental requirement of extending the battery utilization time per charge of mobile devices, this work presents two power-efficient schedulers for mixed streaming services in LTE uplink systems. Our objective is to minimize the total transmission power for all users. The proposed schedulers are subject to rate, delay, contiguous allocation, and maximum transmission power constraints. We first consider an optimal scheduler that uses binary integer programming (BIP). Then, we propose an iterative scheduler that performs a low-complexity greedy algorithm which solves the BIP problem. We compare the performance of the proposed schedulers to the state-of-the-art schedulers such as the energy-aware resource allocation (EARA) [1] and the proportional fair (PF) [2] in terms of rate, delay, average transmission power and complexity. Simulation results show that the proposed schedulers offer a remarkable transmission power reduction as compared to the PF and the EARA schedulers, and satisfy the QoS requirements.

Fairness Considerations for Full Duplex Multi-User MIMO Systems

We consider a cellular system with a full-duplex (FD) base station (BS) serving multiple uplink (UL) and downlink (DL) users simultaneously, where all the nodes are equipped with multiple antennas. The self-interference at the BS and the co-channel interference (CCI) caused by the UL users on the DL users are both taken into account. We address the proportional fairness (PF) issue of this system, which is important for networks with asymmetric topology and/or asymmetric traffic demands. The sum of the logarithm of the achievable rate of UL and DL users is maximized subject to power constraints at the BS and UL users. We develop a gradient projection (GP) method to solve this non-convex optimization problem, and demonstrate that the proposed algorithm provides a good balance between maximizing the sum-rate and maintaining fairness among users.

Optimal downlink power allocation in cellular networks

In this paper, we introduce a novel approach for power allocation in cellular networks. In our model, we use sigmoidal-like utility functions to represent different users’ modulation schemes. Each utility function is a representation of the probability of successfully transmitted packets per unit of power consumed by a user, when using a certain modulation scheme. We consider power allocation with utility proportional fairness policy, where the fairness among users is in utility percentage i.e. percentage of successfully transmitted packets of the corresponding modulation scheme. We formulate our power allocation optimization problem as a product of utilities of all users and prove that it is convex and therefore the optimal solution is tractable. We present a distributed algorithm to allocate base station powers optimally with priority given to users running lower modulation schemes while ensuring non-zero power allocation to users running higher modulation schemes. Our algorithm prevents fluctuation in the power allocation process and is capable of traffic and modulation dependent pricing policy. This can be used to flatten traffic and decrease the service price for users. We also compare our results with a benchmark algorithm and show that our algorithm performs better in allocating powers fairly to all users without dropping any user in order to maximize performance.

A PDF/PMF Function Based Frequency Allocation Scheme in Fractional Frequency Reuse Aided OFDMA Networks

A novel frequency allocation scheme based on probability density/mass function functions (PDF/PMF) of random variables is proposed in this paper, which is capable of adjusting the balance between long term fairness and performance in fractional frequency reuse aided orthogonal frequency division multiple access based networks. This scheme divides the allocation process into two steps, including the intra-group allocation and the inter-group allocation. By adjusting the values of a set of parameters, such as rate parameters of PDF/PMF functions, and the ratio of radii of the center region and the whole cell, the presented scheme achieves a flexible adjustment between user-fairness and cell-throughput. Simulation results indicate that compared with the proportional fairness allocation and the equal rate allocation, the proposed scheme has significant performance advantage when the values of parameters are appropriate, and also has continuous controllability between "absolutely fair" and "absolutely unfair".

Scheduling Based on Maximum PF Selection with Contiguity Constraint for SC-FDMA in LTE Uplink

Single-carrier frequency division multiple access (SC-FDMA), which is similar to multi carrier modulation type with orthogonal frequency domain multiple access (OFDMA), has been used as long term evolution (LTE) uplink access method due to its low PAPR and high UE power efficiency. SC-FDMA, however, has a constraint that subcarriers must be consecutively allocated to each user for every time slot due to the single carrier feature of the access type. This paper proposes an LTE uplink scheduling algorithm satisfying the contiguity constraint of resource allocation and ensuring high cell (system) throughput and fairness with low complexity. The proposed scheme, named MSCC, preferentially considers allocation to RBs based on the highest Proportional Fair scheduling metric in each physical resource block (RB) with the contiguity constraint. As a result of simulation analysis, MSCC has better fairness and cell throughput than the previous schemes (i.e., RME, IRME algorithm) by 10% and 17% at most, respectively. We also analyze cell edge user throughput (the gathering with the cell users who have 5% lowest throughput) and PAPR. In the appendix, complexity analysis shows that the time complexity of the MSCC is better than the previous schemes.

Network Utility Maximization With Asymmetric User Satisfaction

This letter revisits a network utility maximization considering asymmetric user satisfaction (AUS), i.e., a user is more sensitive to a quality of service (QoS) decrement than an increment. Reflecting AUS, we propose a novel fairness criterion called asymmetrically proportional fairness (APF). To achieve APF resource allocation, we devise a utility function and design a bandwidth allocation algorithm. Analytic and numerical results verify that our algorithm achieves APF equilibrium.

Clustering‐based low‐complexity resource allocation in two‐tier femtocell networks with QoS provisioning

SummaryIn this paper, we study the resource allocation problem of the uplink transmission with delay quality‐of‐service constraints in two‐tier femtocell networks. Particularly, to provide statistical delay guarantees, the effective capacity is employed as the network performance measure instead of the conventional Shannon capacity. To make the problem computationally efficient and numerically tractable, we decompose the problem into three subproblems, namely, cluster configuration subproblem, intra‐cluster subchannel allocation subproblem and inter‐cluster power control subproblem. Firstly, we develop a low‐complexity heuristic semi‐dynamic clustering scheme, where the delay of the channel state information feedback via backhaul is considered. We model such system in the framework of networked partial observation Markov decision process and derive a strategy to reduce the search range for the best cluster configuration. Then, for a given cluster configuration, the cluster heads deal with subchannel allocation and power control within each cluster. We propose a subchannel allocation scheme with proportional fairness. Thereafter, the inter‐cluster power control subproblem is modeled as a set of exact potential games, and a channel quality related pricing mechanism is presented to mitigate inter‐cluster interference. The existence and uniqueness of Nash equilibriums for the proposed game are investigated, and an effective decentralized algorithm with guaranteed convergence is designed. Simulation results demonstrate that the proposed algorithms not only have much lower computational complexity but also perform close to the exhaustive search solutions and other existing schemes. Copyright © 2015 John Wiley & Sons, Ltd.

Energy-efficient resource allocation for OFDMA relay systems with imperfect CSIT

In this paper, we propose an energy-efficient resource allocation scheme for the downlink of decode-and-forward relay-assisted orthogonal frequency division multiple access systems. The resource allocation is designed based on imperfect channel state information at the transmitter. We jointly optimize the power allocation, data rate allocation, and subcarrier allocation to maximize the system energy efficiency (EE). We formulate the EE problem as a probabilistic mixed non-convex optimization problem, in which the individual power constraints and the outage probability requirements are considered. An iterative algorithm based on bisection method is given to transform the optimization problem into a standard concave form. Also, we consider a proportional fairness design for the EE maximization problem. Simulation results illustrate the effectiveness of the proposed algorithm.

Multi-Resource Fairness

Designing efficient and fair algorithms for sharing multiple resources between heterogeneous demands is becoming increasingly important. Applications include compute clusters shared by multi-task jobs and routers equipped with middleboxes shared by flows of different types. We show that the currently preferred objective of Dominant Resource Fairness (DRF) has a significantly less favorable efficiency-fairness tradeoff than alternatives like Proportional Fairness and our proposal, Bottleneck Max Fairness. We propose practical algorithms to realize these sharing objectives and evaluate their performance under a stochastic demand model. It is shown, in particular, that the strategyproofness property that motivated the choice of DRF for an assumed fixed set of jobs or flows, is largely irrelevant when demand is dynamic.

Distributed Proportional Fair Load Balancing in Heterogenous Systems

We consider the problem of distributed load balancing in heterogenous parallel server systems, where the service rate achieved by a user at a server depends on both the user and the server. Such heterogeneity typically arises in wireless networks (e.g., servers may represent frequency bands, and the service rate of a user varies across bands). We assume that each server equally shares in time its capacity among users allocated to it. Users initially attach to an arbitrary server, but at random instants of time, they probe the load at a new server and migrate there if this improves their service rate. The dynamics under this distributed load balancing scheme, referred to as Random Local Search (RLS), may be interpreted as those generated by strategic players updating their strategy in a load balancing game. In closed systems, where the user population is fixed, we show that this game has pure Nash Equilibriums (NEs), and that these equilibriums get close to a Proportionally Fair (PF) allocation of users to servers when the user population grows large. We provide an anytime upper bound of the gap between the allocation under RLS and the PF allocation. In open systems, where users randomly enter the system and leave upon service completion, we establish that the RLS algorithm stabilizes the system whenever this it at all possible under centralized load balancing schemes, i.e., it is throughput-optimal. The proof of this result relies on a novel Lyapounov analysis that captures the dynamics due to both users' migration and their arrivals and departures. To our knowledge, the RLS algorithm constitutes the first fully distributed and throughput-optimal load balancing scheme in heterogenous parallel server systems. We extend our analysis to various scenarios, e.g. to cases where users can be simultaneously served by several servers. Finally we illustrate through numerical experiments the efficiency of the RLS algorithm.

Adaptive CSI and feedback estimation in LTE and beyond: a Gaussian process regression approach

The constant increase in wireless handheld devices and the prospect of billions of connected machines has compelled the research community to investigate different technologies which are able to deliver high data rates, lower latency and better reliability and quality of experience to mobile users. One of the problems, usually overlooked by the research community, is that more connected devices require proportionally more signalling overhead. Particularly, acquiring users’ channel state information is necessary in order for the base station to assign frequency resources. Estimating this channel information with full resolution in frequency and in time is generally impossible, and thus, methods have to be implemented in order to reduce the overhead. In this paper, we propose a channel quality estimation method based on the concept of Gaussian process regression to predict users’ channel states for varying user mobility profiles. Furthermore, we present a dual-control technique to determine which is the most appropriate prediction time for each user in order to keep the packet loss rate below a pre-defined threshold. The proposed method makes use of active learning and the exploration-exploitation paradigm, which allow the controller to choose autonomously the next sampling point in time so that the exploration of the control space is limited while still reaching an optimal performance. Extensive simulation results, carried out in an LTE-A simulator, show that the proposed channel prediction method is able to provide consistent gain, in terms of packet loss rate, for users with low and average mobility, while its efficacy is reduced for high-velocity users. The proposed dual-control technique is then applied, and its impact on the users’ packet loss is analysed in a multicell network with proportional fair and maximum throughput scheduling mechanisms. Remarkably, it is shown that the presented approach allows for a reduction of the overall channel quality signalling by over 90 % while keeping the packet loss below 5 % with maximum throughput schedulers, as well as signalling reduction of 60 % with proportional fair scheduling.

Robust Power Control for Cognitive Radio Networks with Proportional Rate Fairness

This paper studies the power control problem in cognitive radio networks where a primary user and multiple secondary users (SUs) coexist. Imperfect channel state information is considered. The objective is to maximize the SUs' sum rate while guaranteeing the proportional rate fairness among SUs. The problem under consideration is non-convex. By doing a transformation, it is equivalently changed to a second-order cone programming problem, which can be efficiently solved by existing standard methods. Simulations have been done to verify the network performance under different channel uncertainty conditions.

Adaptive Mechanism for Distributed Opportunistic Scheduling

Distributed opportunistic scheduling (DOS) techniques have been recently proposed for improving the throughput performance of wireless networks. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits in case the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. A key challenge with DOS is to design a distributed algorithm that optimally adjusts the access probability and the threshold of each station. To address this challenge, in this paper, we first compute the configuration of these two parameters that jointly optimizes throughput performance in terms of proportional fairness. Then, we propose an adaptive algorithm based on control theory that converges to the desired point of operation. Finally, we conduct a control theoretic analysis of the algorithm to find a setting for its parameters that provides a good tradeoff between stability and speed of convergence. Simulation results validate the design of our mechanism and confirm its advantages over previous works.

QoS Constrained Optimization of Cell Association and Resource Allocation for Load Balancing in Downlink Heterogeneous Cellular Networks

This paper considers the optimal cell association and resource allocation for load balancing in a heterogeneous cellular network subject to user’s quality-of-service (QoS) constraints. We adopt the proportional fairness (PF) utility maximization formulation which also accommodates the QoS constraints in terms of minimum rate requirements. With equal resource allocation this joint optimization problem is either infeasible or requires relaxation that yields a solution which is difficult to implement. Nevertheless, we show that this joint optimization problem can be effectively solved without any priori assumption on resource allocation and yields a cell association scheme which enforces single BS association for each user. We re-formulated the joint optimization problem as a network-wide resource allocation problem with cardinality constraints. A reweighted heuristic l1-norm regularization method is used to obtain a sparse solution to the re-formulated problem. The cell association scheme is then derived from the sparsity pattern of the solution, which guarantees a single BS association for each user. Compared with the previously proposed method based on equal resource allocation, the proposed framework results in a feasible cell association scheme and yields a robust solution on resource allocation that satisfies the QoS constraints. Our simulations illustrate the impact of user’s minimum rate requirements on cell association and demonstrate that the proposed approach achieves load balancing and enforces single BS association for users.

SC-FDMA-based resource allocation and power control scheme for D2D communication using LTE-A uplink resource

Abstract Device-to-device (D2D) communication-enabled cellular networks allow cellular devices to directly communicate with each other without any evolved NodeB (eNB). D2D communication aims to improve the spectral efficiency and increases the overall system capacity. For future mobile networks, intelligent radio resource allocation and power control schemes are required to accommodate the increasing number of cellular devices and their growing demand of data traffic. In this paper, a combined resource allocation and power control scheme for D2D communication is proposed. In the proposed scheme, D2D communication reuses the uplink (UL) resources of conventional cellular user equipments (CUEs); therefore, we have adopted single-carrier frequency division multiple access (SC-FDMA) as UL transmission scheme. The proposed scheme uses fractional frequency reuse (FFR)-based architecture to efficiently allocate the resources and mitigate the interference between CUEs and D2D user equipments (DUEs). In order to guarantee the user fairness, the proposed scheme uses the well-known proportional fair (PF) scheduling algorithm for resource allocation. We have also proposed an intelligent power control scheme which provides equal opportunity to both CUEs and DUEs to achieve a certain minimum signal-to-interference and noise ratio (SINR) value. The performance evaluation results show that the proposed scheme significantly improves the overall cell capacity and achieves low peak-to-average power ratio (PAPR).

Optimal Association in Wireless Mesh Networks

The wireless multihop backhaul is a unique feature of wireless mesh networks (WMNs), which necessitates redesign of association control algorithms. In this paper, we formulate and propose approximation algorithms for the problem of optimal joint association and bandwidth allocation in WMNs, considering max–min fairness (MM) and proportional fairness (PF) objectives. We first relax the integral association constraint and get an optimal fractional association solution. Then, we propose two rounding algorithms, namely, largest fraction rounding and bipartite graph rounding, to get an integral solution and analyze their theoretical approximation ratios. Finally, we propose two approximation ratio improvement algorithms so that the improved approximation ratio can more accurately reflect the true performance gap between the produced solution and the optimal one. Our simulation results show that the proposed algorithms achieve performance that is close to the optimal and outperform popular heuristic algorithms. We also compare the performance of PF and MM in WMNs in terms of network throughput and fairness in user bandwidth. Finally, we compare the performances of the proposed rounding algorithms and show that the approximation ratio can be reduced to 1–2 by the proposed ratio improvement algorithms. Therefore, our proposed algorithm is able to achieve nearly optimal association control, as well as bandwidth allocation, considering MM or PF, with small approximation ratios, in WMNs.