Proportional Fairness Research Articles

Distributive justice in network communications

Communication networks often have limited resources to serve all users and flows, and therefore different network resource assignment schemes have been deployed that satisfy fairness notions such as max-min fairness or proportional fairness. The justification of why one notion suits over other must be given due analysis in designing and administrating of communication networks. We explore the development of such fairness notions and resource assignment policies from philosophical and economics roots and how they translate into network communications and its layered protocol architecture. We find that resource assignment policies are not commutative, which impacts communications traffic and its bidirectional nature. We observe the effect of prioritisation in assigned resource amounts under different notions of the α-fairness spectrum and when the proportion of prioritised participants is varied. After an exploratory discourse, we formulate steps for designing and administrating resource allocation schemes for network communications that takes distributive justice into consideration.

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.

Efficient and Fair Network Selection for Integrated Cellular and Drone-Cell Networks

This paper is concerned with the network selection for heterogeneous networks. Although many network selection approaches have been developed for heterogeneous networks, few of them explore the network selection problem for integrated cellular and drone-cell networks. Furthermore, most of the existing designs may be unsuitable for the integrated cellular and drone-cell networks owing to the high dynamic drone-cell-user association and the relatively limited drone-cell network capacity. In this paper, we investigate a network selection problem for the integrated cellular and drone-cell networks with a goal of maximizing a proportional fairness function of time average utilities across users under coarse correlated equilibrium constraints and minimum time average utility constraints. To mitigate this challenging problem, we first convert it into a non-linear integer programming (NLIP) problem based on our derived theoretical results. Next, we propose a repeated-stochastic-game-based efficient and fair network selection (RSG-EF) algorithm to alleviate the NLIP problem by leveraging a linear approximation mechanism. Simulation results show that the RSG-EF algorithm can achieve the highest total utility and a high level of fairness across users compared with three benchmark algorithms.

Extending Proportional Fair Scheduling to Buffer-Aided Relay Access Networks

We investigate downlink scheduling in two-hop relay access networks with a central controller. To address the time-varying nature of wireless channels, a channel-aware scheduling scheme is proposed to exploit both spatial and multihop diversity. Given instantaneous channel state information, scheduling decisions are made at every time slot based on the designed priority indexes. The proposed scheme improves throughput and ensures fair transmission among all the active mobile stations in the system. In addition, with optimized scaling factors, the proposed scheme is shown to be close to Pareto-optimal.

Adaptive Distributed Association in Time-Variant Millimeter Wave Networks

The underutilized millimeter-wave (mm-wave) band is a promising candidate to enable extremely high data rate communications in future wireless networks. However, the special characteristics of the mm-wave systems such as high vulnerability to obstacles (due to high penetration loss) and to mobility (due to directional communications) demand a careful design of the association between the clients and access points (APs). This challenge can be addressed by distributed association techniques that gracefully adapt to wireless channel variations and client mobilities. We formulated the association problem as a mixed-integer optimization aiming to maximize the network throughput with proportional fairness guarantees. This optimization problem is solved first by a distributed dual decomposition algorithm, and then by a novel distributed auction algorithm where the clients act asynchronously to achieve near-to-optimal association between the clients and APs. The latter algorithm has a faster convergence with a negligible drop in the resulting network throughput. A distinguishing novel feature of the proposed algorithms is that the resulting optimal association does not have to be re-computed every time the network changes (e.g., due to mobility). Instead, the algorithms continuously adapt to the network variations and are thus very efficient. We discuss the implementation of the proposed algorithms on top of existing communication standards. The numerical analysis verifies the ability of the proposed algorithms to optimize the association and to maintain optimality in the time-variant environments of the mm-wave networks.

Fairness and Load Balancing in SDWN Using Handoff-Delay-Based Association Control and Load Monitoring

Traffic loads in any 802.11 WLAN are distributed unevenly. This imbalance implies that some access points (APs) suffer from traffic congestions, while others are underutilized. The unbalanced load distributions cause annoying packet delay and throughput degradation which is unacceptable in current and future networks. A load-balancing algorithm should solve two challenges. The first is to accurately identify the APs’ loads to timely find traffic imbalances. And the second is to associate clients with APs to achieve optimal proportional fairness intelligently. Network metrics such as throughput, delay, jitter, and client amount cannot be used individually to accurately identify APs’ loads, because of the complexities of wireless communications. Which metrics to use and how to combine those network metrics to represent AP load are controversial. For intelligent association control, handoff delay (time to move a station from an AP to another) may last for 6 seconds. If the algorithm designers do not consider this delay in their optimization processes, unnecessary re-associations generated in their algorithm will offset the optimization profits. In this paper, we propose novel learning-based methods to monitor the network load to discover real-time load unbalances. We also model the load balancing problem as a utility maximization problem in which costs caused by handoff delay are considered. Then we utilize discretized linear programming theory and general assignment problem theory to solve it. We also compute the approximation ratio of our algorithm. We implement the whole load balancing system and evaluate the performances which show that our method outperforms a state-of-art algorithm in terms of throughput by up to12.7%, and it outperforms the received signal strength indicator (RSSI) based method by up to 28.13%.

Adaptive resource allocation in FSO/RF multiuser system with proportional fairness for UAV application

The combination of free space optic (FSO) and unmanned aerial vehicle (UAV) can be a promising solution to last-mile problem because of FSO high bandwidth and flexibility of UAV. However, FSO is vulnerable to the atmospheric turbulence and transmitter configuration is limited on UAV. Therefore, in this paper, we investigate an adaptive resource allocation strategy to provide relative fair and high transmission capacity for users. In the proposed network model, the downlink scenario is considered and ground station communicates with UAV and its local users by FSO and RF links, respectively. According to the channel conditions and rate requirements from users, channel and power assignments should satisfy the capacity demand in reasonable. To this end, we decompose the original channel and power allocation problem with high computational complexity into low-complexity subproblems, corresponding to channel allocation in RF transmission phase and channel matching and power allocation in FSO transmission phase. Then, a heuristic efficient resource assignment algorithm is proposed to achieve the optimal capacity distribution for users. Numerical results show that the proposed method can asymptotically achieve optimal throughput. Furthermore, system throughput is higher at low transmitting power requirement than those of other existing methods.

Research of the allocation method the time-frequency resource of the LTE downlink using RAT 1

A research on the previously proposed method for allocation of time-frequency resource of the LTE downlink has been conducted. The method is based on the solution of the optimization problem on the allocation of resource blocks in the downlink LTE technology, which corresponds to the RAT 1 scheme. The advantage of the method is that the allocation of resource blocks of the downlink LTE is carried out in a balanced manner taking into account the signal-interference environment, type and level of bandwidth requirements. The results of the comparative analysis confirmed the effectiveness of this method. A number of numerical examples have shown that using the method allows to increase the bandwidth of the LTE network from 16-20% to 33-47% compared to the known solutions presented by the Max C/I Ratio, Round Robin and Proportional Fair algorithms. The article offers recommendations for the practical implementation of the proposed solution and presents a variant of the multi-level functional architecture for the block of allocation of the time-frequency resource of the downlink LTE network.

Security-Aware Proportional Fairness Resource Allocation for Cognitive Heterogeneous Networks

Cognitive radio and heterogeneous wireless networks are important candidate techniques for the fifth generation (5G) communication systems. On the other hand, propagation properties of radio channels in a physical layer are exploited to design efficient secure transmission schemes for 5G wireless networks. In this work, a security-aware joint power and subchannel allocation problem based on the inter-network cooperation is investigated at cognitive heterogeneous networks via imperfect spectral sensing. The security-aware joint power and subchannel allocation is subject to constraints in available subchannels, proportional fairness secrecy transmission rates among secondary mobile terminals (MTs), total interference power threshold, and total power for each secondary MT. In order to determine the impact of imperfect spectral sensing to resource allocation, we first represent the interference power at primary base station in terms of the misdetection probability and false alarm probability. Then, the security-aware power and subchannel allocation problem with inter-network cooperation is formulated as a biconvex optimization problem, and an optimal security-aware power and subchannel allocation algorithm is proposed utilizing the dual decomposition method. Compared to the heuristic power and subchannel allocation algorithms, numerical simulation results show that the proposed algorithms improve the total secrecy throughput and guarantee the proportional fairness among different secondary MTs.

On the Fairness of Wi-Fi and LTE-LAA Coexistence

With both small-cell LTE and 802.11 networks now available as alternatives for deployment in unlicensed bands at 5 GHz, investigation into their coexistence is a topic of great interest. ETSI Rel. 14 has standardized LTE licensed assisted access (LAA) that seeks to make LTE more coexistence friendly with Wi-Fi by incorporating listen before talk. However, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fairness</i> of Wi-Fi and LTE-LAA sharing is a topic that has not been adequately explored. In this paper, we first investigate the 3GPP definition of fair coexistence via new analytical models. By tuning the LTE-LAA parameters, we exemplify scenarios when the 3GPP notion of fairness is achieved and conversely, when not achieved. The formal notions of access and proportional fairness is then considered for these scenarios to compare and contrast with the 3GPP definition.

Optimization a Scheduling Algorithm of CA in LTE ADV

Long-Term Advancement Progressed (LTE-ADV) is the advancement of the long-term evolution, which created via 3GPP. LTE-ADV aims to offer a transmission bandwidth of (100) MHz by using Carrier Aggregation (CA) to aggregate LTE-ADV carriers. To increase the data capacity of the system and resource allocation converts a very good tool. LTE-Advanced multiple Component Carriers (CCs) becomes a difficult optimization problem. In the paper proposes a new scheduling algorithm and compares with a different scheduling traditional algorithms that are proportional fair and round robin in the CA, in order to find the best scheduler that provides high-quality throughput and improves fairness. It i also evaluates mapping model types are Mutual Information Effective SINR Mapping (MIESM) and Exponential Effective SINR Mapping (EESM). The results show that the throughput in the proposed algorithm with MIESM outperforms from others mapping and scheduling.

A fairness resource allocation algorithm for coverage and capacity optimization in wireless self-organized network

To achieve the higher resource efficiency, Coverage and Capacity Optimization (CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network (SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness (PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness (CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin (RR), proportional fairness (PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

Secrecy Performance of Multi-User MISO VLC Broadcast Channels With Confidential Messages

We study, in this paper, the secrecy performance of a multi-user (MU) multiple-input single-output visible light communication broadcast channel with confidential messages. The underlying system model comprises $K +1$ nodes: a transmitter (Alice) equipped with $N$ fixtures of LEDs and $K$ spatially dispersed users, each equipped with a single photo-diode. The MU channel is modeled as deterministic and real-valued and assumed to be perfectly known to Alice, since all users are assumed to be active. We consider typical secrecy performance measures, namely, the max–min fairness, the harmonic mean, the proportional fairness, and the weighted fairness. For each performance measure, we derive an achievable secrecy rate for the system as a function of the precoding matrix. As such, we propose algorithms that yield the best precoding matrix for the derived secrecy rates, where we analyze their convergence and computational complexity. In contrast, what has been considered in the literature so far is zero-forcing (ZF) precoding, which is suboptimal. We present several numerical examples through which we demonstrate the substantial improvements in the secrecy performance achieved by the proposed techniques compared with those achieved by the conventional ZF. However, this comes at a slight increase in the complexity of the proposed techniques compared with ZF.

QoE-Driven Optimized Load Balancing Design for Hybrid LiFi and WiFi Networks

The hybrid light fidelity (LiFi) and wireless fidelity (WiFi) networks are regarded as a promising solution for future indoor wireless communication. In this letter, we investigate the traffic load balancing (LB) issue for this hybrid networks. In our design, considering that user terminals (UTs) may have different data rate demands, we utilize the complementary advantages of the wide bandwidth of the LiFi network and the wide coverage area of the WiFi network to serve the UTs. With considerations of the access point heterogeneity and UTs’ quality of experience (QoE) demands for data rate, we then formulate the LB issue as a mixed-integer non-linear programming problem with the aim of maximizing the system throughput under proportional fairness constraints, and propose a distributed optimal algorithm and a simplified suboptimal sequential algorithm to solve it. Simulation results demonstrate that using the presented QoE-driving LB scheme, more UTs are served with higher QoE while maintaining high system throughput and proportional fairness.

Hierarchical beamforming: Resource allocation, fairness and flow level performance

We consider hierarchical beamforming in wireless networks. For a given population of flows, we propose computationally efficient algorithms for fair rate allocation including proportional fairness and max–min fairness. We next propose closed-form formulas for flow level performance, for both elastic (with either proportional fairness and max–min fairness) and streaming traffic. We further assess the performance of hierarchical beamforming using numerical experiments. Since the proposed solutions have low complexity compared to conventional beamforming, our work suggests that hierarchical beamforming is a promising candidate for the implementation of beamforming in future cellular networks.

User scheduling and power allocation for nonfull buffer traffic in NOMA downlink systems

SummaryNonorthogonal multiple access (NOMA) is one of the key technologies for 5G, where the system capacity can be increased by allowing simultaneous transmission of multiple users at the same radio resource. The most of the proportional fairness (PF)–based resource allocation studies for NOMA systems assumes full buffer traffic model, while the traffic in real‐life scenarios is generally nonfull buffer. In this paper, we propose User Demand–Based Proportional Fairness (UDB‐PF) and Proportional User Satisfaction Fairness (PUSF) algorithms for user scheduling and power allocation in NOMA downlink systems when traffic demands of the users are limited and time‐varying. UDB‐PF extends the PF‐based scheduling by allocating optimum power levels towards satisfying the traffic demand constraints of user pair in each resource block. The objective of PUSF is to maximize the network‐wide user satisfaction by allocating sufficient frequency and power resources according to traffic demands of the users. In both cases, user groups are selected first to simultaneously transmit their signals at the same frequency resource, while the optimal transmission power level is assigned to each user to optimize the underlying objective function. In addition, the genetic algorithm (GA) approach is employed for user group selection to reduce the computational complexity. When the user traffic rate requirements change rapidly over time, UDB‐PF yields better sum rate (throughput) while PUSF provides better network‐wide user satisfaction results compared with the PF‐based user scheduling. We also observed that the GA‐based user group selection significantly reduced the computational load while achieving the comparable results of the exhaustive search.

A Resource Allocation Mechanism for Cloud Radio Access Network Based on Cell Differentiation and Integration Concept

A Self-Organising Cloud Radio Access Network (C-RAN) is proposed, which dynamically adapt to varying capacity demands. The Base Band Units and Remote Radio Heads are scaled semi-statically based on the concept of cell differentiation and integration (CDI) while a dynamic load balancing is formulated as an integer-based optimisation problem with constraints. A Discrete Particle Swarm Optimisation (DPSO) is developed as an Evolutionary Algorithm to solve load balancing optimisation problem. The performance of DPSO is tested based on two problem scenarios and compared to an Exhaustive Search (ES) algorithm. The DPSO deliver optimum performance for small-scale networks and near optimum performance for large-scale networks. The DPSO has less complexity and is much faster than the ES algorithm. Computational results demonstrate significant throughput improvement in a CDI-enabled C-RAN compared to a fixed C-RAN, i.e., an average throughput increase of 45.53 and 42.102 percent, and a decrease of 23.149 and 20.903 percent in the average blocked users is experienced for Proportional Fair (PF) and Round Robin (RR) schedulers, respectively. A power model is proposed to estimate the overall power consumption of C-RAN. A decrease of $\approx 16\%$ is estimated in a CDI-enabled C-RAN when compared to a fixed C-RAN, both serving the same geographical area.

Mobility-Aware Resource Allocation in VLC Networks Using T-Step Look-Ahead Policy

Visible light communication (VLC) uses huge license-free spectral bandwidth of visible light for high-speed wireless communication. Since each VLC access point covers a small area, handovers of mobile users are inevitable. In order to deal with these handovers, developing fast and effective resource allocation algorithms is a challenge. This paper introduces the problem of mobility-aware optimization of resources in VLC networks using the T-step look-ahead policy and employing the notion of handover efficiency. It is shown that the handover efficiency can correlate the overall performance of the network with future actions based on the mobility of users. Due to the stationary nature of indoor optical wireless channels, future channel state information (CSI) can be predicted by anticipating the future locations of users. A resource allocation algorithm is thus proposed, which uses CSI to dynamically allocate network resources to users. To solve this mathematically intractable problem, a novel relaxation method is proposed, which proves to be a useful tool to develop efficient algorithms for network optimization problems with a proportional fairness utility function. The resulting algorithm is extremely faster than the previous method and awareness of mobility enhances the overall performance of the network in terms of rate and fairness utility function.

Game Theoretic Approach for Network Access Control in Heterogeneous Networks

The latest mobile communication devices are usually equipped with multiple network interfaces with the capability of accessing multiple types of wireless networks. Since each network has limited capacity, a network may experience congestion if too many mobile devices access this network. To avoid congestion, an intelligent distributed network selection scheme is needed for each mobile device. In this paper, we propose a game theoretic approach to the problem of network selection. Specifically, we formulate the problem as a repeated stochastic game with a goal of maximizing total utility while achieving high proportional fairness among all users in the service area. To solve this problem, we develop a Lyapunov optimization algorithm to obtain the optimal action for each user from the game manager perspective. Simulation results show that our proposed approach can achieve much higher utility than existing schemes while maintaining good fairness index.

SNR- Versus Rate-Based Proportional Fair Scheduling in Rayleigh Fading Channels

The initial definition of the Proportional Fair scheduling was based on a transmission rate criterion. However, a Signal-to-Noise Ratio (SNR) based definition is commonly used by researchers due to its mathematical tractability, based on the assumption that the performance of both definitions is equivalent. In this work, we derive closed-form expressions for the probability density function of the system output SNR (i.e. received SNR based on the selected scheduling algorithm) and the per-user SNR over Rayleigh fading channels when a rate-based criterion is used. Additionally, we analyze the common assumption of equivalence between both criteria by comparing their per-user performance. Our analysis reveals that their performance are only similar when users experience high values of average SNR. However, significant differences in terms of user fairness have been found in the lower SNR regime.