Fairness Constraints Research Articles

Resource Allocation in Cooperative OFDMA Systems with Fairness Constraint

This letter investigates a subchannel and power allocation (SPA) algorithm which maximizes the throughput of a user under the constraints of total transmit power and fair subchannel occupation among relay nodes. The proposed algorithm reduces computational complexity from exponential to linear in the number of subchannels at the expense of a small performance loss.

On automatic verification of self-stabilizing population protocols

The population protocol model has emerged as an elegant computation paradigm for describing mobile ad hoc networks, consisting of a number of mobile nodes that interact with each other to carry out a computation. The interactions of nodes are subject to a fairness constraint. One essential property of population protocols is that all nodes must eventually converge to the correct output value (or configuration). In this paper, we aim to automatically verify self-stabilizing population protocols for leader election and token circulation in the Spin model checker. We report our verification results and discuss the issue of modeling strong fairness constraints in Spin.

Efficient fair algorithms for message communication

A computer network serves distributed applications by communicating messages between their remote ends. Many such applications desire minimal delay for their messages. Beside this efficiency objective, allocation of the network capacity is also subject to the fairness constraint of not shutting off communication for any individual message. Processor Sharing (PS) is a de facto standard of fairness but provides significantly higher average delay than Shortest Remaining Processing Time (SRPT), which is an optimally efficient but unfair algorithm. In this paper, we explore efficient fair algorithms for message communication where fairness means that no message is delivered later than under PS. First, we introduce a slack system to characterize fair algorithms completely and develop efficient fair algorithms called Pessimistic Fair Sojourn Protocol (PFSP), Optimistic Fair Sojourn Protocol (OFSP), and Shortest Fair Sojourn (SFS). Then, we prove that a fair online algorithm does not assure minimal average delay attainable with fairness. Our analysis also reveals lower bounds on worst-case inefficiency of fair algorithms. We conduct extensive simulations for various distributions of message sizes and arrival times. During either temporary overload or steady-state operation, SFS and other newly proposed fair algorithms support SRPT-like efficiency and consistently provide much smaller average delay than PS.

A Game-Theoretic Framework for Medium Access Control

In this paper, we generalize the random access game model, and show that it provides a general game- theoretic framework for designing contention based medium access control. We extend the random access game model to the network with multiple contention measure signals, study the design of random access games, and analyze different distributed algorithms achieving their equilibria. As examples, a series of utility functions is proposed for games achieving the maximum throughput in a network of homogeneous nodes. In a network with n traffic classes, an N-signal game model is proposed which achieves the maximum throughput under the fairness constraint among different traffic classes. In addition, the convergence of different dynamic algorithms such as best response, gradient play and Jacobi play under propagation delay and estimation error is established. Simulation results show that game model based protocols can achieve superior performance over the standard IEEE 802.11 DCF, and comparable performance as existing protocols with the best performance in literature.

Fairness as a constraint

Purpose – The purpose of this short paper is to provide an alternative method to explain the reason why the outcome of the ultimatum game based on the well‐known self‐interest model is not feasible in real life.Design/methodology/approach – Contrary to traditional models which treat fairness as a variable of the utility function, the paper takes fairness as a constraint to the utility function. The problem is solved through the standard incentive theory and bargaining theory.Findings – It was found that fairness constraints play an important role in determining the eventual outcome of the ultimatum game.Research limitations/implications – The paper's approach may set up a useful framework for incorporating fairness into economics, and bridge the gaps between the fairness approach and the standard incentive theory and bargaining theory.Practical implications – When tackling the problem of distribution based on fairness, the paper shows that the cultural background and special situation should be taken seri...

Practical Radio Link Resource Allocation for Fair QoS-Provision on OFDMA Downlink with Partial Channel-State Information

We address the problem of resource allocation on the downlink of an OFDMA single-cell system under fairness constraints with limited channel state information (CSI). Target QoS corresponds to a minimum user data rate, a target bit-error rate and a maximum BER-outage probability. The channel model includes path-loss, shadowing, and fading. The only available CSI is the channel average gain of each user. This partial CSI defines a shadowed path-loss that yields a modified user distribution. Resource allocation is based on the shadowed user distribution that we characterize analytically. Thus, under the target QoS, we provide the optimal resource allocation that maximizes the user rate. Compared to full-CSI-based allocation schemes, our solution offers a significant complexity and feedback reduction. Finally, the performance of our method is compared to other existing methods and the robustness of its outage performance to CSI errors is shown.

A Link Adaptation Scheme for Efficient Transmission of H.264 Scalable Video Over Multirate WLANs

In this paper, we propose a cross-layer optimization scheme for delivery of scalable video over multirate wireless networks, in particular the popular 802.11 based wireless local area network (WLAN). The 802.11 based networks use a link adaptation mechanism in the physical layer (PHY) to maintain the reliability of transmission under varying channel conditions. When channel condition worsens, the reliability is maintained by employing more robust modulation and coding schemes, at the cost of reduced PHY bit rate. The reduced bit rate will result in lower available throughput for applications. For scalable video streaming applications, the conventional solution to this problem is to reduce the video bit rate by dropping the higher enhancement layers of the scalable video. We show in this article that the video quality can be improved, if the link adaptation scheme uses more intelligent reliability criteria and adjusts the PHY parameters used for delivering each video layer, according to the relative importance of that layer. Our scheme achieves better video quality without increasing the traffic load of the WLAN. For this purpose we present temporal fairness constraints and formulate an optimization problem for assigning different PHY modes to different layers of scalable video; the solution to this problem provides a set of PHY configuration parameters that achieve the highest possible video quality while meeting the admission control constraints in the network. Performance evaluations demonstrate that our method outperforms the existing mechanisms.

Optimal Routing for Wireless Mesh Networks With Dynamic Traffic Demand

Wireless mesh networks have attracted increasing attention and deployment as a high-performance and low-cost solution to last-mile broad-band Internet access. Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best routing solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for wireless mesh network routing under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By studying the traces collected at wireless access points, we first present a traffic estimation method which predicts future traffic demand based on its historical data using time-series analysis. This method provides not only the mean value of the future traffic demand estimation but also its statistical distribution. We further investigate the optimal routing strategies for wireless mesh network which take these two forms of traffic demand estimations as inputs. The goal is to balance the traffic load so that minimum congestion will be incurred. This routing objective could be transformed into the throughput optimization problem where the throughput of aggregated flows is maximized subject to fairness constraints that are weighted by the traffic demands. Based on linear programming, we present two routing algorithms which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate the traffic dynamics in mesh network routing.

Multiclass Utility-Based Scheduling for UWB Networks

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, the optimal scheduling problem for ultrawideband (UWB) networks is formulated as a utility maximization problem, considering heterogeneous traffic characteristics and the fairness constraint, which is an NP-hard problem. The utility function, defined as the user satisfaction level with respect to the allocated bandwidth, takes the link distance as the input parameter that may be noisy due to ranging errors. We solve the stochastic optimization problem by a metaheuristic, called the exclusive-region-based global search algorithm, that can avoid trapping into local optima with reasonable efficiency, complexity, and convergence speed. The concept of exclusive region for concurrent UWB communications is employed to improve algorithm efficiency. Extensive simulations demonstrate that, by appropriately scheduling concurrent transmissions, the total utilities of each traffic class can be significantly improved. In addition, the proposed scheduling algorithm can support heterogeneous applications and guarantee the intraclass and interclass fairness among competing flows. </para>

TCP Flow Level Performance Evaluation on Error Rate Aware Scheduling Algorithms in Evolved UTRA and UTRAN Networks

We present a TCP flow level performance evaluation on error rate aware scheduling algorithms in Evolved UTRA and UTRAN networks. With the introduction of the error rate, which is the probability of transmission failure under a given wireless condition and the instantaneous transmission rate, the transmission efficiency can be improved without sacrificing the balance between system performance and user fairness. The performance comparison with and without error rate awareness is carried out dependant on various TCP traffic models, user channel conditions, schedulers with different fairness constraints, and automatic repeat request (ARQ) types. The results indicate that error rate awareness can make the resource allocation more reasonable and effectively improve the system and individual performance, especially for poor channel condition users.

On scheduling and clustering in hierarchical TH-PPM UWB wireless ad hoc networks

Ultra wideband (UWB) systems are considered as the key wireless infrastructure platforms for efficient short-range communications. In particular, the UWB based mobile computing systems are envisioned to be attractive solutions to various ad hoc networking applications. However, due to UWB's unique physical characteristics, the traditional resource management schemes for ad hoc networks cannot be applied to UWB based systems directly. In this paper, we consider the bandwidth scheduling problem in a UWB based hierarchical wireless ad hoc network, which is typically used in an enterprise-scale mobile computing environment. Based on the mathematical analysis and the computer simulations, it is demonstrated that our proposed scheduling scheme exhibits close-to-optimal performance governed by the proportional fairness (PF) constraint. Moreover, a novel self-organized clustering method is designed to improve the system throughput while meeting the PF constraint. Simulation results suggest that the proposed clustering method is effective under various system configurations.

Resource Allocation Scheme in MIMO-OFDMA System for User's Different Data Throughput Requirements

In the subcarrier and power allocation schemes in Multi-Input Multi-Output and Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) systems, only equal fairness among users has been considered and no scheme for proportional data rate fairness has been considered. In this paper, a subcarrier, bit and power allocation scheme is proposed to maximize the total throughput under the constraints of total power and proportional data rate fairness among users. In the proposed scheme, joint subchannel allocation and adaptive bit loading is firstly performed by using singular value decomposition (SVD) of channel matrix under the constraint of users' data throughput requirements, and then adaptive power loading is applied. Simulation results show that effective performance of the system has been improved as well as each throughput is proportionally distributed among users in MIMO-OFDMA systems.

Multicast scheduling with resource fairness constraints

Integration of multicast and unicast data in future radio access networks will be necessary in order to improve the resource efficiency and provide new services. Such integration requires new and efficient resource sharing mechanisms. These mechanisms need to be optimized to provide the best possible trade-off between resource efficiency and fairness. In this article, we consider a case where streaming multicast users are multiplexed together with elastic unicast users on a common time-slotted channel. We derive a system model to study the performance of various resource allocations strategies under proportional and resource fairness constraints. Fairness is directly defined in terms of the users' utilities rather than of the throughputs they are assigned to. We also describe an extension of the well-known unicast proportional fair scheduler to the multicast scenario. Through extensive simulations we demonstrate the performance of this scheduler for various traffic loads and multicast group sizes.

Optimal bandwidth assignment for packet rings

The network throughput is an important performance criteria for the packet ring networks. Since maximizing the network throughput can lead to severe bias in bandwidth allocation among all flows, fairness should be imposed to prevent bandwidth starvation. The challenge here, therefore, is the joint optimization of the network throughput and fairness. In this paper, we present the optimal bandwidth assignment scheme to decompose this optimization problem into two tasks, one for finding fair bandwidth assignment and the other for finding the optimal routing. The network throughput is maximized under the fairness constraints when these tasks are performed iteratively.

Multi-sender diversity and its application in the uplink of ieee 802.11 WLANs

Multi-sender diversity is an opportunity to exploit channel fluctuation in the situation where multiple senders compete for the same wireless resources. To utilize this diversity in the uplink of WLANs, we propose a novel distributed opportunistic scheduling scheme. Under the constraint of access fairness, the proposed scheme maximizes the system throughput. Analysis and simulation results show that the throughput gain increases with the number of stations.

A two-phase utility maximization framework for wireless medium access control

In multi-hop wireless networks, the optimal medium access control (MAC) design is challenging, partially due to the time-varying nature of the PHY-layer communication channels and the network topology. In this paper, we take a utility maximization approach to study fair MAC design towards QoS provisioning. To this end, we first identify two key challenges of wireless access control, namely the topology dependency and the channel dependency, therein. Based on the observation that the topology change and channel variation occur on different time scales, we decompose the utility maximization to two phases: a "global" optimization phase addresses the topology dependency, and arbitrates fair channel access across the links by adapting the persistence probability to achieve long-term fairness, and a "local" optimization phase deals with the channel dependence, and determines the transmission duration based on local channel conditions while maintaining short-term fairness. Observing that the MAC throughput depends on the realizations of channel contention in random access networks, we use stochastic approximation to investigate in depth the MAC design with the adaptive persistence mechanism in the global phase. Using Lyapunov's Stability Theorems and LaSalle's Invariance Theorem, we establish the stability of the proposed algorithm for the global phase and analyze the fairness under (omegaoarr, kappa)-fair utility functions. Our findings reveal that under the large network assumption, there exists a single equilibrium point for the proposed (omegaoarr, kappa)-fair MAC algorithm provided that kappa > 1. We also present the solution to the local optimization phase under general fairness constraints.

Opportunistic Scheduling for OFDM Systems with Fairness Constraints

We consider the problem of downlink scheduling for multiuser orthogonal frequency-division multiplexing (OFDM) systems. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. Previous work in this area has focused on single-channel systems. Multiuser OFDM allows multiple users to transmit simultaneously over multiple channels. In this paper, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three QoS/fairness constraints formultiuserOFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. Our scheduler decides not only which time slot, but also which subcarrier to allocate to each user. Implementing these optimal policies involves solving a maximal bipartite matching problem at each scheduling time. To solve this problem efficiently, we apply a modified Hungarian algorithm and a simple suboptimal algorithm. Numerical results demonstrate that our schemes achieve significant improvement in system performance compared with nonopportunistic schemes.

An industrial application of symbolic model checking

Model checking techniques are recognized to provide reliable and copious results. Instead of examining a few cases only – as it is done in testing – model checking includes the whole state space in mathematical proofs of correctness. Yet, this completeness is seen as a drawback as the state explosion problem is hard to handle. In our industrial case study, we apply automated model checking techniques to an innovative elevator system, the TWIN by ThyssenKrupp. By means of abstraction and nondeterminism, we cope with runtime behaviour and achieve to efficiently prove our specification’s validity. The elevator’s safety requirements are exhaustively expressed in temporal logic along with real-world and algorithmic prerequisites, consistency properties, and fairness constraints. Beyond verifying system safety for an actual installation, our case study demonstrates the rewarding applicability of model checking at an industrial scale.

The Moral Limits of Criminalizing Remote Harms

I draw on accessorial liability jurisprudence in an attempt to outline the moral limits of criminalizing people for merely influencing the criminal choices of others. A person's conduct is a "remote harm" when it is harmless "but for" the fact that it encourages another independent party to commit a harmful criminal act (a primary harm). For example, the "broken windows" thesis holds that minor incivilities (such as passive begging) are a precursor to more serious crime. Passive begging allegedly sends a signal to criminals that the broken windows area is unpoliced and is an easy target for crime. The beggars are criminalized to deter independent parties from committing crimes in the broken windows area. In this paper, I object to this kind of criminalization because it contravenes the requirements of fairness and individual responsibility, because it aims to punish people for the inadvertent consequences of their actions. I argue that a person should only be held responsible for another's criminal harm when she is normatively involved in it. What is needed are normative reasons for stating from an ex ante perspective that it will be fair to hold X morally responsible for S when it causes Y to do harm N. If this requirement is satisfied then there will be a prima facie case for criminalization. I argue that a person is normatively involved in another's crime when she knowingly assists or intentionally encourages that crime. In addition, a person can become normatively involved in another's criminal harm by underwriting it. I also assert that the fairness constraint should only be overridden as a matter of necessity to prevent harm of an extraordinarily grave kind and that the broken windows harm does not satisfy this requirement.

Optimal and Approximate Mobility-Assisted Opportunistic Scheduling in Cellular Networks

This paper considers the problem of scheduling multiple users in the downlink of a time-slotted cellular data network. For such a network, opportunistic scheduling algorithms improve system performance by exploiting time variations of the radio channel. We present novel optimal and approximate opportunistic scheduling algorithms that combine channel fluctuation and user mobility information in their decision rules. The algorithms modify the opportunistic scheduling framework of Liu et al., (1993) with dynamic constraints for fairness. These fairness constraints adapt according to the user mobility. The adaptation of constraints in the proposed algorithms implicitly results in giving priority to the users that are in the most favorable locations. The optimal algorithm is an offline algorithm that precomputes constraint values according to a known mobility model. The approximate algorithm is an online algorithm that relies on the future prediction of the user mobility locations in time. We show that the use of mobility information in opportunistic scheduling increases channel capacity. We also provide analytical bounds on the performance of the approximate algorithm using the fundamental inequality of Dyer et al., (1986) for linear programs. Simulation results on high data rate (HDR) illustrate the usefulness of the proposed schemes for elastic traffic and macrocell structures