28,867 research outputs found
Practical Resource Allocation Algorithms for QoS in OFDMA-based Wireless Systems
In this work we propose an efficient resource allocation algorithm for OFDMA
based wireless systems supporting heterogeneous traffic. The proposed algorithm
provides proportionally fairness to data users and short term rate guarantees
to real-time users. Based on the QoS requirements, buffer occupancy and channel
conditions, we propose a scheme for rate requirement determination for delay
constrained sessions. Then we formulate and solve the proportional fair rate
allocation problem subject to those rate requirements and power/bandwidth
constraints. Simulations results show that the proposed algorithm provides
significant improvement with respect to the benchmark algorithm.Comment: To be presented at 2nd IEEE International Broadband Wireless Access
Workshop. Las Vegas, Nevada USA Jan 12 200
Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink
This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index
Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport
Increasingly, discrimination by algorithms is perceived as a societal and
legal problem. As a response, a number of criteria for implementing algorithmic
fairness in machine learning have been developed in the literature. This paper
proposes the Continuous Fairness Algorithm (CFA) which enables a
continuous interpolation between different fairness definitions. More
specifically, we make three main contributions to the existing literature.
First, our approach allows the decision maker to continuously vary between
specific concepts of individual and group fairness. As a consequence, the
algorithm enables the decision maker to adopt intermediate ``worldviews'' on
the degree of discrimination encoded in algorithmic processes, adding nuance to
the extreme cases of ``we're all equal'' (WAE) and ``what you see is what you
get'' (WYSIWYG) proposed so far in the literature. Second, we use optimal
transport theory, and specifically the concept of the barycenter, to maximize
decision maker utility under the chosen fairness constraints. Third, the
algorithm is able to handle cases of intersectionality, i.e., of
multi-dimensional discrimination of certain groups on grounds of several
criteria. We discuss three main examples (credit applications; college
admissions; insurance contracts) and map out the legal and policy implications
of our approach. The explicit formalization of the trade-off between individual
and group fairness allows this post-processing approach to be tailored to
different situational contexts in which one or the other fairness criterion may
take precedence. Finally, we evaluate our model experimentally.Comment: Vastly extended new version, now including computational experiment
IST Austria Technical Report
Boolean notions of correctness are formalized by preorders on systems. Quantitative measures of correctness can be formalized by real-valued distance functions between systems, where the distance between implementation and specification provides a measure of “fit” or “desirability.” We extend the simulation preorder to the quantitative setting, by making each player of a simulation game pay a certain price for her choices. We use the resulting games with quantitative objectives to define three different simulation distances. The correctness distance measures how much the specification must be changed in order to be satisfied by the implementation. The coverage distance measures how much the im- plementation restricts the degrees of freedom offered by the specification. The robustness distance measures how much a system can deviate from the implementation description without violating the specification. We consider these distances for safety as well as liveness specifications. The distances can be computed in polynomial time for safety specifications, and for liveness specifications given by weak fairness constraints. We show that the distance functions satisfy the triangle inequality, that the distance between two systems does not increase under parallel composition with a third system, and that the distance between two systems can be bounded from above and below by distances between abstractions of the two systems. These properties suggest that our simulation distances provide an appropriate basis for a quantitative theory of discrete systems. We also demonstrate how the robustness distance can be used to measure how many transmission errors are tolerated by error correcting codes
Fairness Evaluation in Cooperative Hybrid Cellular Systems
Many method has been applied previously to improve the fairness of a wireless communication system. In this paper, we propose using hybrid schemes, where more than one transmission scheme are used in one system, to achieve this objective. These schemes consist of cooperative transmission schemes, maximal ratio transmission and interference alignment, and non-cooperative schemes, orthogonal and non-orthogonal schemes used alongside and in combinations in the same system to improve the fairness. We provide different weight calculation methods to vary the output of the fairness problem. We show the solution of the radio resource allocation problem for the transmission schemes used. Finally, simulation results is provided to show fairness achieved, in terms of Jain's fairness index, by applying the hybrid schemes proposed and the different weight calculation methods at different inter-site distances
Fairness in education: The Italian university before and after the reform
In 2001 the Italian tertiary education system embarked in a broad process of reform. The main novelty brought by the reform was a reduction of the length of study to get a first level degree together with the introduction of a 2-years, second level, master degree. This paper aims at studying the effects of the reform in terms of fairness in educational opportunity. In order to do so we first define fairness criteria following a well-developed responsibility sensitive egalitarian literature, we then discuss existing inequality of opportunity measures consistent with these criteria, we show their relationship, and we adapt them to the educational framework. We finally employ this set of measures to show the evolution of fairness in the access to university in Italy before and after the reform.Equality of opportunity, higher education.
LNCS
Boolean notions of correctness are formalized by preorders on systems. Quantitative measures of correctness can be formalized by real-valued distance functions between systems, where the distance between implementation and specification provides a measure of “fit” or “desirability.” We extend the simulation preorder to the quantitative setting, by making each player of a simulation game pay a certain price for her choices. We use the resulting games with quantitative objectives to define three different simulation distances. The correctness distance measures how much the specification must be changed in order to be satisfied by the implementation. The coverage distance measures how much the implementation restricts the degrees of freedom offered by the specification. The robustness distance measures how much a system can deviate from the implementation description without violating the specification. We consider these distances for safety as well as liveness specifications. The distances can be computed in polynomial time for safety specifications, and for liveness specifications given by weak fairness constraints. We show that the distance functions satisfy the triangle inequality, that the distance between two systems does not increase under parallel composition with a third system, and that the distance between two systems can be bounded from above and below by distances between abstractions of the two systems. These properties suggest that our simulation distances provide an appropriate basis for a quantitative theory of discrete systems. We also demonstrate how the robustness distance can be used to measure how many transmission errors are tolerated by error correcting codes
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