84 research outputs found
Interference Alignment via Message-Passing
We introduce an iterative solution to the problem of interference alignment
(IA) over MIMO channels based on a message-passing formulation. We propose a
parameterization of the messages that enables the computation of IA precoders
by a min-sum algorithm over continuous variable spaces -- under this
parameterization, suitable approximations of the messages can be computed in
closed-form. We show that the iterative leakage minimization algorithm of
Cadambe et al. is a special case of our message-passing algorithm, obtained for
a particular schedule. Finally, we show that the proposed algorithm compares
favorably to iterative leakage minimization in terms of convergence speed, and
discuss a distributed implementation.Comment: Submitted to the IEEE International Conference on Communications
(ICC) 201
Energy and morphology of martensite-twinned martensite interface in CuAlNi shape memory alloy: a phase-field study
Needle-like twins are observed experimentally within the transition layer at
the martensite-twinned martensite interface. We utilize a phase-field approach
to investigate this microstructure. Our goal is to simulate the morphology of
the transition layer and to perform a detailed analysis to characterize its
interfacial and elastic micro-strain energy. To illustrate the micromechanical
framework developed for that purpose, sample computations are carried out for a
CuAlNi shape memory alloy undergoing the cubic-to-orthorhombic martensitic
transformation. A particular focus of the study is on size-dependent morphology
through examining the impact of twin spacing. Additionally, our results reveal
that certain twin volume fractions lead to the emergence of twin branching, as
a way to minimize the total free energy stored in the microstructure
Relay X Channels without Channel State Information at the Transmit Sides: Degrees of Freedom
This paper focuses on the two-user relay-assisted X channel with no channel state information (CSI) available at the transmitter side. Two relaying modes, namely half-duplex decode-and-forward (DF) and cognitive relays, are considered and the degrees of freedom (DoF) are characterized. It is shown that assisted by a half-duplex DF relay that is equipped with 2M antennas, the X channel with two M-antenna users has 4M/3 DoF, which is achievable through interference alignment (IA). Furthermore, it is shown that in this channel, an M-antenna cognitive relay (with non-causal access to information streams) provides 2M DoF using interference cancellation (IC) technique. In this setting, IC outperforms interference alignment in the cognitive relay mode, since the latter achieves 4M/3 DoF
A data-driven decision support framework for DEA target setting:an explainable AI approach
The intention of target setting for Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA) is to perform better than their peers or reach a reference efficiency level. However, most of the time, the logic behind the target setting is based on mathematical models, which are not achievable in practice. Besides, these models are based on decreasing/increasing inputs/outputs that might not be feasible based on DMU's potential in the real world. We propose a data-driven decision support framework to set actionable and feasible targets based on vital inputs-outputs for target setting. To do so, DMUs are classified in their corresponding Efficiency Frontier (EF) levels based on multiple EFs approach and a machine learning classifier. Then, the vital inputs-outputs are determined using an Explainable Artificial Intelligence (XAI) method. Finally, a Multi-Objective Counterfactual Explanation is developed based on DEA (MOCE-DEA) to lead DMU in reaching the reference EF by adjusting actionable and feasible inputs-outputs. We studied Iranian hospitals to evaluate the proposed framework and presented two cases to demonstrate its mechanism. The results show that the performance of the DMUs is improved to reach the reference EF for studied cases. Then, a validation was conducted with the primal DEA model to show the robust improvement of DMUs after adjusting their original value based on the generated solutions by the proposed framework. It demonstrates that the adjusted values can also improve DMUs' performance in the primal DEA model.</p
A data-driven decision support framework for DEA target setting:an explainable AI approach
The intention of target setting for Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA) is to perform better than their peers or reach a reference efficiency level. However, most of the time, the logic behind the target setting is based on mathematical models, which are not achievable in practice. Besides, these models are based on decreasing/increasing inputs/outputs that might not be feasible based on DMU's potential in the real world. We propose a data-driven decision support framework to set actionable and feasible targets based on vital inputs-outputs for target setting. To do so, DMUs are classified in their corresponding Efficiency Frontier (EF) levels based on multiple EFs approach and a machine learning classifier. Then, the vital inputs-outputs are determined using an Explainable Artificial Intelligence (XAI) method. Finally, a Multi-Objective Counterfactual Explanation is developed based on DEA (MOCE-DEA) to lead DMU in reaching the reference EF by adjusting actionable and feasible inputs-outputs. We studied Iranian hospitals to evaluate the proposed framework and presented two cases to demonstrate its mechanism. The results show that the performance of the DMUs is improved to reach the reference EF for studied cases. Then, a validation was conducted with the primal DEA model to show the robust improvement of DMUs after adjusting their original value based on the generated solutions by the proposed framework. It demonstrates that the adjusted values can also improve DMUs' performance in the primal DEA model.</p
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