Coverage probability in wireless networks with determinantal scheduling

• URL: http://arxiv.org/abs/2006.05038v1
• Date: Tue, 9 Jun 2020 04:05:50 GMT
• Title: Coverage probability in wireless networks with determinantal scheduling
• Authors: Bartek B{\l}aszczyszyn, Antoine Brochard, H. Paul Keeler
• Abstract summary: We propose a new class of algorithms for randomly scheduling network transmissions. We show that, similarly to Aloha, they are also subject to elegant analysis of the coverage probabilities and transmission attempts.
• Score: 1.4502611532302039
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new class of algorithms for randomly scheduling network transmissions. The idea is to use (discrete) determinantal point processes (subsets) to randomly assign medium access to various {\em repulsive} subsets of potential transmitters. This approach can be seen as a natural extension of (spatial) Aloha, which schedules transmissions independently. Under a general path loss model and Rayleigh fading, we show that, similarly to Aloha, they are also subject to elegant analysis of the coverage probabilities and transmission attempts (also known as local delay). This is mainly due to the explicit, determinantal form of the conditional (Palm) distribution and closed-form expressions for the Laplace functional of determinantal processes. Interestingly, the derived performance characteristics of the network are amenable to various optimizations of the scheduling parameters, which are determinantal kernels, allowing the use of techniques developed for statistical learning with determinantal processes. Well-established sampling algorithms for determinantal processes can be used to cope with implementation issues, which is is beyond the scope of this paper, but it creates paths for further research.

Related papers

• Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of Multipliers [143.6249073384419]
  In this paper, we propose a distributed sampling scheme based on the alternating direction method of multipliers. We provide both theoretical guarantees of our algorithm's convergence and experimental evidence of its superiority to the state-of-the-art. In simulation, we deploy our algorithm on linear and logistic regression tasks and illustrate its fast convergence compared to existing gradient-based methods.
  arXiv  Detail & Related papers  (2024-01-29T02:08:40Z)
• Approximating a RUM from Distributions on k-Slates [88.32814292632675]
  We find a generalization-time algorithm that finds the RUM that best approximates the given distribution on average. Our theoretical result can also be made practical: we obtain a that is effective and scales to real-world datasets.
  arXiv  Detail & Related papers  (2023-05-22T17:43:34Z)
• Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise [59.47042225257565]
  We present a novel planning method that does not rely on any explicit representation of the noise distributions. First, we abstract the continuous system into a discrete-state model that captures noise by probabilistic transitions between states. We capture these bounds in the transition probability intervals of a so-called interval Markov decision process (iMDP)
  arXiv  Detail & Related papers  (2021-10-25T06:18:55Z)
• A greedy reconstruction algorithm for the identification of spin distribution [0.0]
  We show that the identifiability of a piecewise constant approximation of the probability distribution is related to the invertibility of a matrix. The algorithm aims to design specific controls which ensure that this matrix is as far as possible from a singular matrix.
  arXiv  Detail & Related papers  (2021-08-26T12:40:52Z)
• Asymptotics of Network Embeddings Learned via Subsampling [4.23373349945751]
  We study representation methods using a subsampling approach, such as node2vec, into a single unifying framework. This provides a theoretical foundation to understand what the embedding vectors represent and how well these methods perform on downstream tasks. Notably, we observe that typically used loss functions may lead to shortcomings, such as a lack of Fisher consistency.
  arXiv  Detail & Related papers  (2021-07-06T02:54:53Z)
• Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes [112.38662246621969]
  Reinforcement learning considers the problem of finding policies that maximize an expected cumulative reward in a Markov decision process with unknown transition probabilities. We compute unbiased navigation gradients of the value function which we use as ascent directions to update the policy. A major drawback of policy gradient-type algorithms is that they are limited to episodic tasks unless stationarity assumptions are imposed.
  arXiv  Detail & Related papers  (2020-10-16T15:15:42Z)
• Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain [62.997667081978825]
  Activation Relaxation (AR) is motivated by constructing the backpropagation gradient as the equilibrium point of a dynamical system. Our algorithm converges rapidly and robustly to the correct backpropagation gradients, requires only a single type of computational unit, and can operate on arbitrary computation graphs.
  arXiv  Detail & Related papers  (2020-09-11T11:56:34Z)
• Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization [10.017195276758454]
  It is assumed in this setting that it is intractable to compute objective function and derivative values explicitly. An algorithm is proposed for the deterministic setting that is modeled after a state-of-the-art line-search SQP algorithm. The results of numerical experiments demonstrate the practical performance of our proposed techniques.
  arXiv  Detail & Related papers  (2020-07-20T23:04:26Z)
• Inferring Signaling Pathways with Probabilistic Programming [1.8275108630751837]
  We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference.
  arXiv  Detail & Related papers  (2020-05-28T14:55:11Z)
• Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation [72.40827239394565]
  We propose to compute features only at sparsely sampled locations. We then densely reconstruct the feature map with an efficient procedure. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
  arXiv  Detail & Related papers  (2020-03-19T15:36:31Z)
• Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs) [0.0]
  A novel generative adversarial network (GAN) based data-driven distributionally robust chance constrained programming framework is proposed. GAN is applied to fully extract distributional information from historical data in a nonparametric and unsupervised way. The proposed framework is then applied to supply chain optimization under demand uncertainty.
  arXiv  Detail & Related papers  (2020-02-28T00:05:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.