arXiv subjects

Paul Russell

Publications and source records attributed to Paul Russell.

4 recordsLinked to original sources

arXiv2023

Open Source-based Over-The-Air 5G New Radio Sidelink Testbed

The focus of this paper is to demonstrate an over-the-air (OTA) 5G new radio (NR) sidelink communication prototype. 5G NR sidelink communications allow NR UEs to transfer data independently without the assistance of a base station (gNB), which enables V2X communications, including platooning, autonomous driving, sensor extension, industrial IoT, public safety communication and much more. Our design leverages the open-source OpenAirInterface5G (OAI) software, which operates on software-defined radios (SDRs) and can be easily extended for mesh networking. The software includes all signal processing components specified by the 3GPP 5G sidelink standards, including Low-Density Parity Check (LDPC) encoding/decoding, polar encoding/decoding, data and control multiplexing, modulation/demodulation, and orthogonal frequency-division multiplexing (OFDM) modulation/demodulation. It can be configured to operate with different bands, bandwidths, and antenna settings. The first milestone in this work was to demonstrate the completed Physical Sidelink Broadcast Channel (PSBCH) development, which conducts synchronization between a Synchronization Reference (SyncRef) UE and a nearby UE. The SyncRef UE broadcasts a sidelink synchronization signal block (S-SSB) periodically, which the nearby UE detects and uses to synchronize its timing and frequency components with the SyncRef UE. Once a connection is established, the next developmental milestone is to transmit real data (text messages) via the Physical Sidelink Shared Channel (PSSCH). Our PHY sidelink framework is tested using both an RF simulator and an OTA testbed with multiple nearby UEs. Beyond the development of synchronization and data transmission/reception in 5G sidelink, we conclude with various performance tests and validation experiments. The results of these metrics show that our simulator is comparable to the OTA testbed.

cs.NI↗

arXiv2022

Efficient forecasting and uncertainty quantification for large scale account level Monte Carlo models of debt recovery

We consider the problem of forecasting debt recovery from large portfolios of non-performing unsecured consumer loans under management. The state of the art in industry is to use stochastic processes to approximately model payment behaviour of individual customers based on several covariates, including credit scores and payment history. Monte Carlo simulation of these stochastic processes can enable forecasting of the possible returns from portfolios of defaulted debt, and the quantification of uncertainty. Despite the fact that the individual-level models are relatively simple, it is challenging to carry out simulations at the portfolio level because of the very large number of accounts. The accounts are also heterogeneous, with a broad range of values for the collection variances. We aim to solve two main problems: efficient allocation of computational resources in the simulations to estimate the likely collections as precisely as possible, and quantification of the uncertainty in the forecasts. We show that under certain conditions, robust estimators of population-level variance can be constructed by summing over coarse unbiased estimators of the variance of individual accounts. The proposed methods are demonstrated through application to a model which shares key features with those that are used in practice.

stat.CO↗

arXiv2019

Transport map accelerated adaptive importance sampling, and application to inverse problems arising from multiscale stochastic reaction networks

In many applications, Bayesian inverse problems can give rise to probability distributions which contain complexities due to the Hessian varying greatly across parameter space. This complexity often manifests itself as lower dimensional manifolds on which the likelihood function is invariant, or varies very little. This can be due to trying to infer unobservable parameters, or due to sloppiness in the model which is being used to describe the data. In such a situation, standard sampling methods for characterising the posterior distribution, which do not incorporate information about this structure, will be highly inefficient. In this paper, we seek to develop an approach to tackle this problem when using adaptive importance sampling methods, by using optimal transport maps to simplify posterior distributions which are concentrated on lower dimensional manifolds. This approach is applicable to a whole range of problems for which Monte Carlo Markov chain (MCMC) methods mix slowly. We demonstrate the approach by considering inverse problems arising from partially observed stochastic reaction networks. In particular, we consider systems which exhibit multiscale behaviour, but for which only the slow variables in the system are observable. We demonstrate that certain multiscale approximations lead to more consistent approximations of the posterior than others. The use of optimal transport maps stabilises the ensemble transform adaptive importance sampling (ETAIS) method, and allows for efficient sampling with smaller ensemble sizes. This approach allows us to take advantage of the large increases of efficiency when using adaptive importance sampling methods for previously intractable Bayesian inverse problems with complex posterior structure.

stat.CO↗

arXiv2015

Ensemble Transport Adaptive Importance Sampling

Markov chain Monte Carlo methods are a powerful and commonly used family of numerical methods for sampling from complex probability distributions. As applications of these methods increase in size and complexity, the need for efficient methods increases. In this paper, we present a particle ensemble algorithm. At each iteration, an importance sampling proposal distribution is formed using an ensemble of particles. A stratified sample is taken from this distribution and weighted under the posterior, a state-of-the-art ensemble transport resampling method is then used to create an evenly weighted sample ready for the next iteration. We demonstrate that this ensemble transport adaptive importance sampling (ETAIS) method outperforms MCMC methods with equivalent proposal distributions for low dimensional problems, and in fact shows better than linear improvements in convergence rates with respect to the number of ensemble members. We also introduce a new resampling strategy, multinomial transformation (MT), which while not as accurate as the ensemble transport resampler, is substantially less costly for large ensemble sizes, and can then be used in conjunction with ETAIS for complex problems. We also focus on how algorithmic parameters regarding the mixture proposal can be quickly tuned to optimise performance. In particular, we demonstrate this methodology's superior sampling for multimodal problems, such as those arising from inference for mixture models, and for problems with expensive likelihoods requiring the solution of a differential equation, for which speed-ups of orders of magnitude are demonstrated. Likelihood evaluations of the ensemble could be computed in a distributed manner, suggesting that this methodology is a good candidate for parallel Bayesian computations.

math.NA↗