101 research outputs found
Distributed deep reinforcement learning resource allocation scheme for industry 4.0 Device-To-Device scenarios
Get PDFThis paper proposes a distributed deep reinforcement learning (DRL) methodology for autonomous mobile robots (AMRs) to manage radio resources in an indoor factory with no network infrastructure. Hence, deep neural networks (DNN) are used to optimize the decision policy of the robots, which will make decisions in a distributed manner without signalling exchange. To speed up the learning phase, a centralized training is adopted in which a single DNN is trained using the experience from all robots. Once completed, the pre-trained DNN is deployed at all robots for distributed selection of resources. The performance of this approach is evaluated and compared to 5G NR sidelink mode 2 via simulations. The results show that the proposed method achieves up to 5% higher probability of successful reception when the density of robots in the scenario is high.This work has been partially funded by Junta de Andalucía (projects EDEL4.0:UMA18-FEDERJA-172 and PENTA:PY18-4647) and Universidad de Málaga (I Plan Propio de Investigación, Transferencia y Divulgación Científica). Ramoni Adeogun is supported by the Danish Council for Independent Research, grant no. DFF 9041-00146B. The authors would like to express their profound gratitude to Nokia Standardization Aalborg and Aalborg University for funding the first author’s research stay. The authors thank Assoc. Prof. Gilberto Beradinelli for his comments on the manuscript
Deep Learning for Direction of Arrival Estimation in Massive MIMO Systems
No full textWith the dawn of fifth generation (5G) cellular networks massive MIMO systems have become more applicable. One problem in massive MIMO systems is pilot contamination caused by the dense placement of 5G cellular towers. Therefore it is of interest to use blind source separation instead to determine channel parameters. Traditional methods for blind source separation can be slow so alternatives are of interest. The three methods examined are deep neural networks, sparse estimation methods (ISTA based) and a fusion between the two (Learned-ISTA based). For direction of arrival estimation the methods provide comparable NMSE at the low end of 10e-1 to high end of 10e-2. The Learned-ISTA based methods show promise and further investigation of training it, model parameters and peak-finding algorithms for selecting the directions of arrival is relevant
Federated Interference Management for Industrial 6G Subnetworks
No full text6G in-X subnetværk er kortdistances laveffektsceller, der er designet til at imødekomme ekstreme krav til kommunikation i form af datahastighed, forsinkelse og pålidelighed. Dog udgør interferens en betydelig begrænsende faktor for ekstrem kommunikation i tætte udrulninger af in-X subnetværk. Nylige studier har foreslået løsninger til styring af interferens baseret på ”multi-agent reinforcement learning”, hvor problemet med radioressourceoptimering modelleres som en ”multi-Markov decision process”. Studierne har været baseret på enten centraliseret eller distribueret træning. Mens centraliseret træning drager fordel af erfaringerne fra alle delnetværkene under træningen, kan det medføre kompromittering af privatliv og sikkerhed, da det kræver deling af målinger mellem delnetværkene og en centraliseret agent. Derimod er agenter i distribueret træning udelukkende afhængige af lokale målinger af miljøet for at træffe beslutninger, hvilket ofte fører til konvergensproblemer. For at overvinde disse udfordringer foreslås en klient-til-server ”horizontal federated reinforcement learning” model, hvor viden deles implicit gennem lokalt trænede modelvægte. Simulationer i et industrielt miljø ved hjælp af 3GPP-udbredelsesmodeller har vist lovende resultater med hensyn til hurtig konvergens, marginale præstationsforbedringer og robusthed over for ikke-stationære miljøer.6G in-X subnetworks are short-range low-power cells envisioned to support extreme communication requirements for data rate, latency, and reliability. However, interference represents a major limiting factor to extreme communication in dense deployments of in-X subnetworks. Recent studies have proposed interference management solutions based on multi-agent reinforcement learning, where the radio resource optimization problem is modeled as a multi-Markov decision process. The studies have been based on centralized or distributed training. While centralized training benefits from the experiences of all subnetworks during the training, it may lead to compromised privacy and security issues since it requires sharing of measurements between the subnetworks and a centralized agent. In contrast, agents in distributed training rely solely on only local measurements of the environment for decision which often leads to convergence problems.To overcome these challenges, a client-to-server horizontal federated reinforcement learning framework is proposed, where knowledge is shared implicitly through locally trained model weights.Simulations in an industrial environment using 3GPP propagation models have shown promising results for quick convergence, marginal performance improvement, and robustness to non-stationary environments
Machine Learning and Artificial Intelligence Enabled Failure Prediction for Maritime Propulsion Systems
No full textThis thesis investigates the application of machine learning (ML) and artificial intelligence(AI) techniques for failure prediction in maritime propulsion systems, with a focus on twocritical failures observed in MAN Energy Solutions’ engines: accumulator membrane fail-ure in sequential two-stroke engines and pipe damage during methanol fuel changeover indual-fuel systems. The thesis explores the feasibility of predictive maintenance throughdata-driven approaches, leveraging time series data collected from operational vessels andcontrolled test engines. A comprehensive feature engineering pipeline is developed to ad-dress challenges related to high dimensionality, feature redundancy, and class imbalance.Feature selection methods are applied to construct interpretable and task-relevant fea-ture subsets, resulting in insights into system behavior leading up to failure. Multiple MLand AI models, including Random Forests, 1D Convolutional Neural Networks (CNNs),and Long Short-Term Memory (LSTM) networks, are implemented and evaluated forboth binary failure classification and remaining useful life (RUL) regression tasks, fordifferent reading window (RW) and prediction window (PW) sizes. Here SMOTE andDTW-SMOTE has been used to augment data, increasing the data variance in training.Results demonstrate that methanol changeover failures are highly predictable, with Ran-dom Forest and 1D CNN models achieving strong performance with an accuracy of 91%for changeover classification and an R2 of 0.826 for changeover RUL prediction. In con-trast, accumulator failure prediction proves more challenging due to limited failure dataand lower signal resolution with the 1D CNN providing the greatest accuracy of 71%.The findings highlight the importance of model selection, task-specific window sizing, andfeature interpretability in predictive maintenance applications. The thesis concludes thatML and AI can be used for predicting pipe damage during methanol fuel changeover,while providing valuable insight into the system and the failure through feature engineer-ing. This task is slightly harder for accumulator membrane failure in sequential two-strokeengines, and further work is required to develop high performing and robust models forthis purpose.<br/
Joint Resource Allocation for Dual - Band Heterogeneous Wireless Network
Get PDFIn this paper, we investigate downlink resource allocation in two-tier OFDMA heterogeneous networks comprising a macrocell transmitting at a microwave frequency and dual band small cells utilizing both microwave and millimeter wave frequencies. A non - cooperative game theoretic approach is proposed for adaptively switching the SC transmission frequency based on the location of small cell users and interference to macrocell users. We propose a resource allocation approach which maximizes the sum rate of small cell users while minimizing interference to macrocell users and the total power consumption. The performance of the proposed resource allocation solution is evaluated via rigorous MATLAB simulation
Asymptotic Performance Bound on Estimation and Prediction of Mobile MIMO-OFDM Wireless Channels
Get PDFIn this paper, we derive an asymptotic closed--form expression for the error bound on extrapolation of doubly selective mobile MIMO wireless channels. The bound shows the relationship between the prediction error and system design parameters such as bandwidth, number of antenna elements, and number of frequency and temporal pilots, thereby providing useful insights into the effects of these parameters on prediction performance. Numerical simulations show that the asymptotic bound provides a good approximation to previously derived bounds while eliminating the need for repeated computation and dependence on channel parameters such as angles of arrival and departure, delays and Doppler shifts
A Novel Game Theoretic Method for Efficient Downlink Resource Allocation in Dual Band 5G Heterogeneous Network
No full textHybrid heterogeneous wireless networks utilizing both traditional microwave frequency band and millimetre wave band are currently been investigated as a potential approach to meet the increasing demand for ultra-high rate transmission with the severe microwave spectrum scarcity and requirement for low power network devices. In this paper, we investigate downlink resource allocation in two-tier heterogeneous networks comprising of a macrocell transmitting at a microwave frequency and dual-band small cells utilizing both microwave and millimetre wave frequencies. We present a novel architecture with dual band small cell base stations. The small cell coverage area is divided into two regions where the users in the inner and outer regions are served by the associated small cells on millimetre wave and microwave frequencies, respectively. We formulate a two layer game theory based approach for maximizing energy efficiency and spectral efficiency of the system with optimal usage of available radio resources. The proposed game theoretic approach comprises of a non-cooperative frequency assignment game as its first layer and a multi-objective optimization based game as the second layer. In the frequency assignment game, each small cell base station selects a frequency band from either the microwave band or millimetre wave band for each of its associated users by maximizing the data rate of its users. The solution to the frequency assignment game is obtained via Pure Strategy Nash Equilibrium. The utility function of the game in the second layer involves power and sub-carrier allocation via the joint maximization of both energy efficiency and spectral efficiency of the network. The utility function is formulated as a multi-objective optimization problem which is converted into a single objective problem and solved using Lagrangian dual relaxation. Simulations results show that the proposed dual band heterogeneous network with game theoretic resource allocation offers improved sum rate, energy efficiency and spectral efficiency compared to classical shared spectrum heterogeneous network utilizing only microwave frequency band.</p
Channel Prediction for Mobile MIMO Wireless Communication Systems
No full textTemporal variation and frequency selectivity of wireless channels constitute
a major drawback to the attainment of high gains in capacity
and reliability offered by multiple antennas at the transmitter and receiver
of a mobile communication system. Limited feedback and adaptive transmission
schemes such as adaptive modulation and coding, antenna selection,
power allocation and scheduling have the potential to provide the platform
of attaining the high transmission rate, capacity and QoS requirements in
current and future wireless communication systems. Theses schemes require
both the transmitter and receiver to have accurate knowledge of Channel
State Information (CSI). In Time Division Duplex (TDD) systems, CSI at
the transmitter can be obtained using channel reciprocity. In Frequency Division
Duplex (FDD) systems, however, CSI is typically estimated at the
receiver and fed back to the transmitter via a low-rate feedback link. Due to
the inherent time delays in estimation, processing and feedback, the CSI obtained
from the receiver may become outdated before its actual usage at the
transmitter. This results in significant performance loss, especially in high
mobility environments. There is therefore a need to extrapolate the varying
channel into the future, far enough to account for the delay and mitigate the
performance degradation.
The research in this thesis investigates parametric modeling and prediction
of mobile MIMO channels for both narrowband and wideband systems.
The focus is on schemes that utilize the additional spatial information offered
by multiple sampling of the wave-field in multi-antenna systems to
aid channel prediction. The research has led to the development of several
algorithms which can be used for long range extrapolation of time-varyingchannels. Based on spatial channel modeling approaches, simple and efficient
methods for the extrapolation of narrowband MIMO channels are proposed.
Various extensions were also developed. These include methods for wideband
channels, transmission using polarized antenna arrays, and mobile-to-mobile
systems.
Performance bounds on the estimation and prediction error are vital when
evaluating channel estimation and prediction schemes. For this purpose, analytical
expressions for bound on the estimation and prediction of polarized
and non-polarized MIMO channels are derived. Using the vector formulation
of the Cramer Rao bound for function of parameters, readily interpretable
closed-form expressions for the prediction error bounds were found for cases
with Uniform Linear Array (ULA) and Uniform Planar Array (UPA). The
derived performance bounds are very simple and so provide insight into system
design.
The performance of the proposed algorithms was evaluated using standardized
channel models. The effects of the temporal variation of multipath
parameters on prediction is studied and methods for jointly tracking the
channel parameters are developed. The algorithms presented can be utilized
to enhance the performance of limited feedback and adaptive MIMO
transmission schemes
On Propagation Graph Model for Industrial UWB Channels
Get PDFThis paper investigates the suitability of a propagation graph (PG) model for ultra wideband (UWB) industrial wireless channels. Based on short range UWB channel sounding measurements in typical industrial scenarios, we estimate parameters of the model using a method of moments approach. The measurements are then compared with approximate expressions for the power delay spectrum (PDS) derived based on PG formalism. Results show reasonable agreement between the measured and approximate PDS
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