Journal Article10.1109/TVT.2019.2950474
Multiple Delay Estimation for Collision Resolution in Non-Orthogonal Random Access
17
TL;DR: This paper focuses on the estimation of round-trip delays (RTD) of multiple signals in non-orthogonal random access (NORA) based on the maximum likelihood (ML) criterion and proposes a low-complexity approach based on variational inference, which is widely used in machine learning.
read more
Abstract: In machine-type communications (MTC), contention-based random access is employed to support a number of MTC devices with a limited number of resource blocks (RBs). Since multiple active devices may transmit signals in the same RB or channel, the collision caused by the presence of multiple signals is inevitable and the detection of collision becomes important. Furthermore, if the arrival time and the number of multiple signals can be estimated, successive interference cancellation (SIC) can be employed in time domain to improve the throughput. In this paper, we focus on the estimation of round-trip delays (RTD) of multiple signals in non-orthogonal random access (NORA) based on the maximum likelihood (ML) criterion. Since the computational complexity of the ML approach is high, we propose a low-complexity approach based on variational inference, which is widely used in machine learning. We also show that the number of signals can be reliably estimated from the estimated RTDs.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A Collision Resolution Protocol for Random Access in Massive MIMO
TL;DR: A massive multiple-input multiple-output (MIMO) based grant-free random access (RA) with resolution of preamble collision for massive access with analytic expressions of success probability of the proposed collision resolution with conjugate beamforming and zero-forcing beamforming are derived.
27
Joint Channel Estimation, Activity Detection and Data Decoding Based on Dynamic Message-Scheduling Strategies for mMTC
01 Apr 2022
TL;DR: In this article , a bilinear message-scheduling GAMP (BiMSGAMP) algorithm is proposed for massive machine-type communications, which combines channel estimation, activity detection and data decoding.
16
Random Access and Detection Performance of Internet of Things for Smart Ocean
TL;DR: This article proposes a relay-aided random access (RARA) scheme for the smart ocean, where retransmissions are carried out by maritime buoys with the relay function, to deal with collisions.
15
System Throughput Maximization of Uplink NOMA Random Access Systems
Lifeng Mai,Qi Zhang,Jiayin Qin +2 more
TL;DR: It is shown from simulation results that the proposed uplink NOMA system is more robust to the increase of users than a conventional orthogonal multiple access system.
13
References
•Book
Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
- 01 Aug 2006
TL;DR: Looking for competent reading resources?
10.1K
•Book
Fundamentals Of Statistical Signal Processing
Steven Kay
- 16 Mar 2001
TL;DR: This fundamentals of statistical signal processing volume ii detection theory tends to be the representative book in this website.
7.3K
Paper: Modeling by shortest data description
TL;DR: The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.
6.8K
An introduction to variational methods for graphical models
TL;DR: This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields), and describes a general framework for generating variational transformations based on convex duality.
Variational Inference: A Review for Statisticians
TL;DR: For instance, mean-field variational inference as discussed by the authors approximates probability densities through optimization, which is used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling.