Interference Distribution Prediction for Link Adaptation in
Ultra-Reliable Low-Latency Communications
- URL: http://arxiv.org/abs/2007.00306v1
- Date: Wed, 1 Jul 2020 07:59:35 GMT
- Title: Interference Distribution Prediction for Link Adaptation in
Ultra-Reliable Low-Latency Communications
- Authors: Alessandro Brighente, Jafar Mohammadi, Paolo Baracca
- Abstract summary: Link adaptation (LA) is considered to be one of the bottlenecks to realize URLLC.
In this paper, we focus on predicting the signal to interference plus noise ratio at the user to enhance the LA.
We show that exploiting time correlation of the interference is an important enabler of URLLC.
- Score: 71.0558149440701
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The strict latency and reliability requirements of ultra-reliable low-latency
communications (URLLC) use cases are among the main drivers in fifth generation
(5G) network design. Link adaptation (LA) is considered to be one of the
bottlenecks to realize URLLC. In this paper, we focus on predicting the signal
to interference plus noise ratio at the user to enhance the LA. Motivated by
the fact that most of the URLLC use cases with most extreme latency and
reliability requirements are characterized by semi-deterministic traffic, we
propose to exploit the time correlation of the interference to compute useful
statistics needed to predict the interference power in the next transmission.
This prediction is exploited in the LA context to maximize the spectral
efficiency while guaranteeing reliability at an arbitrary level. Numerical
results are compared with state of the art interference prediction techniques
for LA. We show that exploiting time correlation of the interference is an
important enabler of URLLC.
Related papers
- Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks [55.467288506826755]
Federated learning (FL) has been recognized as a viable solution for local-privacy-aware collaborative model training in wireless edge networks.
Most existing communication-efficient FL algorithms fail to reduce the significant inter-device variance.
We propose a novel communication-efficient FL algorithm, named FedQVR, which relies on a sophisticated variance-reduced scheme.
arXiv Detail & Related papers (2025-01-20T04:26:21Z) - Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models [2.5971582867976934]
GAT-LLM is a novel multivariate wireless link quality prediction model that combines Large Language Models (LLMs) with Graph Attention Networks (GAT)
We show that GAT-LLM significantly improves the accuracy and robustness of link quality prediction, particularly in multi-step prediction scenarios.
arXiv Detail & Related papers (2025-01-20T03:21:20Z) - OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924.
irSinc yields a signal with increased spectral efficiency without sacrificing error performance.
Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
arXiv Detail & Related papers (2024-06-07T09:20:30Z) - Power-Efficient Indoor Localization Using Adaptive Channel-aware
Ultra-wideband DL-TDOA [7.306334571814026]
We propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm.
It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter.
arXiv Detail & Related papers (2024-02-16T09:04:04Z) - Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference [73.63024765499719]
Mobile networks can be compromised by interference from neighboring cells or jammers.
We propose Spectrum Breathing, which cascades-gradient pruning and spread spectrum to suppress interference without bandwidth expansion.
We show a performance tradeoff between gradient-pruning and interference-induced error as regulated by the breathing depth.
arXiv Detail & Related papers (2023-05-10T07:05:43Z) - Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via
Conformal Prediction [72.59079526765487]
The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services.
The main challenge is posed by the uncertainty in the process of URLLC packet generation.
We introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latency irrespective of the quality of the URLLC traffic predictor.
arXiv Detail & Related papers (2023-02-15T14:09:55Z) - Federated Learning over Noisy Channels: Convergence Analysis and Design
Examples [17.89437720094451]
Federated Learning (FL) works when both uplink and downlink communications have errors.
How much communication noise can FL handle and what is its impact to the learning performance?
This work is devoted to answering these practically important questions by explicitly incorporating both uplink and downlink noisy channels in the FL pipeline.
arXiv Detail & Related papers (2021-01-06T18:57:39Z) - Distributional Reinforcement Learning for mmWave Communications with
Intelligent Reflectors on a UAV [119.97450366894718]
A novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed.
In order to maximize the downlink sum-rate, the optimal precoding matrix (at the base station) and reflection coefficient (at the IR) are jointly derived.
arXiv Detail & Related papers (2020-11-03T16:50:37Z) - Reinforcement Learning for Mitigating Intermittent Interference in
Terahertz Communication Networks [4.999585439793266]
Uncoordinated transmissions by a large number of users can cause substantial interference in terahertz networks.
New framework based on reinforcement learning is proposed that uses an adaptive multi-thresholding strategy.
arXiv Detail & Related papers (2020-03-10T16:28:45Z)
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.