Interference Suppression Using Deep Learning: Current Approaches and Open Challenges

• URL: http://arxiv.org/abs/2112.08988v1
• Date: Thu, 16 Dec 2021 16:07:42 GMT
• Title: Interference Suppression Using Deep Learning: Current Approaches and Open Challenges
• Authors: Taiwo Oyedare, Vijay K Shah, Daniel J Jakubisin, Jeff H Reed
• Abstract summary: In this paper, we review a wide range of techniques that have used deep learning to suppress interference. We provide comparison and guidelines for many different types of deep learning techniques in interference suppression. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in interference suppression.
• Score: 2.179313476241343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In light of the finite nature of the wireless spectrum and the increasing demand for spectrum use arising from recent technological breakthroughs in wireless communication, the problem of interference continues to persist. Despite recent advancements in resolving interference issues, interference still presents a difficult challenge to effective usage of the spectrum. This is partly due to the rise in the use of license-free and managed shared bands for Wi-Fi, long term evolution (LTE) unlicensed (LTE-U), LTE licensed assisted access (LAA), 5G NR, and other opportunistic spectrum access solutions. As a result of this, the need for efficient spectrum usage schemes that are robust against interference has never been more important. In the past, most solutions to interference have addressed the problem by using avoidance techniques as well as non-AI mitigation approaches (for example, adaptive filters). The key downside to non-AI techniques is the need for domain expertise in the extraction or exploitation of signal features such as cyclostationarity, bandwidth and modulation of the interfering signals. More recently, researchers have successfully explored AI/ML enabled physical (PHY) layer techniques, especially deep learning which reduces or compensates for the interfering signal instead of simply avoiding it. The underlying idea of ML based approaches is to learn the interference or the interference characteristics from the data, thereby sidelining the need for domain expertise in suppressing the interference. In this paper, we review a wide range of techniques that have used deep learning to suppress interference. We provide comparison and guidelines for many different types of deep learning techniques in interference suppression. In addition, we highlight challenges and potential future research directions for the successful adoption of deep learning in interference suppression.

Related papers

• RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge [66.33067693672696]
  This paper addresses the critical problem of interference rejection in radio-frequency (RF) signals using a novel, data-driven approach. First, we present an insightful signal model that serves as a foundation for developing and analyzing interference rejection algorithms. Second, we introduce the RF Challenge, a publicly available dataset featuring diverse RF signals along with code templates. Third, we propose novel AI-based rejection algorithms, specifically architectures like UNet and WaveNet, and evaluate their performance across eight different signal mixture types.
  arXiv  Detail & Related papers  (2024-09-13T13:53:41Z)
• Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications [8.29063749138322]
  We develop a new strategy called Multi-Agent Context Learning (MACOL) to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions.
  arXiv  Detail & Related papers  (2024-01-04T15:43:55Z)
• Measuring and Mitigating Interference in Reinforcement Learning [30.38857177546063]
  Catastrophic interference is common in many network-based learning systems. We provide a definition and novel measure of interference for value-based reinforcement learning methods.
  arXiv  Detail & Related papers  (2023-07-10T20:20:20Z)
• Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning [95.31679010587473]
  Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks. This article provides a comprehensive overview of the positive and negative effects of interference on over-the-air-based edge learning systems.
  arXiv  Detail & Related papers  (2023-06-17T09:04:48Z)
• Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges [137.47736805685457]
  We show that interference can still be effectively mitigated even without knowing its channel information. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.
  arXiv  Detail & Related papers  (2023-05-11T18:06:46Z)
• 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)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Transfer Learning for Future Wireless Networks: A Comprehensive Survey [49.746711269488515]
  This article aims to provide a comprehensive survey on applications of Transfer Learning in wireless networks. We first provide an overview of TL including formal definitions, classification, and various types of TL techniques. We then discuss diverse TL approaches proposed to address emerging issues in wireless networks.
  arXiv  Detail & Related papers  (2021-02-15T14:19:55Z)
• Reinforcement Learning Assisted Beamforming for Inter-cell Interference Mitigation in 5G Massive MIMO Networks [0.0]
  Beamforming is an essential technology in the 5G massive multiple-input-multiple-output (MMIMO) communications. The inter-cell interference (ICI) is one of the main impairments faced by 5G communications due to frequency-reuse technologies. We propose a reinforcement learning (RL) assisted full dynamic beamforming for ICI mitigation in 5G downlink.
  arXiv  Detail & Related papers  (2021-01-27T07:18:07Z)
• 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.