Add AiFi paper work

content/_index.md

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         ![AgileNN](agilenn.png)
         AgileNN is the first work that achieves real-time inference (<20ms) of mainstream neural network models (e.g., ImageNet) on extremely weak MCUs (e.g., STM32 series with <1MB of memory), without impairing the inference accuracy. The usage of eXplainable AI (XAI) techniques allows >6x improvement of feature compressibility during offloading and >8x reduction of the local device's resource consumption.
         {{< /columns >}}
+        {{< hr >}}
+        [View more...](/tag/on-device-ai/)
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content/authors/ruirong/_index.md

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+---
+# Display name
+title: Chen, Ruirong
+
+# Full Name (for SEO)
+first_name: Ruirong
+last_name: Chen
+
+# Is this the primary user of the site?
+superuser: false
+
+# Role/position
+role: Graduated PhD
+
+# Organizations/Affiliations
+organizations:
+  - name: University of Pittsburgh
+    url: ''
+
+# Short bio (displayed in user profile at end of posts)
+bio: Ph.D. in Electrical and Computer Engineering
+
+interests:
+  - Wireless Networks
+  - Wireless Sensing
+  - Internet of Things
+  - Smart Health Systems
+
+education:
+  courses:
+    - course: PhD in Electrical and Computer Engineering
+      institution: University of Pittsburgh
+      year: 2017-2022
+
+# Social/Academic Networking
+# For available icons, see: https://wowchemy.com/docs/getting-started/page-builder/#icons
+#   For an email link, use "fas" icon pack, "envelope" icon, and a link in the
+#   form "mailto:your-email@example.com" or "#contact" for contact widget.
+social:
+  - icon: envelope
+    icon_pack: fas
+    link: 'mailto:Ruirongchen25@163.com'
+  - icon: google-scholar
+    icon_pack: ai
+    link: https://scholar.google.com/citations?hl=en&user=N-NBveMAAAAJ
+  - icon: github
+    icon_pack: fab
+    link: https://github.com/mmcruirong
+  - icon: linkedin
+    icon_pack: fab
+    link: https:/www.linkedin.com/in/chen-ruirong-35837192/
+  - icon: earth-americas
+    icon_pack: fas
+    link: https://mmcruirong.github.io/RuirongChen.github.io/
+# Link to a PDF of your resume/CV from the About widget.
+# To enable, copy your resume/CV to `static/files/cv.pdf` and uncomment the lines below.
+# - icon: cv
+#   icon_pack: ai
+#   link: files/cv.pdf
+
+# Enter email to display Gravatar (if Gravatar enabled in Config)
+email: ''
+
+# Highlight the author in author lists? (true/false)
+highlight_name: false
+
+# Organizational groups that you belong to (for People widget)
+#   Set this to `[]` or comment out if you are not using People widget.
+user_groups:
+  - Past Students
+---
+
+<!--
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed neque elit, tristique placerat feugiat ac, facilisis vitae arcu. Proin eget egestas augue. Praesent ut sem nec arcu pellentesque aliquet. Duis dapibus diam vel metus tempus vulputate.
+-->

content/publication/2022-aifi/cite.bib

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+@inproceedings{chen2022aifi,
+  title={AiFi: AI-Enabled WiFi Interference Cancellation with Commodity PHY-Layer Information},
+  author={Chen, Ruirong and Huang, Kai and Gao, Wei},
+  booktitle={Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems},
+  pages={134--148},
+  year={2022}
+}

content/publication/2022-aifi/featured.jpg

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+../../../assets/media/aifi.jpg

content/publication/2022-aifi/index.md

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+---
+title: 'AiFi: AI-Enabled WiFi Interference Cancellation with Commodity PHY-Layer Information'
+authors:
+  - ruirong
+  - kai
+  - wei
+date: '2023-07-27T00:00:00Z'
+doi: '10.1145/3560905.3568537'
+
+# Schedule page publish date (NOT publication's date).
+publishDate: '2023-07-26T00:00:00Z'
+
+# Publication type.
+# Legend: 0 = Uncategorized; 1 = Conference paper; 2 = Journal article;
+# 3 = Preprint / Working Paper; 4 = Report; 5 = Book; 6 = Book section;
+# 7 = Thesis; 8 = Patent
+publication_types: ['1']
+
+# Publication name and optional abbreviated publication name.
+publication: In *the the 20th ACM Conference on Embedded Networked Sensor Systems (Sensys'22)*
+publication_short: In *SenSys'22*
+
+abstract: Interference could result in significant performance degradation in WiFi networks. Most existing solutions to interference cancellation require extra RF hardware, which is usually infeasible in many low-power wireless scenarios. In this paper, we present AiFi, a new interference cancellation technique that can be applied to commodity WiFi devices without using any extra RF hardware. The key idea of AiFi is to retrieve knowledge about interference from the locally available physical-layer (PHY) information at the WiFi receiver, including the pilot information (PI) and the channel state information (CSI). AiFi leverages the power of AI to address the possible ambiguity when estimating interference from these PHY information, and incorporates the domain knowledge about WiFi PHY to minimize the neural network complexity. Experiment results show that AiFi can correct 80% of bit errors due to interference and improves the MAC frame reception rate by 18x, with <1ms latency for interference cancellation in each frame.
+
+# Summary. An optional shortened abstract.
+summary: This work applies on-device AI techniques to interference cancellation in WiFi networks and enables generalizable interference cancellation on commodity WiFi devices without any extra RF hardware. By using neural network models to mimic WiFi network's PHY-layer operation, AiFi can be generally applied to different types of interference signals ranging from concurrent WiFi transmissions, ZigBee/Bluetooth to wireless baby monitors or even microwave oven, and improves the MAC-layer frame reception rate by 18x.
+
+tags:
+  - 'sensing'
+  - 'wireless-systems'
+  - 'on-device-ai'
+featured: true
+
+url_pdf: https://doi.org/10.1145/3560905.3568537
+url_code: 'https://github.com/pittisl/AiFi_PHY_Reconstruct'
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+image:
+  caption: ''
+  focal_point: ''
+  preview_only: false
+
+# Associated Projects (optional).
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+#   Simply enter your project's folder or file name without extension.
+#   E.g. `internal-project` references `content/project/internal-project/index.md`.
+#   Otherwise, set `projects: []`.
+#projects:
+#  - internal-project
+
+# Slides (optional).
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+#   Simply enter your slide deck's filename without extension.
+#   E.g. `slides: "example"` references `content/slides/example/index.md`.
+#   Otherwise, set `slides: ""`.
+slides:
+---

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