More Like this

1. CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion

   https://doi.org/10.1109/TWC.2021.3109789  

   Gonultas, Emre ; Lei, Eric ; Langerman, Jack ; Huang, Howard ; Studer, Christoph ( September 2021 , IEEE Transactions on Wireless Communications)

   null (Ed.)

   Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions. In this paper, we propose a positioning pipeline for wireless LAN MIMO-OFDM systems which uses uplink CSI measurements obtained from one or more unsynchronized access points (APs). For each AP receiver, novel features are first extracted from the CSI that are robust to system impairments arising in real-world transceivers. These features are the inputs to a NN that extracts a probability map indicating the likelihood of a UE being at a given grid point. The NN output is then fused across multiple APs to provide a final position estimate. We provide experimental results with real-world indoor measurements under line-of-sight (LoS) and non-LoS propagation conditions for an 80 MHz bandwidth IEEE 802.11ac system using a two-antenna transmit UE and two AP receivers each with four antennas. Our approach is shown to achieve centimeter-level median distance error, an order of magnitude improvement over a conventional baseline. 

   more » « less
2. A Digital Predistortion Scheme Exploiting Degrees-of-Freedom for Massive MIMO Systems

   Yao, Miao ; Sohul, Munawwar ; Nealy, Randall ; Marojevic, Vuk ; Reed, Jeffrey ( July 2018 , IEEE International Conference on Communications)

   The primary source of nonlinear distortion in wireless transmitters is the power amplifier (PA). Conventional digital predistortion (DPD) schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is not practical for scaling to tens or hundreds of PAs in massive multiple-input multiple-output (MIMO) systems. There is more than one candidate precoding matrix in a massive MIMO system because of the excess degrees-of-freedom (DoFs), and each precoding matrix requires a different DPD polynomial order to compensate for the PA nonlinearity. This paper proposes a low-order DPD method achieved by exploiting massive DoFs of next-generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to achieve the same performance as the conventional DPD using an 11th order polynomial for a 10010 massive MIMO configuration. Experimental results show a 70% reduction in computational complexity, enabling ultra-low latency communications. 

   more » « less
3. Learning-Based MIMO Channel Estimation under Practical Pilot Sparsity and Feedback Compression

   https://doi.org/10.1109/TWC.2022.3202750  

   Del Rosario, Mason ; Ding, Zhi ( February 2023 , IEEE transactions on wireless communications)

   Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression networks for delay domain CSI and a BS-side decoding scheme. Vitally, these works assume that the full delay domain CSI is available at the UE, but in reality, the UE must estimate the delay domain based on a limited number of frequency domain pilots. In this work, we propose a linear pilot-to-delay estimator (P2DE) that acquires the truncated delay CSI via sparse frequency pilots. We show the accuracy of the P2DE under frequency downsampling, and we demonstrate the P2DE’s efficacy when utilized with existing CSI estimation networks. Additionally, we propose to use trainable compressed sensing (CS) networks in a differential encoding network for time-varying CSI estimation, and we propose a new network, MarkovNet-ISTA-ENet (MN-IE), which combines a CS network for initial CSI estimation and multiple autoencoders to estimate the error terms. We demonstrate that MN-IE has better asymptotic performance than networks comprised of only one type of network. 

   more » « less
4. Adapting Convolutional Neural Networks for Indoor Localization with Smart Mobile Devices

   Mittal, A. ; Tiku, S. ; Pasricha, S. ( January 2018 , ACM Great Lakes Symposium on VLSI (GLSVLSI))

   Indoor localization is emerging as an important application domain for enhanced navigation (or tracking) of people and assets in indoor locales such as buildings, malls, and underground mines. Most indoor localization solutions proposed in prior work do not deliver good accuracy without expensive infrastructure (and even then, the results may lack consistency). Ambient wireless received signal strength indication (RSSI) based fingerprinting using smart mobile devices is a low-cost approach to the problem. However, creating an accurate ‘fingerprinting-only’ solution remains a challenge. This paper presents a novel approach to transform Wi-Fi signatures into images, to create a scalable fingerprinting framework based on Convolutional Neural Networks (CNNs). Our proposed CNN based indoor localization framework (CNN-LOC) is validated across several indoor environments and shows improvements over the best known prior works, with an average localization error of < 2 meters. 

   more » « less
5. Location-aware smart campus security application

   https://doi.org/10.1109/UIC-ATC.2017.8397588  

   Liu, Kaikai ; Warade, Navjot ; Pai, Tejas ; Gupta, Keertikeya ( August 2017 , 2017 IEEE Smart City Innovation (SCI))

   One of the biggest challenges that Universities face today is the safety of its people on campus from crimes like mugging, battery and even shooting in or around the campus area. Using SJSU campus as an example, over 50 alert cases of burglaries, thefts, batteries, sexual assaults and other incidents have been reported in and around the SJSU campus over the last year. We have Bluelight emergency telephones placed all over the campus, in all buildings, elevators and on the campus grounds. These phones can be used to report emergency situations, suspicious activities, request escorts etc. However, there is a huge delay between the occurrence of incidents and the arrival of the policeman at the site. There is a critical need for a system that would allow the authorities to locate victims and respond faster to these incidents. To reduce the delay in reporting incidents and their occurrence time, we have developed a mobile application that will let users send alerts along with their real-time location to the UPD directly from their mobile phones. However, finding the position of a victim in a building is the most important challenge we are facing. Many existing systems do not work in indoor environment, and the state-of-the-art localization systems are either inconvenience to use or inaccurate enough to pin-point user's locations inside the building. In this paper, we propose a fine-grained location-aware smart campus security systems that leverages hybrid localization approaches with minimum deployment cost. Specifically, we effectively combines the Wi-Fi fingerprinting localization approach with the Bluetooth beacon based trilateration approach, and improves the location accuracy to the meter-level with low cost. 

   more » « less