no code implementations • 19 Apr 2024 • Ankan Dash, Jingyi Gu, Guiling Wang, Nirwan Ansari
Following this, we utilize the encoder portion of the AE models to extract relevant features from labeled data, and finetune an MLP-based Position Estimation Model to accurately deduce user locations.
no code implementations • 4 Jun 2022 • Mohammad Arif Hossain, Abdullah Ridwan Hossain, Nirwan Ansari
Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet).
no code implementations • 7 Sep 2021 • Guanxiong Liu, Hang Shi, Abbas Kiani, Abdallah Khreishah, Jo Young Lee, Nirwan Ansari, Chengjun Liu, Mustafa Yousef
In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC.
no code implementations • 24 Nov 2019 • Jinle Zhu, Qiang Li, Li Hu, Hongyang Chen, Nirwan Ansari
By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector.
no code implementations • 7 Mar 2017 • Xiansheng Guo, Nirwan Ansari, Huiyong Li
Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the localization accuracy and reduce the burden of building fingerprints.
no code implementations • 7 Mar 2017 • Xiansheng Guo, Sihua Shao, Nirwan Ansari, Abdallah Khreishah
A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points.
no code implementations • 1 Sep 2016 • Xiansheng Guo, Nirwan Ansari
We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage.