More Like this

1. Uplink Power Control and SNR-Dependent Beam Alignment Errors in MmWave Cellular Networks

   https://doi.org/10.1109/PIMRC54779.2022.9977460  

   Zia, Muhammad Saad; Blough, Douglas M.; Weitnauer, Mary Ann (January 2022, Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications)

   Beam alignment is a critical aspect in millimeter wave (mm-wave) cellular systems. However, the inherent limitations of channel estimation result in beam alignment errors, which degrade the system performance. For systems with a large number of antennas at the base station, downlink channel estimation is performed using uplink pilot signals. The beam alignment errors, thus, depend on the user equipment (UE) transmit power, which needs to be managed properly as the UEs are battery powered. This paper investigates how the use of uplink power control for the transmission of pilot signals in a mm-wave network affects the downlink beam alignment errors, which depend on various link parameters. We use stochastic geometry and statistics of the Student's t -distribution to develop an analytical model, which captures the interplay between the uplink power control and downlink signal-to-noise ratio (SNR) coverage probability. Our results indicate that using uplink power control significantly reduces UE power consumption without adversely affecting the downlink SNR coverage. 

   more » « less
2. 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
3. Spatial Correlation-Aware Opportunistic Beamforming in IRS-Aided Multi-User Systems

   https://doi.org/10.1109/LWC.2025.3588257  

   Yashvanth, L; Murthy, Chandra R; Rao, Bhaskar D (January 2025, IEEE Wireless Communications Letters)

   This paper addresses the high overheads associated with intelligent reflecting surface (IRS) aided wireless systems. By exploiting the inherent spatial correlation among the IRS elements, we propose a novel approach that randomly samples the IRS phase configurations from a carefully designed distribution and opportunistically schedules the user equipments (UEs) for data transmission. The key idea is that when IRS configuration is randomly chosen from a channel statistics-aware distribution, it will be near-optimal for at least one UE, and upon opportunistically scheduling that UE, we can obtain nearly all the benefits from the IRS without explicitly optimizing it. We formulate and solve a variational functional problem to derive the optimal phase sampling distribution. We show that, when the IRS phase configuration is drawn from the optimized distribution, it is sufficient for the number of UEs to scale exponentially with the rank of the channel covariance matrix, not with the number of IRS elements, to achieve a given target SNR with high probability. Our numerical studies reveal that even with a moderate number of UEs, the opportunistic scheme achieves near-optimal performance without incurring the conventional IRS-related signaling overheads and complexities. 

   more » « less
4. Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning

   https://doi.org/10.1109/TWC.2023.3289929  

   Lin, Yu-Chien; Lee, Ta-Sung; Ding, Zhi (May 2023, IEEE Transactions on Wireless Communications)

   Massive MIMO systems can achieve high spectrum and energy efficiency in downlink (DL) based on accurate estimate of channel state information (CSI). Existing works have developed learning-based DL CSI estimation that lowers uplink feedback overhead. One often overlooked problem is the limited number of DL pilots available for CSI estimation. One proposed solution leverages temporal CSI coherence by utilizing past CSI estimates and only sending CSI-reference symbols (CSIRS) for partial arrays to preserve CSI recovery performance. Exploiting CSI correlations, FDD channel reciprocity is helpful to base stations with direct access to uplink CSI. In this work, we propose a new learning-based feedback architecture and a reconfigurable CSI-RS placement scheme to reduce DL CSI training overhead and to improve encoding efficiency of CSI feedback. Our results demonstrate superior performance in both indoor and outdoor scenarios by the proposed framework for CSI recovery at substantial reduction of computation power and storage requirements at UEs. 

   more » « less
5. 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