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Found 27 papers, 3 papers with code
Feasibility Analysis of In-Band Coexistence in Dense LEO Satellite Communication Systems
We carefully model and study the potential downlink interference between the two systems and investigate how strategic satellite selection may be used by Kuiper to serve its ground users while also protecting Starlink ground users.
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks
Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage.
Joint Uplink-Downlink Capacity and Coverage Optimization via Site-Specific Learning of Antenna Settings
We propose a novel framework for optimizing antenna parameter settings in a heterogeneous cellular network.
Spatial and Statistical Modeling of Multi-Panel Millimeter Wave Self-Interference
Characterizing self-interference is essential to the design and evaluation of in-band full-duplex communication systems.
STEER: Beam Selection for Full-Duplex Millimeter Wave Communication Systems
Modern millimeter wave (mmWave) communication systems rely on beam alignment to deliver sufficient beamforming gain to close the link between devices.
Downlink Analysis and Evaluation of Multi-Beam LEO Satellite Communication in Shadowed Rician Channels
The extension of wide area wireless connectivity to low-earth orbit (LEO) satellite communication systems demands a fresh look at the effects of in-orbit base stations, sky-to-ground propagation, and cell planning.
LoneSTAR: Analog Beamforming Codebooks for Full-Duplex Millimeter Wave Systems
This work develops LoneSTAR, a novel enabler of full-duplex millimeter wave (mmWave) communication systems through the design of analog beamforming codebooks.
Beamformed Self-Interference Measurements at 28 GHz: Spatial Insights and Angular Spread
We present measurements and analysis of self-interference in multi-panel millimeter wave (mmWave) full-duplex communication systems at 28 GHz.
Over-the-Air Design of GAN Training for mmWave MIMO Channel Estimation
More importantly, our proposed framework has the potential to be trained online using real noisy pilot measurements, is not restricted to a specific channel model and can even be utilized for a federated OTA design of a dataset generator from noisy data.
28 GHz Phased Array-Based Self-Interference Measurements for Millimeter Wave Full-Duplex
We present measurements of the 28 GHz self-interference channel for full-duplex sectorized multi-panel millimeter wave (mmWave) systems, such as integrated access and backhaul.
System-Level Analysis of Full-Duplex Self-Backhauled Millimeter Wave Networks
We use this to characterize the network-level improvements seen when upgrading from conventional HD IAB nodes to FD ones by deriving closed-form expressions for (i) latency gain of FD-IAB over HD-IAB and (ii) the maximum number of hops that a HD- and FD-IAB network can support while satisfying latency and throughput targets.
Distributed Proximal Policy Optimization for Contention-Based Spectrum Access
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access that go beyond traditional carrier sensing.
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing
The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access.
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning
The use of unlicensed spectrum for cellular systems to mitigate spectrum scarcity has led to the development of intelligent adaptive approaches to spectrum access that improve upon traditional carrier sensing and listen-before-talk methods.
Downlink Analysis of LEO Multi-Beam Satellite Communication in Shadowed Rician Channels
The coming extension of cellular technology to base-stations in low-earth orbit (LEO) requires a fresh look at terrestrial 3GPP channel models.
Learning Site-Specific Probing Beams for Fast mmWave Beam Alignment
Beam alignment - the process of finding an optimal directional beam pair - is a challenging procedure crucial to millimeter wave (mmWave) communication systems.
Millimeter Wave Analog Beamforming Codebooks Robust to Self-Interference
This paper develops a novel methodology for designing analog beamforming codebooks for full-duplex millimeter wave (mmWave) transceivers, the first such codebooks to the best of our knowledge.
Wideband Channel Estimation with A Generative Adversarial Network
Communication at high carrier frequencies such as millimeter wave (mmWave) and terahertz (THz) requires channel estimation for very large bandwidths at low SNR.
Hybrid Beamforming for Millimeter Wave Full-Duplex under Limited Receive Dynamic Range
To prevent self-interference from saturating the receiver of a full-duplex device having limited dynamic range, our design addresses saturation on a per-antenna and per-RF chain basis.
DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems
In this work, we develop DeepWiPHY, a deep learning-based architecture to replace the channel estimation, common phase error (CPE) correction, sampling rate offset (SRO) correction, and equalization modules of IEEE 802. 11ax based orthogonal frequency division multiplexing (OFDM) receivers.
Millimeter Wave Full-Duplex Radios: New Challenges and Techniques
Equipping millimeter wave (mmWave) systems with full-duplex capability would accelerate and transform next-generation wireless applications and forge a path for new ones.
High Dimensional Channel Estimation Using Deep Generative Networks
This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network.
Deep Learning Predictive Band Switching in Wireless Networks
Our proposed classifier-based band switching policy instead exploits spatial and spectral correlation between radio frequency signals in different bands based on knowledge of the UE location.
Autoencoder-Based Error Correction Coding for One-Bit Quantization
It is empirically shown that this design gives nearly the same performance as to the hypothetically perfectly trained autoencoder, and we also provide a theoretical proof of why that is so.
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning
Our results illustrate that the proposed algorithm approaches the performance of the multi-agent RL, which requires millions of trials, with hundreds of online trials, assuming relatively low environmental dynamics, and performs much better than a single agent RL.
Unified Analysis of HetNets using Poisson Cluster Process under Max-Power Association
Owing to its flexibility in modeling real-world spatial configurations of users and base stations (BSs), the Poisson cluster process (PCP) has recently emerged as an appealing way to model and analyze heterogeneous cellular networks (HetNets).
Information Theory Networking and Internet Architecture Information Theory
One-Bit OFDM Receivers via Deep Learning
For channel estimation (using pilots), we design a novel generative supervised deep neural network (DNN) that can be trained with a reasonable number of pilots.
