Navid NaderiAlizadeh

Journal Article IEEE Transactions on Signal Processing · January 1, 2023 We consider the problems of user selection and power control in wireless interference networks, comprising multiple access points (APs) communicating with a group of user equipment devices (UEs) over a shared wireless medium. To achieve a high aggregate ra ... Full text Cite

A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks

Preprint · October 28, 2022 Link to item Cite

State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

Preprint · July 5, 2022 Link to item Cite

Adaptive Wireless Power Allocation with Graph Neural Networks

Conference ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · May 23, 2022 Full text Cite

Learning Resilient Radio Resource Management Policies with Graph Neural Networks

Preprint · March 7, 2022 Link to item Cite

Guest Editorial: An End-to-End Machine Learning Perspective on Industrial IoT

Journal Article IEEE Internet of Things Magazine · March 2022 Full text Cite

A Lagrangian Duality Approach to Active Learning

Preprint · February 8, 2022 Link to item Cite

State-Augmented Learnable Algorithms for Resource Management in Wireless Networks

Journal Article IEEE Transactions on Signal Processing · January 1, 2022 We consider resource management problems in multi-user wireless networks, which can be cast as optimizing a network-wide utility function, subject to constraints on the long-term average performance of users across the network. We propose a state-augmented ... Full text Cite

A Lagrangian Duality Approach to Active Learning

Conference ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022) · 2022 Cite

Optimizing the Configuration of Intelligent Reflecting Surfaces using Deep Learning

Conference 2021 IEEE Globecom Workshops (GC Wkshps) · December 2021 Full text Cite

Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels

Preprint · October 4, 2021 Link to item Cite

Contrastive Self-Supervised Learning for Wireless Power Control

Conference ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · June 6, 2021 Full text Cite

Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

Journal Article IEEE Transactions on Wireless Communications · June 1, 2021 We propose a mechanism for distributed resource management and interference mitigation in wireless networks using multi-agent deep reinforcement learning (RL). We equip each transmitter in the network with a deep RL agent that receives delayed observations ... Full text Cite

Set Representation Learning with Generalized Sliced-Wasserstein Embeddings

Preprint · March 5, 2021 Link to item Cite

Pooling by Sliced-Wasserstein Embedding

Conference ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021) · 2021 Cite

Contrastive Self-Supervised Learning for Wireless Power Control

Preprint · October 22, 2020 Link to item Cite

Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning

Preprint · October 9, 2020 Link to item Cite

Wasserstein Embedding for Graph Learning

Preprint · June 16, 2020 Link to item Cite

Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

Conference 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · May 2020 Full text Cite

Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

Conference 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · May 2020 Full text Cite

SAR automatic target recognition with less labels

Conference Automatic Target Recognition XXX · April 24, 2020 Full text Cite

Wireless Power Control via Counterfactual Optimization of Graph Neural Networks

Preprint · February 17, 2020 Link to item Cite

Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

Preprint · February 14, 2020 Link to item Cite

On the Communication Latency of Wireless Decentralized Learning

Preprint · February 10, 2020 Link to item Cite

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Preprint · December 22, 2019 Link to item Cite

Learning to Code: Coded Caching via Deep Reinforcement Learning

Preprint · December 9, 2019 Link to item Cite

Learning to Code: Coded Caching via Deep Reinforcement Learning

Conference 2019 53rd Asilomar Conference on Signals, Systems, and Computers · November 2019 Full text Cite

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Conference 2019 53rd Asilomar Conference on Signals, Systems, and Computers · November 2019 Full text Cite

Monitoring Under-Modeled Rare Events for URLLC

Conference 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) · July 2019 Full text Cite

When Multiple Agents Learn to Schedule: A Distributed Radio Resource Management Framework

Preprint · June 20, 2019 Link to item Cite

Cache-Aided Interference Management in Wireless Cellular Networks

Journal Article IEEE Transactions on Communications · May 1, 2019 We consider the problem of interference management in wireless cellular networks with caches at both base stations and receivers, and we characterize the degrees of freedom (DoFs) per cell to within an additive gap of (1/3) and a multiplicative gap of 2 fo ... Full text Cite

Deep CNN for Wideband Mmwave Massive Mimo Channel Estimation Using Frequency Correlation

Conference ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · May 2019 Full text Cite

Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems

Preprint · April 14, 2019 Link to item Cite

A Power Efficient Fully Digital Beamforming Architecture for mmWave Communications

Conference 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring) · April 2019 Full text Cite

Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems

Journal Article IEEE Journal on Selected Topics in Signal Processing · January 1, 2019 For millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, hybrid processing architecture is usually used to reduce the complexity and cost, which poses a very challenging issue in channel estimation. In this paper, deep convolutio ... Full text Cite

Feedback-Based Interference Management in Ultra-Dense Networks via Parallel Dynamic Cell Selection and Link Scheduling

Conference 2018 IEEE International Conference on Communications (ICC) · May 2018 Full text Cite

Topological Interference Management With Reconfigurable Antennas

Conference IEEE Transactions on Communications · November 2017 Full text Cite

Fundamental Limits of Non-Coherent Interference Alignment via Matroid Theory

Journal Article IEEE Transactions on Information Theory · October 1, 2017 We consider the problem of non-coherent interference alignment, in which the goal is to align the signals of multiple interfering transmitters at a single receiver where the transmitters are not aware of the channel state information. We cast this problem ... Full text Cite

Ultra-Dense Networks in 5G: Interference Management via Non-Orthogonal Multiple Access and Treating Interference as Noise

Conference 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall) · September 2017 Full text Cite

On the optimality of separation between caching and delivery in general cache networks

Conference 2017 IEEE International Symposium on Information Theory (ISIT) · June 2017 Full text Cite

Fundamental Limits of Cache-Aided Interference Management

Journal Article IEEE Transactions on Information Theory · May 1, 2017 We consider a system, comprising a library of N files (e.g., movies) and a wireless network with a KT transmitters, each equipped with a local cache of size of MT files and a KR receivers, each equipped with a local cache of size of MR files. Each receiver ... Full text Cite