26 research outputs found
Deep HyperNetwork-Based MIMO Detection
Optimal symbol detection for multiple-input multiple-output (MIMO) systems is
known to be an NP-hard problem. Conventional heuristic algorithms are either
too complex to be practical or suffer from poor performance. Recently, several
approaches tried to address those challenges by implementing the detector as a
deep neural network. However, they either still achieve unsatisfying
performance on practical spatially correlated channels, or are computationally
demanding since they require retraining for each channel realization. In this
work, we address both issues by training an additional neural network (NN),
referred to as the hypernetwork, which takes as input the channel matrix and
generates the weights of the neural NN-based detector. Results show that the
proposed approach achieves near state-of-the-art performance without the need
for re-training
HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO
In order to unlock the full advantages of massive multiple input multiple
output (MIMO) in the downlink, channel state information (CSI) is required at
the base station (BS) to optimize the beamforming matrices. In frequency
division duplex (FDD) systems, full channel reciprocity does not hold, and CSI
acquisition generally requires downlink pilot transmission followed by uplink
feedback. Prior work proposed the end-to-end design of pilot transmission,
feedback, and CSI estimation via deep learning. In this work, we introduce an
enhanced end-to-end design that leverages partial uplink-downlink reciprocity
and temporal correlation of the fading processes by utilizing jointly downlink
and uplink pilots. The proposed method is based on a novel deep learning
architecture -- HyperRNN -- that combines hypernetworks and recurrent neural
networks (RNNs) to optimize the transfer of long-term channel features from
uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a
lower normalized mean square error (NMSE) performance, and that it reduces
requirements in terms of pilot lengths.Comment: To be presented at SPAWC 202
On Investigations of Machine Learning and Deep Learning Techniques for MIMO Detection
This paper reviews in detail the various types of multiple input multiple output (MIMO) detector algorithms. The current MIMO detectors are not suitable for massive MIMO (mMIMO) scenarios where there are a large number of antennas. Their performance degrades with the increase in number of antennas in the MIMO system. For combatting the issues, machine learning (ML) and deep learning (DL) based detection algorithms are being researched and developed. An extensive survey of these detectors is provided in this paper, alongwith their advantages and challenges. The issues discussed have to be resolved before using them for final deployment
Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues
Data-driven machine learning (ML) is promoted as one potential technology to
be used in next-generations wireless systems. This led to a large body of
research work that applies ML techniques to solve problems in different layers
of the wireless transmission link. However, most of these applications rely on
supervised learning which assumes that the source (training) and target (test)
data are independent and identically distributed (i.i.d). This assumption is
often violated in the real world due to domain or distribution shifts between
the source and the target data. Thus, it is important to ensure that these
algorithms generalize to out-of-distribution (OOD) data. In this context,
domain generalization (DG) tackles the OOD-related issues by learning models on
different and distinct source domains/datasets with generalization capabilities
to unseen new domains without additional finetuning. Motivated by the
importance of DG requirements for wireless applications, we present a
comprehensive overview of the recent developments in DG and the different
sources of domain shift. We also summarize the existing DG methods and review
their applications in selected wireless communication problems, and conclude
with insights and open questions
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Combining the classical Kalman filter (KF) with a deep neural network (DNN)
enables tracking in partially known state space (SS) models. A major limitation
of current DNN-aided designs stems from the need to train them to filter data
originating from a specific distribution and underlying SS model. Consequently,
changes in the model parameters may require lengthy retraining. While the KF
adapts through parameter tuning, the black-box nature of DNNs makes identifying
tunable components difficult. Hence, we propose Adaptive KalmanNet (AKNet), a
DNN-aided KF that can adapt to changes in the SS model without retraining.
Inspired by recent advances in large language model fine-tuning paradigms,
AKNet uses a compact hypernetwork to generate context-dependent modulation
weights. Numerical evaluation shows that AKNet provides consistent state
estimation performance across a continuous range of noise distributions, even
when trained using data from limited noise settings
New Environment Adaptation with Few Shots for OFDM Receiver and mmWave Beamforming
Few-shot learning (FSL) enables adaptation to new tasks with only limited
training data. In wireless communications, channel environments can vary
drastically; therefore, FSL techniques can quickly adjust transceiver
accordingly. In this paper, we develop two FSL frameworks that fit in wireless
transceiver design. Both frameworks are base on optimization programs that can
be solved by well-known algorithms like the inexact alternating direction
method of multipliers (iADMM) and the inexact alternating direction method
(iADM). As examples, we demonstrate how the proposed two FSL frameworks are
used for the OFDM receiver and beamforming (BF) for the millimeter wave
(mmWave) system. The numerical experiments confirm their desirable performance
in both applications compared to other popular approaches, such as transfer
learning (TL) and model-agnostic meta-learning
Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications
Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave
(mmWave) and terahertz (THz) systems to achieve both coverage and capacity
enhancement, where the design of hybrid precoders, combiners, and the IRS
typically relies on channel state information. In this paper, we address the
problem of uplink wideband channel estimation for IRS aided multiuser
multiple-input single-output (MISO) systems with hybrid architectures.
Combining the structure of model driven and data driven deep learning
approaches, a hybrid driven learning architecture is devised for joint
estimation and learning the properties of the channels. For a passive IRS aided
system, we propose a residual learned approximate message passing as a model
driven network. A denoising and attention network in the data driven network is
used to jointly learn spatial and frequency features. Furthermore, we design a
flexible hybrid driven network in a hybrid passive and active IRS aided system.
Specifically, the depthwise separable convolution is applied to the data driven
network, leading to less network complexity and fewer parameters at the IRS
side. Numerical results indicate that in both systems, the proposed hybrid
driven channel estimation methods significantly outperform existing deep
learning-based schemes and effectively reduce the pilot overhead by about 60%
in IRS aided systems.Comment: 30 pages, 8 figures, submitted to IEEE transactions on wireless
communications on December 13, 202
Investigation of the performance of multi-input multi-output detectors based on deep learning in non-Gaussian environments
The next generation of wireless cellular communication networks must be energy efficient, extremely reliable, and have low latency, leading to the necessity of using algorithms based on deep neural networks (DNN) which have better bit error rate (BER) or symbol error rate (SER) performance than traditional complex multi-antenna or multi-input multi-output (MIMO) detectors. This paper examines deep neural networks and deep iterative detectors such as OAMP-Net based on information theory criteria such as maximum correntropy criterion (MCC) for the implementation of MIMO detectors in non-Gaussian environments, and the results illustrate that the proposed method has better BER or SER performance