Ioannis N. Psaromiligkos’s research while affiliated with McGill University and other places

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Publications (105)


FIGURE 1: CANDECOMP/PARAFAC Decomposition.
FIGURE 2: Full duplex MIMO RIS communication model.
FIGURE 5: Performance of channel estimation methods vs SNR. (M = 4, K = 3, N = 5 × 5 = 25)
FIGURE 6: Performance of estimating the individual channels. (M = 4, K = 3, N = 5 × 5 = 25)
FIGURE 7: Performance of pilot transmissions schemes 1 and 2 for channel estimation methods, with L pilots transmitted per block and a total of B = 26 block transmissions. (M = 4, K = 3, N = 5 × 5 = 25)

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Tensor Signal Modeling and Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO
  • Article
  • Full-text available

November 2024

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15 Reads

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1 Citation

IEEE Open Journal of the Communications Society

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Ioannis N. Psaromiligkos

Channel estimation is one of the main challenges for reconfigurable intelligent surface (RIS) assisted communication systems with passive reflective elements due to the high number of parameters to estimate. In this paper, we consider channel estimation for a MIMO FD RIS-assisted wireless communication system and use tensor (multidimensional array) signal modelling techniques to estimate all channel state information (CSI) involving the self-interference, direct-path, and the RIS assisted channel links. We model the received signal as a tensor composed of two CANDECOMP/PARAFAC (CP) decomposition terms for the non-RIS and the RIS assisted links. Based on this model we extend the alternating least squares algorithm to jointly estimate all channels, then derive the corresponding Cramér-Rao Bounds (CRB). Numerical results show that compared to recent previous works which estimate the non-RIS and RIS links during separate training stages, our method provides a more accurate estimate by efficiently using all pilots transmitted throughout the full training duration without turning the RIS off when comparing the same number of total pilots transmitted. For a sufficient number of transmitted pilots, the proposed method’s accuracy comes close to the CRB for the RIS channels and attains the CRB for the direct-path and self-interference channels.

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Load Estimation in a Two-Priority mMTC Random Access Channel

May 2024

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2 Reads

The use of cellular networks for massive machine-type communications (mMTC) is an appealing solution due to the wide availability of cellular infrastructure. Estimating the number of devices (network load) is vital for efficient allocation of the available resources, especially for managing the random access channel (RACH) of the network. This paper considers a two-priority RACH and proposes two network load estimators: a maximum likelihood (ML) estimator and a reduced complexity (RCML) variant. The estimators are based on a novel model of the random access behavior of the devices coupled with a flexible analytical framework to calculate the involved probabilities. Monte Carlo simulations demonstrate the accuracy of the proposed estimators for different network configurations.


A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition

January 2024

IEEE Transactions on Neural Networks and Learning Systems

We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected (FC) layers finally combine the extracted features to perform classification. The proposed architecture restrains each of the subband CNNs from learning using the entire input signal spectrum, resulting in structural regularization. Our proposed CNN architecture is fully compatible with the end-to-end learning mechanism of typical CNN architectures and learns the subband decomposition from the input dataset. We show that the proposed CNN architecture has attractive properties, such as robustness to input and weight-and-bias quantization noise, compared to regular full-band CNN architectures. Importantly, the proposed architecture significantly reduces computational costs, while maintaining state-of-the-art classification accuracy. Experiments on image classification tasks using the MNIST, CIFAR-10/100, Caltech-101, and ImageNet-2012 datasets show that the proposed architecture allows accuracy surpassing state-of-the-art results. On the ImageNet-2012 dataset, we achieved top-5 and top-1 validation set accuracy of 86.91% and 69.73%, respectively. Notably, the proposed architecture offers over 90% reduction in computation cost in the inference path and approximately 75% reduction in back-propagation (per iteration) with just a single-layer subband decomposition. With a two-layer subband decomposition, the computational gains are even more significant with comparable accuracy results to the single-layer decomposition.



A Survey On Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

January 2024

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1,021 Reads

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3 Citations

IEEE Open Journal of the Communications Society

Wireless networks are increasingly relying on machine learning (ML) paradigms to provide various services at the user level. Yet, it remains impractical for users to offload their collected data set to a cloud server for centrally training their local ML model. Federated learning (FL), which aims to collaboratively train a global ML model by leveraging the distributed wireless computation resources across users without exchanging their local information, is therefore deemed as a promising solution for enabling intelligent wireless networks in the data-driven society of the future. Recently, reconfigurable intelligent metasurfaces (RIMs) have emerged as a revolutionary technology, offering a controllable means for increasing signal diversity and reshaping transmission channels, without implementation constraints traditionally associated with multi-antenna systems. In this paper, we present a comprehensive survey of recent works on the applications of FL to RIM-aided communications. We first review the fundamental basis of FL with an emphasis on distributed learning mechanisms, as well as the operating principles of RIMs, including tuning mechanisms, operation modes, and deployment options. We then proceed with an in-depth survey of literature on FL-based approaches recently proposed for the solution of three key interrelated problems in RIM-aided wireless networks, namely: channel estimation (CE), passive beamforming (PBF) and resource allocation (RA). In each case, we illustrate the discussion by introducing an expanded FL (EFL) framework in which only a subset of active users partake in the distributed training process, thereby allowing to reduce transmission overhead. Lastly, we discuss some current challenges and promising research avenues for leveraging the full potential of FL in future RIM-aided extremely large-scale multiple-input-multiple-output (XL-MIMO) networks.


Minimization of Internally Reflected Power Via Waveform Design in Cognitive MIMO Radar

December 2023

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23 Reads

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3 Citations

IEEE Transactions on Aerospace and Electronic Systems

State-of-the-art cognitive MIMO radars maximize the signal-to-interference-plus-noise ratio (SINR) for an extended target of interest by matching the transmitted waveforms to the target impulse response (TIR). Existing methods to match the transmitted waveforms do not consider the problem of internally-reflected power due to the mutual coupling between the transmitting antenna array elements, which results in transmitter inefficiency and possible hardware damage. While the mutual coupling problem in MIMO radars has been handled using microwave techniques heretofore, we herein advocate a signal-processing approach to this problem in cognitive MIMO radars. Specifically, we pro-pose an effective waveform design formalism allowing to jointly maximize the SINR and minimize the reflected power from the transmitting antennas under a TIR matching constraint, while achieving waveform orthogonality in the Doppler domain. Mini-mizing the reflected power is achieved through the incorporation of a regularization term, taking the form of an ll_{\infty} -norm, in the objective function of a minimum variance distortionless response criterion. An efficient proximal gradient method is developed to solve the resulting non-smooth optimization problem. Simulations with different TIR distributions and transmitting antenna array sizes show that the proposed waveform design algorithm results in lower active reflection coefficients for the antenna elements than selected benchmarks. Furthermore, our algorithm offers a competitive SINR performance compared to these benchmarks and can cope with the fast-varying TIR.


Citations (50)


... The application of FSS patch antennas in vehicle-to-vehicle (V2V) communication signifies a notable progression owing to their distinctive attributes tailored to meet the specific demands of this evolving technology [22]. FSS patch antennas offer a multitude of advantages, encompassing amplified gain for stronger and more reliable vehicle connections, enhanced directivity that precisely concentrates signals where required, and diminished multipath propagation to minimize signal distortion and augment overall system performance [23], [24]. ...

Reference:

Design and enhancement of microstrip patch antenna with frequency selective surface backing for vehicle-to-vehicle communication
A Survey On Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

IEEE Open Journal of the Communications Society

... This is an important issue to highlight as improving communication through FD transmission requires knowledge of estimating both the RIS assisted channels and the non-RIS assisted channels: the direct-path between the AP and UE or the self-interference at the AP and UE. However, having separate channel estimation stages where the RIS is turned "off" then "on" leads to extra pilot overhead compared to jointly estimating all channels with what the least squares solution provides in [38], [39] for HD and [16] for FD scenarios. ...

Channel Estimation for Reconfigurable Intelligent Surface-Assisted Full-Duplex MIMO With Hardware Impairments
  • Citing Article
  • October 2023

IEEE Wireless Communications Letters

... Under the strict delay requirements, the system's energy consumption is minimized by optimizing resource allocation and sub-channel allocation. In [20], the author studied the device-to-device (D2D) assisted fog computing scenario in minimizing the system's energy consumption under the probabilistic constraint of task processing time. The above researches are to reduce the system's energy consumption through different optimization methods in various application scenarios. ...

Energy-Efficient D2D-Aided Fog Computing under Probabilistic Time Constraints
  • Citing Conference Paper
  • December 2021

... Task offloading is treated as a non-convex optimization problem and solved using a meta-heuristic algorithm. Similarly, Karatalay et al. [23] investigated energy-efficient resource allocation in device-todevice (D2D) fog computing scenarios. They proposed a low-complexity heuristic resource allocation strategy to minimize overall energy consumption due to limited transmission power, computational resources, and task processing time. ...

Energy-Efficient Resource Allocation for D2D-Assisted Fog Computing
  • Citing Article
  • December 2022

IEEE Transactions on Green Communications and Networking

... As reported in [10], an universal pulse-based joint estimation algorithm of clock skew and offset in WSNs is introduced with the propagation delay estimated under a reference clock. In addition, typical pulse-based synchronization methods without reference clock are presented in [11][12][13]. The clock offset estimation methods are given in [11] and [12] with assuming the ideal condition that takes neither propagation delay nor clock skew into account. ...

A Distributed Pulse-Based Synchronization Protocol for Half-Duplex D2D Communications

IEEE Open Journal of the Communications Society

... The spatial diversity in MIMO radars brings several advantages, including improvement of spatial resolution, parameter estimation accuracy, ground moving target identification, and detection performance [1]. For extended target models, where the target occupies several range cells [2], the so-called cognitive MIMO radars, adapt the transmitted waveforms to the target impulse response (TIR) to improve target detection [3]. ...

Extended Target Frequency Response Estimation Using Infinite Hmm in Cognitive Radars

... In our previous work [30], we proposed the Wavelet Subband Decomposition (WSD) structure that decomposes an input image using wavelets and then processes each of the resulting subbands using separate convolutional layers whose outputs are combined by a fully connected (FC) layer. As we discuss in [30], the network exhibits several attractive properties such as structural regularization, immunity from input and weight quantization noise, etc. ...

A Structurally Regularized Convolutional Neural Network for Image Classification Using Wavelet-Based Subband Decomposition
  • Citing Conference Paper
  • September 2019

... The second is the Kdistribution which, although encountered in real applications [47], has been scarcely explored in the literature. In both cases, the TIR between each pair of the transmitting and receiving antenna elements is generated as a spherical invariant random vector (SIRV) [48], [49] which is assumed to be known by the radar system as in [10], [11], [21]. For a comprehensive evaluation of the proposed method, we use K = 1000 Monte Carlo simulation trials, each with a different TIR for both considered TIR distributions and antenna sizes. ...

Covariance-Free Nonhomogeneity STAP Detector in Compound Gaussian Clutter Based on Robust Statistics

... Based on this algorithm, we then propose a novel fully-distributed pulse-based synchronization protocol for half-duplex D2D communications in 5G networks. This work significantly extends upon our previous contributions in [17], [24], where we focus on a simplified version of the problem by neglecting the presence of clock skew and its influence on data communication. Specifically, our main contributions in this work are summarized as follows: ...

Fast Converging Distributed Pulse-coupled Clock Synchronization for Half-duplex D2D Communications over Multipath Channels
  • Citing Conference Paper
  • December 2018