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| Publications |
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S. Hamidi-Rad and J. Kaewell, Quantum Channel Decoding, 2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Broomfield, CO, USA, 2022, pp. 847-850, doi: 10.1109/QCE53715.2022.00141.
Abstract:
Channel Coding is the technique that enables reliable delivery of digital data over unreliable communication channels. For most high performance channel coding techniques, the existing classical algorithms are computationally expensive, making them impractical for throughput-demanding applications with large code sizes. Today’s Noisy Intermediate-Scale Quantum (NISQ) computers, although limited due to a modest number of qubits, short coherence time, and poor gate fidelity, are useful tools for exploring and experimenting with possible solutions to a wide variety of computational problems.In this paper we show how careful initialization of qubits combined with a simple quantum circuit, enables us to perform channel decoding for different linear block codes. We first explain our novel qubit initialization technique which we call "Quantum Soft Decision". We then show how to build a simple quantum circuit based on the Generator or Parity-check matrix using another technique called "Quantum Generator". Using these universal concepts, we implement Quantum Decoders for two different types of linear block codes, namely Hamming codes and Polar codes. Our simple quantum circuits achieve decoding performances comparable with best classical algorithms such as Maximum Likelihood (ML) for Hamming codes and Successive Cancellation (SC) and Successive Cancellation List (SCL) for Polar codes. Using Qiskit, we implemented and compared the decoding performance at different code sizes and noise levels on simulated (both ideal and noisy) quantum computers. Also using Amazon Braket, we verified the algorithm on real quantum computers.
K. Srikar, J. Kaewell, S. Hamidi-Rad, and K. Jamieson. Decoding Polar Codes via Noisy Quantum Gates: Quantum Circuits and Insights. arXiv preprint arXiv:2210.10854 (2022).
Abstract:
The use of quantum computation for wireless network ap- plications is emerging as a promising paradigm to bridge the performance gap between in-practice and optimal wire- less algorithms. While today’s quantum technology offers limited number of qubits and low fidelity gates, application- based quantum solutions help us to understand and improve the performance of such technology even further. This pa- per introduces QGateD-Polar, a novel Quantum Gate-based Maximum-Likelihood Decoder design for Polar error correc- tion codes, which are becoming widespread in today’s 5G and tomorrow’s NextG wireless networks. QGateD-Polar uses quantum gates to dictate the time evolution of Polar code decoding—from the received wireless soft data to the final decoded solution—by leveraging quantum phenomena such as superposition, entanglement, and interference, mak- ing it amenable to quantum gate-based computers. Our early results show that QGateD-Polar achieves the Maximum Like- lihood performance in ideal quantum simulations, demon- strating how performance varies with noise.
S. Hamidi-Rad and S. Jain, MCformer: A Transformer Based Deep Neural Network for Automatic Modulation Classification, 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685815.
Abstract:
In this paper, we propose MCformer - a novel deep neural network for the automatic modulation classification task of complex-valued raw radio signals. MCformer architecture leverages convolution layer along with self-attention based encoder layers to efficiently exploit temporal correlation between the embeddings produced by convolution layer. MCformer provides state of the art classification accuracy at all signal-to-noise ratios in the RadioML2016.10b data-set with significantly less number of parameters which is critical for fast and energy-efficient operation.
T. H. Huang, A. Malhotra and S. Hamidi-Rad, A Deep Learning Method for Joint Compression and Unsupervised Denoising of CSI Feedback, ICC 2023 - IEEE International Conference on Communications, Rome, Italy, 2023, pp. 4150-4156, doi: 10.1109/ICC45041.2023.10279775.
Abstract:
In this work, we propose a deep learning approach for jointly compressing and denoising the CSI feedback in massive MIMO systems. We consider a practical scenario where only noisy CSI is available for training and inference. To jointly denoise and compress the CSI feedback for improved reconstruction quality without having access to true CSI, we propose a novel generic loss function based on the Stein's unbiased risk estimator (SURE) for unsupervised denoising, and the evidence lower bound (ELBO) for CSI compression. This is in contrast to most existing supervised denoising methods that either require knowledge of the true CSI or are limited to high SNR regimes. Empirically, we show that the proposed approach improves the reconstruction quality of the state-of-the-art method. Moreover, the proposed approach is independent of the choice of the encoder-decoder architecture and can be easily extended to the existing volume of work on this topic.
A. Kumar, A. Malhotra and S. Hamidi-Rad, Group Sparsity via Implicit Regularization for MIMO Channel Estimation, 2023 IEEE Wireless Communications and Networking Conference (WCNC), Glasgow, United Kingdom, 2023, pp. 1-6, doi: 10.1109/WCNC55385.2023.10118737.
Abstract:
To compensate for the path losses in millimeter wave (mmWave) communication, multiple-input-multiple-output (MIMO) systems leverage beamforming-based solutions to boost the received SNR. Since beamforming requires knowledge of the wireless channel, its performance is also closely tied with the channel estimation accuracy. In mmWave communication, wireless channels are known to have only a few dominant paths. Several channel estimation methods leverage this characteristic by either using low-rank methods or by modeling element-wise sparsity of channels in angular domain. In this work we propose that the channel in angular domain is better characterized as group sparse rather than element-wise sparse. We further leverage the recent advancements in implicit regularization with gradient descent to develop a non-convex formulation that implicitly enforces group sparsity without additional regularization terms. We further compare the performance of the proposed approach against existing low rank or sparsity based matrix completion methods for channel estimation.
S. Kumar, A. Malhotra and S. Hamidi-Rad, Channel Parameter Estimation in Wireless Communication: A Deep Learning Perspective, 2024 IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), Stockholm, Sweden, 2024, pp. 511-516, doi: 10.1109/ICMLCN59089.2024.10624782.
Abstract:
While advances in Machine Learning have revolutionized certain areas (computer vision, robotics, natural language processing, etc.), the application in wireless communications has been less dramatic. One limiting factor is the (potentially) high computational complexity. Yet another important inhibitor is the lack of realistic datasets. To fully understand the potential of deep learning based methods to solve wireless communication problems, it is critical to leverage representative datasets, i.e., datasets that are captured in a very wide variety of scenarios, conditions and configurations – that fully reflect the places where wireless networks are deployed and used. Collecting over-the-air data (OTA) at scale in all of these settings – the gold standard – is extremely challenging and impractical. In the absence of such, machine learning practitioners are forced to rely on a handful of indoor and outdoor simulation scenarios with limited variability for model training and validation. Adding new scenarios into simulation has been limited by the significant effort and time required to accurately capture the multipath characteristics in real environments. Having the capability to extract multipath parameters from OTA channel samples would allow for faster characterization of different scenarios and a quick turn-around time towards having them introduced in simulation. Taking a step towards this goal, in this paper, we explore the use of deep learning based inverse modeling frameworks for extracting multipath channel information from the channel impulse response. Specifically, we focus on extracting the multipath parameters from captured Multiple-Input-Multiple-Output (MIMO) channel matrices.
A. S. M. M. Jameel, A. Malhotra, A. E. Gamal and S. Hamidi-Rad, Deep OFDM Channel Estimation: Capturing Frequency Recurrence in IEEE Communications Letters, vol. 28, no. 3, pp. 562-566, March 2024, doi: 10.1109/LCOMM.2024.3350369.
Abstract:
In this letter, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios.
M. S. Ibrahim, A. Malhotra, M. Beluri, A. Roy and S. Hamidi-Rad, Vandermonde Constrained Tensor Decomposition for Hybrid Beamforming in Multi-Carrier MIMO Systems, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 2474-2479, doi: 10.1109/GLOBECOM48099.2022.10001539.
Abstract:
Hybrid beamforming has evolved as a promising technology that offers the balance between system performance and design complexity in mmWave MIMO systems. Existing hybrid beamforming methods either impose unit-modulus constraints or a codebook constraint on the analog precoders/combiners, which in turn results in a performance-overhead tradeoff. This paper puts forth a tensor framework to handle the wideband hybrid beamforming problem, with Vandermonde constraints on the analog precoders/combiners. The proposed method strikes the balance between performance, overhead and complexity. Numerical results on a 3GPP link-level test bench reveal the efficacy of the proposed approach relative to the codebook-based method while attaining the same feedback overhead. Moreover, the proposed method is shown to achieve comparable performance to the unit-modulus approaches, with substantial reductions in overhead.
A. Malhotra, M. S. Ibrahim, S. Hamidi-Rad, M. Beluri and A. Roy, Fast Global Optimization for Hybrid Beamforming in Limited Feedback mmWave Systems, in IEEE Wireless Communications Letters, vol. 11, no. 12, pp. 2590-2594, Dec. 2022, doi: 10.1109/LWC.2022.3210475.
Abstract:
Hybrid beamforming provides a cost effective strategy towards practical deployment of massive multiple-input multiple-output (MIMO) systems. Since the hybrid precoder-combiner evaluation requires channel state information, the computation is performed at the receiver and the evaluated precoders are communicated back to the transmitter. This transmission overhead associated with the precoder feedback can be large when the number of transmit antennas is high. In this letter, we study the optimization associated with hybrid beamforming in limited feedback systems. We propose an efficient solution for the optimization under a codebook constraint and prove that the proposed solution is globally optimal when the codebook is orthonormal. The proposed approach is also shown to provide superior performance compared to existing approaches in a limited feedback setup. Further, it exhibits a computational complexity far lower than existing alternatives, making it feasible for deployment in real, resource constrained systems.
S. Jain, S. Hamidi-Rad and F. Racape, Low Rank Based End-to-End Deep Neural Network Compression, 2021 Data Compression Conference (DCC), Snowbird, UT, USA, 2021, pp. 233-242, doi: 10.1109/DCC50243.2021.00031.
Abstract:
Deep neural networks (DNNs), despite their performance on a wide variety of tasks, are still out of reach for many applications as they require significant computational resources. In this paper, we present a low-rank based end-to-end deep neural network compression framework with the goal of enabling DNNs performance to computationally constrained devices. The proposed framework includes techniques for low-rank based structural approximation, quantization and lossless arithmetic coding. Many of these techniques have been accepted in the MPEG working draft on compressed Neural Network Representations. We demonstrate the efficacy of the proposed framework via extensive experiments on a variety of DNNs for various tasks considered in this standardization activity. These techniques provide impressive performance on DNNs used in ImageNet Large-Scale Visual Recognition Challenge by compressing VGG16 by 61x, ResNet50 by almost 15x, and MobileNetV2 by almost 7x.
H. Kirchhoffer et al., Overview of the Neural Network Compression and Representation (NNR) Standard, in IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 5, pp. 3203-3216, May 2022, doi: 10.1109/TCSVT.2021.3095970.
Abstract:
Neural Network Coding and Representation (NNR) is the first international standard for efficient compression of neural networks (NNs). The standard is designed as a toolbox of compression methods, which can be used to create coding pipelines. It can be either used as an independent coding framework (with its own bitstream format) or together with external neural network formats and frameworks. For providing the highest degree of flexibility, the network compression methods operate per parameter tensor in order to always ensure proper decoding, even if no structure information is provided. The NNR standard contains compression-efficient quantization and deep context-adaptive binary arithmetic coding (DeepCABAC) as core encoding and decoding technologies, as well as neural network parameter pre-processing methods like sparsification, pruning, low-rank decomposition, unification, local scaling and batch norm folding. NNR achieves a compression efficiency of more than 97% for transparent coding cases, i.e. without degrading classification quality, such as top-1 or top-5 accuracies. This paper provides an overview of the technical features and characteristics of NNR.
S. Hamidi-Rad, K. Lyons and N. Goela, Infrastructure-less indoor localization using light fingerprints, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA, 2017, pp. 5995-5999, doi: 10.1109/ICASSP.2017.7953307.
Abstract:
An infrastructure-less indoor localization system is proposed based on fingerprints of light signals acquired at high frequencies. In contrast to other systems that modulate lights, the proposed system distinguishes lights by learning from training samples. Due to slight differences in the electronic components used in the construction of compact fluorescent light (CFL) and light emitting diode (LED) bulbs, the optical signals emitted by each light bulb have slight differences with other light bulbs even within the same brand and model. Light signals are digitized with a fast and accurate analog-to-digital converter (ADC) at up to 1 mega-samples/second, segmented, and mapped into the frequency domain using the Fast Fourier Transform (FFT). Spectral features based on the FFT are filtered, normalized, and used as training data for supervised machine learning algorithms. Results are provided for two classifiers of varying complexity: (1) A k-Nearest Neighbor (KNN) classifier; (2) A Convolutional Neural Net (CNN) classifier. A hardware system for indoor localization was designed to analyze the performance of the classifiers. Under certain restrictions, results show that light bulbs may be identified with high accuracy without special infrastructure for modulation. Identifying a light bulb is meant to be synonymous with identifying its associated location.
H. Choi, F. Racape, S. Hamidi-Rad, M. Ulhaq and S. Feltman, Frequency-aware Learned Image Compression for Quality Scalability, 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP), Suzhou, China, 2022, pp. 1-5, doi: 10.1109/VCIP56404.2022.10008818.
Abstract:
Spatial frequency analysis and transforms serve a central role in most engineered image and video lossy codecs, but are rarely employed in neural network (NN)-based approaches. We propose a novel NN-based image coding framework that utilizes forward wavelet transforms to decompose the input signal by spatial frequency. Our encoder generates separate bitstreams for each latent representation of low and high frequencies. This enables our decoder to selectively decode bitstreams in a quality-scalable manner. Hence, the decoder can produce an enhanced image by using an enhancement bitstream in addition to the base bitstream. Furthermore, our method is able to enhance only a specific region of interest (ROI) by using a corresponding part of the enhancement latent representation. Our experiments demonstrate that the proposed method shows competitive rate-distortion performance compared to several non-scalable image codecs. We also showcase the effectiveness of our two-level quality scalability, as well as its practicality in ROI quality enhancement.