arXiv subjects

Deniz Gunduz

Publications and source records attributed to Deniz Gunduz.

At least 37 records · Page 2Linked to original sources

arXiv2025

Towards AI-Native Fronthaul: Neural Compression for NextG Cloud RAN

The rapid growth of data traffic and the emerging AI-native wireless architectures in NextG cellular systems place new demands on the fronthaul links of Cloud Radio Access Networks (C-RAN). In this paper, we investigate neural compression techniques for the Common Public Radio Interface (CPRI), aiming to reduce the fronthaul bandwidth while preserving signal quality. We introduce two deep learning-based compression algorithms designed to optimize the transformation of wireless signals into bit sequences for CPRI transmission. The first algorithm utilizes a non-linear transformation coupled with scalar/vector quantization based on a learned codebook. The second algorithm generates a latent vector transformed into a variable-length output bit sequence via arithmetic encoding, guided by the predicted probability distribution of each latent element. Novel techniques such as a shared weight model for storage-limited devices and a successive refinement model for managing multiple CPRI links with varying Quality of Service (QoS) are proposed. Extensive simulation results demonstrate notable Error Vector Magnitude (EVM) gains with improved rate-distortion performance for both algorithms compared to traditional methods. The proposed solutions are robust to variations in channel conditions, modulation formats, and noise levels, highlighting their potential for enabling efficient and scalable fronthaul in NextG AI-native networks as well as aligning with the current 3GPP research directions.

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arXiv2025

Over-the-Air Inference over Multi-hop MIMO Networks

A novel over-the-air machine learning framework over multi-hop multiple-input and multiple-output (MIMO) networks is proposed. The core idea is to imitate fully connected (FC) neural network layers using multiple MIMO channels by carefully designing the precoding matrices at the transmitting nodes. A neural network dubbed PrototypeNet is employed consisting of multiple FC layers, with the number of neurons of each layer equal to the number of antennas of the corresponding terminal. To achieve satisfactory performance, we train PrototypeNet based on a customized loss function consisting of classification error and the power of latent vectors to satisfy transmit power constraints, with noise injection during training. Precoding matrices for each hop are then obtained by solving an optimization problem. We also propose a multiple-block extension when the number of antennas is limited. Numerical results verify that the proposed over-the-air transmission scheme can achieve satisfactory classification accuracy under a power constraint. The results also show that higher classification accuracy can be achieved with an increasing number of hops at a modest signal-to-noise ratio (SNR).

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arXiv2025

Learning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding

We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional communication systems minimize interference by orthogonally allocating resources (time and/or bandwidth) among users, which limits their capacity. We introduce a machine learning (ML)-aided wireless image transmission method that merges compression and channel coding using a multi-view autoencoder, which allows the transmitters to use all the available channel resources simultaneously, resulting in a non-orthogonal multiple access (NOMA) scheme. The receiver must recover all the images from the received superposed signal, while also associating each image with its transmitter. Traditional ML models deal with individual samples, whereas our model allows signals from different users to interfere in order to leverage gains from NOMA under limited bandwidth and power constraints. We introduce a progressive fine-tuning algorithm that doubles the number of users at each iteration, maintaining initial performance with orthogonalized user-specific projections, which is then improved through fine-tuning steps. Remarkably, our method scales up to 16 users and beyond, with only a 0.6% increase in the number of trainable parameters compared to a single-user model, significantly enhancing recovered image quality and outperforming existing NOMA-based methods over a wide range of datasets, metrics, and channel conditions. Our approach paves the way for more efficient and robust multi-user communication systems, leveraging innovative ML components and strategies.

cs.NI↗

arXiv2025

CRC-Assisted Channel Codes for Integrated Passive Sensing and Communications

We propose a novel coded integrated passive sensing and communication (CIPSAC) system with orthogonal frequency division multiplexing (OFDM), where a multi-antenna base station (BS) passively senses the parameters of the targets and decodes the information bit sequences transmitted by a user. The transmitted signal is comprised of pilot and data OFDM symbols where the data symbols adopt cyclic redundancy check (CRC)-assisted channel codes to facilitate both the decoding and sensing procedures. In the proposed scheme, CRC not only enhances the reliability of communication but also provides guidance to the parameter sensing procedure at the BS. In particular, a novel iterative parameter sensing and channel decoding (IPSCD) algorithm is proposed, where the correctly decoded codewords that pass CRC are utilized for sensing to improve the parameter estimation accuracy, and in return, more accurate parameter estimates lead to a larger number of correctly decoded data symbols. Conventional sensing algorithms rely only on the received pilot signals, while we utilize both the data and pilot signals for sensing. We provide a detailed analysis of the optimal strategy, in which the wrongly decoded data packets are replaced by zero codewords. To further improve the performance, we introduce learning-based near-orthogonal superposition (NOS) codes, which exhibit superior error correction capability especially in the short block length regime. NOS codes are trained using a weighted loss function, where a hyper parameter is introduced to balance the sensing and the communication losses. Simulation results show the effectiveness of the proposed CIPSAC system and the IPSCD algorithm, where both the sensing and decoding performances are significantly improved with a few iterations. We also carry out extensive ablation studies for a comprehensive understanding of the proposed scheme.

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arXiv2025

ODMA-Based Cell-Free Unsourced Random Access with Successive Interference Cancellation

We consider the unsourced random access problem with multiple receivers and propose a cell-free type solution for that. In our proposed scheme, the active users transmit their signals to the access points (APs) distributed in a geographical area and connected to a central processing unit (CPU). The transmitted signals are composed of a pilot and polar codeword, where the polar codeword bits occupy a small fraction of the data part of the transmission frame. The receiver operations of pilot detection and channel and symbol estimation take place at the APs, while the actual message bits are detected at the CPU by combining the symbol estimates from the APs forwarded over the fronthaul. The effect of the successfully decoded messages is then subtracted at the APs. Numerical examples illustrate that the proposed scheme can support up to 1400 users with a high energy efficiency, and the distributed structure decreases the error probability by more than two orders of magnitude.

cs.ET↗

arXiv2025

Private Collaborative Edge Inference via Over-the-Air Computation

We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accurate decision collaboratively. In addition to maximizing the inference accuracy, we also want to ensure the privacy of local models. To this end, we leverage the superposition property of the multiple access channel to implement bandwidth-efficient multi-user inference methods. We propose different methods for ensemble and multi-view classification that exploit over-the-air computation (OAC). We show that these schemes perform better than their orthogonal counterparts with statistically significant differences while using fewer resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed OAC approach to multi-user inference, and perform an ablation study to demonstrate the effectiveness of our design choices. We share the source code of the framework publicly on Github to facilitate further research and reproducibility.

cs.LG↗

arXiv2024

Variable-Length Feedback Codes via Deep Learning

Variable-length feedback coding has the potential to significantly enhance communication reliability in finite block length scenarios by adapting coding strategies based on real-time receiver feedback. Designing such codes, however, is challenging. While deep learning (DL) has been employed to design sophisticated feedback codes, existing DL-aided feedback codes are predominantly fixed-length and suffer performance degradation in the high code rate regime, limiting their adaptability and efficiency. This paper introduces deep variable-length feedback (DeepVLF) code, a novel DL-aided variable-length feedback coding scheme. By segmenting messages into multiple bit groups and employing a threshold-based decoding mechanism for independent decoding of each bit group across successive communication rounds, DeepVLF outperforms existing DL-based feedback codes and establishes a new benchmark in feedback channel coding.

cs.IT↗

arXiv2024

Process-and-Forward: Deep Joint Source-Channel Coding Over Cooperative Relay Networks

We introduce deep joint source-channel coding (DeepJSCC) schemes for image transmission over cooperative relay channels. The relay either amplifies-and-forwards its received signal, called DeepJSCC-AF, or leverages neural networks to extract relevant features from its received signal, called DeepJSCC-PF (Process-and-Forward). We consider both half- and full-duplex relays, and propose a novel transformer-based model at the relay. For a half-duplex relay, it is shown that the proposed scheme learns to generate correlated signals at the relay and source to obtain beamforming gains. In the full-duplex case, we introduce a novel block-based transmission strategy, in which the source transmits in blocks, and the relay updates its knowledge about the input signal after each block and generates its own signal. To enhance practicality, a single transformer-based model is used at the relay at each block, together with an adaptive transmission module, which allows the model to seamlessly adapt to different channel qualities and the transmission powers}. Simulation results demonstrate the superior performance of DeepJSCC-PF compared to the state-of-the-art BPG image compression algorithm operating at the maximum achievable rate of conventional decode-and-forward and compress-and-forward protocols, in both half- and full-duplex relay scenarios over AWGN and Rayleigh fading channels.

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arXiv2024

Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk in Edge Computing

Privacy-preserving inference in edge computing paradigms encourages the users of machine-learning services to locally run a model on their private input and only share the models outputs for a target task with the server. We study how a vicious server can reconstruct the input data by observing only the models outputs while keeping the target accuracy very close to that of a honest server by jointly training a target model (to run at users' side) and an attack model for data reconstruction (to secretly use at servers' side). We present a new measure to assess the inference-time reconstruction risk. Evaluations on six benchmark datasets show the model's input can be approximately reconstructed from the outputs of a single inference. We propose a primary defense mechanism to distinguish vicious versus honest classifiers at inference time. By studying such a risk associated with emerging ML services our work has implications for enhancing privacy in edge computing. We discuss open challenges and directions for future studies and release our code as a benchmark for the community at https://github.com/mmalekzadeh/vicious-classifiers .

cs.LG↗

arXiv2024

High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models

We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver. Specifically, we are interested in the perception-distortion trade-off in the practical finite block length regime, in which separate source and channel coding can be highly suboptimal. We introduce a novel scheme, where the conventional DeepJSCC encoder targets transmitting a lower resolution version of the image, which later can be refined thanks to the generative model available at the receiver. In particular, we utilize the range-null space decomposition of the target image; DeepJSCC transmits the range-space of the image, while DDPM progressively refines its null space contents. Through extensive experiments, we demonstrate significant improvements in distortion and perceptual quality of reconstructed images compared to standard DeepJSCC and the state-of-the-art generative learning-based method.

eess.IV↗

arXiv2024

Graph Neural Networks over the Air for Decentralized Tasks in Wireless Networks

Graph neural networks (GNNs) model representations from networked data and allow for decentralized inference through localized communications. Existing GNN architectures often assume ideal communications and ignore potential channel effects, such as fading and noise, leading to performance degradation in real-world implementation. Considering a GNN implemented over nodes connected through wireless links, this paper conducts a stability analysis to study the impact of channel impairments on the performance of GNNs, and proposes graph neural networks over the air (AirGNNs), a novel GNN architecture that incorporates the communication model. AirGNNs modify graph convolutional operations that shift graph signals over random communication graphs to take into account channel fading and noise when aggregating features from neighbors, thus, improving architecture robustness to channel impairments during testing. We develop a channel-inversion signal transmission strategy for AirGNNs when channel state information (CSI) is available, and propose a stochastic gradient descent based method to train AirGNNs when CSI is unknown. The convergence analysis shows that the training procedure approaches a stationary solution of an associated stochastic optimization problem and the variance analysis characterizes the statistical behavior of the trained model. Experiments on decentralized source localization and multi-robot flocking corroborate theoretical findings and show superior performance of AirGNNs over wireless communication channels.

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arXiv2024

Adaptive Compression in Federated Learning via Side Information

The high communication cost of sending model updates from the clients to the server is a significant bottleneck for scalable federated learning (FL). Among existing approaches, state-of-the-art bitrate-accuracy tradeoffs have been achieved using stochastic compression methods -- in which the client $n$ sends a sample from a client-only probability distribution $q_{ϕ^{(n)}}$, and the server estimates the mean of the clients' distributions using these samples. However, such methods do not take full advantage of the FL setup where the server, throughout the training process, has side information in the form of a global distribution $p_θ$ that is close to the clients' distribution $q_{ϕ^{(n)}}$ in Kullback-Leibler (KL) divergence. In this work, we exploit this closeness between the clients' distributions $q_{ϕ^{(n)}}$'s and the side information $p_θ$ at the server, and propose a framework that requires approximately $D_{KL}(q_{ϕ^{(n)}}|| p_θ)$ bits of communication. We show that our method can be integrated into many existing stochastic compression frameworks to attain the same (and often higher) test accuracy with up to $82$ times smaller bitrate than the prior work -- corresponding to 2,650 times overall compression.

cs.LG↗

arXiv2024

Deep Joint Source-Channel Coding Over Cooperative Relay Networks

This paper presents a novel deep joint source-channel coding (DeepJSCC) scheme for image transmission over a half-duplex cooperative relay channel. Specifically, we apply DeepJSCC to two basic modes of cooperative communications, namely amplify-and-forward (AF) and decode-and-forward (DF). In DeepJSCC-AF, the relay simply amplifies and forwards its received signal. In DeepJSCC-DF, on the other hand, the relay first reconstructs the transmitted image and then re-encodes it before forwarding. Considering the excessive computation overhead of DeepJSCC-DF for recovering the image at the relay, we propose an alternative scheme, called DeepJSCC-PF, in which the relay processes and forwards its received signal without necessarily recovering the image. Simulation results show that the proposed DeepJSCC-AF, DF, and PF schemes are superior to the digital baselines with BPG compression with polar codes and provides a graceful performance degradation with deteriorating channel quality. Further investigation shows that the PSNR gain of DeepJSCC-DF/PF over DeepJSCC-AF improves as the channel condition between the source and relay improves. Moreover, DeepJSCC-PF scheme achieves a similar performance to DeepJSCC-DF with lower computational complexity.

cs.IT↗

arXiv2024

Distributed Deep Joint Source-Channel Coding with Decoder-Only Side Information

We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side (the Wyner-Ziv scenario). In particular, we are interested in developing practical schemes using a data-driven joint source-channel coding (JSCC) approach, which has been previously shown to outperform conventional separation-based approaches in the practical finite blocklength regimes, and to provide graceful degradation with channel quality. We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side. Our results demonstrate that the proposed method succeeds in integrating the side information, yielding improved performance at all channel conditions in terms of the various quality measures considered here, especially at low channel signal-to-noise ratios (SNRs) and small bandwidth ratios (BRs). We have made the source code of the proposed method public to enable further research, and the reproducibility of the results.

cs.CV↗

arXiv2024

A Hybrid Joint Source-Channel Coding Scheme for Mobile Multi-hop Networks

We propose a novel hybrid joint source-channel coding (JSCC) scheme for robust image transmission over multi-hop networks. In the considered scenario, a mobile user wants to deliver an image to its destination over a mobile cellular network. We assume a practical setting, where the links between the nodes belonging to the mobile core network are stable and of high quality, while the link between the mobile user and the first node (e.g., the access point) is potentially time-varying with poorer quality. In recent years, neural network based JSCC schemes (called DeepJSCC) have emerged as promising solutions to overcome the limitations of separation-based fully digital schemes. However, relying on analog transmission, DeepJSCC suffers from noise accumulation over multi-hop networks. Moreover, most of the hops within the mobile core network may be high-capacity wireless connections, calling for digital approaches. To this end, we propose a hybrid solution, where DeepJSCC is adopted for the first hop, while the received signal at the first relay is digitally compressed and forwarded through the mobile core network. We show through numerical simulations that the proposed scheme is able to outperform both the fully analog and fully digital schemes. Thanks to DeepJSCC it can avoid the cliff effect over the first hop, while also avoiding noise forwarding over the mobile core network thank to digital transmission. We believe this work paves the way for the practical deployment of DeepJSCC solutions in 6G and future wireless networks.

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arXiv2024

Remote Estimation of Markov Processes over Costly Channels: On the Benefits of Implicit Information

In this paper, we study the remote estimation problem of a Markov process over a channel with a cost. We formulate this problem as an infinite horizon optimization problem with two players, i.e., a sensor and a monitor, that have distinct information, and with a reward function that takes into account both the communication cost and the estimation quality. We show that the main challenge in solving this problem is associated with the consideration of implicit information, i.e., information that the monitor can obtain about the source when the sensor is silent. Our main objective is to develop a framework for finding solutions to this problem without neglecting implicit information a priori. To that end, we propose three different algorithms. The first one is an alternating policy algorithm that converges to a Nash equilibrium. The second one is an occupancy-state algorithm that is guaranteed to find a globally optimal solution. The last one is a heuristic algorithm that is able to find a near-optimal solution.

eess.SY↗

arXiv2024

Friendly Attacks to Improve Channel Coding Reliability

This paper introduces a novel approach called "friendly attack" aimed at enhancing the performance of error correction channel codes. Inspired by the concept of adversarial attacks, our method leverages the idea of introducing slight perturbations to the neural network input, resulting in a substantial impact on the network's performance. By introducing small perturbations to fixed-point modulated codewords before transmission, we effectively improve the decoder's performance without violating the input power constraint. The perturbation design is accomplished by a modified iterative fast gradient method. This study investigates various decoder architectures suitable for computing gradients to obtain the desired perturbations. Specifically, we consider belief propagation (BP) for LDPC codes; the error correcting code transformer, BP and neural BP (NBP) for polar codes, and neural BCJR for convolutional codes. We demonstrate that the proposed friendly attack method can improve the reliability across different channels, modulations, codes, and decoders. This method allows us to increase the reliability of communication with a legacy receiver by simply modifying the transmitted codeword appropriately.

cs.IT↗

arXiv2024

Point Cloud in the Air

Acquisition and processing of point clouds (PCs) is a crucial enabler for many emerging applications reliant on 3D spatial data, such as robot navigation, autonomous vehicles, and augmented reality. In most scenarios, PCs acquired by remote sensors must be transmitted to an edge server for fusion, segmentation, or inference. Wireless transmission of PCs not only puts on increased burden on the already congested wireless spectrum, but also confronts a unique set of challenges arising from the irregular and unstructured nature of PCs. In this paper, we meticulously delineate these challenges and offer a comprehensive examination of existing solutions while candidly acknowledging their inherent limitations. In response to these intricacies, we proffer four pragmatic solution frameworks, spanning advanced techniques, hybrid schemes, and distributed data aggregation approaches. In doing so, our goal is to chart a path toward efficient, reliable, and low-latency wireless PC transmission.

cs.IT↗