arXiv subjects

Fehmi Emre Kadan

Publications and source records attributed to Fehmi Emre Kadan.

7 recordsLinked to original sources

arXiv2026

ETSI ISG MAT: Bridging Multiple Access Techniques Research and 6G Standardisation

Improved spectral efficiency and inter-user interference mitigation are important aspects of IMT-2030 and the ongoing 3GPP 6G study. ETSI established the Industry Specification Group on Multiple Access Techniques (ISG MAT) as a research and pre-standardisation activity to build wider consensus on downlink MAT for 3GPP-based 6G systems. Its first report, ETSI GR MAT 001 V1.1.1, provides a standards-oriented comparison of 3GPP-specified techniques, including OMA, MU-MIMO, and MUST, with candidate techniques comprising power-domain NOMA, RSMA, and cache-aided MU-MIMO. The report identifies operating conditions in which candidate MAT can improve spectral efficiency and assesses implications for transceiver processing, network assistance information, and reference-signal requirements. Ongoing work includes realistic 5G NR link-level evaluations and the study of MAT for non-terrestrial networks, providing timely technical evidence for 3GPP 6G standardisation discussions.

eess.SP↗

arXiv2025

NeuromorphicRx: From Neural to Spiking Receiver

In this work, we propose a novel energy-efficient spiking neural network (SNN)-based receiver for 5G-NR OFDM system, called neuromorphic receiver (NeuromorphicRx), replacing the channel estimation, equalization and symbol demapping blocks. We leverage domain knowledge to design the input with spiking encoding and propose a deep convolutional SNN with spike-element-wise residual connections. We integrate an SNN with artificial neural network (ANN) hybrid architecture to obtain soft outputs and employ surrogate gradient descent for training. We focus on generalization across diverse scenarios and robustness through quantized aware training. We focus on interpretability of NeuromorphicRx for 5G-NR signals and perform detailed ablation study for 5G-NR signals. Our extensive numerical simulations show that NeuromorphicRx is capable of achieving significant block error rate performance gain compared to 5G-NR receivers and similar performance compared to its ANN-based counterparts with 7.6x less energy consumption.

cs.NE↗

arXiv2025

Low-Complexity OFDM Deep Neural Receivers

Deep neural receivers (NeuralRxs) for Orthogonal Frequency Division Multiplexing (OFDM) signals are proposed for enhanced decoding performance compared to their signal-processing based counterparts. However, the existing architectures ignore the required number of epochs for training convergence and floating-point operations (FLOPs), which increase significantly with improving performance. To tackle these challenges, we propose a new residual network (ResNet) block design for OFDM NeuralRx. Specifically, we leverage small kernel sizes and dilation rates to lower the number of FLOPs (NFLOPs) and uniform channel sizes to reduce the memory access cost (MAC). The ResNet block is designed with novel channel split and shuffle blocks, element-wise additions are removed, with Gaussian error linear unit (GELU) activations. Extensive simulations show that our proposed NeuralRx reduces NFLOPs and improves training convergence while improving the decoding accuracy.

cs.IT↗

arXiv2023

Alamouti-Like Transmission Schemes in Distributed MIMO Networks

The purpose of the study is to investigate potential benefits of using Alamouti-like orthogonal space-time-frequency block codes (STFBC) in distributed multiple-input multiple-output (D-MIMO) systems to increase the diversity at the UE side when instantaneous channel state information (CSI) is not available at radio units (RUs). Most of the existing transmission techniques require instantaneous CSI to form precoders which can only be realized together with accurate and up-to-date channel knowledge. STFBC can increase the diversity at UE side without estimating the downlink channel. Under challenging channel conditions, the network can switch to a robust mode where a certain data rate is maintained for users even without knowing the channel coefficients by means of STFBC. In this study, it will be mainly focused on clustering of RUs and user equipment, where each cluster adopts a possibly different orthogonal code, so that overall spectral efficiency is optimized. Potential performance gains over known techniques that can be used when the channel is not known will be shown and performance gaps to sophisticated precoders making use of channel estimates will be identified.

cs.IT↗

arXiv2023

Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network

In distributed multiple-input multiple-output (D-MIMO) networks, power control is crucial to optimize the spectral efficiencies of users and max-min fairness (MMF) power control is a commonly used strategy as it satisfies uniform quality-of-service to all users. The optimal solution of MMF power control requires high complexity operations and hence deep neural network based artificial intelligence (AI) solutions are proposed to decrease the complexity. Although quite accurate models can be achieved by using AI, these models have some intrinsic vulnerabilities against adversarial attacks where carefully crafted perturbations are applied to the input of the AI model. In this work, we show that threats against the target AI model which might be originated from malicious users or radio units can substantially decrease the network performance by applying a successful adversarial sample, even in the most constrained circumstances. We also demonstrate that the risk associated with these kinds of adversarial attacks is higher than the conventional attack threats. Detailed simulations reveal the effectiveness of adversarial attacks and the necessity of smart defense techniques.

eess.SP↗

arXiv2021

Beamformer Design with Smooth Constraint-Free Approximation in Downlink Cloud Radio Access Networks

It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network, which is a candidate solution in 5G and beyond, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink scenario and aim to minimize total power spent by designing beamformers. We consider the case where perfect channel state information is not available in the CP. The original problem includes discontinuous terms with many constraints. We propose a novel method which transforms the problem into a smooth constraint-free form and a solution is found by the gradient descent approach. As a comparison, we consider the optimal method solving an extensive number of convex sub-problems, a known heuristic search algorithm and some sparse solution techniques. Heuristic search methods find a solution by solving a subset of all possible convex sub-problems. Sparse techniques apply some norm approximation ($\ell_0/\ell_1, \ell_0/\ell_2$) or convex approximation to make the objective function more tractable. We also derive a theoretical performance bound in order to observe how far the proposed method performs off the optimal method when running the optimal method is prohibitive due to computational complexity. Detailed simulations show that the performance of the proposed method is close to the optimal one, and it outperforms other methods analyzed.

cs.IT↗

arXiv2021

A Theoretical Performance Bound for Joint Beamformer Design of Wireless Fronthaul and Access Links in Downlink C-RAN

It is known that data rates in standard cellular networks are limited due to inter-cell interference. An effective solution of this problem is to use the multi-cell cooperation idea. In Cloud Radio Access Network (C-RAN), which is a candidate solution in 5G and future communication networks, cooperation is applied by means of central processors (CPs) connected to simple remote radio heads with finite capacity fronthaul links. In this study, we consider a downlink C-RAN with a wireless fronthaul and aim to minimize total power spent by jointly designing beamformers for fronthaul and access links. We consider the case where perfect channel state information is not available in the CP. We first derive a novel theoretical performance bound for the problem defined. Then we propose four algorithms with different complexities to show the tightness of the bound. The first two algorithms apply successive convex optimizations with semi-definite relaxation idea where other two are adapted from well-known beamforming design methods. The detailed simulations under realistic channel conditions show that as the complexity of the algorithm increases, the corresponding performance becomes closer to the bound.

cs.IT↗