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Ufuk Altun

Bio: Ufuk Altun is an academic researcher from Istanbul Technical University. The author has contributed to research in topics: Computer science & Communication channel. The author has an hindex of 1, co-authored 6 publications receiving 4 citations.

Papers
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Journal ArticleDOI
TL;DR: This article focuses its attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security.
Abstract: Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising physical layer concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output systems, sophisticated multi-carrier waveform designs, reconfigurable intelligent surface-empowered communications, and physical layer security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based multiple-input multiple-output by sharing user-friendly code snippets, which might be useful for interested readers.

11 citations

Proceedings ArticleDOI
01 Nov 2017
TL;DR: The usage of analog function computation (AFC) strategy is studied to combine communication and computation as a case study targeting a wireless sensor network and the obtained benefits when compared to a time division multiple access (TDMA) based transmission scheme are highlighted.
Abstract: Traditional wireless sensor network applications separate the functionalities for communication and computation. However, through simultaneous transmission and superposition property, multiple access wireless channels can enable joint communication and computation. Recent studies successfully highlight the benefit that can be harnessed from the wireless transmission channel in order to improve the network throughput and to reduce the computational load. In this paper, the usage of analog function computation (AFC) strategy is studied to combine communication and computation as a case study targeting a wireless sensor network. The proposed network executes summation, multiplication, maximum and minimum operations on channel with the help of pre-processing and post-processing functions. Mathematical model of the network is supported with computer simulations and the obtained benefits when compared to a time division multiple access (TDMA) based transmission scheme are highlighted. The real-time implementation of the sensor network is achieved by using software defined radio (SDR) nodes and extensive tests are run for summation operation in order to investigate the performance of the network.

9 citations

Journal ArticleDOI
TL;DR: This letter study distinguishing individual observations from the aggregated data by including unique prime identifiers to pre-processing functions to equip the network with a simple authentication mechanism while removing the dependency on public key algorithms or trusted third parties.
Abstract: Conventional analog function computation (AFC) is an effective data aggregation technique that combines communication and computation to improve time efficiency and scalability. In this letter, different from the conventional AFC, we study distinguishing individual observations from the aggregated data by including unique prime identifiers to pre -processing functions. Prime identifiers can be used to equip the network with a simple authentication mechanism while removing the dependency on public key algorithms or trusted third parties. The resilience of the proposed scheme to spoofing attacks and non-ideal conditions including the channel estimation errors and the thermal noise is investigated with computer simulations.

4 citations

Posted Content
TL;DR: In this paper, the authors classify the existing literature on the superposition property and present their contributions for wireless communication techniques that benefit from superposition of signals in the Internet of Things (IoT).
Abstract: In conventional communication systems, any interference between two communicating points is regarded as unwanted noise since it distorts the received signals. On the other hand, allowing simultaneous transmission and intentionally accepting the interference of signals and even benefiting from it have been considered for a range of wireless applications. As prominent examples, non-orthogonal multiple access (NOMA), joint source-channel coding, and the computation codes are designed to exploit this scenario. They also inspired many other fundamental works from network coding to consensus algorithms. Especially, federated learning is an emerging technology that can be applied to distributed machine learning networks by allowing simultaneous transmission. Although various simultaneous transmission applications exist independently in the literature, their main contributions are all based on the same principle; the superposition property. In this survey, we aim to emphasize the connections between these studies and provide a guide for the readers on the wireless communication techniques that benefit from the superposition of signals. We classify the existing literature depending on their purpose and application area and present their contributions. The survey shows that simultaneous transmission can bring scalability, security, low-latency, low-complexity and energy efficiency for certain distributed wireless scenarios which are inevitable with the emerging Internet of things (IoT) applications.

4 citations

Proceedings ArticleDOI
05 Oct 2020
TL;DR: In this paper, the effect of least square and linear minimum mean squared error (LMMSE) channel estimation algorithms on the performance of OFDM-IM is investigated in the presence of barrage jamming.
Abstract: Orthogonal frequency division multiplexing (OFDM) with index modulation (OFDM-IM), which transmits information bits both by M-ary constellation symbols and indices of active subcarriers that are selected according to input bits, has attracted the attention of researchers in the recent years. OFDM-IM is a promising candidate for future wireless communication systems, due to its performance gain over OFDM. In this paper, the effect of least square and linear minimum mean squared error (LMMSE) channel estimation algorithms on the performance of OFDM-IM are investigated in the presence of barrage jamming. LMMSE algorithm's robustness against pilot jamming attacks is shown via computer simulations.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: The vision for 6G is furnishes the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology and main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals.
Abstract: The fifth-generation mobile network (5G), as the fundamental enabler of Industry 4.0, has facilitated digital transformation and smart manufacturing through AI and cloud computing (CC). However, B5G is viewed as a turning point that will fundamentally transform existing global trends in wireless communication practices as well as in the lives of masses. B5G foresees a world where physical–digital confluence takes place. This study intends to see the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology. However, despite several developments, the dream of an era without latency, unprecedented speed internet, and extraterrestrial communication has yet to become a reality. This article explores main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals. This article furnishes the vision for 6G, facilitating technology infrastructures, challenges, and research leads towards the ultimate achievement of “technology for humanity” objective and better service to underprivileged people.

15 citations

Journal ArticleDOI
TL;DR: This paper provides an overview of current theoretical and application prospects of IoT, AI, cloud computing, edge computing, deep learning techniques, blockchain technologies, social networks, robots, machines, privacy, and security techniques in intersection with COVID-19 pandemic.
Abstract: The origin of the COVID-19 pandemic has given overture to redirection, as well as innovation to many digital technologies. Even after the progression of vaccination efforts across the globe, total eradication of this pandemic is still a distant future due to the evolution of new variants. To proactively deal with the pandemic, the health care service providers and the caretaker organizations require new technologies, alongside with improvements in existing related technologies, Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning in terms of infrastructure, efficiency, privacy, and security. This paper provides an overview of current theoretical and application prospects of IoT, AI, cloud computing, edge computing, deep learning techniques, blockchain technologies, social networks, robots, machines, privacy, and security techniques. In consideration of these prospects in intersection with COVID-19 pandemic, we reviewed the technologies within the broad umbrella of AI-IoT technologies in the most concise classification scheme. In this review, we illustrated that AI-IoT technological applications and innovations have most impacted the field of healthcare. The essential AI-IoT technologies found for healthcare were fog computing in IoT, deep learning, and blockchain. Furthermore, we highlighted several aspects of these technologies and their future impact with a novel methodology of using techniques from image processing, machine learning and differential system modeling.

6 citations

Posted Content
TL;DR: In this paper, an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL) is proposed.
Abstract: In this paper, we develop an orthogonal-frequency-division-multiplexing (OFDM)-based over-the-air (OTA) aggregation solution for wireless federated learning (FL). In particular, the local gradients in massive IoT devices are modulated by an analog waveform and are then transmitted using the same wireless resources. To this end, achieving perfect waveform superposition is the key challenge, which is difficult due to the existence of frame timing offset (TO) and carrier frequency offset (CFO). In order to address these issues, we propose a two-stage waveform pre-equalization technique with a customized multiple access protocol that can estimate and then mitigate the TO and CFO for the OTA aggregation. Based on the proposed solution, we develop a hardware transceiver and application software to train a real-world FL task, which learns a deep neural network to predict the received signal strength with global positioning system information. Experiments verify that the proposed OTA aggregation solution can achieve comparable performance to offline learning procedures with high prediction accuracy.

6 citations

Journal ArticleDOI
TL;DR: In this article , the authors proposed two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence-based models used in the millimeter-wave (mmWave) beamforming prediction.
Abstract: The design of a security scheme for beamforming prediction is critical for next-generation wireless networks (5G, 6G, and beyond). However, there is no consensus about protecting beamforming prediction using deep learning algorithms in these networks. This paper presents the security vulnerabilities in deep learning for beamforming prediction using deep neural networks in 6G wireless networks, which treats the beamforming prediction as a multi-output regression problem. It is indicated that the initial DNN model is vulnerable to adversarial attacks, such as Fast Gradient Sign Method , Basic Iterative Method , Projected Gradient Descent , and Momentum Iterative Method , because the initial DNN model is sensitive to the perturbations of the adversarial samples of the training data. This study offers two mitigation methods, such as adversarial training and defensive distillation, for adversarial attacks against artificial intelligence-based models used in the millimeter-wave (mmWave) beamforming prediction. Furthermore, the proposed scheme can be used in situations where the data are corrupted due to the adversarial examples in the training data. Experimental results show that the proposed methods defend the DNN models against adversarial attacks in next-generation wireless networks.

5 citations

Journal ArticleDOI
20 Oct 2022
TL;DR: This study provides a comprehensive survey on practical OAC methods and discusses the available OAC schemes with their pros and cons, and summarizes the potential applications of OAC and point out some future directions.
Abstract: Communication and computation are often viewed as separate tasks. This approach is very effective from the perspective of engineering as isolated optimizations can be performed. However, for many computation-oriented applications, the main interest is a function of the local information at the devices, rather than the local information itself. In such scenarios, information theoretical results show that harnessing the interference in a multiple access channel for computation, i.e., over-the-air computation (OAC), can provide a significantly higher achievable computation rate than separating communication and computation tasks. Moreover, the gap between OAC and separation in terms of computation rate increases with more participating nodes. Given this motivation, in this study, we provide a comprehensive survey on practical OAC methods. After outlining fundamentals related to OAC, we discuss the available OAC schemes with their pros and cons. We provide an overview of the enabling mechanisms for achieving reliable computation in the wireless channel. Finally, we summarize the potential applications of OAC and point out some future directions.

4 citations