arXiv subjects

Caner Goztepe

Publications and source records attributed to Caner Goztepe.

4 recordsLinked to original sources

arXiv2023

An Experimental Study of NOMA for Connected Autonomous Vehicles

Connected autonomous vehicles (CAV) constitute an important application of future-oriented traffic management .A vehicular system dominated by fully autonomous vehicles requires a robust and efficient vehicle-to-everything (V2X) infrastructure that will provide sturdy connection of vehicles in both short and long distances for a large number of devices, requiring high spectral efficiency (SE). Power domain non-orthogonal multiple access (PD-NOMA) technique has the potential to provide the required high SE levels. In this paper, a vehicular PD-NOMA testbed is implemented using software defined radio (SDR) nodes. The main concerns and their corresponding solutions arising from the implementation are highlighted. The bit error rates(BER) of vehicles with different channel conditions are measured for mobile and stationary cases. The extent of the estimation errors on the success rate beyond the idealized theoretical analysis view is investigated and the approaches to alleviate these errors are discussed. Finally, our perspective on possible PD-NOMA based CAV deployment scenarios is presented in terms of performance constraints and expectancy along with the overlooked open issues.

eess.SP↗

arXiv2022

A Glimpse of Physical Layer Decision Mechanisms: Facts, Challenges, and Remedies

Communications are realized as a result of successive decisions at the physical layer, from modulation selection to multi-antenna strategy, and each decision affects the performance of the communication systems. Future communication systems must include extensive capabilities as they will encompass a wide variety of devices and applications. Conventional physical layer decision mechanisms may not meet these requirements, as they are often based on impractical and oversimplifying assumptions that result in a trade-off between complexity and efficiency. By leveraging past experiences, learning-driven designs are promising solutions to present a resilient decision mechanism and enable rapid response even under exceptional circumstances. The corresponding design solutions should evolve following the lines of learning-driven paradigms that offer more autonomy and robustness. This evolution must take place by considering the facts of real-world systems and without restraining assumptions. In this paper, the common assumptions in the physical layer are presented to highlight their discrepancies with practical systems. As a solution, learning algorithms are examined by considering the implementation steps and challenges. Furthermore, these issues are discussed through a real-time case study using software-defined radio nodes to demonstrate the potential performance improvement. A cyber-physical framework is presented to incorporate future remedies.

cs.LG↗

arXiv2021

Localization Threats in Next-Generation Wireless Networks

The impact of localization systems in our daily lives is increasing. As next-generation networks will introduce hyperconnectivity with the emerging applications, this impact will undoubtedly further increase, proliferating the importance of the location information's reliability. As society becomes more dependent on this information in terms of the products and services, security solutions will have to be enriched to provide countermeasures sufficiently advanced to ever-evolving threats, forcing the joint design of communication and localization systems. This paper envisions integrated communication and localization systems by focusing on localization security. Also, conventional and next-generation attacks on localization are discussed along with an efficient attack detection method and test-bed-based demonstration, highlighting the need for effective countermeasures.

cs.CR↗

arXiv2019

Transmit Antenna Selection for Massive MIMO-GSM with Machine Learning

A dynamic and flexible generalized spatial modulation (GSM) framework is proposed for massive MIMO systems. Our framework is leveraged on the utilization of machine learning methods for GSM in order to improve the error performance in presence of correlated channels and channel estimation errors. Both decision tree and multi-layer perceptrons approaches are adopted for the GSM transmitter. Simulation results indicate that in presence of real-life impairments machine learning based approaches provide a superior performance when compared to the classical Euclidean distance based approach. The observations are validated through measurement results over the designed $16\times 4$ MIMO test-bed using software defined radio nodes.

eess.SP↗