Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated into applications throughout all network layers. However, the security concerns on network functions of NextG using AI-based models, i.e., model poisoning, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cybersecurity threats, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks while mitigation methods can make models more robust against adversarial attacks. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack. The results indicate that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.

Generalized frequency division multiplexing (GFDM) is considered as a candidate waveform for 5G wireless communication systems. It is the generalize form of the orthogonal frequency division multiplexing (OFDM) that includes a number of subcarriers and subsymbols. An alternative waveform referred as the superimposed waveform in this study, was recently proposed to increase the data rate. The superimposed waveform is based on using orthogonal polynomial in each grid of time-frequency lattice structure. In this way, more than one symbols are transmitted with the different orthogonal pulses. While the proposed waveform showed promising results for the increasing data rate, there were some issues regarding the implementations. Motivated by this, we design transceiver structure as a low complex way with mathematical derivation by using inverse discrete Fourier transform (IDFT) and DFT operations. The bandwidth efficiency is almost 2.4 much higher than GFDM with Gaussian pulses. The performances of the superimposed waveform over a Rayleigh Fading channel and its comparison over additive white Gaussian noise (AWGN) channel are done.

Waveform Design Considerations for 5G Wireless Networks | InTechOpen, Published on: 2016-12-14. Authors: Evren Çatak and Lütfiye Durak‐Ata