Taneli Riihonen

Muhammad Umar Farooq Qaisar, Weijie Yuan, Onur Günlü et al.

The commencement of the sixth-generation (6G) wireless networks represents a fundamental shift in the integration of communication and sensing technologies to support next-generation applications. Integrated sensing and communication (ISAC) is a key concept in this evolution, enabling end-to-end support for both communication and sensing within a unified framework. It enhances spectrum efficiency, reduces latency, and supports diverse use cases, including smart cities, autonomous systems, and perceptive environments. This tutorial provides a comprehensive overview of ISAC's role in 6G networks, beginning with its evolution since 5G and the technical drivers behind its adoption. Core principles and system variations of ISAC are introduced, followed by an in-depth discussion of the enabling technologies that facilitate its practical deployment. The paper further analyzes current research directions to highlight key challenges, open issues, and emerging trends. Design insights and recommendations are also presented to support future development and implementation. This work ultimately tries to address three central questions: Why is ISAC essential for 6G? What innovations does it bring? How will it shape the future of wireless communication?

Satu Johansson, Taneli Riihonen

In this paper, military use cases or applications and implementation thereof are considered for natural language processing and large language models, which have broken into fame with the invention of the generative pre-trained transformer (GPT) and the extensive foundation model pretraining done by OpenAI for ChatGPT and others. First, we interrogate a GPT-based language model (viz. Microsoft Copilot) to make it reveal its own knowledge about their potential military applications and then critically assess the information. Second, we study how commercial cloud services (viz. Microsoft Azure) could be used readily to build such applications and assess which of them are feasible. We conclude that the summarization and generative properties of language models directly facilitate many applications at large and other features may find particular uses.

Jaakko Pihlajasalo, Dani Korpi, Mikko Honkala et al.

In this paper, we propose a machine learning (ML) based physical layer receiver solution for demodulating OFDM signals that are subject to a high level of nonlinear distortion. Specifically, a novel deep learning based convolutional neural network receiver is devised, containing layers in both time- and frequency domains, allowing to demodulate and decode the transmitted bits reliably despite the high error vector magnitude (EVM) in the transmit signal. Extensive set of numerical results is provided, in the context of 5G NR uplink incorporating also measured terminal power amplifier characteristics. The obtained results show that the proposed receiver system is able to clearly outperform classical linear receivers as well as existing ML receiver approaches, especially when the EVM is high in comparison with modulation order. The proposed ML receiver can thus facilitate pushing the terminal power amplifier (PA) systems deeper into saturation, and thereon improve the terminal power-efficiency, radiated power and network coverage.