AI in 5G/6G是当下通信技术研究的热点之一，源于5G/6G网络技术越来越灵活，一张网络能够支持越来越多的应用场景，给网络规划、部署、优化和运维带来了越来越高的要求。一张高效的通信网络，必然对应高效的网络运维和优化，AI技术的不断发展给这种要求提供新的技术手段发展思路。此外，无线技术的突破同样是令人振奋的关注热点，比如灵活可重构的变频设备，使调频变得更加容易；移动场景下的无线充电技术可以让通信产品延伸出更多的应用场景，甚至催生新的行业生态。
本期选取了4篇文献，讨论的内容包括AI in 5G/6G的发展方向以及具体的算法策略，以及可重构变频设备的介绍和移动场景下的无线充电技术等内容，推送给相关领域的科研人员，共同探讨。
Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G
Rubayet Shafin, etc.
IEEE Wireless Communications,2020,27(2):212 - 217
Mobile network operators (MNOs) are in the process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in fifth generation (5G) and beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top five challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond- 5G and sixth generation (6G) networks.
A Waveform Parameter Assignment Framework for 6G with the Role of Machine Learning
Ahmet Yazar, etc.
IEEE Open Journal of Vehicular Technology,2020:1-17
5G enables a wide variety of wireless communications applications and use cases. There are different requirements associated with the applications, use cases, channel structure, network and user. To meet all of the requirements, several new configurable parameters are defined in 5G New Radio (NR). It is possible that 6G will have even higher number of configurable parameters based on new potential conditions. In line with this trend, configurable waveform parameters are also varied and this variation will increase in 6G considering the potential future necessities. In this paper, association of users and possible configurable waveform parameters in a cell is discussed for 6G communication systems. An assignment framework of configurable waveform parameters with different types of resource allocation optimization mechanisms is proposed. Most of all, the role and usage of machine learning (ML) in this framework is described. A case study with a simulation based dataset generation methodology is also presented.
Reconfigurable frequency multiplication with a ferroelectric transistor
Halid Mulaosmanovic, etc
Frequency multiplication is essential in wireless communication systems, where stable high-frequency oscillations are required. However, multipliers typically employ power- and area-hungry filtering and amplification circuits. Here, we show that a single ferroelectric field-effect transistor, made from ferroelectric hafnium oxide, can be used as a full-wave rectifier and frequency doubler. This is achieved by using the parabolic shape of the transistor’s transfer characteristics, which can be tailored by accurately tuning the partial polarization switching and the band-to-band tunnelling drain current. Due to the reversible polarization switching, our approach is fully reconfigurable, allowing either multiplication or simple transmission of the input frequency to be activated within a single ferroelectric transistor. With our devices, we also implement two practical cases of the frequency modulation scheme without any additional filtering circuits.
Robust and efficient wireless power transfer using a switch-mode implementation of a nonlinear parity–time symmetric circuit
Sid Assawaworrarit, etc.
Stationary wireless power transfer has been deployed commercially and can be used to charge a variety of devices, including mobile phones and parked electric vehicles. However, wireless power transfer set-ups typically suffer from an inherent sensitivity to the relative movement of the device with respect to the power source. Nonlinear parity–time symmetric circuits could be used to deliver robust wireless power transfer even while a device is moving rapidly, but previous implementations have relied on an inefficient gain element based on an operation-amplifier circuit, which has inherent loss, and hence have exhibited poor total system efficiency. Here we show that robust and efficient wireless power transfer can be achieved by using a power-efficient switch-mode amplifier with current-sensing feedback in a parity–time symmetric circuit. In this circuit, the parity–time symmetry guarantees that the effective load impedance on the switch-mode amplifier remains constant, and hence the amplifier maintains high efficiency despite variation of the transfer distance. We experimentally demonstrate a nonlinear parity–time symmetric radiofrequency circuit that can wirelessly transfer around 10?W of power to a moving device with a nearly constant total efficiency of 92% and over a distance from 0 to 65?cm.