The commercial deployment of the fifth-generation mobile communications system (5G) is progressing, and applications and user experiences are gradually evolving. In wireless communications, the technologies must be applied to guarantee high-quality transmission in spite of propagation channel fluctuations. Taking linear equalization as an example, in this paper, several signal processing technologies that support 5G are introduced and the characteristics of linear and nonlinear signal processing methods are described. It is shown that the linear and nonlinear signal processing methods reduce computational complexity and improve system performance, respectively. The characteristics of 5G standardization trends are also introduced, the situations in which use cases of use are subdivided and added are described, and the concept of cell-free systems, which is the system model of 6G, the next generation of 5G, is presented. In 6G, the number of elements in communication resources allocation will increase exponentially, and multiple objective functions will also arise. We describe how to quickly derive a quasi-optimal solution using machine learning while separating and integrating interference, and how to control the 6G system, including wireless resources. This makes it possible to accommodate future expansions of super-use cases that cover all applications, areas, and communication styles.

This paper is a review of the history of active noise control (ANC), which uses sound to control and reduce unpleasant noise, and describes recent trends in the field. ANC is a technology for controling and reducing noise from noise sources (primary sources) by using controlled sounds from loudspeakers (secondary sources). This technology has become widely known to the public through noise-canceling headphones. However, three-dimentional (3D) noise control in public spaces such as offices and event halls, as well as in traffic and residential environments such as inside cars and houses, has not yet been widely implemented. In order to realize 3D noise control in such sound spaces, there are still many problems to overcome. In this paper, three representative virtual sensing methods for moving the zone of quiet (ZoQ) to an arbitrary location are first introduced, and their performance characteristics are compared. In addition, the causality constraint that is important in feed-forward ANC systems is explained, and an ANC system using oversampling is introduced as one solution to this problem. Furthermore, an application of machine learning, which has been used in various fields in recent years, to ANC systems is presented.

Accelerometers are used for many applications from smartphones and game consoles to automobiles, robots and drones. In particular, MEMS-based accelerometers are promising for many future applications since they are suitable for mass production and easy to implement in a small device. However, conventional MEMS accelerometers suffer from various noise contributors and hence, it was not easy to attain high sensitivity and low power consumption simultaneously. Recently, we have successfully developed a MEMS accelerometer with both extremely high sensitivity (ultralow noise) and low power consumption. In this article, the operation principle of the accelerometer and several key techniques, as well as new application are explained.

Sparse superposition codes are error-correcting codes proposed in 2010, which construct codewords by sparsely superposing column vectors of a matrix called a dictionary. These codes can be directly applied to Gaussian channels and have been shown to achieve transmission rates arbitrarily close to the channel capacity. To understand a theoretical limitation, the error probability of optimal decoding (maximum likelihood decoding) without regard to computational complexity has been analyzed. Furthermore, efficient decoding methods have been studied for practical applications, such as approximate message passing (AMP), which is one of the solutions for compressed sensing. In this paper, these topics are introduced with the author's research, in which the performance of maximum likelihood decoding is evaluated when the distribution used for generating the dictionary is greatly simplified.

Since Shor discovered polynomial time quantum algorithms to solve integer factorization and discrete logarithm problems, lots of studies have been done on post-quantum public key cryptography. In recent years, some interesting attacks using quantum computers have been found on not only public key schemes but also symmetric key schemes. This article provides an overview of the history and recent trends in research on the post-quantum security of symmetric key schemes, mainly in terms of cryptanalysis.

The aim of Society 5.0 is to create a human-oriented society that balances economic development and social problem solving using “digital twin”, in which various user behaviors in physical space are replicated in the form of data, analyzed and managed in cyberspace, to a highly level of integration through search and recommendation. We have been studying user behavior data, including SNS data and “review” data along with location information obtained from mobile devices (smartphones), publicly available data on crime, purchases, and infections including time and location information, and geographic data such as Google Street View images and building and road information, in order to develop a system for recommending routes, stores and attractions. We have also conducted research and development on routes, stores, attraction recommendations and hazard area prediction. In this paper, we present the role of digital twin in solving social problems, introduce the basic mechanisms of data collection, management, analysis, and visualization by digital twin using a smartphone device, as well as application technologies that provide a safe and comfortable mobility experience for pedestrians and cyclists using smartphone.