BITS: Bits of Information,
Transmitted and Stored

KURKOSKI Laboratory
Professor：KURKOSKI, Brian Michael

E-mail：
［Research areas］
Information theory, coding theory, communications theory
［Keywords］
reliable communications, wireless communications, data storage

Skills and background we are looking for in prospective students

We welcome students with motivation and ability in three areas.
（1） An interest in truly improving real-world systems through the prac cal applica on of mathema cs, par cularly linear algebra and probability
（2） Computer programming skills, such as C/C++, Java, or Matlab.
（3） Passion to use English as a technical language.

What you can expect to learn in this laboratory

Graduating students will have knowledge of fundamental methods for understanding and designing state-of-the-art communication and data storage systems. These systems are implemented as algorithms, and so students will gain understanding of mathematical techniques underlying these algorithms. Students will be able to read a paper, understand the contents, implement the algorithm in a program, and evaluate by computer simulations. Most students study and gain deep knowledge of error-correcting codes for reliable communications.

【Job category of graduates】 communications, data storage

Research outline

Information, Transmitted and Stored

Information transmission is sending data from one point to another point, for example, from the mobile phone in your hand, to a base station on the top of a building. Information storage is the sending of data from one point in time, to another point in time, for example files saved to your hard drive or SSD today can be recovered next week. Noise in the environment and unreliable storage media can corrupt signals and cause errors in data.

Information Theory and Coding Theory

The BITS Lab studies information theory and coding theory, to provide reliable communications and reliable storage of information. Information theory deals with the fundamental limits of reliable information transmission and compression. Remarkably, information can be transmitted reliably over a communications channel, even if the channel is unreliable. A central result states that the information rate R of transmission can be no greater than the channel capacity:

R <1/2 log （1 + SNR）

for a channel with signal-to-noise ratio SNR.

Coding theory deals with error-correcting codes, a concrete method to correct some errors, and even achieve the channel capacity. One such code can be represented using three circles, as shown in the figure. The number of 1’s inside each circle must be even. The code consists of seven bits, each either a 0 or a 1. But some bits have been erased to an unknown “?”. Can you recover the original bits?

Codes for Data Storage

Data storage is at the core of the information technology revolution, from the smartphones in our hands to data centers in the cloud. Flash memory, hard disk drives and distributed storage networks combine to provide ubiquitous access to data. But these exciting new systems pose new problems of storage density, reliability and efficiency. Coding theory provides an answer.

Cooperative Wireless Communications

With the arrival of the smartphone, the demand for wireless network communications has exploded. But new electromagnetic spectrum is scarce. To increase future data rates, cooperative wireless communications is the new way forward. In cooperative wireless communications, users, relays and base stations work together to increase data rates, as shown in the figure.

Lattices are codes which use the same real-number algebra for both the code and the channel, where electromagnetic signals are superimposed. Lattice codes correct errors introduced by channel noise, satisfy transmission power constraints, and possess properties needed for network coding. We are developing lattice coding theory to enable next-generation cooperative wireless communications.

Key publications

1. M. N. Hasan, B. M. Kurkoski, A. Sakzad and E. Viterbo, "Steepest Gradient-Based Orthogonal Precoder for Integer-Forcing MIMO," in IEEE Transac ons on Wireless Communica ons, Feb. 2020.
2. T. Matsumine, B. M. Kurkoski, and H. Ochiai, “Construc on D la ce decoding and its applica on to BCH code la ces,” in 2018 IEEE Global Communica ons Conference, December 2018.
3. B. M. Kurkoski, “Encoding and indexing of la ce codes,” IEEE Transac ons on Informa on Theory, vol. 64, pp. 6320-6332, September 2018.