All posts filed under: Misc

A year in the life of Lakeside Labs

Did you observe that lately more and more tech products and business processes have features of self-organization? Almost every major car maker has been testing self-driving vehicles and is now introducing them to the market. The industry is crazy about “industry 4.0”—which promises self-organizing production with humans, machines, and products collaborating to make decentralized decisions. These are just two examples for the ongoing trend toward more self-* properties in tech systems, e.g., self-configuration, self-optimization, and self-healing. Such paradigm shift from centralized, managed systems to decentralized, autonomous systems has been the core of Lakeside Labs — the small nonprofit research company that I have been co-leading as scientific director — since its foundation in 2008. Lakeside Labs is a hub for science and innovation in self-organizing networked systems. A space for inspiration, creativity, and multidisciplinarity. At the time of our foundation several people believed that research on self-organization is a pure academic “exercise” by professors without any practical application of benefit to industry. This attitude has changed in the course of 2015/16 when many companies have increasingly become interested in …

Jorge F. Schmidt explaining interference alignment

Interference alignment in a nutshell

There are different strategies on how to handle and manage interference in wireless networks. The choice of strategy mainly depends on the signal-to-interference ratio (SIR) at the receiver. If interference is very weak, it should be ignored and treated as noise. If interference is stronger—about as strong as the desired signal—we want to avoid it and allow only one node to transmit at a given time or frequency in a certain spatial region (orthogonalization). Both strategies are commonly used in wireless technologies in practice. If interference is even stronger than the signal of interest, it is favorable to allow interference and decode it at the receiver rather than to avoid it. A major limitation of all three strategies is that their per-node throughput decreases with an increasing number of transmitting nodes.