About the Project

Introduction

In dense urban environments, spectrum is a scarce resource. One way to improve the spectral efficiency is to use multiple antennas to exploit spatial diversity. With recent advances in MIMO, specialized devices are also being deployed with an increasing number of antennas; massive MIMO arrays have been built, featuring hundreds of small antennas, designed for applications such as cellular base stations, where denser deployment is difficult. A consequence of the rise of MIMO is that it gives rise to much richer information about the condition of the channel between two nodes. Whereas previously, a source and a destination could learn about the channel condition between the two nodes and how it varied with time, with MIMO we can learn that information on a per-transmitter chain and per-receiver chain basis. In this project, we address the following specific areas in and through which security can be improved in MIMO commodity systems

  1. Physical layer security
  2. Radiometric fingerprinting
  3. Interference Alignment
  4. Massive MIMO
  5. Collaborative and Heterogeneous network

Research Approach:

Physical Layer Security in an Adversarial Environment

It is well known that the wireless channel condition between a transmitter and a receiver varies with time. This channel condition can be used as a shared secret between the transmitter and the receiver, because in theory it is difficult to predict the multi-path characteristics of a location that is at least half a wavelength away. In recent years, the area of physical layer security has emerged; this field of study has shown that it is possible for a transmitter and receiver to share a key that has sufficient entropy as compared to an eavesdropper that is merely in the vicinity of the receiver. In this project, we will explore a variety of attacker models, including the precision with which the attacker might know the positions of the transmitter and receiver antennas. We can then determine the extent to which physical layer security can use multiple spatial streams to thwart channel-influencing attackers by determining the quality of the bits extracted by such schemes and using entropy pools to discard bits that might be known or influenced by the attacker

MIMO Fingerprinting

Recent work has shown that radio transmitter chains and antennas have a distinct fingerprint based on the imperfection of analog components in that radio chain. Previous work on fingerprinting has shown that over a sufficiently long period of time, a single transmitter can be authenticated to a limited extent through these analog imperfections. We explore MIMO fingerprinting as MIMO provides additional sources of fingerprintable information, and perhaps information that is not subject to the spoofing behavior. For example, the distance between multiple transmitter antennas allows a transmitter to perform beamforming, creating a signal that is stronger in some places than others. Minor imperfections in the location of the transmitter antennas, however, may create a signal strength pattern that is different from the ideal pattern, which could potentially be detected by a receiver with more than one antenna. Furthermore, because each transmitter chain must have independent analog components, each transmitter chain and antenna will have its own characteristic signature, allowing us to combine identifying bits from each of several transmitters to perform more accurate identification and authentication.

MIMO for Wireless Service Providers

Antennas can be useful network resources, much like spectral frequency or time (the latter two of which are typically used as dimensions along which a medium access control protocol divides the spectrum use). As discussed above, utilizing multiple antennas can benefit the network in increasing capacity because it can take advantage of antenna diversity (and consequentially provide higher-quality links between users) and decrease interference to the spatially coexisting users (by beamforming, for example). We envision a collaborative environment where a third-party MIMO user facilitates such gains for other users (for instance, a single-antenna user, which by itself would not have been able to achieve such benefits) and call these MIMO users service entities as they serve for the better of the network’s common good. For instance, relay-based service entities, such as wireless MIMO routers and base stations, are deployed for our everyday Internet use.