Wireless communication technology has evolved rapidly during the last 20 years. Nowadays, there are huge networks providing communication infrastructures to not only people but also to machines, such as unmanned air and ground vehicles, cars, household appliances and so on. There is no doubt that new wireless communication technologies must be developed, that support the data traffic in these emerging, large networks. While developing these technologies, it is also important to investigate the vulnerability of these technologies to different malicious attacks. In particular, spoofing and jamming attacks should be investigated and new countermeasure techniques should be developed. In this context, spoofing refers to the situation in which a receiver identifies falsified signals, that are transmitted by the spoofers, as legitimate or trustable signals. Jamming, on the other hand, refers to the transmission of radio signals that disrupt communications by decreasing the signal-to-interference-and-noise ratio (SINR) on the receiver side. 

In this thesis, we analyze the effects of spoofing and jamming both on global navigation satellite system (GNSS) and on massive multiple-input multiple-output (MIMO) communications. GNSS is everywhere and used to provide location information. Massive MIMO is one of the cornerstone technologies in 5G. We also propose countermeasure techniques to the studied spoofing and jamming attacks. 

More specifically, in paper A we analyze the effects of distributed jammers on massive MIMO and answer the following questions: Is massive MIMO more robust to distributed jammers compared with previous generation’s cellular networks? Which jamming attack strategies are the best from the jammer’s perspective, and can the jamming power be spread over space to achieve more harmful attacks? In paper B, we propose a detector for GNSS receivers that is able to detect multiple spoofers without having any prior information about the attack strategy or the number of spoofers in the environment. 

In this thesis, we focus on vulnerabilities and robustness of two wireless communication technologies: global navigation satellite system (GNSS), a technology that provides position-velocity-time information, and massive multiple-input-multiple-output (MIMO), a core cellular 5G technology. In particular, we investigate spoofing and jamming attacks to GNSS and massive MIMO, respectively, and the robust massive MIMO receiver against impulsive noises. In this context, spoofing refers to the situation in which a receiver identifies falsified signals, that are transmitted by the spoofers, as legitimate or trustable signals.

Jamming, on the other hand, refers to the transmission of radio signals that disrupt communications by decreasing the signal to interference plus noise ratio (SINR) on the receiver side.

The reason why we investigate impulsive noises is that the standard wireless receivers assume that the noise has Gaussian distribution. However, the impulsive noises may appear in any communication link. The difference between impulsive noises and standard Gaussian noises is that it is more likely to observe outliers in impulsive noises. Therefore, we question whether the standard Gaussian receivers are robust against impulsive noises and design robust receivers against impulsive noises.

More specifically, in paper A we analyze the effects of distributed jammers on massive MIMO and answer the following questions: Is massive MIMO more robust to distributed jammers compared with previous generation's cellular networks? Which jamming attack strategies are the best from the jammer's perspective, and can the jamming power be spread over space to achieve more harmful attacks?

In paper B, we propose a detector for GNSS receivers that is able to detect multiple spoofers without having any prior information about the attack strategy or the number of spoofers in the environment.

In paper C and D, we design robust receivers for massive MIMO against impulsive noise. In paper C, we model the noise having a Cauchy distribution and present a channel estimation technique, achievable rates and soft-decision metrics for coded signals. The main observation in paper C is that the proposed receiver works well in the presence of Cauchy and Gaussian noises, although the standard Gaussian receiver performs very bad when the noise has Cauchy distribution. In paper D, we compare two types of receivers, the Gaussian-mixture and the Cauchy-based, when the noise has symmetric alpha-stable (SαS) distributions. Based on the numerical results, the Gaussian-mixture receiver outperforms the Cauchy-based receiver.