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- Gülgün, Ziya, 1992-
(författare)
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GNSS and Massive MIMO : Spoofing, Jamming and Robust Receiver Design for Impulsive Noise
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 2. |
- Mishra, Kumar Vijay, et al.
(författare)
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Toward Millimeter-Wave Joint Radar Communications A signal processing perspective
- 2019
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Ingår i: IEEE signal processing magazine (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1053-5888 .- 1558-0792. ; 36:5, s. 100-114
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Tidskriftsartikel (refereegranskat)abstract
- Synergistic design of communications and radar systems with common spectral and hardware resources is heralding a new era of efficiently utilizing a limited radio-frequency (RF) spectrum. Such a joint radar communications (JRC) model has advantages of low cost, compact size, less power consumption, spectrum sharing, improml performance, and safety due to enhanced information sharing. Today, millimeter-wave (mm-wave) communications have emerged as the preferred technology for short distance wireless links because they provide transmission bandwidth that is several gigahertz wide. This band is also promising for short-range radar applications, which benefit from the high-range resolution arising from large transmit signal bandwidths. Signal processing techniques are critical to the implementation of mm-wave JRC systems. Major challenges are joint waveform design and performance criteria that would optimally trade off between communications and radar functionalities. Novel multiple-input, multiple-output (MIMO) signal processing techniques are required because mm-wave JRC systems employ large antenna arrays. There are opportunities to exploit recent advances in cognition, compressed sensing, and machine learning to reduce required resources and dynamically allocate them with low overheads. This article provides a signal processing perspective of mm-wave JRC systems with an emphasis on waveform design.
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