arXiv subjects

Amirmohammad Haddad

Publications and source records attributed to Amirmohammad Haddad.

2 recordsLinked to original sources

arXiv2025

Scaling Wideband Hybrid Beamforming for sub-THz Communication

We investigate the capacity attainable for a multiuser MIMO uplink as we scale both array size and bandwidth for regimes in which all-digital arrays incur excessive hardware complexity and power consumption. We consider a tiled hybrid beamforming architecture in which each tile, or subarray, is a phased array performing analog (or RF) beamforming, followed by DSP on the tile outputs. For parameters compatible with sub-THz fixed access links, we discuss hardware and power consumption considerations for choosing tile size and the number of tiles. Noting that the problem of optimal multiuser MIMO in our wideband regime is open even for the simplest possible channel models, we compare the spectral efficiencies attainable by a number of reasonable strategies for tile-level RF beamforming, assuming flexibility in the digital signal processing (DSP) of the tile outputs. We consider a number of beam broadening approaches for addressing the ``beam squint'' incurred by RF beamforming in our wideband regime, along with strategies for sharing tiles among users. Information-theoretic benchmarks are computed for an idealized MIMO-OFDM system, with linear per-subcarrier multiuser detection compared against an unconstrained complexity receiver.

eess.SP↗

arXiv2025

To See or Not to See -- Fingerprinting Devices in Adversarial Environments Amid Advanced Machine Learning

The increasing use of the Internet of Things raises security concerns. To address this, device fingerprinting is often employed to authenticate devices, detect adversaries, and identify eavesdroppers in an environment. This requires the ability to discern between legitimate and malicious devices which is achieved by analyzing the unique physical and/or operational characteristics of IoT devices. In the era of the latest progress in machine learning, particularly generative models, it is crucial to methodically examine the current studies in device fingerprinting. This involves explaining their approaches and underscoring their limitations when faced with adversaries armed with these ML tools. To systematically analyze existing methods, we propose a generic, yet simplified, model for device fingerprinting. Additionally, we thoroughly investigate existing methods to authenticate devices and detect eavesdropping, using our proposed model. We further study trends and similarities between works in authentication and eavesdropping detection and present the existing threats and attacks in these domains. Finally, we discuss future directions in fingerprinting based on these trends to develop more secure IoT fingerprinting schemes.

cs.CR↗