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2021-09-01
Ahmed, MMeraj, Vashist, Abhishek, Pudukotai Dinakarrao, Sai Manoj, Ganguly, Amlan.  2020.  Architecting a Secure Wireless Interconnect for Multichip Communication: An ML Approach. 2020 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :1—6.
Compute-intensive platforms such as micro-servers and embedded systems have already undergone a shift from a single-chip to multichip architecture to achieve better yield and lower cost. However, performance of multichip systems is limited by the latency and power-hungry chip-to-chip wired I/Os. On the other hand, wireless interconnections are emerging as an energy-efficient and low latency interconnect solution for such multichip systems as it can mask long multi-hop off-chip wired I/O communication. Despite efficient communication, the unguided on and off-chip wireless communication introduce security vulnerabilities in the system. In this work, we propose a reconfigurable, secure millimeter-wave (mm-Wave) wireless interconnection architecture (AReS) for multichip systems capable of detecting and defending against emerging threats including Hardware Trojans (HTs) and Denial-of-Service (DoS) using a Machine Learning (ML)-based approach. The ML-based approach is used to classify internal and external attack to enable the required defense mechanism. To serve this purpose, we design a reconfigurable Medium Access Control (MAC) and a suitable communication protocol to enable sustainable communication even under jamming attack from both internal and external attackers. The proposed architecture also reuses the in-built test infrastructure to detect and withstand a persistent jamming attack in a wireless multichip system. Through simulation, we show that, the proposed wireless interconnection can sustain chip-to-chip communication even under persistent jamming attack with an average 1.44xand 1.56x latency degradation for internal and external attacks respectively for application-specific traffic.
2018-05-02
Jian, R., Chen, Y., Cheng, Y., Zhao, Y..  2017.  Millimeter Wave Microstrip Antenna Design Based on Swarm Intelligence Algorithm in 5G. 2017 IEEE Globecom Workshops (GC Wkshps). :1–6.

In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.