Architecting a One-to-many Traffic-Aware and Secure Millimeter-Wave Wireless Network-in-Package Interconnect for Multichip Systems

With the aggressive scaling of device geometries, the yield of complex Multi Core Single Chip(MCSC) systems with many cores will decrease due to the higher probability of manufacturing defects especially, in dies with a large area. Disintegration of large System-on-Chips(SoCs) into smaller chips called chiplets has shown to improve the yield and cost of complex systems. Therefore, platform-based computing modules such as embedded systems and micro-servers have already adopted Multi Core Multi Chip (MCMC) architectures overMCSC architectures. Due to the scaling of memory intensive parallel applications in such systems, data is more likely to be shared among various cores residing in different chips resulting in a significant increase in chip-to-chip traffic, especially one-to-many traffic. This one-to-many traffic is originated mainly to maintain cache-coherence between many cores residing in multiple chips. Besides, one-to-many traffics are also exploited by many parallel programming models, system-level synchronization mechanisms, and control signals. How-ever, state-of-the-art Network-on-Chip (NoC)-based wired interconnection architectures do not provide enough support as they handle such one-to-many traffic as multiple unicast trafficusing a multi-hop MCMC communication fabric. As a result, even a small portion of such one-to-many traffic can significantly reduce system performance as traditional NoC-basedinterconnect cannot mask the high latency and energy consumption caused by chip-to-chipwired I/Os. Moreover, with the increase in memory intensive applications and scaling of MCMC systems, traditional NoC-based wired interconnects fail to provide a scalable inter-connection solution required to support the increased cache-coherence and synchronization generated one-to-many traffic in future MCMC-based High-Performance Computing (HPC) nodes. Therefore, these computation and memory intensive MCMC systems need an energy-efficient, low latency, and scalable one-to-many (broadcast/multicast) traffic-aware interconnection infrastructure to ensure high-performance. Research in recent years has shown that Wireless Network-in-Package (WiNiP) architectures with CMOS compatible Millimeter-Wave (mm-wave) transceivers can provide a scalable, low latency, and energy-efficient interconnect solution for on and off-chip communication. In this dissertation, a one-to-many traffic-aware WiNiP interconnection architecture with a starvation-free hybrid Medium Access Control (MAC), an asymmetric topology, and a novel flow control has been proposed. The different components of the proposed architecture are individually one-to-many traffic-aware and as a system, they collaborate with each other to provide required support for one-to-many traffic communication in a MCMC environment. It has been shown that such interconnection architecture can reduce energy consumption and average packet latency by 46.96% and 47.08% respectively for MCMC systems. Despite providing performance enhancements, wireless channel, being an unguided medium, is vulnerable to various security attacks such as jamming induced Denial-of-Service (DoS), eavesdropping, and spoofing. Further, to minimize the time-to-market and design costs, modern SoCs often use Third Party IPs (3PIPs) from untrusted organizations. An adversary either at the foundry or at the 3PIP design house can introduce a malicious circuitry, to jeopardize an SoC. Such malicious circuitry is known as a Hardware Trojan (HT). An HTplanted in the WiNiP from a vulnerable design or manufacturing process can compromise a Wireless Interface (WI) to enable illegitimate transmission through the infected WI resulting in a potential DoS attack for other WIs in the MCMC system. Moreover, HTs can be used for various other malicious purposes, including battery exhaustion, functionality subversion, and information leakage. This information when leaked to a malicious external attackercan reveals important information regarding the application suites running on the system, thereby compromising the user profile. To address persistent jamming-based DoS attack in WiNiP, in this dissertation, a secure WiNiP interconnection architecture for MCMC systems has been proposed that re-uses the one-to-many traffic-aware MAC and existing Design for Testability (DFT) hardware along with Machine Learning (ML) approach. Furthermore, a novel Simulated Annealing (SA)-based routing obfuscation mechanism was also proposed toprotect against an HT-assisted novel traffic analysis attack. Simulation results show that,the ML classifiers can achieve an accuracy of 99.87% for DoS attack detection while SA-basedrouting obfuscation could reduce application detection accuracy to only 15% for HT-assistedtraffic analysis attack and hence, secure the WiNiP fabric from age-old and emerging attacks

Security of Electrical, Optical and Wireless On-Chip Interconnects: A Survey

The advancement of manufacturing technologies has enabled the integration of more intellectual property (IP) cores on the same system-on-chip (SoC). Scalable and high throughput on-chip communication architecture has become a vital component in today's SoCs. Diverse technologies such as electrical, wireless, optical, and hybrid are available for on-chip communication with different architectures supporting them. Security of the on-chip communication is crucial because exploiting any vulnerability would be a goldmine for an attacker. In this survey, we provide a comprehensive review of threat models, attacks, and countermeasures over diverse on-chip communication technologies as well as sophisticated architectures.Comment: 41 pages, 24 figures, 4 table

Model-Free Learning of Two-Stage Beamformers for Passive IRS-Aided Network Design

Electronically tunable metasurfaces, or Intelligent Reflective Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflective elements. Capitalizing on key practical limitations of IRS-aided beamforming pertaining to system modeling and channel sensing/estimation, we propose a novel, fully data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for general two-stage (i.e., short/long-term), fully-passive IRS-aided stochastic utility maximization. ZoSGA learns long-term optimal IRS beamformers jointly with short-term optimal precoders (e.g., WMMSE-based) via minimal zeroth-order reinforcement and in a strictly model-free fashion, relying solely on the \textit{effective} compound channels observed at the terminals, while being independent of channel models or network/IRS configurations. Another remarkable feature of ZoSGA is being amenable to analysis, enabling us to establish a state-of-the-art (SOTA) convergence rate of the order of $\boldsymbol{O}(\sqrt{S}\epsilon^{-4})$ under minimal assumptions, where $S$ is the total number of IRS elements, and $\epsilon$ is a desired suboptimality target. Our numerical results on a standard MISO downlink IRS-aided sumrate maximization setting establish SOTA empirical behavior of ZoSGA as well, consistently and substantially outperforming standard fully model-based baselines. Lastly, we demonstrate that ZoSGA can in fact operate \textit{in the field}, by directly optimizing the capacitances of a varactor-based electromagnetic IRS model (unknown to ZoSGA) on a multiple user/IRS, compute-heavy network setting, with essentially no computational overheads or performance degradation

Model-Free Learning of Two-Stage Beamformers for Passive IRS-Aided Network Design

Electronically tunable metasurfaces, or Intelligent Reflecting Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflecting elements. Capitalizing on key practical limitations of IRS-aided beamforming pertaining to system modeling and channel sensing/estimation, we propose a novel, fully data-driven Zeroth-order Stochastic Gradient Ascent (ZoSGA) algorithm for general two-stage (i.e., short/long-term), fully-passive IRS-aided stochastic utility maximization. ZoSGA learns long-term optimal IRS beamformers jointly with short-term optimal precoders (e.g., WMMSE-based) via minimal zeroth-order reinforcement and in a strictly model-free fashion, relying solely on the effective compound channels observed at the terminals, while being independent of channel models or network/IRS configurations. Another remarkable feature of ZoSGA is being amenable to analysis, enabling us to establish a state-of-the-art (SOTA) convergence rate of the order of O Ο(√Sϵ-4) under minimal assumptions, where S is the total number of IRS elements, and ϵ is a desired suboptimality target. Our numerical results on a standard MISO downlink IRS-aided sumrate maximization setting establish SOTA empirical behavior of ZoSGA as well, consistently and substantially outperforming standard fully model-based baselines. Lastly, we demonstrate that ZoSGA can in fact operate in the field, by directly optimizing the capacitances of a varactor-based electromagnetic IRS model (unknown to ZoSGA) on a multiple user/IRS, link-dense network setting, with essentially no computational overheads or performance degradation

Adversarial Machine Learning in Wireless Communication Systems

We consider adversarial machine learning settings in wireless communication systems with adversaries that attempt to manipulate the deep learning (DL)-based wireless communication tasks, such as modulation classification and signal classification. In particular, we consider the evasion attack, i.e., adversarial attack, to which deep neural networks (DNNs) are known to be highly susceptible even under small-scale attacks. The shared and broadcast nature of wireless medium increases the potential for adversaries to tamper with DL-based wireless communication tasks. In this dissertation, we study the vulnerability of the DNNs used for various wireless communication applications to adversarial attacks. First, we present channel-aware adversarial attacks against DL-based wireless signal classifiers where a DNN is used at each receiver to classify over-the-air received signals to modulation types. We propose realistic attacks by considering channel effects from the adversary to each receiver, and a broadcast adversarial attack by crafting a common adversarial perturbation to simultaneously fool classifiers at different receivers. To mitigate the effect of the adversarial attack, we develop a certified defense scheme to guarantee the robustness of the classifier. Next, we consider an adversary that transmits adversarial perturbations using its multiple antennas to fool the classifier into misclassifying the received signals. From the adversarial machine learning perspective, we show how to utilize multiple antennas at the adversary to improve the adversarial attack performance. We consider power allocation among antennas and utilization of channel diversity while exploiting the multiple antennas at the adversary. We show that attack success increases as the number of antennas at the adversary increases. Then, we consider the privacy of wireless communications from an eavesdropper that employs a DL classifier to detect transmissions. In this setting, a transmitter transmits to its receiver in the presence of an eavesdropper, where a cooperative jammer (CJ) with multiple antennas transmits carefully crafted adversarial perturbations over-the-air to fool the eavesdropper into classifying the received superposition of signals as noise. We show that this adversarial perturbation causes the eavesdropper to misclassify the received signals as noise with a high probability while increasing the bit error rate (BER) at the legitimate receiver only slightly. Next, we consider an adversary that generates adversarial perturbation using a surrogate DNN model that is trained at the adversary. This surrogate model may differ from the transmitter's classifier significantly because the adversary and the transmitter experience different channels from the background emitter and therefore their classifiers are trained with different distributions of inputs. We consider different topologies to investigate how different surrogate models that are trained by the adversary (depending on the differences in channel effects experienced by the adversary) affect the performance of the adversarial attack. Then, we consider beam prediction problem using DNN for initial access (IA) in 5G and beyond communication systems where the user equipments (UEs) select the beam with the highest received signal strength (RSS) to establish their initial connection. We propose an adversarial attack to manipulate the over-the-air captured RSSs as the input to the DNN. This attack reduces the IA performance significantly and fools the DNN into choosing the beams with small RSSs. Next, we consider adversarial attacks on power allocation where the base station (BS) allocates its transmit power to multiple orthogonal subcarriers by using a DNN to serve multiple UEs. The DNN corresponds to a regression model which is trained with channel gains as the input and allocated transmit powers as the output. While the BS allocates the transmit power to the UEs to maximize rates for all UEs, an adversary aims to minimize these rates. We show that the regression-based DNN is susceptible to adversarial attacks, where the rate of communication is significantly affected. Finally, we consider reconfigurable intelligent surface (RIS)-aided wireless communication systems that improve the spectral efficiency and the coverage of wireless systems by electronically controlling the electromagnetic material in the presence of an eavesdropper. While there is an ongoing transmission boosted by the RIS, both the intended receiver and an eavesdropper individually aim to detect this transmission using their own DNN classifiers. The RIS interaction vector is designed by balancing two potentially conflicting objectives of focusing the transmitted signal to the receiver and keeping the transmitted signal away from the eavesdropper. To boost covert communications, adversarial perturbations are added to signals at the transmitter to fool the eavesdropper's classifier while keeping the effect on the receiver low. We show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications

Rapidly time-varying reconfigurable intelligent surfaces for downlink multiuser transmissions

Until now, researchers in wireless communications have mainly focused their attention on slowly time-varying designs of reconfigurable intelligent surfaces (RISs), where the spatial-phase gradient across the RIS is varied at the rate equal to the inverse of the channel coherence time. Additional degrees of freedom for controlling EM waves can be gained by applying a time modulation to the reflection response of RISs during the channel coherence time interval, thereby attaining rapidly time-varying RISs. In this paper, we develop a general framework where a downlink multiuser transmission over single-input single-output slow fading channels is assisted by a digitally controlled rapidly time-varying RIS. We show that reconfiguring the RIS at a rate greater than the inverse of the channel coherence time might be beneficial from a communication perspective depending on the considered network utility function and the available channel state information at the transmitter (CSIT). The conclusions of our analysis in terms of system design guidelines are as follows: (i) if the network utility function is the sum-rate time-averaged network capacity, without any constraint on fair resource allocation, and full CSIT is available, it is unnecessary to change the electronic properties of the RIS within the channel coherence time interval; (ii) if partial CSIT is assumed only, a rapidly time-varying randomized RIS allows to achieve a suitable balance between sum-rate time-averaged capacity and user fairness, especially for a sufficiently large number of users; (iii) regardless of the available amount of CSIT, the design of rapid temporal variations across the RIS is instrumental for developing scheduling algorithms aimed at maximizing the network capacity subject to some fairness constraints.Comment: Accepted for publication in IEEE Transactions on Communications. Cite as: F. Verde, D. Darsena, and V. Galdi, "Rapidly time-varying reconfigurable intelligent surfaces for downlink multiuser transmissions," in IEEE Transactions on Communications, 2024, doi: https://doi.org/10.1109/TCOMM.2024.335895

A dynamic distributed multi-channel TDMA slot management protocol for ad hoc networks

With the emergence of new technologies and standards for wireless communications and an increase in application and user requirements, the number and density of deployed wireless ad hoc networks is increasing. For deterministic ad hoc networks, Time-Division Multiple Access (TDMA) is a popular medium access scheme, with many distributed TDMA scheduling algorithms being proposed. However, with increasing traffic demands and the number of wireless devices, proposed protocols are facing scalability issues. Besides, these protocols are achieving suboptimal spatial spectrum reuse as a result of the unsolved exposed node problem. Due to a shortage of available spectrum, a shift from fixed spectrum allocation to more dynamic spectrum sharing is anticipated. For dynamic spectrum sharing, improved distributed scheduling protocols are needed to increase spectral efficiency and support the coexistence of multiple co-located networks. Hence, in this paper, we propose a dynamic distributed multi-channel TDMA (DDMC-TDMA) slot management protocol based on control messages exchanged between one-hop network neighbors and execution of slot allocation and removal procedures between sender and receiver nodes. DDMC-TDMA is a topology-agnostic slot management protocol suitable for large-scale and high-density ad hoc networks. The performance of DDMC-TDMA has been evaluated for various topologies and scenarios in the ns-3 simulator. Simulation results indicate that DDMC-TDMA offers near-optimal spectrum utilization by solving both hidden and exposed node problems. Moreover, it proves to be a highly scalable protocol, showing no performance degradation for large-scale and high-density networks and achieving coexistence with unknown wireless networks operating in the same wireless domain