Bin Hu

Bin Hu, Michael Lemmon

Vehicular Networked Systems (VNS) are mobile ad hoc networks where vehicles exchange information over wireless communication networks to ensure safe and efficient operation. It is, however, challenging to ensure system safety and efficiency as the wireless channels in VNS are often subject to state-dependent deep fades where the data rate suffers a severe drop and changes as a function of vehicle states. Such couplings between vehicle states and channel states in VNS thereby invalidate the use of separation principle to design event-based control strategies. By adopting a state-dependent fading channel model that was proposed to capture the interaction between vehicle and channel states, this paper presents a novel self-triggered scheme under which the VNS ensures efficient use of communication bandwidth while preserving stochastic stability. The novelty of the proposed scheme lies in its use of the state-dependent fading channel model in the event design that enables an adaptive and effective adjustment on transmission frequency in response to dynamic variations on channel and vehicle states. Under the proposed self-triggered scheme, this paper presents a novel source coding scheme that tracks vehicle's states with performance guarantee in the presence of state-dependent fading channels. The efficacy and advantages of the proposed scheme over other event-based strategies are verified through both simulation and experimental results of a leader-follower example.

Jiazhao Wang, Wenchao Jiang, Ruofeng Liu et al.

A physical-layer modulator is a vital component for an IoT gateway to map the symbols to signals. However, due to the soldered hardware chipsets on the gateway's motherboards or the diverse toolkits on different platforms for the software radio, the existing solutions either have limited extensibility or are platform-specific. Such limitation is hard to ignore when modulation schemes and hardware platforms have become extremely diverse. This paper presents a new paradigm of using neural networks as an abstraction layer for physical layer modulators in IoT gateway devices, referred to as NN-defined modulators. Our approach addresses the challenges of extensibility and portability for multiple technologies on various hardware platforms. The proposed NN-defined modulator uses a model-driven methodology rooted in solid mathematical foundations while having native support for hardware acceleration and portability to heterogeneous platforms. We conduct the evaluation of NN-defined modulators on different platforms, including Nvidia Jetson Nano and Raspberry Pi. Evaluations demonstrate that our NN-defined modulator effectively operates as conventional modulators and provides significant efficiency gains (up to $4.7\times$ on Nvidia Jetson Nano and $1.1\times$ on Raspberry Pi), indicating high portability. Furthermore, we show the real-world applications using our NN-defined modulators to generate ZigBee and WiFi packets, which are compliant with commodity TI CC2650 (ZigBee) and Intel AX201 (WiFi NIC), respectively.