Driver distraction caused by over-reliance on automotive technology is one of the leading causes of accidents in semi-autonomous vehicles. Existing driver's attention-gauging approaches are intrusive and as such emphasize constant driver engagement. In case of an urgent traffic event, they fail to measure the event's criticality and subsequently generate timely alerts. In this paper, we re-position the driver's attention-gauging approach as a way to improve the driver's situational awareness during critical situations. We exploit how a vehicle captures its surroundings information to convert an automotive decision into defining the criticality and timeliness of an alert. For this, we identify the relationship between the traffic event, the type of automotive sensing technologies, and its processing resources to capture that event to design the driver's attention gauge. We evaluate the timeliness of alerts for different traffic scenarios over a prototype built using NVIDIA Jetson Xavier AGX and Carla. Our results show that we can improve the timeliness of an alert by up to 75x as compared to existing state-of-the-art approaches, while also providing feedback on its criticality.

Multi-stream 60 GHz communication can potentially achieve data rates up to 100 Gbps via multiplexing multiple data streams. Unfortunately, establishing multi-stream directional links is a high overhead procedure as the search space increases with the number of spatial streams and the product of AP-client beam resolution. In this paper, we present MUlti-stream beam-Training for mm-wavE networks (MUTE) a novel system that leverages channel sparsity, GHz-scale sampling rate, and the knowledge of mm-Wave RF codebook beam patterns to construct a set of candidate beams for efficient multi-stream beam steering. MUTE repurposes the mandatory periodic beam sweeps in 60 GHz WLANs to discover the dominant paths of the mmWave channel between the AP and any client with zero additional overhead. Coupling path estimates with beam pattern knowledge, MUTE selects a set of candidate beams that capture diverse or ideally orthogonal paths to obtain maximum stream separability. Our over-the-air experiments demonstrate that MUTE achieves 90% of the maximum achievable aggregate PHY rate while incurring only 1.2% of exhaustive search's training overhead.

Multi-stream 60 GHz communication has the potential to achieve data rates up to $100$ Gbps via multiplexing multiple data streams. Unfortunately, establishing multi-stream directional links can be a high overhead procedure as the search space increases with the number of spatial streams and the product of AP-client beam resolution. In this paper, we present MUlti-stream beam-Training for mm-wavE networks (MUTE) a novel system that leverages channel sparsity, GHz-scale sampling rate, and the knowledge of mm-Wave RF codebook beam patterns to construct a set of candidate beams for multi-stream beam steering. In 60 GHz WLANs, the AP establishes and maintains a directional link with every client through periodic beam training. MUTE repurposes these beam acquisition sweeps to estimate the Power Delay Profile (PDP) of each beam with zero additional overhead. Coupling PDP estimates with beam pattern knowledge, MUTE selects a set of candidate beams that capture diverse or ideally orthogonal paths to obtain maximum stream separability. Our experiments demonstrate that MUTE achieves 90% of the maximum achievable aggregate rate while incurring only 0.04% of exhaustive search's training overhead.

We present LiSteer, a novel system that steers mmWave beams at mobile devices by repurposing indicator LEDs on wireless Access Points (APs) to passively acquire direction estimates using off-the-shelf light sensors. We demonstrate that LiSteer maintains beam alignment at the narrowest beamwidth level even in case of device mobility, without incurring any training overhead at mobile devices. Our extensive evaluation on a custom dual-band hardware platform comprising highly directional horn antennas as well as practical phased antenna arrays with electronic beam steering shows that LiSteer achieves direction estimates within 2.5 degrees of ground truth on average. Moreover, it achieves beam steering accuracy of more than 97% while in tracking mode, without incurring any client beam training or feedback overhead.

We present SearchLight, a system that enables adaptive steering of highly directional 60 GHz beams via passive sensing of visible light from existing illumination sources. The key idea is to simultaneously track a mobile device's position and orientation using intensity measurements from lighting infrastructure, and to adapt client and AP beams to maintain beam alignment, without training overhead or outages in the 60 GHz band. Our implementation on custom dual-band hardware with 2 GHz wide channels and 24-element, electronically steerable phased array antennas shows that SearchLight successfully tracks client mobility and achieves up to 3× throughput gains compared to an in-band training strategy, and eliminates millisecond-scale in-band training epochs.

We introduce X60, the first SDR-based testbed for 60 GHz WLANs, featuring fully programmable MAC/PHY/Network layers, multi-Gbps rates, and a user-configurable 12-element phased antenna array. These features provide us with an unprecedented opportunity to revisit the most important aspects of 60 GHz signal propagation and obtain new insights on performance expected from practical 60 GHz systems. X60's unique capabilities make it an ideal platform for experimentation and prototyping across layers.

High directivity of 60 GHz links introduces new link training and adaptation challenges due to both client and environmental mobility. In this paper, we design, implement and evaluate MOCA, a protocol for Mobility resilience and Overhead Constrained Adaptation for directional 60 GHz links. Since mobility-induced link blockage and misalignment cannot be countered with data rate adaptation alone, we introduce Beam Sounding as a mechanism invoked before each data transmission to estimate the link quality for selected beams, and identify and adapt to link impairments. We devise proactive techniques to restore broken directional links with low overhead and design a mechanism to jointly adapt beamwidth and data rate, targeting throughput maximization that incorporates data rate, overhead for beam alignment, and mobility resilience. We implement a programmable node and testbed using software defined radios with commercial 60 GHz transceivers, and conduct an extensive over-the-air measurement study to collect channel traces for various environments. Based on trace based emulations and the IEEE 802.11ad channel model, we evaluate MOCA under a variety of propagation environments and mobility scenarios. Our experiments show that MOCA achieves up to 2x throughput gains compared to a baseline WLAN scheme in a diverse set of operational conditions.

In this paper, we use recently developed error estimation coding (EEC) to devise an efficient relaying framework for a multi-relay network. EEC utilizes an added redundancy to form an estimate of the bit-error rate (BER) of data received over a noisy channel. Utilizing the BER estimate, we propose a relaying selection strategy that uses the BER estimate at any given relay to switch between Amplify-Forward (AF) and Detect-Forward (DF) cooperation. In addition, we propose a signal combining rule at the destination that weighs different copies of the same data using the corresponding BER estimates. For performance evaluations, we implement the proposed scheme on a USRP-based platform, and test its performance by conducting experiments in an indoor office environment. Our results validate the efficacy of the proposed strategy; the proposed scheme significantly outperforms standard equal gain combining-based AF and DF cooperation.