Smart multiple-input multiple-output (MIMO) antennas with advanced signal processing algorithms are necessary in future wireless networks, such as 6G and beyond, for accurate space division multiplexing and beamforming. Such a MIMO antenna will yield better network coverage and tracking. This paper presents a smart MIMO antenna configuration with a highly innovative beamforming technique using several nonlinear configurations of dipole arrays. Phase delay factors are optimized at the transmitter to form a single beam and then to steer the beam towards a particular direction. A number of phase shifters are added in order to obtain maximum directional gain. This configuration also significantly increases the power gain of the MIMO antenna at a low cost and with operational simplicity. The paper also demonstrates how the beam width and beamsteering can be effectively controlled. Wolfram Mathematica software was used to generate the three-dimensional radiated beam patterns of the transmitter antenna. There are two approaches to configure the receiver antenna. In the first approach, the received signal magnitude is maximized by aligning the contribution of all elements of the receiver antenna to the same phase. With this approach, the field gain of the proposed system is 25.52 (14.07 dBi). The signal processing gain at the receiver is 64 (18.06 dBi). Therefore, the overall power gain for this proposed new digital/geometrical smart MIMO system is 32.13 dBi. In the second approach, the receiver beam is directed towards the transmitter by optimizing the phase delay coefficients of the receiver. Here, the overall gain of the system is found to be 134.56 (21.28 dBi). Even though the system gain in the second approach is lower, it has the advantage of low interference at the receiver side.

Wireless embedded systems assist us in collecting data from the physical world, through sensor data analysis, such systems allow us to understand our environment. However, deploying wireless embedded systems and analyzing the collected data remains significantly challenging. This is due to the steep learning curve required to implement custom machine-learning models and other algorithms for data analysis. Furthermore, it is also challenging to program individual embedded devices. The diversity of the available platforms and their capabilities further compounds this problem. In response, we introduce an end-to-end system, called the Otter. It facilitates simple sensor data collection using commodity-embedded platforms. Moreover, it employs a large language model to design a natural language interface for the analysis and extraction of useful information from the sensor data. We present our preliminary work on prototyping this system, applying it to a specific use case of hand gesture detection. Otter represents one of the first systems to leverage the enhanced capabilities of large language models for simplifying wireless embedded system deployments.

Wireless embedded systems have seen rapid growth. Nonetheless, the further growth of these systems is now threatened due to the complexities of programming, networking, and deploying them. Developing embedded systems today is a challenging task primarily due to the vast diversity in radio standards, network stacks, microcontrollers, sensors, and other parts of the embedded system ecosystem. This complexity makes it a task suited only for experts, thus hindering the broader adoption of embedded systems. In this early work, we tackle a particular aspect of this challenge related to the complexities of wireless communication. Specifically, we explore the hypothesis that the emergent properties of large language models could help mitigate the complexities of programming the wireless communication stack. We adopt a complex radio transmitter design based on a backscatter mechanism and explore the generation of the transmitter's logic using state-of-the-art language models. Our exploration leads to insights that, with appropriate prompting, today's state-of-the-art large language models are already capable of generating complex modulation and backscatter logic. This finding warrants further efforts to design language model generated radio transmitters to simplify embedded systems.

We have seen rapid deployment of augmented reality devices. However, these devices currently suffer from limited battery life. They require frequent recharge, especially when capturing and streaming information wirelessly. In particular, these devices stream information over the radio spectrum using transceivers that are power-hungry, and wireless communication dominates the energy budget of these devices. We present our ongoing work to design a system called Li-FiAR, which proposes integrating Li-Fi with augmented reality devices. Specifically, we implement a transmitter and receiver and integrate them with an augmented reality device. We argue that Li-Fi receivers may consume less power than their radio counterparts while benefiting from the spatial nature of light propagation. This enables novel application scenarios for augmented reality. We demonstrate the transmission of images and audio through Li-Fi, presented on an augmented reality device in this work.