Augmenting Deep Learning for Efficient Nextg Wireless Communication and Sensing Systems

Wireless networks have become an integral aspect of our daily lives. Over the years, earlier generations of wireless networks have enabled some innovative applications, such as wireless gaming, fast internet browsing, and home automation, which were previously unattainable. However, to support emerging technologies like autonomous driving, virtual reality, telemedicine, and intelligent manufacturing, which require high data throughput and low latency, there is a need for advanced wireless networks. Legacy networks like WiFi/LTE, which operate below 6 GHz, have limited bandwidth and are insufficient to fulfill the high data throughput demands of several applications. Millimeter-wave (mmWave) networks, operating between 30 GHz to 300 GHz, promise to offer a broader contiguous spectrum and low latency and might solve various applications’ data throughput and latency requirements. However, due to the directional nature of mmWave, it is prone to frequent outages while using Line-of-Sight (LoS) paths only, and the network deployment needs to consider Non-Line-of-Sight (NLoS) paths to provide reliable network connectivity. Additionally, due to the high operating frequency of mmWave network devices, these devices can also work as sensors to detect objects in their surroundings and work in all weather conditions. However, the specularity of mmWave and the weak reflectivity of objects cause most object details to be lost, making objects imperceptible. Besides, sharing a single mmWave device for networking and sensing applications reduces data throughput and sensing accuracy. In this dissertation, we first design and evaluate a deep learning-based model to find optimal locations of 5G base stations (called “picocell”) for indoor and outdoor environments. Due to the lack of open-source data samples at high frequency, we collect actual data samples and verify our deep-learning models. Our model uses semantic features of the environment, such as the wall, ceiling, floor, and door, to make it generalizable and requires only a few data samples from the new environment to fine-tune, and it removes the need for an extensive site survey for optimal deployment. We then enable accurate sensing and high-throughput networking with a single mmWave device. We use conditional Generative Adversarial Networks to get an accurate depth image of the objects and residual networks to predict unobservable data samples from previously observed ones during networking. Finally, we design and implement a deep learning framework to detect the 3D bounding boxes of vehicles and pedestrians from mmWave devices in harsh weather conditions and compare their performance with vision-based methods. We use the non-anchor bounding box prediction method to enable 3D object detection for outdoor environments.