Parth H. Pathak’s research while affiliated with George Mason University and other places

With the Internet of Things (IoT) anticipated to grow up to a trillion devices by 2035 [1] and 6G aiming at 10/m2 Massive Machine Type Communications [2], massive connectivity has long been considered a key to the success of IoT and diverse emerging services. Backscatter is an attractive option for scalability, operating at tens of µW [3] by reflecting the wireless signal without generating the power-hungry passband signal itself. The ultra-low power operation of backscatter offers long-term sustainability. In particular, recent advances in mmWave backscatters [4,5,6] present a great potential for scalability by exploiting the abundant spectrum resource in the mmWave ranging up to 14 GHz (60 GHz band); a bandwidth over two orders of magnitude greater than 26 MHz and 100 MHz in the popular 900 MHz and 2.4 GHz bands, respectively.

This position paper explores the challenges and opportunities for high-quality immersive volumetric video streaming for multiple users over millimeter-wave (mmWave) WLANs. While most of the previous work has focused on single-user streaming, there is a growing need for multi-user immersive applications such as virtual collaboration, classroom education, teleconferencing, etc. While mmWave wireless links can provide multi-gigabit per second data rates, they suffer from blockages and high beamforming overhead. This paper investigates an environment-driven approach to address the challenges. It presents a comprehensive research agenda that includes developing a collaborative 3D scene reconstruction process, material identification, ray tracing, blockage mitigation, and cross-layer multi-user video rate adaptation. Our preliminary results show the feasibility and identify the limitations of existing solutions. Finally, we discuss the open challenges of implementing a practical system based on the proposed research agenda.

With MIMO and enhanced beamforming features, IEEE 802.11ay is poised to create the next generation of mmWave WLANs that can provide over 100 Gbps data rate. However, beamforming between densely deployed APs and clients incurs unacceptable overhead. On the other hand, the absence of up-to-date beamforming information restricts the diversity gains available through MIMO and multi-users, reducing the overall network capacity. This paper presents a novel approach of "coordinated beamforming" (called CoBF) where only a small subset of APs are selected for beamforming in the 802.11ay mmWave WLANs. Based on the concept of uncertainty, CoBF predicts the APs whose beamforming information is likely outdated and needs updating. The proposed approach complements existing per-link beamforming solutions and extends their effectiveness from link-level to network-level. Furthermore, CoBF leverages the AP uncertainty to create MU-MIMO groups through interference-aware scheduling in 802.11ay WLANs. With extensive experimentation and simulations, we show that CoBF can significantly reduce beamforming overhead and improve network capacity for 802.11ay WLANs.

The scarcity of training data available for IMUs in wearables poses a serious challenge for IMU-based American Sign Language (ASL) recognition. In this paper, we ask the following question: can we "translate" the large number of publicly available, in-the-wild ASL videos to their corresponding IMU data? We answer this question by presenting a video to IMU translation framework (Vi2IMU) that takes as input user videos and estimates the IMU acceleration and gyro from the perspective of user's wrist. Vi2IMU consists of two modules, a wrist orientation estimation module that accounts for wrist rotations by carefully incorporating hand joint positions, and an acceleration and gyro prediction module, that leverages the orientation for transformation while capturing the contributions of hand movements and shape to produce realistic wrist acceleration and gyro data. We evaluate Vi2IMU by translating publicly available ASL videos to their corresponding wrist IMU data and train a gesture recognition model purely using the translated data. Our results show that the model using translated data performs reasonably well compared to the same model trained using measured IMU data.

With MIMO and enhanced beamforming features, IEEE 802.11ay is poised to create the next generation of mmWave WLANs that can provide over 100 Gbps data rate. However, beamforming between densely deployed APs and clients incurs unacceptable overhead. On the other hand, the absence of up-to-date beamforming information restricts the diversity gains available through MIMO and multi-users, reducing the overall network capacity. This paper presents a novel approach of "coordinated beamforming" (called CoBF) where only a small subset of APs are selected for beamforming in the 802.11ay mmWave WLANs. Based on the concept of uncertainty, CoBF predicts the APs whose beamforming information is likely outdated and needs updating. The proposed approach complements the existing per-link beamforming solutions and extends their effectiveness from link-level to network-level. Furthermore, CoBF leverages the AP uncertainty to create MU-MIMO groups through interference-aware scheduling in 802.11ay WLANs. With extensive experimentation and simulations, we show that CoBF can significantly reduce beamforming overhead and improve network capacity for 802.11ay WLANs.