Mobile Millimeter Research Articles

Joint Relay Selection and Beam Management Based on Deep Reinforcement Learning for Millimeter Wave Vehicular Communication

Cooperative relays improve reliability and coverage in wireless networks by providing multiple paths for data transmission. Relaying will play an essential role in vehicular networks at higher frequency bands, where mobility and frequent signal blockages cause link outages. To ensure connectivity in a relay-aided vehicular network, the relay selection policy should be designed to efficiently find unblocked relays. Inspired by recent advances in beam management in mobile millimeter wave (mmWave) networks, this paper addresses the question: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how can the best relay be selected with minimal overhead from beam management?</i> In this regard, we formulate a sequential decision problem to jointly optimize relay selection and beam management. We propose a joint relay selection and beam management policy based on deep reinforcement learning (DRL) using the Markov property of beam indices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numerical experiments demonstrate that the proposed algorithm outperforms baselines without prior channel knowledge. Moreover, the DRL-based algorithm can maintain high spectral efficiency under fast-varying channels.

Soft Molds with Micro-Machined Internal Skeletons Improve Robustness of Flapping-Wing Robots.

Mobile millimeter and centimeter scale robots often use smart composite manufacturing (SCM) for the construction of body components and mechanisms. The fabrication of SCM mechanisms requires laser machining and laminating flexible, adhesive, and structural materials into small-scale hinges, transmissions, and, ultimately, wings or legs. However, a fundamental limitation of SCM components is the plastic deformation and failure of flexures. In this work, we demonstrate that encasing SCM components in a soft silicone mold dramatically improves the durability of SCM flexure hinges and provides robustness to SCM components. We demonstrate this advance in the design of a flapping-wing robot that uses an underactuated compliant transmission fabricated with an inner SCM skeleton and exterior silicone mold. The transmission design is optimized to achieve desired wingstroke requirements and to allow for independent motion of each wing. We validate these design choices in bench-top tests, measuring transmission compliance, kinematics, and fatigue. We integrate the transmission with laminate wings and two types of actuation, demonstrating elastic energy exchange and limited lift-off capabilities. Lastly, we tested collision mitigation through flapping-wing experiments that obstructed the motion of a wing. These experiments demonstrate that an underactuated compliant transmission can provide resilience and robustness to flapping-wing robots.

User Equipment Tracking for a Millimeter Wave System Using Vision and RSSI

In a mobile millimeter wave (mmWave) communication system, blockages cause disconnections or serious degradation of communications. Several techniques have been proposed to control radio links between multiple base stations based on blockage prediction using camera images to avoid these problems. However, blockage prediction requires continuously determining the position of user equipment (UE) with decimeter precision, which is difficult when sensors and resources on the UE side are not available, and there are many moving objects around the UE. To resolve this problem, we propose a UE tracking method that uses a received signal strength indicator (RSSI) and RGB-D camera images from the base station. The proposed method consists of a combination of visual tracking and reidentification of the UE using radio information and camera images. We use the synchronization of the RSSI variation with the occlusion on the image by movements of the UE and objects for reidentification. We evaluated the proposed method experimentally in an outdoor environment by simulating a communication area formed by mmWave band base stations. The proposed method achieved an 11.5% improvement in tracking accuracy compared with conventional visual tracking.

Bayesian Beamforming for Mobile Millimeter Wave Channel Tracking in the Presence of DOA Uncertainty

This paper proposes a Bayesian approach for angle-based hybrid beamforming and tracking that is robust to uncertain or erroneous direction-of-arrival (DOA) estimation in millimeter wave (mmWave) multiple input multiple output (MIMO) systems. Because the resolution of the phase shifters is finite and typically adjustable through a digital control, the DOA can be modeled as a discrete random variable with a prior distribution defined over a discrete set of candidate DOAs, and the variance of this distribution can be introduced to describe the level of uncertainty. The estimation problem of DOA is thereby formulated as a weighted sum of previously observed DOA values, where the weights are chosen according to a posteriori probability density function (pdf) of the DOA. To alleviate the computational complexity and cost, we present a motion trajectory-constrained a priori probability approximation method. It suggests that within a specific spatial region, a directional estimate can be close to true DOA with a high probability and sufficient to ensure trustworthiness. We show that the proposed approach has the advantage of robustness to uncertain DOA, and the beam tracking problem can be solved by incorporating the Bayesian approach with an expectation-maximization (EM) algorithm. Simulation results validate the theoretical analysis and demonstrate that the proposed solution outperforms a number of state-of-the-art benchmarks.

UAV Beam Alignment for Highly Mobile Millimeter Wave Communications

Unmanned Aerial Vehicles (UAVs) have the advantages of high mobility and flexibility that can be potentially used as aerial base stations (BSs) to provide millimeter-wave (mmWave) communication with ground users. However, the UAV mmWave beamforming must be carefully designed to provide directional transmission and support reliable connections. In this paper, we consider a UAV mmWave system, where the UAV serves as a BS to cover mobile users on the ground. Firstly, we study the beam training and propose beam training codebook designs for both insufficient and sufficient beam training cases. Then, we propose efficient beam tracking methods to predict the beam direction with user mobility, where both random and inertial motion are considered. Simulation results show that the average downlink capacity increases with the proposed beam training codebook. Further, the mean angular error of our proposed beam tracking is lower than existing method.

EKF-Based Beam Tracking for mmWave MIMO Systems

Beam tracking is an effective way to maintain high-quality links in mobile millimeter wave MIMO systems, which is achieved by accurately estimating angle-of-departure (AoD) and angle-of-arrival (AoA) of the desired path. Considering the slow variations of angular velocity for time-varying AoD and AoA, a second-order AoD/AoA variation model is proposed for extended Kalman filter (EKF) based beam tracking. By utilizing two well configured tracking beam pairs, the unknown path gain is removed from EKF’s measurement model and the optimal estimation of AoD/AoA is derived. Simulation results demonstrate that the proposed EKF-based beam tracking method achieves higher AoD estimation accuracy than previous methods.

Multi-Armed Bandit Beam Alignment and Tracking for Mobile Millimeter Wave Communications

We propose a novel beam alignment and tracking algorithm for time-varying millimeter wave channels with a dynamic channel support. Millimeter wave beam alignment is challenging due to the expected large number of antennas. A multi-armed bandit training beam selection policy is used to balance exploration of the set of feasible beams. We track the channel using a synthesis of sparse Bayesian learning and Kalman filtering and smoothing. Results show our algorithm has a more rapid rate of initial beam alignment compared to other beam selection policies and, for dynamic channel support, long-term beamforming gain commensurate to omni-directional training.

Exploring AoA/AoD Dynamics in Beam Alignment of Mobile Millimeter Wave MIMO Systems

In this paper, we present a priori-aided beam alignment scheme for time-varying millimeter wave multiple-input multiple-output (MIMO) systems, where the temporal correlations of the angle of departure (AoD) and angle of arrival (AoA) of the propagation path are exploited. Specifically, the variations of AoD and AoA between two consecutive channel realizations are described by a set of transition probabilities, respectively, which characterize the temporal correlations. We aim to maximize the average successful estimation probability (ASEP) by jointly optimizing the selected transmitting–receiving training beam pairs and their associated transmit power. When the transition probabilities are known, this problem is formulated as a mixed integer nonlinear programming (MINLP). Due to the non-convex nature of the formulated MINLP, we propose a semi-exhaustive search algorithm (SESA) to obtain a suboptimal solution. Moreover, in order to achieve maximum ASEP, we prove that the training beam pairs corresponding to the directions which have higher transition probabilities should be used more times than those with lower transition probabilities. Numerical results show that the proposed joint optimization with the SESA can achieve a higher ASEP than the existing works.