Learning-Based Phase Shift Optimization of Liquid Crystal RIS in Dynamic mmWave Networks

To enhance coverage and signal quality in millimeter-wave (mmWave) frequencies, reconfigurable intelligent surfaces (RISs) have emerged as a game-changing solution to manipulate the wireless environment. Traditional semiconductor-based RISs face scalability issues due to high power consumption. Meanwhile, liquid crystal-based RISs (LC-RISs) offer energy-efficient and cost-effective operation even for large arrays. However, this promise has a caveat. LC-RISs suffer from long reconfiguration times, on the order of tens of milliseconds, which limits their applicability in dynamic scenarios. To date, prior works have focused on hardware design aspects or static scenarios to address this limitation, but little attention has been paid to optimization solutions for dynamic settings. Our paper fills this gap by proposing a reinforcement learning-based optimization framework to dynamically control the phase shifts of LC-RISs and maximize the data rate of a moving user. Specifically, we propose a Deep Deterministic Policy Gradient (DDPG) algorithm that adapts the LC-RIS phase shifts without requiring perfect channel state information and balances the tradeoff between signal-to-noise ratio (SNR) and configuration time. We validate our approach through high-fidelity ray tracing simulations, leveraging measurement data from an LC-RIS prototype. Our results demonstrate the potential of our solution to bring adaptive control to dynamic LC-RIS-assisted mmWave systems.

Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing

The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP's and MUs' conflicting goals, and the uncertainty about the MUs' required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP's and MUs' individual goals while the MUs learn their efforts online. Our innovative "free-sensing" mechanism significantly improves the MU's learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs' and the MCSP's satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%.