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Free, publicly-accessible full text available May 29, 2025
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Multi-band operation in wireless networks can improve data rates by leveraging the benefits of propagation in different frequency ranges. Distinctive beam management procedures in different bands complicate band assignment because they require considering not only the channel quality but also the associated beam management overhead. Reinforcement learning (RL) is a promising approach for multi-band operation as it enables the system to learn and adjust its behavior through environmental feedback. In this paper, we formulate a sequential decision problem to jointly perform band assignment and beam management. We propose a method based on hierarchical RL (HRL) to handle the complexity of the problem by separating the policies for band selection and beam management. We evaluate the proposed HRL-based algorithm on a realistic channel generated based on ray-tracing simulators. Our results show that the proposed approach outperforms traditional RL approaches in terms of reduced beam training overhead and increased data rates under a realistic vehicular channel.more » « less
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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 address the question: how can the best relay be selected with minimal overhead from beam management? 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 in- dices and beam measurements. The proposed DRL-based algorithm learns time-varying thresholds that adapt to the dynamic channel conditions and traffic patterns. Numeri- cal 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.more » « less
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null (Ed.)Fast and accurate interrogation of complex samples containing diseased cells or pathogens is important to make informed decisions on clinical and public health issues. Inertial microfluidics has been increasingly employed for such investigations to isolate target bioparticles from liquid samples with size and/or deformability-based manipulation. This phenomenon is especially useful for the clinic, owing to its rapid, label-free nature of target enrichment that enables further down- stream assays. Inertial microfluidics leverages the principle of inertial focusing, which relies on the balance of inertial and viscous forces on particles to align them into size-dependent laminar stream- lines. Several distinct microfluidic channel geometries (e.g., straight, curved, spiral, contraction-ex- pansion array) have been optimized to achieve inertial focusing for a variety of purposes, including particle purification and enrichment, solution exchange, and particle alignment for on-chip assays. In this review, we will discuss how inertial microfluidics technology has contributed to improving accuracy of various assays to provide clinically relevant information. This comprehensive review expands upon studies examining both endogenous and exogenous targets from real-world samples, highlights notable hybrid devices with dual functions, and comments on the evolving outlook of the field.more » « less