A deep geoelectrical survey in the Southern Central Alps

Alfano, L.; Lorenzoni, G.; Melandri, C.; Mocchi, M.; Nuttini, G.; Pirera, F.; Soffientini, M. F.

doi:10.4401/ag-4166

Cited by 1 publication

(2 citation statements)

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“…After that, DDPG samples B number of transitions from the replay buffer to form a mini-batch B. With mini-batch inputs, the target actor network μ (s t+1 |θ µ ) outputs the action to the target critic network Q , where the resulting action-value can be evaluated based on (3). Therefore, the critic network is updated by minimizing the loss function…”

Section: Action Embedding and Training Proceduresmentioning

confidence: 99%

“…While the traditional mobile network confronts challenges on spectrum insufficiency, the multiple-inputmultiple-output (MIMO) technology, which contributes crucial progress in system capacity and reliability, is regarded as a necessary feature in the fifth-generation (5G) and beyond (B5G) wireless network systems [2]. Multi-user MIMO (MU-MIMO) has been widely exploited in current wireless systems and furnishes significant improvement through conventional MIMO [3]. However, with a roughly equal number of service antennas and terminals under frequency-division duplex operation, MU-MIMO lacks scalability in various scenarios [4].…”

Section: Introductionmentioning

confidence: 99%

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Joint QoS-Aware Scheduling and Precoding for Massive MIMO Systems via Deep Reinforcement Learning

Huang¹,

Chou²,

Chen³

et al. 2021

Preprint

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The rapid development of mobile networks proliferates the demands of high data rate, low latency, and highreliability applications for the fifth-generation (5G) and beyond (B5G) mobile networks. Concurrently, the massive multipleinput-multiple-output (MIMO) technology is essential to realize the vision and requires coordination with resource management functions for high user experiences. Though conventional crosslayer adaptation algorithms have been developed to schedule and allocate network resources, the complexity of resulting rules is high with diverse quality of service (QoS) requirements and B5G features. In this work, we consider a joint user scheduling, antenna allocation, and precoding problem in a massive MIMO system. Instead of directly assigning resources, such as the number of antennas, the allocation process is transformed into a deep reinforcement learning (DRL) based dynamic algorithm selection problem for efficient Markov decision process (MDP) modeling and policy training. Specifically, the proposed utility function integrates QoS requirements and constraints toward a longterm system-wide objective that matches the MDP return. The componentized action structure with action embedding further incorporates the resource management process into the model. Simulations show 7.2% and 12.5% more satisfied users against static algorithm selection and related works under demanding scenarios.

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Section: Action Embedding and Training Proceduresmentioning

confidence: 99%