We explore the use of caching both at the network edge and within User Equipment(UE)to alleviate traffic load of wireless *** develop a joint cache placement and delivery policy that maximizes the Quality of Service(Q...
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We explore the use of caching both at the network edge and within User Equipment(UE)to alleviate traffic load of wireless *** develop a joint cache placement and delivery policy that maximizes the Quality of Service(QoS)while simultaneously minimizing backhaul load and UE power consumption,in the presence of an unknown time-variant file *** file requests in a time slot being affected by download success in the previous slot,the caching system becomes a non-stationary Partial Observable Markov Decision Process(POMDP).We solve the problem in a deep reinforcement learning framework based on the Advantageous Actor-Critic(A2C)algorithm,comparing Feed Forward Neural Networks(FFNN)with a Long Short-Term Memory(LSTM)approach specifically designed to exploit the correlation of file popularity distribution across time *** results show that using LSTM-based A2C outperforms FFNN-based A2C in terms of sample efficiency and optimality,demonstrating superior performance for the non-stationary POMDP *** caching at the UEs,we provide a distributed algorithm that reaches the objectives dictated by the agent controlling the network,with minimum energy consumption at the UEs,and minimum communication overhead.
Human action recognition (HAR) systems need to process large volumes of data posing several challenges including, but not limited to, accurately identifying the actions and classifying them in near real time. Most of ...
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Recently, IoT (Internet-of-Things) devices are very widely used in consumer electronics areas and their design and manufacturing are often outsourced to third parties to make them at a low cost. Meanwhile, malfunction...
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We propose a training method for a heterogeneous multi-agent system to improve the learning efficiency in sparse-reward environments. Although extensive research on multi-agent deep reinforcement learning are conducte...
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This paper proposes a smartphone-based pedestrian dead reckoning to track stair ascent and descent. By using accelerometer and barometer information, pedestrians' gait patterns are collected under different carryi...
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Glass, though ubiquitous, is difficult to recognize in an image due to its transparency. Fine-grained low-level features indicating the presence of glass, such as refraction and reflection, are weak and subtle. This c...
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We propose a method based on particle swarm optimization (PSO) for the multi-agent pattern formation problem (MAPFP), which allows agents to form formation patterns with a high completion rate without overlaps. MAPFP ...
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IoT (Internet-of-Things) devices are tremendously widespread in our daily lives and these devices are very often outsourced to third-party companies to save cost. However, it is pointed out that the risk to insert mal...
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Joint Radar Communication (JRC) system achieves radar detection and communication transmission using a shared hardware platform, making it more suitable for integration, miniaturization, and efficient spectrum utiliza...
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In the current era of pervasive short video content, the exposure of passersby's data frequently raises privacy concerns. Traditional anonymization techniques for passersby, like blurring and mosaicing, are often ...
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