GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

Stable Zr(IV)-Based Metal–Organic Frameworks with Predesigned Functionalized Ligands for Highly Selective Detection of Fe(III) Ions in Water

Metal–organic frameworks are a class of attractive materials for fluorescent sensing. Improvement of hydrolytic stability, sensitivity, and selectivity of function is the key to advance application of fluorescent MOFs in aqueous media. In this work, two stable MOFs, [Zr₆O₄(OH)₈(H₂O)₄(L¹)₂] (BUT-14) and [Zr₆O₄(OH)₈(H₂O)₄(L²)₂] (BUT-15), were designed and synthesized for the detection of metal ions in water. Two new ligands utilized for construction of the MOFs, namely, 5′,5‴-bis­(4-carboxyphenyl)-[1,1′:3′,1″:4″,1‴:3‴,1⁗-quinquephenyl]-4,4⁗-dicarboxylate (L¹) and 4,4′,4″,4‴-(4,4′-(1,4-phenylene)­bis­(pyridine-6,4,2-triyl))­tetrabenzoate (L²), are structurally similar with the only difference being that the latter is functionalized by pyridine N atoms. The two MOFs are isostructural with a <b>sqc</b>-<b>a</b> topological framework structure, and highly porous with the Brunauer–Emmett–Teller (BET) surface areas of 3595 and 3590 m² g^–1, respectively. Interestingly, they show intense fluorescence in water, which can be solely quenched by trace amounts of Fe³⁺ ions. The detection limits toward the Fe³⁺ ions were calculated to be 212 and 16 ppb, respectively. The efficient fluorescent quenching effect is attributed to the photoinduced electron transfer between Fe³⁺ ions and the ligands in these MOFs. Moreover, the introduced pyridine N donors in the ligand of BUT-15 additionally donate their lone-pair electrons to the Fe³⁺ ions, leading to significantly enhanced detection ability. It is also demonstrated that BUT-15 exhibits an uncompromised performance for the detection of Fe³⁺ ions in a simulated biological system