A novel Twitter context aided content caching (TAC) framework is proposed for enhancing the caching efficiency by taking advantage of the legibility and massive volume of Twitter data. For the purpose of promoting the caching efficiency, three machine learning models are proposed to predict latent events and events popularity, utilizing collected Twitter data with geo-tags and geographic information of the adjacent base stations (BSs). Firstly, we propose a latent Dirichlet allocation (LDA) model for latent events forecasting because of the superiority of LDA model in natural language processing (NLP). Then, we conceive long short-term memory (LSTM) with skip-gram embedding approach and LSTM with continuous skip-gram-Geo-aware embedding approach for the events popularity forecasting. Furthermore, we associate the predict latent events and the popularity of the events with the caching strategy. Lastly, we propose a non-orthogonal multiple access (NOMA) based content transmission scheme. Extensive practical experiments demonstrate that: 1) the proposed TAC framework outperforms conventional caching framework and is capable of being employed in practical applications thanks to the associating ability with public interests; 2) the proposed LDA approach conserves superiority for natural language processing (NLP) in Twitter data; 3) the perplexity of the proposed skip-gram based LSTM is lower compared with conventional LDA approach; and 4) evaluation of the model demonstrates that the hit rates of tweets of the model vary from 50% to 65% and the hit rate of the caching contents is up to approximately 75% with smaller caching space compared to conventional algorithms. Simulation results also shows that the proposed NOMA-enabled caching scheme outperforms conventional least frequently used (LFU) scheme by 25%.

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves a joint optimization of NOMA user partitioning and RIS phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. To solve the challenging joint optimization problem, we invoke a modified object migration automation (MOMA) algorithm to partition the users into equal-size clusters. To optimize the RIS phase shifting matrix, we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Different from conventional training-then-testing processing, we consider a long-term self-adjusting learning model where the intelligent agent is capable of learning the optimal action for every given state through exploration and exploitation. Extensive numerical results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves enhanced sum data rate compared with the conventional orthogonal multiple access (OMA) framework. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy in a long-term manner. 3) The performance of the proposed RIS-aided NOMA framework can be improved by increasing the granularity of the RIS phase shifts. The numerical results also show that increasing the number of reflecting elements (REs) is an efficient method to improve the sum data rate of the MUs.

Caching popular contents at the network edge has been considered as a promising enabler to relieve the pressure on networks due to the fact that a substantial portion of global data traffic is repeatedly requested by many subscribers and thus redundantly generated. Recommendation, on the other hand, has attracted spiraling attention for its capability of reshaping users’ contents demand patterns. In this paper, we examine the practicability of recommendation in boosting the gains of edge caching with uncharted users’ feature information. To this end, we first characterize the average system cost for a generic network model, disclosing its dependence on the recommendation and caching strategies. Then, we formulate the joint caching and recommendation decision oriented cost minimization problem, taking the constraints on each content provider’s cache capacity budget, each individual user’s recommendation size and recommendation quality into account. However, the implicit information regarding users’ preference makes the problem inextricable. To address this issue, a versatile long short term memory (LSTM) network assisted prediction paradigm is proposed to attain the preference schema of users with the assistance of their historical behavior data. Based on that, we rigorously prove the NP-hardness of obtaining the optimal recommendation and caching policies that jointly minimize the system cost. Therewith, an iterative suboptimal algorithm is developed, which has provable polynomial time complexity and convergence guarantee. Extensive simulation results validate the effectiveness of our proposed LSTM enabled feature information prediction approach and the convergence performance of the devised joint decision making methodology. In addition, it is shown that the proposed scheme outperforms numerous benchmarks significantly.

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves the optimization of phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. For intelligently adjusting the phase shifting matrix of the access point (AP), we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Extensive simulation results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves better sum data rate compared with orthogonal multiple access (OMA) networks. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy, while conventional optimization approaches can not. 3) Compared with increasing the transmit power of the AP, increasing the number of reflecting elements (REs) is a more efficiency method to improve the sum data rate.

An optimization problem of content placement in cooperative caching is formulated, with the aim of maximizing the sum mean opinion score (MOS) of mobile users. Firstly, as user mobility and content popularity have significant impacts on the user experience, a recurrent neural network (RNN) is invoked for user mobility prediction and content popularity prediction. More particularly, practical data collected from GPS-tracker app on smartphones is tackled to test the accuracy of user mobility prediction. Then, based on the predicted mobile users' positions and content popularity, a learning automata based Q-learning (LAQL) algorithm for cooperative caching is proposed, in which learning automata (LA) is invoked for Q-learning to obtain an optimal action selection in a random and stationary environment. It is proven that the LA based action selection scheme is capable of enabling every state to select the optimal action with arbitrary high probability if Q-learning is able to converge to the optimal Q value eventually. In the LAQL algorithm, a central processor acts as the intelligent agent, which allocate contents to BSs according to the reward or penalty from the feedback of the BSs and users, iteratively. To characterize the performance of the proposed LAQL algorithms, sum MOS of users is applied to define the reward function. Extensive simulation results reveal that: 1) the prediction error of RNNs based algorithm lessen with the increase of iterations and nodes; 2) the proposed LAQL achieves significant performance improvement against traditional Q-learning algorithm; and 3) the cooperative caching scheme is capable of outperforming non-cooperative caching and random caching of 3% and 4%, respectively.

A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge computing (MEC) framework is proposed. For the purpose of efficiently allocating communication and computation resources to users' computation tasks requests, we propose a long-short-term memory (LSTM) network to predict the task popularity. Based on the predicted task popularity, a long-term reward maximization problem is formulated that involves a joint optimization of the task offloading decisions, computation resource allocation, and caching decisions. To tackle this challenging problem, a single-agent Q-learning (SAQ-learning) algorithm is invoked to learn a long-term resource allocation strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions. More specifically, a BLA based action select scheme is proposed for the agents in MAQ-learning to select the optimal action in every state. We prove that the BLA based action selection scheme is instantaneously self-correcting and the selected action is an optimal solution for each state. Extensive simulation results demonstrate that: 1) The prediction error of the proposed LSTMs based task popularity prediction decreases with increasing learning rate. 2) The proposed framework significantly outperforms the benchmarks like all local computing, all offloading computing and non-cache computing. 3) The proposed BLA based MAQ-learning achieves an improved performance compared to conventional MAQ-learning algorithm.

This article investigates a wireless caching framework based on tweets and their location data collected from Twitter. The tweet texts are associated with the location information of the corresponding base stations (BSs) for improving the caching efficiency at BSs. Extracted latent topics and predicted content probability are applied to reduce caching redundancy at BSs. A machine learning approach, namely latent Dirichlet allocation (LDA), is invoked to extract location-aware latent topics for better caching performances. In an effort to predict content probability for caching, a novel skip-gram based long short-term memory (LSTM) model is proposed to cluster words with similar semantics for content probability prediction. Moreover, practical data collected from Twitter is tackled to verify the performance of the proposed framework. Extensive practical tests demonstrate that: 1) Our proposed framework is capable of perceiving caching peaks while the conventional counting method fails; 2) The proposed machine learning approaches are capable of generating accurate topics extraction and content probability prediction results; 3) Our proposed framework maintains superiority over conventional caching approaches and possesses considerable application potential due to its ability of associating with indigenous public preferences.

A novel resource allocation scheme for cache-aided mobile-edge computing (MEC) is proposed, to efficiently offer communication, storage and computing service for intensive computation and sensitive latency computational tasks. In this paper, the considered resource allocation problem is formulated as a mixed integer non-linear program (MINLP) that involves a joint optimization of tasks offloading decision, cache allocation, computation allocation, and dynamic power distribution. To tackle this non-trivial problem, Markov decision process (MDP) is invoked for mobile users and the access point (AP) to learn the optimal offloading and resource allocation policy from historical experience and automatically improve allocation efficiency. In particular, to break the curse of high dimensionality in the state space of MDP, a deep reinforcement learning (DRL) algorithm is proposed to solve this optimization problem with low complexity. Moreover, extensive simulations demonstrate that the proposed algorithm is capable of achieving a quasi-optimal performance under various system setups, and significantly outperform the other representative benchmark methods considered. The effectiveness of the proposed algorithm is confirmed from the comparison with the results of the optimal solution.

Caching during off-peak times can bring popular contents closer to users, and hence improves quality of experience (QoE) of users in wireless networks. We formulate an optimization problem of cooperative content caching, with the aim of maximizing the sum mean opinion score (MOS) of all users in the network. To solve the challenging content caching problem, we cluster users by global K-means (GKM), based on content popularity. For improving the effectiveness of caching, we propose a low complexity ε-greedy Q-learning based content caching algorithm which obtains a near-optimal solution. To characterize the performance of the proposed cooperative caching algorithms, sum MOS of users is used to define the reward function in Q- learning. The proposed Q-learning algorithm is capable of assisting the network to efficiently utilize the caching resource of the BSs. Simulation results reveal that: The proposed low complexity ε-greedy Q-learning based content caching algorithm achieves a near-optimal performance and is capable of outperforming GKM based caching.