Network slicing provides personalized services by building isolated logical networks. Besides, through deploying Virtual Network Function (VNF) chains on Mobile Edge Computing (MEC) servers, network slicing can guarantees the performance of low-latency services. To reduce maintenance costs, it is necessary to optimize the deployment of both the slices’ access functions on the base stations and the VNF chains on the MEC servers. Meanwhile, optimizing MEC server activation can save energy while meeting service demands. There have been many studies focusing on MEC server activation, access function deployment, and VNF chain deployment, but no work has considered them simultaneously. To fill this research gap, we construct a joint server activation as well as the access functions and VNF chains deployment model, aiming to guarantee performance while minimizing the cost. This problem is a mixed-integer nonlinear programming problem which is hard to be solved. Thus, we decompose the problem and design a Deep Q Network (DQN)based three-stage algorithm. Numerical results demonstrate the superiority of the proposed scheme over the baseline methods.

Antenna tuning plays an essential role in ensuring high quality wireless communications. Targeting for higher Quality of Service (QoS), many existing network antenna tuning schemes are based on expert knowledge, rule-based policies or conventional optimization theory. However, maximizing the traffic-related QoS does not guarantee that all customers experience good services. In addition, existing schemes are often limited to some handcrafted rules or heuristics and lack of adaptability especially in a time-varying environment. Quality of Experience (QoE), a user-centric metric, can better measure users' satisfaction for services in wireless networks. This paper proposes the cooperative tuning of antennas based on QoE, a paradigm shift from network-centric QoS to user-centric QoE domain. In a normal cellular network, besides the need of improving the overall QoE, it requires handling faults from different cells. As Multi-agent Reinforcement Learning (MARL) has the capability of self-learning the dynamics of environment, we propose an antenna configuration algorithm based on multi-goal MARL. In our framework, agents from different cells not only need to cooperate with each other to achieve the global goal of increasing the overall QoE of the wireless network but also complete some personal goals by combating the faults encountered in their own cells. To accelerate the training efficiency, we introduce a novel two-stage curriculum learning. To reduce the collection time of each QoE sample, we develop an accurate and timely QoE/QoS mapping model with the cascading of a Random Forest Classifier (RFC) and a Deep Neural Network (DNN) (abbreviated as RFC-DNN), which can help us obtain QoE by collecting QoS measurements and perform QoE-based antenna configurations with smaller time granularity. Our proposed RFC-DNN model can reduce the time by 70% when predicting the QoE of a single sample. A huge amount of time will be saved in MARL when tens of thousands of transitions/samples need to be collected. The performance results show that our proposed antenna tuning schemes can not only address specific faults in each cell, but also significantly improve the global average QoE with a faster and more stable convergence speed.

The slice-based Open Radio Access Network (O-RAN) enables rapid deployment of logical networks and provides personalized services. In designing the inter-slice resource sharing scheme, multiple factors need to be considered. Firstly, resource isolation level (RIL) and interference isolation level (IIL) are two key factors in ensuring slice isolation. Secondly, the new business models brought by network slicing imply the need to consider tenants’ resource costs. However, to date, no prior studies have considered RIL, IIL and costs simultaneously, often involving only one or two of them. Accordingly, we propose an inter-slice resource block (RB) leasing and association adjustment scheme (ISRLA) that allows slices to borrow RBs from the shared resource pool and other slices. ISRLA is divided into three modules, namely, prediction module, leasing module, and RB association adjustment module. Firstly, the prediction module uses a Long Short-Term Memory model to predict RB demand and guide subsequent resource optimization. Then, the leasing module balances RIL and costs to determine the number of RBs to be leased in or leased out. This is defined as a nonlinear integer programming problem, which is solved through an iterative strategy based on camp swapping (ISBCS). ISBCS is further proven to be convergent. Finally, the RB association adjustment module determines specific RB adjustment strategies based on the results obtained from the leasing module, aiming to ensure IIL. Here, we design a potential game based on the Lagrangian relaxation (LR) method to obtain an approximate optimal solution while reducing computational complexity. Compared with camp enumeration (CE), solver (MOSEK), and genetic algorithm (GA), the proposed ISRLA reduces simulation time by up to 84%, 95%, and 99%, respectively. When the number of slices is 4, the performance of ISRLA is the same as that of CE. Moreover, the IIL gap between ISRLA and MOSEK is less than 4.5%. The simulation results show that the proposed ISRLA can obtain the approximate optimal solution with low computational complexity.

Cellular traffic prediction is crucial for intelligent network operations, such as load-aware resource management and proactive network optimization. In this paper, to explicitly characterize the temporal dependence and spatial relationship of nonstationary real-world cellular traffic, we propose a novel prediction method. First, we decompose traffic data into three components which represent various cellular traffic patterns. Second, to capture the spatial relationship among base stations (BSs), we model each component as a directed causal graph by variable-lag transfer entropy (VLTE) based causal structure learning. Third, we design a deep learning model combining graph attention network (GAT) and gated recurrent unit (GRU) to predict each component. GRU is used to capture temporal dependence. GAT is trained to quantitatively analyze spatial relationship and aggregate spatial features. Finally, we integrate the prediction results of three components to obtain the cellular traffic prediction result. We conduct extensive experiments on real-world traffic data, and the results show that our proposed method outperforms other common methods.

Relay cooperation with energy harvesting provides a promising solution to alleviate coverage limitations and energy constraints in hybrid visible light communication (VLC)/radio frequency (RF) Internet of Things (IoT) system with simultaneous lightwave information and power transfer (SLIPT). In the hybrid system, the VLC service provider (VLCSP) seeks the cooperation of relay nodes (RNs) for information delivery to a certain end node (EN). However, considering the autonomous behaviors of the RNs, there are two challenging issues to address in facilitating relay cooperation: 1) selfishness and 2) information asymmetry. Hence, this article proposes a novel incentive scheme for relay cooperation based on an agency selling format. Unlike previous incentive scheme designs, first, the VLCSP charges the cooperating RN for energy harvesting per unit energy price. Second, once the information is successfully transmitted to the EN, the VLCSP pays a portion of future revenue as an agency payment to the RN, in a format seen as agency selling. By giving the VLCSP pricing power, it needs to design a mutually agreeable contract that includes a menu of unit energy prices and agency payments. Here, aiming to maximize the VLCSP’s expected utility, we apply a joint adverse selection and moral hazard model to formulate and optimize the contract design problem in the presence of information asymmetry. Then, the optimal contract solution is derived by using Lagrangian dual analysis. Besides, we present two extreme scenarios where only adverse selection or moral hazard model is applied. Numerical results illustrate that the proposed incentive scheme outperforms the considered benchmarks in terms of the expected utility of VLCSP and RNs, as well as the expected social welfare. We also demonstrate the incentive efficiency of our scheme

With the development of telecommunication technology and the popularity of intelligent devices, user experience in cellular network has become the primary factor of concern. At the same time, network operators are plagued by two problems: Prediction of users real-time experience and find network parameters that have a decisive impact on user experience. We proposed a novel scheme for user experience prediction to deal with these two problems. Causal structure learning for cellular networks was used to analyze numerous performance indicators (KPIs) collected from base stations and key quality indicators (KQIs). Through causal structure learning, a directed causal graph based on the association between KPIs and KQI can be obtained. This causal structure can be embedded in graph attention network. Among them, attention mechanism was selected to further strengthen the correlation between parameters. This correlation between each KPI and between KPIs and KQI was used to predict future value of cell level user experience. Results showed that proposed method performance well in cellular network data analysis and user experience prediction.

Quality of experience (QoE) is a vital metric that indicates how well the wireless network provides transmission services to users, while quality of service (QoS) help better configure the network parameters for higher performance. The evaluation time of QoE is usually several orders of magnitude larger than that of QoS, because QoE is the perception of users over a period of time, but QoS can be collected every millisecond. Therefore, the implementation of QoE/QoS mapping model can help us obtain QoE by collecting the QoS measurements, and perform QoE-based network configurations with smaller time granularity. Many studies are made to obtain the QoS to QoE mapping, including the use of machine learning (ML) methods. However, traditional ML-based regression methods for QoE/QoS mapping face the challenge of high regression error and catastrophic forgetting in dealing with continuously arriving data. In this paper, we propose a novel QoE model based on continual deep learning in wireless network. This model is formed with two deep neural networks (DNNs) concatenated. The first DNN classifies data into different subsets, which are then fed into the second DNN for regression. The second DNN dynamically form the corresponding subnets, each with nodes and connections adaptively selected in each new time period with new arriving data. We solve the catastrophic forgetting problem with the use of node splitting and hidden state augmentation. Our proposed learning framework greatly reduces the regression error to as low as 0.9314%. The experimental results demonstrate that our proposed model reduces the root mean square error (RMSE) by$21 \sim 86$times compared with several existing approaches, specially, the testing error of our proposed model is more than 80 times lower than that of traditional DNN. Compared with other DNN-based cascade models, our proposed method provides good performance in both training time and RMSE.

Millimeter-wave (mmWave) communication has been regarded as one of the most promising means to improve system throughput in the fifth-generation (5G) era. However, there exists signal blockage problem in mmWave network. We consider the combination of mmWave communication with ultradense network (UDN) and virtual cell structure. UDN allows multiple small base stations (SBSs) to be deployed within a limited area. user-centric virtual cell structure enables users to associate multiple mmWave SBSs simultaneously by using coordinated multi-point (CoMP) transmission technology. Therefore, we formulate user association problem for ultra-dense mmWave networks to maximize system throughput while guaranteeing quality of service (QoS) requirements of users. To solve this nonlinear binary integer programming optimization problem, a novel user association scheme based on the many - to-many two-sided matching game with externalities is proposed. To characterize the properties of proposed scheme, we prove that it converges to the two-sided exchange stability within a limited number of iterations. Simulation results show that proposed scheme significantly outperforms other schemes in improving user rate and system throughput while guaranteeing QoS requirements of users.

Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm for future high-speed indoor wireless communication. In order to fully capitalize on the benefits from hybrid VLC and RF technologies, rational resource allocation may play an important role in mitigating mutual interference and increasing system capacity. In this paper, we target the problem of the uplink resource allocation of DUs when multiple DUs and CUs coexist in hybrid VLC-RF networks. To solve this problem, an exploratory coalition formation game is proposed to maximize the sum rate of the D2D system while ensuring the quality of service (QoS) requirements of the CUs and DUs. In the initial stage, we design a priority sequence to guide DUs to choose their own appropriate resources. And in the formation stage, DUs can decide whether to perform the switch operation according to the transfer rule of the cooperative game combined with the greedy strategy. Simulation results corroborate that the proposed algorithm reaches near optimal performance compared with the exhaustive algorithm, and outperforms several other practical schemes in terms of the D2D system throughput.

Self-healing in SON (Self-Organizing Network) can automatically detect, diagnose, and compensate small cell (SC) outage in 5G heterogeneous networks. This paper proposes an efficient self-healing algorithm to reduce system performance loss after small cell outage. The proposed algorithm first finds a compensation cell set (compensation cluster) for each outage user considering channel gain and cell load, and further increase system capacity by using graph-coloring based resource scheduling and power optimization. The QoS requirements of outage users are also considered in scheduling to guarantee the user fairness. Simulation results show that the proposed algorithm can effectively improve system capacity and user fairness.

This paper investigates the Device-to-Device (D2D) communications uplink resource allocation algorithm facing to arbitrary D2D pairs sharing subchannels with multiple cellular users (CUEs). Aiming to optimize the network sum rate, the complexity is considerable due to the flexible interference relation. To reduce the complexity, the problem can be decomposed based on the time variance by two sub-problems: subchannel allocation and power allocation. For subchannel allocation, a clustering algorithm based on the hypergraph similarity of UEs is proposed, which constructs the D2D sharing set to enhance the clustering efficiency and conduces the complexity to be 0(N2). For power allocation, a bisection algorithm which can rapidly converge to the optimal solution is introduced to further promote the system throughput. Simulation results demonstrate that the proposed algorithm can achieve a superior sum rate performance compared to the conventional graph algorithm.

Considering a wireless relay network composed of a source, several relays and a destination where the source transmits data packets to the destination through both direct and relay paths, a multi-timeslots network coding (MTNC) scheme is proposed to combat the lossy nature of wireless channel where the relay nodes with satisfying relay-destination (r - d) channel state firstly perform network coding on the successfully decoded data packets received from the source at previous timeslots and then forward the coded version to the destination. We focus on three metrics - Ergodic capacity, outage probability and symbol error rate to evaluate the performance of MTNC scheme and the replication-based relay (RR) scheme where a "best" relay node is selected out to replicate and forward the data packet. Precise closed form expression of ergodic capacity, tight estimated closed form expression of outage probability (OP) and symbol error rate (SER) are derived for both schemes, and also numerical results are shown to prove the gain of network coding. However, with increasing of the relay number, the improvement of outage probability and SER is limited.

Current deployed emergency communications systems are only available to the rescuing workers. In this paper, a framework of wireless emergency communications is proposed for common communications in the disasters based on relaying and cognitive radio. In this framework, relaying provides small coverage expansion and high capacity for common communications. On the other hand, cognitive radio based frequency lowering provides large coverage expansion and low system capacity for special number communications. The coverage performance is evaluated by the calculation and simulation. To balance the tradeoff between coverage and capacity, both two-hop relaying and cognitive radio are adopted appropriately to satisfy the requirements of emergency communications according to their characteristics. The performance of the proposed framework is also investigated in this paper.