It is widely acknowledged that network slicing can tackle the diverse use cases and connectivity services of the forthcoming next generation mobile networks (5G). Resource scheduling is of vital importance for improving resource-multiplexing gain among slices while meeting specific service requirements for Radio Access Network (RAN) slicing. Unfortunately, due to the performance isolation, diversified service requirements and network dynamics (including user mobility and channel states, etc.), resource scheduling in RAN slicing is very challenging. In this paper, we propose an intelligent resource scheduling strategy (iRSS) for 5G RAN slicing. The main idea of iRSS is to exploit a collaborative learning framework which consists of deep learning (DL) in conjunction with Reinforcement Learning (RL). Specifically, DL is used to perform large time-scale resource allocation, while RL is used to perform on-line resource scheduling for tackling small time-scale network dynamics, including inaccurate prediction and unexpected network states. Depending on the amount of available historical traffic data, iRSS can flexibly adjust the significance between the prediction and online decision modules for assisting RAN in making resource scheduling decisions. Numerical results show that the convergence of iRSS satisfies on-line resource scheduling requirement and can significantly improve resource utilization while guaranteeing performance isolation between slices, compared with other benchmark algorithms.

The ongoing increasing traffic in the era of big data yields unprecedented demands in user experience and network capacity expansion. The users of next generation mobile networks (5G) should be able to use 3GPP, IEEE, and other technologies simultaneously. The integration of multiple Radio Access Technologies (RATs) of licensed or unlicensed bands has been widely deemed as a cost-efficient way to greatly increase network capacity. In this paper, we propose a Smart Aggregated RAT Access (SARA) strategy with aim of maximizing the long-term network throughput while meeting diverse traffic Quality of Service (QoS) requirements. We consider the scenario that users with different QoS requirements access to a Heterogeneous Network (HetNet) with coexisting Cellular-WiFi. In order to maximize system throughput while meeting diverse traffic QoS requirements in such a complex and dynamic environment, we exploit multi-agent reinforcement learning to perform RAT selection in conjunction with resource allocation for individual user access requests, through sensing dynamic channel states and traffic QoS requirements. In SARA, we first use Nash Q-learning to provide a set of feasible RAT selection strategies while decreasing the strategy space in learning process, and then employ Monte-Carlo Tree Search (MCTS) based Q-learning to perform resource allocation. Numerical results reveal that the network throughput can be maximized while meeting various traffic QoS requirements with limited number of searches by using our proposed SARA algorithm. For bulk arrival access requests, a suboptimal solution can be obtained as high computational complexity is incurred for achieving global optimality. Another attractive feature of SARA is that a tradeoff between the solution optimality and learning time can be readily made by terminating the search of MCTS according to the time constraint. Compared with traditional WiFi offloading schemes, SARA can significantly improve network throughput while guaranteeing traffic QoS requirements.

The recently emerging blockchain technology provides a promising tool to enable endogenous security in Internet-of-Things (IoT) systems. However, when applying the legacy blockchain technology to the existing IoT systems, some technical bottlenecks due to resource constraints, such as storage resource, should be carefully addressed. In this article, we propose an optimal blockchain deployment mechanism for wireless IoT systems, where IoT devices have limited resources and the wireless links connection IoT devices are vulnerable, to improve the storage efficiency of massive blockchain data and realize the organic integration of blockchain technology and the communication process. We propose to maintain a complete blockchain by a set of proximity IoT nodes in a collaborative way on the premise of ensuring that each node can check every transaction. Through dynamically adjusting optimal block assignment, the tradeoff between the length of the blockchain to be stored and the security level provided can be well made. Moreover, a chaotic-based genetic algorithm is developed to obtain the near-optimal block assignment solution efficiently. Simulation results show that our proposed blockchain-based mechanism can effectively address the security issue in wireless IoT systems.

4G systems employ Discontinuous Reception (DRX) mechanism to conserve energy by intermittently suspending network connections. Moving to 5G, a wide range of applications with diverse characteristics need to be supported. Especially, machine-type communication (MTC) has been identified as one of the three generic 5G services. Compared with that of human type communication (HTC), the traffic patterns of MTC could be very bursty and even nonstationary. Thus using the legacy DRX mechanism will cause longer access delay and/or higher power consumption. In this paper, we propose a new online learning based DRX mechanism, called AC-DRX, with aim to improve device energy efficiency for MTC services by adapting to varying traffic pattern. In AC-DRX, the time is slotted into intervals and actor-critic (AC) algorithm is used for adjusting DRX cycles by learning the traffic statistics at the beginning of every time interval. To accelerate the learning process, we propose a symmetric sampling method in the AC algorithm. Numerical results show that our proposed AC-DRX mechanism significantly outperforms the legacy DRX and extended DRX mechanisms in terms of both delay and energy efficiency. The performance is fairly close to the upper bound where perfect traffic knowledge is assumed known.

Federated learning (FL) has become a promising technique for developing intelligent wireless networks. In traditional FL paradigms, local models are usually required to be homogeneous for aggregation. However, due to heterogeneous models coming with wireless sysTem heterogeneity, it is preferable for user equipments (UEs) to undertake appropriate amount of computing and/or data transmission work based on sysTem constraints. Meanwhile, considerable communication costs are incurred by model training, when a large number of UEs participate in FL and/or the transmitted models are large. Therefore, resource-efficient training schemes for heterogeneous models are essential for enabling FL-based intelligent wireless networks. In this paper, we propose an adaptive quantization scheme based on ensemble distillation (AQeD), to facilitate heterogeneous model training. We first partition and group the participating UEs into clusters, where the local models in specific clusters are homogeneous with different quantization levels. Then we propose an augmented loss function by jointly considering ensemble distillation loss, quantization levels and wireless resources constraints. In AQeD, model aggregations are performed at two levels: model aggregation for individual clusters and distillation loss aggregation for cluster ensembles. Numerical results show that the AQeD scheme can significantly reduce communication costs and training time in comparison with some state-of-the-art solutions.

Radio Access Network (RAN) slicing is a promising architectural technology to address extremely diversified service demands and provide profitable business models for future mobile networks. In RAN slicing architecture, self-healing is an important functional module to minimize the impact of network failings on the performance of RAN slices. However, self-healing of burgeoning sliced RAN is vastly different from that of traditional RAN and has been rarely investigated. In this paper, we address the Self-healing of RAN Slice (SRANS) problem by modeling it as a Pareto optimization problem with the aim of maximizing the self-healing utilities of individual RAN slices. To deal with the weakness of diversity maintenance in traditional Pareto optimization methods, we propose a Generative Adversarial Network (GAN) based Pareto Optimization (GPO) framework. Specifically, we employ self-conditioned GANs to replace the offspring reproduction module in the traditional Evolutionary Algorithm (EA), where the insufficiency of diversity maintenance in EAs is effectively overcome. Furthermore, we theoretically prove that GPO framework is guaranteed to converge to the optimal Pareto solution set. Numerical results demonstrate that the convergence of proposed GPO framework can be expedited by enhancing the diversity of solution sets in solving the SRANS problem. Compared with traditional schemes, GPO can achieve significant performance gain in terms of the utilities and isolation level of repaired RAN slices.