Mobile data traffic is currently growing exponentially and these rapid increases have caused the backhaul data rate requirements to become the major bottleneck to reducing costs and raising revenue for operators. To address this problem, caching techniques have attracted significant attention since they can effectively reduce the backhaul traffic by eliminating duplicate data transmission that carries popular content. In addition, other system performance metrics can also be improved through caching techniques, e.g., spectrum efficiency, energy efficiency, and transmission delay. In this paper, we provide a systematical survey of the state-of-the-art caching techniques that were recently developed in cellular networks, including macro-cellular networks, heterogeneous networks, device-to-device networks, cloud-radio access networks, and fog-radio access networks. In particular, we give a tutorial on the fundamental caching techniques and introduce caching algorithms from three aspects, i.e., content placement, content delivery, and joint placement and delivery. We provide comprehensive comparisons among different algorithms in terms of different performance metrics, including throughput, backhaul cost, power consumption, and network delay. Finally, we summarize the main research achievements in different networks, and highlight main challenges and potential research directions.

As one of the three main scenarios in the fifth generation (5G) cellular networks, ultra-reliable and low-latency communication (URLLC) can be served as an enabler for real-time wireless control systems. In such a system, the communication resource consumption in URLLC and the control subsystem performance are mutually dependent. To optimize the overall system performance, it is critical to integrate URLLC and control subsystems together by formulating a co-design problem. In this paper, based on uplink transmission, we study the resource allocation problem for URLLC in real-time wireless control systems. The problem is conducted by optimizing bandwidth and transmission power allocation in URLLC and control convergence rate subject to the constraints on communication and control. To formulate and solve the problem, we first convert the control convergence rate requirement into a communication reliability constraint. Then, the co-design problem can be replaced by a regular wireless resource allocation problem. By proving the converted problem is concave, an iteration algorithm is proposed to find the optimal communication resource allocation. Based on that, the optimal control convergence rate can be obtained to optimize overall system performance. Simulation results show remarkable performance gain in terms of spectral efficiency and control cost. Compared with the scheme of satisfying fixed quality-of-service in traditional URLLC design, our method can adjust optimal spectrum allocation to maximize the communication spectral efficiency and maintain the actual control requirement.

The metaverse has the potential to revolutionize the next generation of the Internet by supporting highly interactive services with satisfactory user experience. The synchronization between devices in the physical world and their digital models in the metaverse is crucial. This work proposes a sampling, communication and prediction co-design framework to minimize the communication load subject to a constraint on the tracking error. To optimize the sampling rate and the prediction horizon, we exploit expert knowledge and develop a constrained deep reinforcement learning algorithm. We validate our framework on a prototype composed of a real-world robotic arm and its digital model. The results show that our framework achieves a better trade-off between the average tracking error and the average communication load compared with a communication system without sampling and prediction. For example, the average communication load can be reduced up to 87% when the average track error constraint is 0.007°. In addition, our policy outperforms the benchmark with the static sampling rate and prediction horizon optimized by exhaustive search, in terms of the tail probability of the tracking error. Furthermore, with the assistance of expert knowledge, the proposed algorithm achieves better convergence time, stability, communication load, and average tacking error.

In this work, we propose a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for Mission-Critical Internet-of-Things (MC-IoT) services with short packets in Mobile Edge Computing (MEC) systems, where enhanced Mobile BroadBand (eMBB) services with long packets are considered as background services. To reduce communication delay, the 5th generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, Processor-Sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed-form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the non-convex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. Simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers.

New technologies such as mobile phones, social media and artificial intelligence, have significant impacts on every aspect of education, where digital connectivity is the foundation to support the way people learn. Current Internet and pre-5G cellular communication networks can deliver visual and auditory data, which enable distance/virtual learning. However, remote physical interaction between students and learning facilities, which is an essential part of a new education paradigm i.e., Education 4.0, is still missing. The 5G cellular network with excellent latency and reliability performance would be a game changer by enabling students to feel the physical objects and control them remotely. In this paper, we identify and discuss the unique opportunities the 5G networks can bring to Education 4.0, their technical challenges and potential solutions. We also showcase our Education 4.0 prototype of remote lab.

In industrial internet of things (IIoT), ultra-reliable and low-latency communication (URLLC) is proposed to guarantee the requirement of real-time wireless control systems in worst case, so as to maintain the system working in all cases. However, it is extremely challenging to maintain URLLC throughout the whole control process due to the scarcity of wireless resource. This paper develops an autonomous device-to-device (D2D) communication scheme by jointly considering reliability in URLLC and control requirement. In the proposed scheme, we consider the actual control requirement, i.e., control convergence rate, into communication design, where we find that it can be converted into a constraint on communication reliability. Then, the communication reliability constraint comes from control aspect, instead of URLLC, which leads to that the system does not need to guarantee worst case in URLLC. Second, the sensors autonomously decide whether to be activated with optimal probabilities to participate in the control process, which can maintain the communication reliability requirement with significantly less resource consumption. Simulation results show remarkable performance gain of our method. For instance, compared with fixed activation probability 40% only considering URLLC, the average power consumption of the proposed method can be reduced by at most about 100%.