To satisfy tight latency constraints, ultra–reliable low latency communications (URLLC) traffic is scheduled by overlapping the on–going enhanced mobile broad band (eMBB) transmissions (i.e. puncturing approach), which causes eMBB users unprecedented rate loss and hence degraded quality–of–service (QoS). To tackle this issue, this letter proposes to achieve QoS tradeoff between eMBB and URLLC in 5G networks. We jointly optimize bandwidth allocation and overlapping positions of URLLC users’ traffic with deep deterministic policy gradient algorithm observing channel variations and URLLC traffic arrivals. Simulation results show that the proposed system–wide tradeoff method achieves the best tradeoff performance.

Mobile edge computing (MEC) is one of the key technologies for 5G networks and beyond, which brings computation resources in proximity to end users. While enabling computing capability at the network edge, it also introduces extra energy consumption to the network operation. Moreover, the non-uniform traffic distribution of mobile network makes the utilization of MEC platforms unbalanced. In this paper, we propose an online optimization strategy of MEC server (MECS) computation task offloading with sleep control scheme to minimize the long term energy consumption of the MECS network. First, the energy optimization problem under delay constraint is formulated which considers both the radio and computation resources. Then, a Lyapunov based approach is proposed to convert the long term optimization problem to a per-slot optimization problem which only requires information of current time slot. An online offloading algorithm is proposed to make decisions in each time slot. Finally, the system performance is evaluated and the impacts of several key parameters are analyzed. Simulation results demonstrate that our proposed strategy can significantly reduce the long term average energy consumption of the mobile edge network by 30% to 90% under different workload scenarios, while keeping a relatively low system delay compared with the schemes without MEC cooperation and sleep control.

Caching on the edge has been recognized as an effective solution to tackle the backhaul constraint of network densification. However, most related works ignored user mobility in wireless networks, which is unreasonable under the background of network densification. For a more flexible and context-aware caching decision, the concept of caching on the edge can be extended to mobile edge computing (MEC) that enables computation and storage resources at mobile edge networks. With MEC servers deployed on base stations, a huge amount of collected radio access network context data can be analyzed and utilized to render a caching scheme adaptive to user’s context-aware information. In this regard, a novel mobility-aware coded probabilistic caching scheme is proposed for MEC-enabled small cell networks (SCNs). Different from previous mobility-aware caching schemes, user mobility and distributed storage are incorporated into a conventional probabilistic caching scheme, with the aim of throughput maximization. Based on stochastic geometry theory and a modified mobility model of discrete random jumps, the explicit expression of throughput is derived. Due to the complexity of the expression, two light-weight heuristic algorithms are provided to numerically obtain the optimal solutions. Moreover, a significant trade-off among the gains of mobility diversity, content diversity, and channel selection diversity is discussed, and we further numerically analyze how such a trade-off is influenced by user mobility, content popularity, and backhaul capacity, with some fundamental insights into the application of the proposed scheme in MEC-enabled SCNs. The superiority of our proposed scheme is demonstrated by the comparisons with the classical M most popular caching scheme and the conventional probabilistic caching scheme. Numerical results show that our proposed caching scheme achieves higher throughput than those of the other two, especially when users of intense mobility request contents, of which the popularity profile is not skewed, in MEC-enabled SCNs with poor backhaul capacity, indicating that the proposed caching scheme is a promising solution for network densification.

In 5G ultra-dense heterogeneous network, backhaul plays an important role to provide connection to core networks for small base stations (SBSs). With the densification, non-ideal backhaul will be deployed extensively, which constrains the throughput improvement due to the limited backhaul capacity. This paper models the SBSs and users as independent Poisson point processes (PPPs) and derives a semi closed-form expression which analyzes the ergodic throughput of the network where SBSs have limited backhaul capacity. Based on analysis, to accomplish flexible coverage with limited backhaul, an optimization problem is formulated which accounts for cell association and aims at maximizing the number of offloading users while simultaneously minimizing the biasing factors and guaranteeing the received signal-to-interference-and-noise ratio (SINR) of users when offloading. The optimization problem is shown to be a mixed integer nonlinear problem (MINLP), which can obtain a sub-optimal solution by convex relaxation and further a distributed solution by problem decomposition via primal-dual method. Considering the complexity of ultra-dense network and practical implementation, a backhaul-aware $\eta$-optimal biasing adjustment model is proposed for flexible coverage. This model aims to optimize the coverage for throughput improvement while matching the backhaul capacity. The simulation results show that average user and SBS throughput can be improved significantly by 2$\times$ when backhaul is constrained. Due to the transmit power and interference constraints on access link, there is no need to allocate too much bandwidth to backhaul, which provides guidance for self-backhaul resource allocation.