For coordinated multipoint transmission in heterogeneous ultra dense networks (HUDN), clustering and multicell resource scheduling should be carried out jointly. Based on the α-fairness function, the joint optimization problem is formulated that is NP-hard. A two-step joint clustering and scheduling (TS-JCS) scheme is proposed. Considering the extremely imbalanced cell loads in HUDN, the impact of cell loads on resource availability must be concerned, and a novel load aware clustering (LAC) is designed at the first step and solved by game theory. Based on the clustering results, a fair graph-coloring based inter-cell resource scheduling can be employed at the second step to maximize the resource utilization. Simulations are carried out to evaluate the performance of TS-JCS in a three-layer HUDN. It is shown that the utility of TS-JCS is close to the optimal value, demonstrating the high effectiveness of TS-JCS to solve the joint optimization problem. Compared with non-joint schemes, when fairness is considered in TS-JCS with α = 1 and α = 2, the average throughput can be improved by about 30% while keeping similar performance in cell-edge throughput. Finally, it is also shown that in HUDN with imbalanced load, the performance gain of TS-JCS with α = 1 and α = 2 over the baseline scheme is obvious compared to that in regular hexagonal networks with uniformly distributed loads, demonstrating the importance of the load aware scheme LAC for HUDN.

Due to the high mobility of vehicles, service migration is inevitable in vehicular edge computing (VEC) networks. Frequent service migrations incur prohibitive migration cost including the computing cost (e.g., increased computing delay) and communication cost (e.g., occupied backhaul bandwidth). Yet existing service migration schemes are usually designed without considering the impact of the computing cost. This paper considers the impact of computing and communication cost jointly, and proposes a computing and communication cost-aware service migration scheme for VEC networks (i.e., CA-migration). Taking the service delay as a QoS metric for VEC networks, this paper formulates a migration optimization problem aiming to maximize the services’ satisfaction degree of delay (i.e., the probability that the service delay is smaller than the service delay requirement), where both the communication cost and computing cost affect the services’ satisfaction degree. Since the optimization problem is a constrained non-linear integer programming problem, it is difficult to solve. Moreover, the VEC networks are highly dynamic. Thus, a fast transfer reinforcement learning (fast-TRL) method combining transfer learning and reinforcement learning is proposed to provide an adaptive service migration scheme in dynamic VEC networks. Simulation results show that compared with existing schemes, the proposed CA-migration scheme can increase the satisfaction degree by up to 30%, and needs 25% less training time to obtain the optimal service migration policy.

In cache-enabled heterogeneous cloud radio access networks (HC-RANs), mobile station (MS) association for multimedia content delivery should consider both the content caching location and the wireless channel quality. This paper studies economically optimal MS association to tradeoff the cache-hit ratio and the ratio of MSs with satisfied quality of service (QoS). When the associated enhanced remote radio unit (eRRU) stores the requesting content, the content can be fetched directly from the local cache. Otherwise, fronthaul has to be used to fetch the content. The use of fronthaul resource and cache is treated as costs, and payments of QoS-satisfied MSs are treated as incomes. Thus, the economic MS association is formulated as an optimization problem to maximize the system utility, i.e., total profit of the network operator, which is defined as the difference between incomes and costs. A belief propagation-based method is employed to solve the problem on a developed factor graph. Simulation results show that the proposed economically optimal MS association achieves much higher profit than the existing schemes and works well in the network with various loads. Moreover, the profit of the proposed scheme can be improved with inter-cell interference coordination. For the case with extremely skewed content popularity, the proposed scheme can avoid MS overloading at eRRUs storing most popular multimedia contents. Furthermore, it can support more MSs with satisfied QoS, which leads to a higher profit.

With the explosive growth of computation-intensive and latency-sensitive vehicular applications, limited on-board computing resources can hardly satisfy these heterogeneous requirements and task offloading becomes a potential solution. However, task offloading in vehicular networks may face the dilemma of unaffordable uploading time caused by the huge amount of uploading traffic. Therefore, considering the applications which use the environmental data as their input, the sensing abilities of serving nodes (SNs) are exploited and a traffic-aware task offloading (TATO) mechanism based on convergence of communication and sensing is proposed. In the TATO mechanism, a task vehicle can adaptively upload the input data to some SNs and transmit the computation instructions to others which use the environmental data sensed by themselves as input. The objective is to minimize the overall response time (ORT) by jointly optimizing the task and wireless bandwidth ratios. Next, a binary search and feasibility check (BSFC) algorithm is designed to solve the optimization problem. Simulation results demonstrate the effectiveness of the proposed BSFC algorithm and show that the TATO mechanism always outperforms the benchmark mechanisms (i.e., communication-based offloading and sensing-based offloading) in terms of the ORT. Specifically, when the task offloading traffic is huge and the wireless transmission capability becomes a bottleneck, TATO can reduce the ORT by 42.8% compared with that of the communication-based offloading.

We consider a heterogeneous ultra dense network (HUDN) with both the hexagon and Poisson point process (PPP) layouts, which is more relevant for practical scenarios. A user-centric and adaptive interference-aware non-coherent coordinated multi-point transmission (IA-CoMP) scheme is used as a system setup to reduce both the cross-tier and the co-tier inter-cell interference (ICI) for the HUDN with range expansion (RE) in small cells. Due to the involvement of hexagonal macrocells, it is intractable to analyze the coverage performance of HUDNs with IA-CoMP. To this end, we present a mobile station GrouPing (MSGP)-based coverage analysis method, which partitions all MSs into four groups according to their main interference, and the whole coverage is obtained as the sum of the coverage of each MS group. We demonstrate that the proposed MSGP-based coverage analysis method can provide a tight upper bound when compared with Monte Carlo simulations. As the small cell density increases, the system coverage of HUDNs with hexagonal macrocells reduces exponentially, while the system coverage of HUDNs with PPP-based macrocells remains unchanged. Moreover, the system coverage increases with the larger one of the main ICI judging coefficient and the RE bias.