The use of aerial base stations (ABSs) has gained significant interest due to their deployment flexibility for coverage and capacity enhancements. However, the load balancing problem in multi-ABS networks, which is crucial for quality of service (QoS) provisioning and fair ABS utilization, remains largely unresolved. This paper investigates the load balancing problem for multi-ABS networks with consideration of backhaul limitations, while taking into account the delay-QoS performance of the networks. Firstly, we quantify the load of each ABS with the effective capacity (EC), which is characterized by a latency parameter known as the delay-QoS exponent. Then, we design a load balancing utility function based on the generalized α-fairness concept, which provides the flexibility to the network to achieve various degrees of load balance among ABSs. Next, a joint ABS placement and user association problem is formulated with the aim to maximize the load balancing utility function, with consideration of backhaul capacity constraints, physical isolation between ABSs and ABSs’ limited user capacity. Since the problem is a mixed-integer programming problem which is generally difficult to solve optimally, we develop an efficient solution framework, which optimizes the 3D placements of ABSs using swarm intelligence and user association via convex optimization. Simulation results show that our proposed scheme outperforms existing schemes in terms of load balance, probability of blocking, and EC.

Backscatter communications (BC) have recently emerged as a promising technology for sixth-generation (6G) networks with device-to-device (D2D) communications to support energy-efficient Internet of Things communications. This paper investigates the power allocation problem for energy-efficient 6G networks with backscatter-enabled D2D (BC-D2D) communications. To this end, we formulate the power allocation problem as a biobjective optimization problem that jointly maximizes the sum data rate and minimizes the power consumption of the networks, subject to energy-harvesting, reflection coefficient (RC) and power constraints. To solve this problem, suboptimal RCs are first analytically obtained based on the energy-harvesting constraints of the BC-D2D transmitters. Next, the biobjective optimization problem is transformed into a convex, single-objective power allocation optimization problem using the weighted sum approach, which is then solved using convex optimization. Results show that the proposed scheme outperforms the baseline schemes in energy efficiency under scenarios with different numbers of cellular user equipment and BC-D2D pairs.

With the rapidly increasing data traffic in vehicle-to-infrastructure (V2I) networks, the demand for communication and computing rises proportionally. As such, joint communication, computing and sensing becomes crucial for addressing the demands in V2I networks. This paper proposes an energy-efficient joint communication, sensing and mobile edge computing (MEC) system for V2I networks. In the proposed system, the roadside unit (RSU) is equipped with the functions of communication, sensing, and cache-aided computing. By combining beam prediction and tracking, the optimization problem of system energy consumption is formulated. The problem is divided into three parts, which are solved using the joint communication, sensing and MEC online (JCSMO) strategy. Simulation results show that the proposed JCSMO strategy outperforms the baseline schemes in system energy consumption.

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants' priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS), and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association, and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness, and QoS performance compared with several baseline schemes.

In Long Term Evolution (LTE)/LTE-Advanced (LTE-A) networks, hybrid access femtocells have been introduced as a tradeoff between closed and open access femtocells. However, quality of service (QoS) provisioning in hybrid access femtocells has become more challenging due to different user priority and traffic classes. Existing studies do not consider the LTE/LTE-A protocols and QoS priority of different traffic classes. In this work, a resource management scheme for prioritizing closed subscriber group (CSG) members and QoS provisioning in LTE/LTE-A hybrid access femtocells is proposed. Firstly, an admission control problem and a packet scheduling problem, which account for different user priority and QoS of different traffic classes, are formulated. A lexicographic greedy algorithm and a delay-prioritized scheduling algorithm are proposed to solve the two problems respectively. Simulation results show that the proposed scheme achieves a substantial performance gain in terms of packet loss and fairness for real-time traffic flows, albeit at the cost of lower fairness for non-real-time traffic flows.