User-centric ultra-dense network (UUDN) has become a promising paradigm with the advantages over traditional cell-centric structure with respect to spectrum and power usage. This paper introduces full duplex (FD) technology into UUDN and presents a joint scheme of user access, subchannel allocation and power control for FD UUDN. The scheme firstly judges the feasibility of the constraints of the transmit power and rate requirements. If they are feasible, the total network capacity is maximized under the transmit power constraints. Otherwise, the minimal total power of the network is found to satisfy the rate requirements. The sum rate maximization is solved by multiplicative linear fractional programming (MLFP) based power allocation (MAPEL) algorithm and particle swarm optimization (PSO) algorithm, respectively. The power minimization problem is transformed by averaging the total rate in each subchannel of the user, and then solved by time-sharing method. Simulation demonstrates the enhancement of spectrum efficiency and the reduction of consumed power for UUDN compared with cell-centric UDN. Moreover, the advantage of FD over half duplex (HD) is shown in terms of spectrum efficiency and power consumption.

In cognitive heterogeneous networks, when multiple adjacent femtocells are deployed, uncontrolled transmission power can lead to severe mutual interference. In order to reduce interference while satisfying capacity requirements, we propose a power management scheme for an adjacent femtocell network. Firstly, we build an interference model for adjacent femtocells, in which distance-dependent interference to terminals within the whole femtocell coverage area is considered. Then, we calculate the total capacity of adjacent femtocells based on the mutual interference and formulate a non-convex optimization problem to maximize the capacity under the transmission power constraints. To solve the non-convex problem, we divide it into two convex subproblems and solve them with the Lagrange dual theory and linear programming method. Finally, we derive the closed-form expression of the optimal power configuration to maximize capacity while minimizing energy consumption simultaneously. The simulation results indicate that the proposed power scheme demonstrates obvious improvement in terms of capacity and power economization, compared with the maximal power configuration method.

In recent years, digital twin technology has demonstrated remarkable potential in intelligent transportation systems, leveraging its capabilities of high-precision virtual mapping and real-time dynamic simulation of physical entities. By integrating multi-source data, it constructs virtual replicas of vehicles, roads, and infrastructure, enabling in-depth analysis and optimal decision-making for traffic scenarios. In vehicular networks, existing information caching and transmission systems suffer from low real-time information update and serious transmission delay accumulation due to outdated storage mechanism and insufficient interference coordination, thus leading to a high age of information (AoI). In response to this issue, we focus on pairwise road side unit (RSU) collaboration and propose a digital twin-integrated framework to jointly optimize information caching and communication power allocation. We model the tradeoff between information freshness and resource utilization to formulate an AoI-minimization problem with energy consumption and communication rate constraints, which is solved through deep reinforcement learning within digital twin systems. Simulation results show that our approach reduces the AoI by more than 12 percent compared with baseline methods, validating its effectiveness in balancing information freshness and communication efficiency.

A coupling of wireless access via non-orthogonal multiple access and wireless backhaul via beamforming is a promising way for downlink user-centric ultra-dense networks (UDNs) to improve system performance. However, ultra-dense deployment of radio access points in macrocell and user-centric view of network design in UDNs raise important concerns about resource allocation and user association, among which notably is energy efficiency (EE) balance. To overcome this challenge, we develop a framework to investigate the resource allocation problem for energy efficient user association in such a scenario. The joint optimization framework aiming at the system EE maximization is formulated as a large-scale non-convex mixed-integer nonlinear programming problem, which is NP-hard to solve directly with lower complexity. Alternatively, taking advantages of sum-of-ratios decoupling and successive convex approximation methods, we transform the original problem into a series of convex optimization subproblems. Then we solve each subproblem through Lagrangian dual decomposition, and design an iterative algorithm in a distributed way that realizes the joint optimization of power allocation, sub-channel assignment, and user association simultaneously. Simulation results demonstrate the effectiveness and practicality of our proposed framework, which achieves the rapid convergence speed and ensures a beneficial improvement of system-wide EE.

In space-air-ground integrated networks (SAGIN)-enabled IoT networks, secure access has become a significant challenge due to the increasing risks of eavesdropping attacks. To address these threats to data confidentiality, this paper proposes a Digital Twin (DT)-driven secure access strategy. The strategy leverages a virtual replica of the physical SAGIN environment within the DT framework to continuously assess dynamic eavesdropping risks by quantifying secrecy capacity. Operating within this DT framework, an evolutionary game model dynamically balances the DT-updated secrecy capacity against queuing delay, steering IoT devices toward more secure and efficient access decisions. Furthermore, a novel distributed algorithm, integral to the DT operation, is developed to obtain the equilibrium access strategy for each device in a scalable manner. Simulation results demonstrate that the proposed DT-based approach substantially improves the security of SAGIN-enabled IoT networks. Additionally, it effectively balances system load, prevents overload occurrences, and decreases queuing delay compared to benchmark schemes, thereby comprehensively improving overall network performance.