The multiple-unicast conjecture asserts that network coding offers no throughput advantage over routing in undirected networks. Its validity is known to imply fundamental lower bounds in computational complexity. We propose a Session Interaction Framework that reduces the conjecture to a central equivalence: the conjecture holds universally if and only if every irreducible core is independent. This result transforms the global feasibility problem into a two-stage process. First, to make the reduction phase tractable, we provide simplified sufficient conditions for session dominance, offering geometric criteria to iteratively simplify complex session sets. Second, for the remaining "irreducible core," we propose a Session Decoupling Theorem, reducing the conjecture's validity for a session set to its independent subsets. Topologically, we prove that sessions separated by high-cost cuts or cut-vertices are guaranteed to be independent. By integrating these reduction and decomposition mechanisms, our framework offers a systematic methodology to verify the conjecture across general network topologies.

Satellite networks play a vital role in providing global connectivity to remote areas, including mountains, forests, and regions affected by natural disasters. The primary challenge lies in the limited communication timeframe between satellites and earth stations (ESs) due to the swift motion of satellites, making timely satellite data downloads through ESs challenging. To address this, we propose a method named priority-aware and throughput-optimized intersatellite cooperative data transmission (PACT). PACT optimizes network throughput while maximizing download priorities for ESs by leveraging intersatellite links (ISLs), considering diverse download priorities for different data types. To comprehensively capture constellation characteristics, PACT models low Earth orbit (LEO) constellations using a spatiotemporal graph. Within the graph, data priorities are assigned as edge weights, and the allocation of initial download windows to ESs is achieved by maximum weighted matching. Subsequently, PACT organizes a contact plan through cooperative scheduling leveraging ISLs. A bipartite graph is constructed based on data awaiting download and link allocation to redistribute remaining download windows optimally through maximum matching. This iterative process enhances network throughput and maintains data priority. Performance assessments in the ndnSIM framework, covering diverse load scenarios, demonstrate the efficiency and benefits of PACT, particularly in prioritizing data downloads.

As in-vehicle services grow, the increasing size of cached content prolongs wait times for users. For electric vehicles, balancing efficient communication with reduced energy consumption remains a challenge. In this paper, a vehicle clustering cooperative caching model for urban Internet of Vehicles (IoV) systems is proposed. This model decreases system energy consumption and task delay by leveraging buses as regular mobile Roadside Units (RSUs) and pre-caching nodes. It includes a kinetic energy recovery scheme for vehicles and an Energy Harvesting (EH) mechanism for RSUs, both intended to further reduce energy consumption. Terahertz (THz) technology is harnessed for Vehicle-to-Vehicle (V2V) communication to accelerate caching tasks and reduce tasks delay. To address these challenges, we propose the Deep Deterministic Policy Gradient (DDPG)-based Power Splitting (DPS) algorithm to address the needs of information transmission and energy recharging of buses while in motion. The proposed ($1+1$)-Evolutionary Strategy (ES)-based joint Task Decomposition and Bandwidth Allocation (TDBA) algorithm, which transmits the decomposed task file segments in parallel on different paths, reduces the additional delay and energy consumption caused by frequent task switching. Furthermore, the proposed Time-Location Preference User Rated Recommendation (TLPURR) algorithm recommends appropriate content based on the vehicle user’s time and location preferences, reducing the delay and energy consumption of obtaining content from remote cloud resources. Simulation results demonstrate that our proposed algorithms have a significant improvement in the delay and energy consumption metrics compared to other algorithms.

Reconfigurable intelligent surface (RIS) is playing a growing and ever-more significant role in constructing six-generation (6G) wireless networks due to its properties of low-cost and easy-integration. Current studies about RIS-assisted communication generally assume the RISs are deployed at fixed position or devices, however with the rapid development of unmanned aerial vehicle (UAV) technology, this readily flying wireless access platform is increasingly used to realize reliable communication. If the RISs are mounted on random 3-D (three-dimension) mobile UAVs, how to investigate the uplink transmission of RIS-assisted UAV communication systems would be a great challenge. To resolve this open issue, we establish a novel theoretical model to analyze the uplink performance of RIS-assisted UAV communication systems with random 3-D mobile pattern. In the modeling process, we firstly provide a random 3-D mobile model for UAVs, where the random waypoint and uniform mobility models are simultaneously used. Next we build an end-to-end (E2E) transmission model for RIS-assisted UAV communication system, where the impacts of channel fading type, RIS configuration, UAV’s mobility and association policies are comprehensively considered. Combining the above two models, we derive the analytical expressions of uplink transmission metrics, and make a bound performance analysis on this base. Finally, we evaluate the uplink performance of RIS-assisted UAV communication system with random 3-D mobile pattern, and verify the proposed theoretical model.

Drastic changes in network topology of Flying Ad Hoc Networks (FANETs) result in the instability of the single-hop delay and link status accordingly. Therefore, it is difficult to implement the congestion control with delay-sensitive traffic according to the instantaneous link status. To solve the above difficulty effectively, we formulate the delay-aware congestion control as a network utility maximization, which considers the link capacity and end-to-end delay as constraints. Next, we combine the Lagrange dual method and delay auxiliary variable to decouple the link capacity and delay threshold constraints, as well as to update single-hop delay bound with the delay-outage mode. Built on the methods above, a distributed optimization algorithm is proposed in this work by considering the estimated single-hop delay bound for each transmission, which only uses the local channel information to limit the end-to-end delay. Finally, we deduce the relationship between the primal and dual solutions to underpin the advantages of the proposed algorithm. Simulation results demonstrate that the proposed algorithm effectively can improve network performances in terms of packet time-out rate and network throughput.

Due to the dynamically changing topology of Internet of Vehicles (IoV), it is a challenging issue to achieve efficient data dissemination in IoV. This paper considers strongly connected IoV with a number of heterogenous vehicular nodes to disseminate information and studies distributed replication-based data dissemination algorithms to improve the performance of data dissemination. Accordingly, two data replication algorithms, a deterministic algorithm and a distributed randomised algorithm, are proposed. In the proposed algorithms, the number of message copies spread in the network is limited and the network will be balanced after a series of average operations among the nodes. The number of communication stages needed for network balance shows the complexity of network convergence as well as network convergence speed. It is proved that the network can achieve a balanced status after a finite number of communication stages. Meanwhile, the upper and lower bounds of the time complexity are derived when the distributed randomised algorithm is applied. Detailed mathematical results show that the network can be balanced quickly in complete graph; thus highly efficient data dissemination can be guaranteed in dense IoV. Simulation results present that the proposed randomised algorithm outperforms the present schemes in terms of transmissions and dissemination delay.

Software Defined Networking (SDN) has emerged as a new paradigm that offers the programmability required to dynamically configure and control a network. In this paper, we present a path assignment method based on load balancing(PA-LB) for a centralized controller to calculate optimal end-to-end virtual paths over the underlying network infrastructure, considering multiple requests and load balance simultaneously. In the paper, theoretical analysis shows that the path selection is NP-hard, and strongly polynomial time inapproximable under the assumption P not equal to NP. We also give a constant-factor polynomial time approximation algorithm for this problem under some bounded parameters. Extensive simulation results, over a wide range of underlying network topologies and input parameters, demonstrate that the proposed method outperforms traditional shortest path first (SPF) approaches not only in paths mapping number, but also in network load balance. Under the condition of limited resources, such as bandwidth, the proposed method has a better performance with the increase of the number of nodes. Moreover, optimal load balance can be kept in the network, in order to avoid network congestion and minimize the average packet transmission latency.