In this paper, the backhauling topology of an integrated and backhaul (IAB) network is designed to sustain bursty traffic with the highest probability. To ensure sequential DU-MT or MT-DU transmissions, the topology is characterized by a directed acyclic graph (DAG). First, the Bayes' theorem is used to transform the probability of sustaining the UE traffic into the probability of generating a DAG, and then the transformed problem is decomposed into two subproblems: 1) The link traffic load is determined under the condition that a DAG is formed; 2) Based on the link traffic load, the links that are critical for sustaining the UE traffic are identified, and then a new DAG is generated by maximizing the joint probability of links in the new DAG. Given random initial DAG, the two subproblems are iteratively solved until obtaining a final DAG. Theoretical analysis validates that the above iterative procedures converge to a single DAG, and simulations confirm that the convergence can be achieved within tens of iterations. Simulation results also show that the backhauling approach developed in this paper can support 56.41% higher traffic variations and achieve a 29.32% lower average hop-count than the existing topology generation schemes.

A coordinated carrier sense multiple access (C-CSMA) scheme is developed to schedule packet transmissions in multihop wireless networks. It integrates distributed scheduling and centralized coordination at different time scales. At a small time scale, a distributed scheduling algorithm, which is incorporated into CSMA, runs on each node to determine key parameters for CSMA. These parameters are optimized by ensuring multiple network attributes, i.e., average link date rate, node transmission probability, and the conditional probability of a link being selected for transmission, approaching their corresponding optimal values at a large time scale, e.g., a few seconds. Such optimal values are obtained from a centralized algorithm running on a portal node. The algorithm collects topology, link, and traffic information from the network at a large time scale and determines the attributes above to fulfill the optimal tradeoff between throughput and fairness. Since the distributed algorithm is shepherded by the centralized algorithm, C-CSMA has high scalability: first, it achieves long-term optimal performance with low information collection overhead; second, it schedules packet transmissions in a distributed way and responds quickly to changes in networks. It is proved that network attributes resulting from the distributed scheduling algorithm converge to optimal values provided by the centralized algorithm. Simulation experiments demonstrate that the convergence speed is fast. Moreover, extensive simulation results show that C-CSMA achieves much higher throughput, better fairness, and lower delay than existing scheduling schemes under different carrier sensing thresholds and traffic conditions.

In a 5G integrated backhaul and access (IAB) network, an IAB-donor and multiple IAB-nodes form a multi-hop wireless backhaul network. When a terminal is connected to an IAB-node, the traffic flows generated by the terminal can be backhauled to the IAB-donor. Due to the mobility of terminals, access links are error-prone and cause difficulty in ensuring end-to-end quality of service (QoS) of traffic flows. To improve access link reliability, multi-connectivity is considered in IAB, i.e., a terminal is connected to multiple IAB-nodes. However, to ensure end-to-end QoS, multi-connectivity must be considered together with multi-hop backhauling. Thus, an effective-capacity based resource allocation (eReal) scheme is developed to establish end-to-end multi-connectivity. The objective of the scheme is to guarantee end-to-end QoS and ensure outage probability is below the given threshold. Since traffic flows are classified into the guaranteed bit rate (GBR) and the non-GBR types, the scheme is formulated as two joint route selection and resource allocation problems to provide differentiated services with minimum resource consumption. Since both problems are NP-hard, they are solved using column generation. Performance results show that eReal serves various traffic flows with over 95% QoS guarantees and also significantly outperforms existing schemes.