Ying Loong Lee

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 article 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. First, 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 $\alpha $ -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 3-D 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.

Connectivity remains a significant challenge in rural regions, where existing network infrastructures often fail to meet the growing need for connectivity. To address these limitations, hybrid networking solutions—such as hybrid wireless-wireline access networks (HWWANs) and hybrid satellite-3GPP networks (HSNs)—have emerged as promising alternatives, garnering considerable research attention. Building on these advancements, this paper introduces a novel hybrid wireless-wireline and satellite access network (HWWSAN) framework, which integrates both technologies to enhance network performance, service reliability, and user experience. The proposed HWWSAN framework strategically assigns latency-critical users and hybrid customer premises equipment (HCPE) to terrestrial base stations, while allowing latency-tolerant users to access both the base station and the satellite network. This dual-access approach not only improves system robustness but also ensures efficient resource utilization. To further optimize network performance, we introduce two schemes: a user association scheme and a resource allocation scheme. In the user association scheme, the user association problem is formulated as a combinatorial optimization problem and solved using a greedy algorithm with a fairness-throughput trade-off that efficiently allocates resources based on network conditions and service priorities. The resource allocation scheme is designed based on modeling and solving a quality of service (QoS)-constrained optimization problem that maximizes the weighted system throughput using convex optimization techniques. The proposed methodologies incorporate base station and satellite resource availability, HCPE throughput gap (TG), and diverse user QoS requirements. Extensive simulations demonstrate that the HWWSAN framework reduces call-blocking probability by up to 50%, improves user satisfaction rates beyond 90%, and nearly doubles aggregate network throughput, validating its effectiveness for enhanced rural connectivity.

Accurate prediction of future traffic matrix (TM) for communication networks can help network managers adjust traffic scheduling policies in advance, which can reduce the probability of link congestion and improve the efficiency of network operation. This paper proposes a TM prediction framework based on multilevel discrete wavelet transform network and long and short‐term memory neural network (MDWTN‐LSTM). Discrete wavelet transform (DWT) is introduced into TM prediction to extract multi‐scale time‐frequency features, which can help the neural network model to grasp traffic trends. And then we approximately realized the DWT scheme through the linear layer in the neural network, so that the wavelet transform is embedded in the neural network in a tightly coupled form and participates in the training of model parameters, finally achieves the effect of global parameter optimization and improves both prediction accuracy and adaptability of the prediction framework. The MDWTN‐LSTM based model is verified by a variety of benchmarks using the real‐world data sets, the experimental results show that the proposed framework can achieve relatively superior prediction accuracy. And compared with the theoretical optimal result, 98.6% and 91.1% of the maximum link utilization bias for traffic scheduling based on MDWTN‐LSTM is less than 10%, which is sufficient to support reliable traffic engineering.

In real-time applications requiring frequent status updates, the freshness of information, quantified by the age of information (AoI), is of utmost importance. This study focuses on non-orthogonal multiple access (NOMA)-assisted wireless backscatter networks comprising freshness-sensitive and non-sensitive nodes. We formulate a joint optimization problem encompassing backscatter coefficient configuration, node matching, and time slot allocation. To solve this, we introduce a computationally efficient backscatter node matching algorithm. Our iterative approach optimizes backscatter coefficients and time slot allocation alternately to minimize the average AoI of freshness-sensitive nodes. Specifically, we transform the fractional programming problem into a linear one using quadratic transform and Taylor expansion. Dynamic time slot allocation is achieved by adjusting constraints. Simulations verify our algorithm’s effectiveness in selectively reducing the average AoI of critical nodes, ensuring high information freshness and timely status updates.

A hybrid wireless-wireline access network (HWWAN) is an integrated network infrastructure that combines wireless and wired technologies to offer users seamless and reliable connectivity. However, the existing resource management schemes for HWWANs have largely overlooked the stringent quality of service (QoS) requirements of latency-critical users. Moreover, the surge in the demands for ultra-broadband applications with extremely strict real-time requirements has resulted in an explosive growth of data traffic, rendering network resource allocation challenging for HWWANs. This paper proposes an efficient radio resource allocation scheme for HWWANs that effectively addresses the requirements of both latency-critical user equipment (UEs) and hybrid customer premises equipment (HCPEs) with throughput gap (TG) requirements. Specifically, we formulate a resource optimization problem aimed at maximizing the UEs’ effective capacity (EC) while considering the TG requirements of HCPEs, and the availability of physical resource blocks (PRBs) at the base station. The problem is solved using convex optimization. Simulation results indicate the efficacy of the proposed scheme in terms of percentages of satisfied UEs and HCPEs, sum EC of UEs, and aggregate throughput of all the HCPEs.