TL;DR
Network Sharing strategies aiming at sharing the bandwidth capacity among BSs owned by different MOs are proved effective to deactivate unneeded network resources for long periods of time under proper configuration settings, hence saving energy and reducing operational cost for MOs.
The evolution of sixth-generation (6G) networks increasingly necessitates seamless and on-demand coverage across heterogeneous environments, particularly maritime regions where traditional terrestrial infrastructure is limited. In this paper, we aim to enhance the quality of service (QoS) for maritime users in the 6G space-air-sea integrated networks (SASINs). To shed light on the design of SASIN, we consider a communication model consisting of a single satellite, a single decode-and-forward (DF) uncrewed aerial vehicle (UAV) relay, and multiple maritime users. A novel on-demand coverage performance metric, service efficiency, is proposed to evaluate the QoS of maritime users. Particularly, in order to explore the boundary performance of the proposed architecture, both uplink and downlink communications are analyzed under the assumption of perfect channel state information (CSI). Furthermore, we formulate optimization problems to maximize the service efficiency for both uplink and downlink transmissions, subject to the user scheduling and decoding order, beamforming design, and placement of the relay UAV, respectively. To address the uplink optimization problems, we propose an alternating optimization (AO) algorithm that integrates a greedy randomized adaptive search procedure (GRASP)-based user scheduling algorithm with a successive convex approximation (SCA)-based UAV placement strategy to obtain a high-quality suboptimal solution. Analogously, for the downlink optimization problem, we develop an AO algorithm that combines a low-complexity greedy user scheduling scheme based on an initial beamforming design with the joint optimization of UAV placement and beamforming, effectively balancing performance and computational efficiency. Finally, extensive numerical results demonstrate that the proposed schemes achieve near-optimal performance with significantly reduced complexity, offering a strong solution for high-efficiency SASIN in future 6G maritime communications.
This research Paper focuses on an Ultra-Dense Network (UDN) is a core enabling technology for 5G and 6G wireless systems, proposed to meet escalating capacity demands and support new high-rate, low-latency services. The fundamental principle is network densification, achieved by deploying a massive number of low-power Access Points (APs) and communication links per unit area, dramatically shortening the distance between transmitters and receivers to improve signal quality and spatial frequency reuse. Ultimately, the successful operation of UDNs relies heavily on advanced, AI-driven management systems to dynamically optimize resources, manage interference, and ensure seamless, high-performance connectivity in an inherently complex environment: Our key objectives are improving the Massive Capacity and Data Rates, Enhanced Coverage and Reliability, Ultra-Low Latency, Massive Connectivity Internet of Things (IoT) to maintaining the advanced resilient communication system all the time and every times.
Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.
LoRa-LQE, a link quality estimation scheme specifically designed for mobile environments, outperforms state-of-the-art solutions and achieves packet delivery ratio improvements and energy efficiency improvements, thereby enabling more reliable communication for mobile IoT assets.
SOVANET+ is presented, an extended scheduling technique that jointly accounts for service criticality, network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services, supporting its viability for next-generation intelligent transportation systems.