Retrieval-augmented generation (RAG) enables large language models (LLMs) to incorporate external knowledge at inference.Graphbased RAG extends this by organizing corpora into knowledge graphs, improving multi-hop reasoning and offering a global understanding of the corpus.However, triplet-based graphs generated by LLMs are often fragmented and poorly connected, which reduces coherence and hinders reasoning.Prior enrichment methods such as clustering, community detection, or approximate graph algorithms attempt to restore connectivity but incur high computational cost and risk semantic distortion.To address these issues, we propose TH-RAG, a hierarchical framework that organizes triplets into subtopics and topics, enhancing connectivity, integrating dispersed information, and supporting robust multi-hop reasoning.Experiments on abstractive and specific QA benchmarks show that TH-RAG outperforms strong baselines in accuracy and robustness while remaining efficient, providing a scalable foundation for graph-based RAG systems.To support further research, we release our code in our GitHub repository.

Cell-free massive MIMO (CF-mMIMO) ensures uniform service across large areas by jointly serving user equipments (UEs) with densely distributed access points (APs). Dynamic cooperation clustering (DCC) provides a scalable framework for CF-mMIMO, but extending it to multiple central processing units (CPUs) increases complexity to the order of the total APs, leading to excessive signaling overhead. To address this, we propose multi-CPU DCC (MCC), a scalable approach that reduces large-scale fading coefficient (LSFC) exchanges from the total APs to a single LSFC. Using this shared LSFC, MCC estimates remaining AP–UE LSFCs to form a user-centric AP cluster while limiting signaling. Experimental results show that MCC achieves 99% of the performance of an idealized DCC while reducing UE–AP, AP–CPU, CPU–CPU, and total signaling by 74.06%, 51.72%, 65.56%, and 57.93%, respectively. Thus, MCC enhances scalability in multi-CPU environments while maintaining performance and minimizing signaling overhead.

This study addresses the challenge of high power collision rates in Grant-Free Non-Orthogonal Random Access (GF-NORA) for ultra-massive machine-type communication (umMTC) in ultra-dense networks (UDN). We analyze the impact of power collision and inter-cell interference, defining the key factors affecting successive interference cancellation (SIC) decoding failure. To tackle power collision problem, we propose a multi-agent reinforcement learning (MARL) framework, QMIX algorithm, with joint optimization of access control and power-level design. We evaluate the performance of the proposed scheme with extensive random access simulations in an umMTC environment. Our approach outperforms state-of-the-art schemes, achieving at most 10% increase in successful SIC decoding rate with lower access delay.

Cell-free massive multiple-input multiple-output (CF-mMIMO) can provide uniformly great service to user equipment (UE) by utilizing a large number of distributed access points (APs) connected to a single central processing unit (CPU). For addressing scalability and feasibility issues inherent in single-CPU CF-mMIMO systems, multiple-CPU CF-mMIMO (MC-CF-mMIMO) has been considered. However, the MC-CF-mMIMO system inevitably encounters performance degradation at the boundary area of the CPUs, referred to as the CPU edge. Cooperation among CPUs can improve the performance in the CPU edge region; however, scalability problems resurface owing to inter-CPU information sharing during the cooperation process. In this study, we propose a scalable CPU cooperation scheme that focuses on power control to address the performance degradation issue in the CPU edge region. Initially, we propose a policy estimation scheme for other CPUs to reduce the overhead of information sharing. Based on these estimated policies, each CPU can independently derive power control policies for the max-min optimization problem without simultaneous information sharing. Simulation results show that the proposed power control scheme achieves a performance improvement of up to 4.3234 dB in terms of CPU edge region performance and minimum spectral efficiency compared to baseline schemes based on the degree of CPU cooperation.

Cell-free massive MIMO (CF-mMIMO) provides flattened spectral efficiency throughout the network. Recently, multi-CPU CF-mMIMO (MC-CF-mMIMO) has been considered for addressing a scalability problem. However, MC-CF-mMIMO now suffers from a performance degradation problem in the boundary of the CPU overage, referred the CPU-edge. In this study, we analyze and improve the performance of MC-CF-mMIMO, focusing on the CPU-edge. First, we present a simple CPU cooperation scheme to mitigate the performance degradation problem on the CPU-edge. Then, we provide the closed-form expression of the downlink spectral efficiency according to the precoding schemes. Experimental results indicate that performance without CPU cooperation on the CPU-edge is degraded by 28.66% compared with CPU cooperation.

With the development of IoT technology, 6G defines ultra-massive machine type communication (umMTC) as a core service type. Since umMTC in 6G is composed of a huge number of devices and various IoT service types, an efficient random access (RA) scheme for massive devices is required. We study a scheme that maximizes the successful RA ratio by applying multi-agent reinforcement learning (MARL) in the delay-constrained 6G umMTC environment. We define the necessary information for the optimal RA strategy and describe how to obtain the RA information with machine-type communication device (MTCD) grouping and learning framework. We utilize the QMIX learning framework to solve the non-stationarity problem in MARL and design the learning framework to select optimal RA for each MTCD group. We conduct a simulation to verify the proposed scheme and simulation results show that a successful RA ratio can be improved up to 20% compared to the state-of-the-art in non-uniform device distribution.

Space–air–ground-integrated networks (SAGINs) are receiving a lot of attention as a candidate for an extension to nonterrestrial networks beyond the limit of terrestrial networks. SAGIN can be a means to satisfy various Quality of Service (QoS) by providing several communication links in different characteristics. To fully utilize the advantage of SAGIN, a protocol for informing user equipment (UE) of an appropriate base station layer of SAGIN is needed according to QoS requirements. In addition, the development of the protocol should consider the characteristics of the UE. In particular, when the UE is a machine-type communication device (MTCD) constituting a massive Internet of Things network, random access congestion issue should be considered. To solve the problems, we propose a random access protocol. We propose an indicator named the virtual deadline indicator (VDI) to inform MTCD which layer to try for random access. MTCD attempts random access to one layer of SAGIN by comparing the remaining deadlines of traffic and the point of the VDI. To minimize the deadline expiration rate, we propose an algorithm to shift the VDI position. The base station estimates the number of MTCDs and remaining deadline distribution by using the number of idle preambles to find the VDI location. Finally, we compare the performance of the protocol through simulation with three benchmark schemes. We confirm that the proposed protocol reduces the deadline expiration rate without compromising the Age of Information and energy efficiency.

Sensor clustering and trajectory optimization are a hot topic for last decade to improve energy efficiency of wireless sensor network (WSN). Most of existing studies assume that the sensor is uniformly deployed or all regions in the WSN coverage have the same level of interest. However, even in the same WSN, areas with high probability of disaster will have to form a “hotspot” with more sensors densely placed in order to be sensitive to environmental changes. The energy hole can be serious if sensor clustering and trajectory optimization are formulated without considering the hotspot. Therefore, we need to devise a sensor clustering and trajectory optimization algorithm considering the hotspots of WSN. In this paper, we propose an iterative algorithm to minimize the amount of energy consumed by components of WSN named ISCTO. The ISCTO algorithm consists of two phases. The first phase is a sensor clustering phase used to find the suitable number of clusters and cluster headers by considering the density of sensor and residual battery of sensors. The second phase is a trajectory optimization phase used to formulate suitable trajectory of multiple mobile sinks to minimize the amount of energy consumed by mobile sinks. The ISCTO algorithm performs two phases repeatedly until the amount of energy consumed by the WSN is not reduced. In addition, we show the performance of the proposed algorithm in terms of the total amount of energy consumed by sensors and mobile sinks.