2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 7703-7724· 0 citations· 64 references
Computer Science
Abstract
This article addresses the planning and allocation of spectral resource blocks for unicast (UC) and Multicast-Broadcast Single Frequency Network (MB-SFN) transmissions in dense Sixth-Generation (6G) cellular networks, where the choice of transmission mode directly influences spectral efficiency and Quality of Service (QoS). The objective is to identify the conditions under which the intercellular cooperation inherent to MB-SFN becomes more efficient than the UC mode for spectral resource block utilization under QoS constraints. To this end, we conduct a comparative performance analysis based on: i) Monte Carlo (MC) simulations, used as a numerical benchmark to accurately capture complex radio interactions, and ii) a fluid analytical framework, based on a continuous approximation of the network in which the discrete structure of base stations is replaced by a homogeneous surface density. Within this framework, we derive analytical expressions for the Signal-to-Interference-plus-Noise Ratio (SINR), enabling a tractable characterization of aggregate interference. Resource block allocation expressions are then proposed for both modes, incorporating SINR and outage probability as QoS constraints. The main contribution of this paper lies in deriving, using the fluid framework, an explicit analytical expression for the critical user threshold that characterizes the switch from UC mode to MB-SFN mode, beyond which the latter becomes more spectrum-efficient. The switching decision highlights the duality between the two modes: MB-SFN is constrained by the minimum SINR with resource consumption independent of the number of users, whereas UC mode depends on the average SINR and consumption proportional to the number of users. An in-depth analysis of the combined effect of network parameters is also conducted, highlighting their interactions and their influence on the switching threshold. Finally, the strong agreement with MC simulations validates the accuracy of the fluid framework, providing an effective analytical tool for optimizing adaptive transmission strategies.
The next generation of wireless systems extends ultra-reliable low-latency communications (URLLC) to the realm of massive connections, termed mURLLC. To address the inherent conflict between stringent quality of service (QoS) requirements in URLLC and the problem of severe and highly fluctuating interference behind demands of massive connectivity, effective fast fading (FF) mitigation and resource allocation strategies are crucial. Through in-depth analysis of FF characteristics, this paper derives optimized configurations for two FF mitigation approaches: protection margin reservation and $K$ -repetition. Furthermore, we integrate these FF mitigation strategies into a hierarchical-clustering (HC)-based resource allocation algorithm for configured-grant in mURLLC. This results in a highly practical and efficient algorithm for managing radio resources and interference in mURLLC scenarios. Simulation results demonstrate that our proposed algorithm achieves over 65% reduction in resource consumption without compromising reliability, significantly enhancing network capacity to support demanding mURLLC applications.
Yichen Guo, Lili Xu, Yihang Cheng et al.· IEEE Transactions on Wireles...· 0 citations
This formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks.
E. Spyrou, Chrysostomos D. Stylios, V. Kappatos et al.· Future Internet· 0 citations
Non-orthogonal multiple access (NOMA) is a kind of 5G and 6G radio access technology, which not only enhances spectrum efficiency but also enables several users at the same time to access the network and share the same frequency resource. This paper studies the problem of jointly optimizing power allocation and channel resource assignment in the downlink multi-carrier NOMA system, with the aim of maximizing the weighted sum rate under individual quality-of-service (QoS) constraints, per-user minimum rate requirements, and total transmit power budget. We cast the problem as a mixed-integer non-linear programming (MINLP) task and decompose it into two tractable subproblems: A low-complexity channel allocation step using a bipartite matching framework, followed by an successive convex approximation (SCA) solution to the power control step with Lagrangian duality. A closed-form expression for the optimal power ratio under fixed channel assignment is derived to achieve efficient iteration between the two stages. To further reduce the computational burden for dense deployment of the network, we combined the iterative scheme with a DRL module based on the deep deterministic policy gradient (DDPG) algorithm to enable the system to respond to changes in channel state without having to solve the optimization problem at each time slot. Simulation results show that when deployed in a 3GPP-compliant urban macro-cell environment, the proposed joint scheme can achieve 38 percent more sum throughput than orthogonal frequency-division multiple access (OFDMA) baselines, a 22 percent increase over fixed NOMA power allocation, and converges within 15 iterations under moderate user density. The energy efficiency gain is 3.62 bits/J/Hz when combining the DRL-based dynamic policy, and the practical feasibility of the proposed framework for next-generation network deployment is verified.
Yuming Fu, Xiaofeng Chang, Wanze Gan· Digital Signal and Computer...· 0 citations
A Proportional Fairness (PF)-driven framework for allocating Co-SR transmit power on a per-Transmission Opportunity (TXOP) basis is introduced, and it is proved that any Pareto-optimal power pair keeps at least one AP at its maximum power.
In this work, we investigate the performance of the distributed cooperative massive access (DCMA) framework in large-scale network setups by incorporating stochastic geometry modeling. A partially centralized cell-free cloud-radio access (C-RAN) architecture is considered where remote radio heads (RRHs) decode transmitted messages and cooperate with each other to enhance system performance. Specifically, they can share decoded messages via feedback links, allowing receivers to cancel inter-user interference through successive interference cancellation (SIC), thus improving the decoding capabilities of the system. For such a network, we propose a novel synergetic decoding algorithm that efficiently resolves the assignment and message sharing routing for each user while accounting for practical network constraints. Furthermore, using game theory, we develop a merge-and-split algorithm with lexicographic preference to solve the problem of minimizing the RRHs utilized without compromising the performance. Simulation results show that the proposed framework significantly outperforms systems that do not implement SIC or take advantage of the cooperation between RRHs in terms of outage probability. Finally, we evaluate the performance of the proposed algorithms and validate their efficiency.
Christoforos I. Dallas, A. Tegos, Sotiris A. Tegos et al.· 0 citations
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.
Yingqi He, Jinpeng Xu, Lin Zhou et al.· IEEE Transactions on Wireles...· 0 citations