Jul 2026· Journal on Wireless Communications and Networking· 0 citations
Abstract
This paper proposes a cross-layer scheduling algorithm for joint flow control and resource allocation to optimize the time-average utility function for the dual-connectivity multi-queue multi-server (D-C MQMS) system with a stochastic arrival process. First, we derive the capacity region of the D-C MQMS system by a finite set of linear inequalities. The capacity region characterizes the maximum input rate supported by the system, which is useful for network management. Furthermore, the characterization of the distance between the input rate vector and the boundary of capacity region is exhibited when the channel model is an ON/OFF channel. Then we consider two cases: (I) input rates within the capacity region, and (II) input rates exceeding the capacity region. Two scheduling algorithms for joint flow control and resource allocation, FCRA-I and FCRA-II, are proposed by Lyapunov optimization method. Next, the performance on the utility function and queue delay is analyzed. Finally, by comparing the proposed method with the single-connectivity (S-C) system, we verify the algorithm’s effectiveness by evaluating the average system throughput, system user perceived throughput (SUPT), the throughput-fairness utility function, and the average system backlog.
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
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
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.
M. Younes, Clency Perrine· IEEE Open Journal of the Com...· 0 citations
The rapid development of 6G makes space–air–ground integrated networks (SAGIN) a promising solution to the coverage and capacity limitations of traditional cellular systems. However, time-varying topologies, stochastic channels, imbalanced user demands, and limited resources hinder on-demand service provisioning in wide-area environments. To address this challenge, this paper proposes an on-demand service framework that prioritizes users who contribute greater system utility once their demands are satisfied. The objective is to improve system utility through on-demand services without requiring prior knowledge of user demands or channel statistics. We first develop an on-demand utility model that captures diminishing returns in demand satisfaction while incorporating heterogeneous priority levels and latency constraints. Based on this model, we formulate a joint on-demand resource allocation and task offloading problem (ODRA-TO) to maximize system utility under long-term queue stability constraints. To efficiently solve ODRA-TO, we design an alternating direction method with three-stage iterations (ADMI) that decomposes the problem into learning-assisted task offloading, swap-stable matching based subchannel assignment, and gradient-based successive convex approximation for power control. Simulation results show that ADMI reduces the average queue length by 44.18%, improves on-demand utility by 34.59%, and achieves a 99.00% completion rate for high-priority services under dynamic SAGIN conditions.
Luqiao Wang, Changle Li, Yao Zhang et al.· IEEE Transactions on Wireles...· 0 citations
This paper proposes an advanced resource allocation technique based on multi-objective optimization (MOO) to jointly optimize spectrum and power, mitigating nonlinear impairments and enhancing network performance, thereby improving the optical signal-to-noise ratio.
S. A. Silva, Carmelo J. A. Bastos-Filho, Danilo R. B. Araújo et al.· Journal of Microwaves, Optoe...· 0 citations
Simulation results demonstrate that, compared with P-DQN, SAC, and PADDPG, the proposed framework achieves superior joint performance, thereby verifying its effectiveness for multi-UAV ISAC joint optimization.
Guifen Chen, Zeli Gong· Digital Signal and Computer...· 0 citations