Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 800-805· 0 citations· 15 references
Abstract
Co-channel interference (CCI) management through inter-system consensus, where CCI is kept below a predetermined threshold, has been studied as an approach to proactive spectrum sharing among multiple local 5G systems. In our prior work, we established a resource allocation method that exploits the fact that CCI varies depending on user equipment (UE) positions when beamforming is steered to track each UE. However, the previous study did not incorporate a mechanism to guarantee compliance with interference constraints that keep CCI below a specified level. Moreover, since frequency access opportunities for individual UEs were not guaranteed, there was a concern that some UEs could be left without any allocated resources. In this paper, we formulate the mobility-prediction-based resource allocation as a 0-1 integer linear program (ILP) and introduce both an interference constraint and a minimum allocation constraint as hard constraints. The proposed method is positioned as a hard-constraint scheduler that prioritizes interference compliance and prevention of zero-allocation UEs, while the remaining degrees of freedom are used for SNR-based throughput maximization. The effectiveness and throughput–fairness tradeoff of the proposed method are demonstrated through computer simulations.
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
The evolution of wireless networks toward 6G and Open Radio Access Network (O-RAN) architectures brings unprecedented demands for flexible and energy-efficient resource allocation mechanisms. A key challenge is to allocate radio resources effectively among heterogeneous units while satisfying diverse quality-of-service (QoS) requirements. Traditional allocation methods often fail to capture energy efficiency considerations or lack adaptability in highly dynamic and decentralized environments. To address this, we formulate the resource allocation problem as a non-cooperative game among SDN-enabled Central Units (CUs) and Distributed Units (DUs), where each player’s utility captures a trade-off between throughput gains and resource costs under threshold-based SINR QoS constraints. We show that the game admits an exact generalized potential function, guaranteeing the existence of a pure-strategy Nash equilibrium and convergence under sequential best response dynamics. The SDN controller supervises the network by adjusting system-level parameters, such as the resource price, to guide the network toward efficient and fair allocations. 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. The proposed framework is evaluated against both classical resource allocation strategies (equal and greedy allocation) and advanced optimization-based and game-theoretic baselines, including convex optimization, proportional fairness, water-filling, and Stackelberg formulations, and shows competitive performance.
E. Spyrou, Chrysostomos D. Stylios, V. Kappatos et al.· Future Internet· 0 citations
This paper considers priority-aware partial computation offloading in an uplink mobile edge computing (MEC) network. Devices assigned to different groups occupy orthogonal subbands, whereas devices within each group use power-domain non-orthogonal multiple access (NOMA) with successive interference cancellation. Task-input size determines the transmitted and processed workload, while queue backlog and application urgency determine the service weight. The Gaussian multiple-access-channel rate region is convex, but the complete allocation problem is not jointly convex in the adopted variables because the offloaded workload is coupled with reciprocal transmission rate and reciprocal edge-CPU allocation. A structure-exploiting block-coordinate projected-gradient method is developed. It combines exact finite-candidate offloading updates, an exact edge-CPU allocation bounded below by deadline feasibility and above by local-path saturation, and an analytical projected power step with Armijo backtracking. For eight users at 23 dBm, pairwise group-based NOMA reduces the weighted delay–energy cost and device energy by 6.18% and 23.44%, respectively, relative to orthogonal access. Queue-aware weighting reduces upper-backlog-quartile delay by 2.69 ms (95% confidence half-width: 0.78 ms) while increasing lower-quartile delay by 8.34 ms (half-width: 2.07 ms). In a paired 15-iteration ablation, generic projected block-coordinate updates have a cost ratio of 1.0098 (half-width: 0.0086) relative to the structured method. A hybrid deep deterministic policy-gradient policy, evaluated over five training seeds, has an 11.77% higher cost while requiring 0.84% of the median online decision time. Of 432 allocations, 392 satisfy the residual-qualified stopping tests and 40 are explicitly reported as iteration-safeguard terminations.
Jamil K. J. Bataineh, Ahlam Jawarneh, K. Hayajneh et al.· Italian National Conference...· 0 citations
To achieve high-reliability in the Industrial Internet of Things (IIoT) and satisfy the low-latency requirements of industrial equipment, this paper proposes a resource optimization scheme that jointly controls information transmission blocklength and power allocation. Specifically, Short Packet Communication (SPC) and Non-Orthogonal Multiple Access (NOMA) technologies are introduced to construct a Radio Frequency (RF)-aided Visible Light Communication (VLC) network system. The successful transmission probability of multiple User Equipment (UE) is analyzed, and the Service Capacity (SC) of each channel is quantified. Then, an optimization problem is formulated to maximize the SC, subject to constraints on statistical Quality of Service (QoS), Service Reliability (SR), and transmission power. To solve this optimization problem, we design a resource optimization algorithm joint blocklength and power allocation. Simulation results demonstrate that the proposed resource optimization scheme for NOMA VLC/RF networks outperforms NOMA VLC and OMA VLC in ensuring highly reliable data transmission. Furthermore, the proposed algorithm could maximize the SC of the NOMA VLC/RF networks by utilizing shorter blocklength.
Hongliang Sun, Dejun Xu, Chao Wang et al.· PLoS ONE· 0 citations
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
Dynamic wireless resource allocation in multi-cell networks is challenging due to non-stationary traffic, intercell interference coupling, and heterogeneous quality-of-service (QoS) constraints. Conventional schedulers and standalone metaheuristics lack adaptability across operating regimes, while deep reinforcement learning (DRL) methods often incur high training complexity and stability limitations. This paper proposes a context-aware reinforcement hyper-heuristic framework for dynamic wireless resource allocation. A contextual bandit controller hierarchically selects among multiple low-level optimization heuristics based on real-time network state features. A multi-objective reward design jointly optimizes throughput, fairness, power efficiency, and allocation stability. We establish sublinear regret guarantees under the contextual bandit model and prove convergence under standard stochastic approximation conditions. Extensive simulations over 5,000 large-scale multi-cell instances demonstrate consistent improvements over proportional fair scheduling, evolutionary methods, and DRL-based allocators in throughput, Jain's fairness index, convergence speed, and robustness to traffic perturbations. Statistical tests confirm the significance of the gains. The results indicate that reinforcementdriven hyper-heuristic orchestration provides a scalable and theoretically grounded solution for dynamic wireless resource management.
K. Danach, Samir Haddad, J. Sayah et al.· 2026 6th International Confe...· 0 citations