Joint power and channel resource allocation in NOMA-based 5G/6G wireless networks

QoS-Aware Joint Subcarrier and Power Allocation for OFDM-ISAC V2X Systems

This paper investigates joint subcarrier and power allocation for a multi-user Orthogonal Frequency Division Multiplexing (OFDM)-based Integrated Sensing and Communication (ISAC) system in Vehicle-to-Everything (V2X) environments. The goal is to maximize a weighted sum of the capped effective radar Signal-to-Noise Ratio (SNR) and aggregate communication rate, under per-user constraints on minimum effective radar SNR, communication rate, and range resolution. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model. To address its non-convexity, we develop a Block Coordinate Descent–based Joint Resource Allocation (BCD-JRA) algorithm that alternates between nonlinear power allocation and mixed-integer subcarrier assignment and is used as a benchmark in our study. To support real-time V2X operation, we further propose a low-complexity two-stage heuristic, termed Phased Constraint Satisfaction and Greedy Allocation (PSGA). PSGA first allocates the minimum resources needed to satisfy the Quality of Service (QoS) constraints, and then greedily assigns remaining resources based on marginal utility gains while accounting for effective radar SNR capping. The simulation results show that PSGA attains utility close to the BCD-JRA benchmark with millisecond-level latency and satisfies all QoS constraints in the reported experiments.

Energy-Efficient Resource Allocation for Heterogeneous Services in OFDMA Downlink Networks: Systematic Perspective

QoS-Aware Power Allocation in Mixed-Numerology NOMA

Mixed Numerology Non-Orthogonal Multiple Access (MN-NOMA) is a promising technique for beyond-5G wireless networks, but practical deployments face two major challenges: inter-numerology interference (INI) caused by overlapping subcarriers and residual interference due to imperfect successive interference cancellation (SIC). Most existing studies address these two issues separately. This paper proposes a QoS-aware power allocation framework that jointly mitigates INI and imperfect SIC in downlink MN-NOMA systems. The resource allocation problem is formulated with a logarithmic utility function, that aims to balance the overall spectral efficiency and user fairness. The resulting optimization problem is highly non-convex because of the coupled interference terms. To solve it efficiently, an alternating optimization and successive convex approximation (AO-SCA) framework is developed, where the original problem is iteratively transformed into tractable convex sub problems. Simulation results demonstrate clear performance gains over Equal Power Allocation (EPA) and Fixed Power Allocation (FPA) schemes. The proposed framework improves spectral efficiency, particularly in the low-to-moderate SNR region, while maintaining reliable performance under practical interference conditions. Unlike schemes that favor only strong-channel users, the proposed method provides a balanced fairness-efficiency tradeoff, maintaining a Jain's fairness index of approximately 0.67 while reducing outage probability to near-zero levels at SNR values above 30 dB. These results indicate that the proposed AO-SCA framework provides an effective and practical solution for fairness-aware

Resource optimization algorithm for RF-aided NOMA VLC network joint blocklength control and power allocation

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.

Delay–Energy-Aware Partial Offloading and Coupled Resource Allocation in Hybrid NOMA-MEC Networks: Derivations and Reproducible Evaluation

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.

Hierarchical RSMA in Multi-Antenna Downlink Systems: Outage Performance and Power Allocation

Rate-Splitting Multiple Access (RSMA) has emerged as a robust interference management strategy for future wireless networks. This paper investigates the performance of a hierarchical RSMA scheme in the downlink of a multi-antenna system, designed to efficiently serve clustered user deployments. We derive exact and asymptotic closed-form expressions for the outage probability of users under Nakagami- $m$ fading channels, considering a two-layer message splitting architecture (systemcommon, group-common, and private streams). Furthermore, to ensure fairness and reliability, we formulate a min-max power allocation problem to minimize the worst-case outage probability among users. A Geometric Programming-based algorithm is proposed to solve the resulting non-convex optimization problem. The numerical results validate the theoretical analysis and demonstrate the impact of different strategies for using this model, such as the number of users per group, user allocation strategies, and the number of base station transmit antennas.