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

Resource allocation scheme for VLC-RF networks under QoE constraints

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

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.

Energy-Efficient Joint Optimization of VLC and Fog Computing Resources Allocation

A mixed-integer linear programming (MILP) model is developed that jointly optimizes access points (APs) selection, wavelength assignment and fog computing resources allocation to serve the requests of users and achieves lower average total power consumption.

Multi-objective Optimization for Resource Allocation in Elastic Optical Networks: Integrating Spectrum and Power Assignment

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.

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.

Joint relay selection and resource optimization in BS-assisted multi-hop D2D networks with RF energy harvesting

Device-to-device (D2D) communication is a key enabling technology for sixth-generation wireless networks due to its potential to improve spectral efficiency, extend network coverage, and reduce communication latency. However, the performance of multi-hop D2D communications is constrained by limited relay energy, dynamic wireless channels, and the complexity of relay selection and resource allocation. To address these challenges, this paper proposes a base station (BS)-assisted multi-hop D2D communication framework with radio frequency (RF) energy harvesting. Relay nodes harvest energy from dedicated BS transmissions and subsequently participate in decode-and-forward multi-hop forwarding. The energy-efficiency maximization problem is formulated as a mixed-integer nonlinear fractional programming (MINFP) problem that jointly optimizes relay selection, transmit power allocation, and transmission time scheduling under energy-causality and transmit-power constraints. To efficiently solve this problem, a Dinkelbach-based alternating optimization framework integrating utility-based relay selection and graph-based multi-hop routing is developed. Simulation results demonstrate that, under the considered simulation settings, the proposed framework improves energy efficiency by approximately 20–40% compared with the considered benchmark schemes.