TL;DR
A novel EE resource allocation scheme for orthogonal frequency division multiple access (OFDMA) networks, where both transmitter energy consumption and receiver energy consumption are considered, and an iteration algorithm is developed to obtain the joint time-frequency-power resource allocation strategy.
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.
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.
This article constructs a dual layer optimization for heterogeneous network user access and energy consumption perception. The upper layer uses mixed integer programming to solve user access decisions, while the lower layer uses continuous optimization to handle resource and power allocation, and integrates NSGA-III multi-objective algorithm to achieve Pareto optimization of QoS and energy consumption. The core innovation of this model includes a heterogeneous resource pool management mechanism, where macro-base stations are configured with 100 resource blocks with a power range of 10-40dBm, and three micro-base stations are each configured with 50 resource blocks and a power range of 10-30dBm, as well as decision-making strategies for energy consumption perception. It integrates a comprehensive energy consumption model of static power consumption, dynamic power consumption, and cooling power consumption. Design robust stochastic differential evolution algorithms at the algorithmic level to handle channel uncertainty and ensure multi-objective convergence mechanisms. The optimization results show that among the optimal access strategies of 70 users, 45% of users choose macro-base station services, 55% of users choose nearby micro-base station services, and the total QoS of the system is 6534.28.
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.
Comparison shows that, under the evaluated DeepMIMO-based scenarios, the examined assignment-based methods exhibit different trade-offs in throughput, energy-consumption-related performance, bandwidth utilization, and adaptability to varying user demands, offering useful insights for 5G MIMO resource allocation studies.
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.