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

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

In this paper, we consider an indoor networking architecture in which visible light communication (VLC) provides wireless connectivity between users and distributed fog computing resources and an energy-efficient passive optical network (PON)- based backhaul interconnects the VLC access points (APs). We investigate the joint optimization of the allocation of VLC and fog computing resources aiming to minimize processing and networking power consumption. We develop a mixed-integer linear programming (MILP) model that jointly optimizes access points (APs) selection, wavelength assignment and fog computing resources allocation to serve the requests of users. Compared to a statically preconfigured AP selection and wavelength assignment based on maximizing signal-to-interference-plus-noise ratio (SINR) independently of fog resource placement, the joint optimization achieves lower average total power consumption, driven by a reduction in processing power. These savings are attributed to the ability of the joint optimization to allow AP sharing across users and selecting more energy-efficient wavelengths that still satisfy quality of service (QoS) requirements.

Optimization of Resource Allocation in 5G MIMO Networks Using Linear Assignment Algorithms

Massive Multiple Input Multiple Output (MIMO) is an essential technology that can significantly improve the performance of 5G wireless networks by using multiple antennas in base stations, improving coverage, reducing interference, and increasing data throughput. In this comprehensive study, we propose and analyze advanced optimization techniques for resource allocation in 5G MIMO networks, focusing on three distinct approaches: simple sorting, Hungarian Algorithm, and Minimum Cost Flow Algorithm. Simulations are performed using the publicly available DeepMIMO dataset, where we evaluate each method under both static and dynamic scenarios, aiming to optimize bandwidth distribution and minimize power consumption. A key contribution of this work is the formulation and comparative evaluation of the resource allocation problem as an assignment-based model, allowing the examined methods to be compared under common DeepMIMO-based static and dynamic scenarios. The technical contribution of this work lies in the common assignment-based formulation and comparative evaluation of simple sorting, Hungarian, and Minimum Cost Flow allocation methods under the same DeepMIMO-based static and dynamic 5G MIMO scenarios. Our comparative analysis 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.

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.

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

Efficient routing and spectrum allocation in arbitrary flex-grid entanglement networks

As practical quantum networks approach large-scale deployment, the need for efficient user-to-user frequency allocation is increasing, yet current approaches only provide partial solutions to the routing and spectrum allocation problem for an arbitrary quantum network. We address this challenge for repeater-less flex-grid quantum networks based on hyperentangled photons using an efficient three-stage pipeline combining leading tools in classical networking with recent advances in numerical optimization. First, double instantiations of Yen's algorithm obtain low-loss route candidates between each pair of users and the entanglement sources. Second, the advanced process optimizer (APOPT) obtains frequency channel allocations that maximize distribution rates under fidelity constraints. Finally, the constraint programming solver using satisfiability methods (CP-SAT) assigns specific frequency bins to each link, ensuring that there is no contention between frequencies from different sources. We numerically demonstrate this approach on a representative ring network and a Manhattan incumbent local exchange carrier topology, realizing significant improvements over prior genetic algorithm approaches in speed, accuracy, and scalability. Overall, this pipeline provides an efficient heuristic workflow for optimizing broadband entanglement distribution, applicable to arbitrarily connected quantum networks integrated within the existing lightwave infrastructure.