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

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

Simulation results demonstrate that the proposed resource optimization scheme for NOMA VLC/RF networks outperforms NOMA VLC and OMA VLC and OMA VLC in ensuring highly reliable data transmission.

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.

Resource allocation scheme for VLC-RF networks under QoE constraints

Empowering Rural Areas With Energy-Efficient 5G IAB-Based Fixed Wireless Access Network

Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.

Dynamic Resource Allocation Algorithm for Vehicle-to-Vehicle 6G Visible Light Communication

The intelligent transportation systems (ITS), including autonomous driving technologies, have increased the need for a capable communication system. The optical domain offers a promising spectrum for supporting multi-connection and high-data-rate applications. This paper proposes a dynamic resource allocation algorithm for vehicle-to-vehicle (V2V) 6G visible light communication (VLC) systems. A wavelength division multiple access (WDMA) method is utilized as a technique for supporting multiple connections. Two optimization objectives of the resource allocation are evaluated, which are referred to as Max SNR and Max spectral efficiency (SE) objectives. The best resource assignment for each vehicle is obtained by using the optimized resource allocation model. Five scenarios are examined where vehicles are moved in this work. The Max SE objective shows a fair allocation of resources based on the SNR compared to the Max SNR objective. In addition, a dynamic algorithm is developed for real-time solutions. The proposed dynamic algorithm can provide suboptimal resource allocation at 0.001 s, whereas the Max SE MILP model provides the optimal resource allocation in around 1 min. Thus, the proposed dynamic scheme can be used in real-time applications.

Energy Efficiency Optimization in Distributed MIMO vRAN via Cross-Layer Link Abstraction

Virtualized radio access networks (vRAN) run the compute-intensive multiple-input multiple-output (MIMO) baseband as software on shared servers, which makes energy efficiency (EE) a primary design objective. Distributed MIMO vRAN consumes power across virtualized distributed unit (vDU) baseband, fronthaul transport, and per-radio-unit operation. We build a power model that resolves these three components. We then develop a framework that jointly selects modulation, transmission rank, and per-subcarrier power to maximize system EE. Exponential effective SNR mapping induces a convex per-subcarrier power constraint, which yields a convex power minimization problem with a closed-form waterfilling-like solution. We show that radio frequency-only models underestimate the spectral efficiency range where single-input multiple-output (SIMO) transmission saves power, and our power model extends this range by 24%. We further extend the framework to a traffic-aware setting with realistic user trajectories from the multi-agent transport simulator. We propose a traffic-aware strategy that switches each radio unit among MIMO, SIMO, and sleep modes based on demand. Simulation results over 3GPP NR compliant fading channels show that, after a one-time offline calibration, the framework predicts link performance without further link-level simulation. The proposed framework achieves higher average EE than a traffic-agnostic always-on MIMO baseline, while maintaining comparable throughput at peak hours.