This paper establishes a system model for IRS-assisted aggregated VLC-RF vehicular networks, and a resource allocation scheme is developed to improve the total achievable rate, and decomposes the maximization of the total achievable rate into five subproblems.
Abstract
With advances in emerging material technologies, intelligent reflecting surface (IRS)-assisted vehicular networks have been gaining growing interest. By adaptively shaping the wireless propagation environment, IRSs can improve vehicular network quality of service (QoS). However, most IRS-assisted vehicular network studies are limited to individual RF or VLC frameworks, while only a few investigate IRS-assisted aggregated VLC-RF vehicular networks that combine wide RF coverage with high VLC data rates. In this paper, aggregated VLC-RF vehicular networks are supported by both optical IRSs (OIRSs) and RF IRSs, and a resource allocation scheme is developed to improve the total achievable rate. First, we establish a system model for IRS-assisted aggregated VLC-RF vehicular networks, and then formulate a problem to maximize the total achievable rate. Furthermore, we decompose the maximization of the total achievable rate into five subproblems and solve them iteratively via an efficient alternating optimization scheme based on block coordinate descent (BCD). Moreover, simulation results validate the convergence and efficiency of our algorithm, while highlighting the effects of crucial parameters on system performance, providing valuable insights for resource allocation in IRS-assisted aggregated VLC–RF vehicular networks.
This paper investigates a UAV-assisted multi-panel intelligent reflecting surface (IRS) architecture to enhance link reliability and spectral efficiency in next-generation wireless networks. Unlike conventional IRS deployments with fixed geometries, UAV mobility introduces rapidly varying propagation conditions that challenge classical diversity and combining schemes. To address this, a Maximal Ratio Combining with Generalized Selection (MRC-GS) framework is proposed, in which the UAV optimizes its aerial position while IRS panels are selectively activated based on their contribution to the received signal. A joint optimization problem is formulated to design the IRS phase shifts, UAV position, and receiver combining weights. Hardware impairments are explicitly incorporated in the receiver combining design via impairments-aware noise covariance. The IRS phase optimization adopts a tractable effective-channel formulation and establishes the equivalence between maximizing the effective channel gain and signal-to-impairments-plus-noise ratio (SINR) under the adopted hardware impairment model. By activating only a dominant subset of IRS panels, the proposed approach improves energy efficiency and reduces computational overhead compared to full-activation schemes. Moreover, a low-complexity closed-form phase update is also provided for large-scale deployments. Simulation results demonstrate that the proposed MRC-GS-enabled UAV-IRS system reduces bit error rate (BER) and enhances spectral efficiency compared to benchmark single-IRS and full-combining schemes.
Nasir Saeed, Shumaila Javaid, Naveed Khan et al.· IEEE Open Journal of the Com...· 0 citations
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.
Wafaa B. M. Fadlelmula, S. Mohamed, T. El-Gorashi et al.· 0 citations
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.
Hongliang Sun, Dejun Xu, Chao Wang et al.· PLoS ONE· 0 citations
Simulation results demonstrate that the IRFO-JORA algorithm could efficiently allocate resource according to system parameters and user QoE requirements, thereby significantly improving RF utilization efficiency.
This letter investigates resource allocation for a generalized coordinated direct and relay transmission (CDRT) framework assisted by an amplify-and-forward unmanned aerial vehicle (UAV), where the considered UAV simultaneously forwards the base station’s signals and delivers its own local information, yielding a unified transmission architecture well aligned with practical sensing and control applications. To manage the highly coupled interference topology among direct, forwarded, and relay-originated streams, we adopt the rate-splitting multiple access (RSMA) strategy. We formulate a joint optimization of precoding vectors, the UAV amplification matrix, and rate-splitting parameters to maximize the system sum rate. A penalty-based alternating optimization algorithm that combines semidefinite relaxation and successive convex approximation is customized to tackle the intertwined non-convex fractional terms. Simulation results demonstrate substantial sum-rate gains over the pure-forwarding, NOMA, and SDMA schemes in most considered cases, with the advantage becoming more pronounced under full-load and moderately overloaded conditions.
Dan Jiang, Yuanyuan Gao, Qiao Su et al.· IEEE Wireless Communications...· 0 citations
In dense indoor Internet of Things (IoT) environments, simultaneously transmitting and reflecting intelligent surfaces (STAR-IRS) can provide full-space coverage by steering energy to both sides of the surface. However, most STAR-IRS works focus on the physical layer and overlook medium access control (MAC) challenges, such as how to collect channel state information (CSI), dynamically assign each STAR-IRS element to either the reflection or transmission mode, and coordinate concurrent users. We propose a two-stage MAC protocol that couples carrier sense multiple access with collision avoidance (CSMA/CA) for contention and time division multiple access (TDMA) for scheduled data transmission. During contention, devices send extended request-to-send (eRTS) frames to enable CSI acquisition; during transmission, the access point (AP) configures the STAR-IRS, allocates TDMA slots, and pairs one reflection-region device (RD) with one transmission-region device (TD) for non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC). Analysis and simulations show that the proposed scheme increases throughput and reduces delay compared with reflective-only IRS baselines without NOMA, while remaining practical for dynamic indoor IoT.