Aug 2026· Bulletin of Electrical Engineering and Informatics· Vol 15, pp. 3504-3515· 0 citations· 29 references
TL;DR
This paper explores the application of a fractional frequency reuse (FFR) strategy purposed for networks that integrate both D2D and RIS technologies in multicell cellular network scenarios, and demonstrates its effectiveness in improving network reliability and efficiency.
Abstract
Device-to-device (D2D) communication and reconfigurable intelligent surfaces (RISs) are two technologies that are attractive components of future wireless networks. D2D allows nearby devices to communicate directly, which helps boost network efficiency by reducing the load on base stations. However, since D2D often uses the same frequency bands as regular cellular users, it can cause interferences. RIS, on the other hand, enhances signal coverage by intelligently reflecting signals toward their intended destinations, but it can also unintentionally reflect interfering signals, especially in crowded multicell networks during downlink transmission. To address these interference challenges, this paper explores the application of a fractional frequency reuse (FFR) strategy purposed for networks that integrate both D2D and RIS technologies in multicell cellular network scenarios. The simulation outcomes imply that the proposed FFR method notably expands performances i.e., raising the signal quality in term of signal-to-interference-plus-noise-ratio (SINR) from 0.47 dB to 1.93 dB, enhancing throughput from 10.8 Mbps to 13 Mbps, and improving the bit error rate (BER) to 21 errors per 100 bits, demonstrating its effectiveness in improving network reliability and efficiency.
Dense deployment of LiFi and visible light communication attocells increases sensitivity to inter-cell interference, requiring coordinated interference and resource management. Angle-diversity transmitters (ADTs) for space-division multiple access (SDMA), OFDMA allocation, and fractional frequency reuse (FFR) have been studied separately under inconsistent assumptions, making it difficult to separate reuse-driven from activity- and resource-sharing effects. This work presents a protocol-level framework that unifies ADT-enabled SDMA, OFDMA, and strict FFR within an intensity-modulation direct-detection DCO-OFDM model. Each macrocell is served by a co-located multi-beam ADT defining beam-based microcells; users are classified as center or edge, and protected edge sub-bands are assigned using adjacency-aware graph coloring. Full-load and random-one-per-region regimes are evaluated in 10,000-trial Monte Carlo simulations with sensitivity sweeps over center-edge partitioning and user density. Both regimes achieve a mean SINR of about 28 dB, with no outage observed at 10 or 20 dB in the finite LOS sample. A dynamic extension incorporating mobility, receiver-orientation variation, a parametric first-order NLOS stress model, and variable multi-user OFDMA admission shows that OMN-PF improves edge-frame service success over conventional PF, while OMN-PF-A further improves high-threshold edge reliability with similar scheduled spectral efficiency. The framework provides a reproducible benchmark for dense LiFi resource management.
Mostafa Mohamed, H. Ibrahim, Ali H. Ahmed et al.· Scientific Reports· 0 citations
Reconfigurable intelligent surface (RIS)-assisted wireless-powered communication networks (WPCNs) introduce a new degree of freedom: the same passive beamforming array can concentrate the downlink energy toward harvesting devices and simultaneously shape the uplink interference environment. In this paper, we study a system where the base station (BS) waveform serves the dual role of wireless energy transfer (WET) and passive target sensing. Using the position error bound (PEB) derived from the equivalent Fisher information matrix (EFIM) as the sensing quality metric, we formulate a joint resource allocation problem that maximizes weighted uplink sum-rate subject to a PEB constraint, energy-causality, block-time sharing, and unit-modulus RIS phase constraints. Focusing on the practically important WET-only sensing case, we show that the problem separates into four tractable subproblems and propose a block coordinate descent (BCD) algorithm: (i) closed-form water-filling for WIT time-power allocation, (ii) semidefinite relaxation (SDR) with Dinkelbach iterations for per-slot WIT-RIS beamforming, (iii) golden-section search for the optimal WET duration, and (iv) a convex SDP for WET-RIS optimization under a PEB constraint. The BCD iterates converge monotonically. Simulations confirm a fundamental rate–sensing tradeoff and demonstrate significant gains from joint RIS-assisted optimization.
Yongjie Li, Jing Shen, Jizhao Lu et al.· 2026 8th International Confe...· 0 citations
In this paper, we consider a coexisting network of cellular transmission and device-to-device (D2D) communication, in which BS wants to communicate with far user, meanwhile, a D2D source desires to transmit information to a D2D destination. However, due to heavy shadowing or severe path loss, their direct links are not available. To cope with this problem, a relay is employed to assist the involved transmission. For such a relay-assisted spectrum sharing network, two transmission schemes are designed, i.e., multiple access broadcast NOMA (M-NOMA) scheme and time division broadcast NOMA (T-NOMA) scheme. For each scheme, we first perform power optimization to minimize the outage probability (OP) of D2D communication under the quality of service (QoS) constraint of cellular transmission. Based on the optimization results, we derive the OPs for both cellular and D2D signals. To gain more insights, the asymptotic OPs and the average throughput for both schemes are provided as well. On this basis, we further propose a more superior hybrid M/T-NOMA cognitive communication scheme, in which the system will adaptively select the one with higher system throughput between M-NOMA and T-NOMA as the final transmission scheme. Simulation results validate the accuracy of our analyses, and reveal the performance gain of our schemes over the benchmark schemes.
Yafang Zhang, Ye Tian, Haixia Li et al.· Scientific Reports· 0 citations
Integrated sensing, communication, and computing (ISCC) technology significantly improves spectrum efficiency and reduces hardware costs by unifying the functionalities of sensing, communication, and computation. However, the degradation of wireless link quality caused by obstacles may lead to severe offloading latency. This paper investigates the application of reconfigurable intelligent surface (RIS) technology in ISCC network to enhance the reliability of wireless links and improve the efficiency of computation offloading. In the proposed network, integrated sensing and communication (ISAC) devices employ the same hardware and signaling mechanisms to perform both sensing and communication tasks, while mobile edge computing (MEC) technology is leveraged to process sensing data. To address the cross-layer resource management problem, we formulate a total latency minimization problem for both communication and computation under sensing accuracy constraints, by jointly optimizing the waveform precoding design matrix of ISAC devices, the beam pattern scaling factor, the RIS’s reflective beamforming, and the computing frequency of edge server (ES). Since this problem is highly non-convex, we propose an iterative algorithm based on block coordinate descent (BCD) framework, which leverages semi-definite relaxation (SDR) method, the Charnes-Cooper transform (CCT) method, and closed-form solution to alternately optimize three variable blocks until convergence is achieved for the final solution. Extensive simulations validate the effectiveness of the proposed scheme, demonstrating that the RIS-assisted joint optimization scheme significantly reduces the total system latency. Moreover, we reveal the trade-off between sensing accuracy and system latency.
Yingsheng Peng, Jinbei Zhang, Jingpu Duan et al.· IEEE Transactions on Green C...· 0 citations
Non-terrestrial networks (NTNs) have emerged as a promising technology for providing ubiquitous connectivity in remote, underserved, and disaster-stricken regions. In particular, high-altitude platforms (HAPs) can offer wide-area coverage; however, their performance is often limited by severe path loss, multi-user interference, and spectrum-sharing constraints. To address these challenges, this paper investigates a multi-antenna HAP-based underlay aerial-to-ground communication network employing rate-splitting multiple access (RSMA) and assisted by a terrestrial beyond-diagonal reconfigurable intelligent surface (BD-RIS). The objective is to maximize the system sum rate while satisfying user rate requirements, HAP transmit-power constraints, and interference-temperature constraints imposed to protect the primary network. The design further accounts for imperfect channel state information (CSI) through a worst-case robust optimization framework. The resulting problem is highly non-convex due to the coupled optimization of RSMA precoding and BD-RIS beamforming. To address this challenge, the transmit precoding design is reformulated as a convex semidefinite program and solved using successive convex approximation and the MOSEK solver, while the BD-RIS scattering matrix is optimized over the unitary manifold using Riemannian manifold optimization. Simulation results demonstrate the effectiveness of the proposed framework and show that the BD-RIS-assisted system achieves up to a 45.2% sum-rate improvement compared with conventional single-connected RIS (SC-RIS) architectures while maintaining robustness against CSI uncertainty and satisfying all system constraints.
Zain Ali, Muhammad Asif, S. Althunibat et al.· IEEE Access· 0 citations
This letter investigates a multi-cell reconfigurable intelligent surface (RIS)-assisted cloud radio access network (Cloud-RAN), where multiple remote radio heads (RRHs) are connected to a central unit (CU) via limited-capacity fronthaul links, and multiple user equipments (UEs) in each cell are supported by dedicated RISs. In particular, we provide a unified comparative analysis of three RIS architectures—active RIS, passive RIS with continuous phase shifts, and passive RIS with discrete phase shifts—highlighting their fundamental performance trade-offs under practical fronthaul and RIS structural constraints. To mitigate the effects of fronthaul limitations and inter-cell interference, we propose a joint optimization framework for fronthaul compression, coordinated transmit power allocation, and RIS beamforming to maximize the system sum-rate. Numerical results verify that the proposed approach effectively balances fronthaul resource constraints and RIS configuration flexibility, providing key insights into the performance trade-offs of different RIS architectures for practical network deployments.