Aug 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 36 references
TL;DR
A Dinkelbach-based alternating optimization framework integrating utility-based relay selection and graph-based multi-hop routing is developed and improves energy efficiency by approximately 20–40% compared with the considered benchmark schemes.
Abstract
Device-to-device (D2D) communication is a key enabling technology for sixth-generation wireless networks due to its potential to improve spectral efficiency, extend network coverage, and reduce communication latency. However, the performance of multi-hop D2D communications is constrained by limited relay energy, dynamic wireless channels, and the complexity of relay selection and resource allocation. To address these challenges, this paper proposes a base station (BS)-assisted multi-hop D2D communication framework with radio frequency (RF) energy harvesting. Relay nodes harvest energy from dedicated BS transmissions and subsequently participate in decode-and-forward multi-hop forwarding. The energy-efficiency maximization problem is formulated as a mixed-integer nonlinear fractional programming (MINFP) problem that jointly optimizes relay selection, transmit power allocation, and transmission time scheduling under energy-causality and transmit-power constraints. To efficiently solve this problem, a Dinkelbach-based alternating optimization framework integrating utility-based relay selection and graph-based multi-hop routing is developed. Simulation results demonstrate that, under the considered simulation settings, the proposed framework improves energy efficiency by approximately 20–40% compared with the considered benchmark schemes.
In this paper, the joint radio resource management issues in a cognitive radio network driven by radio frequency energy harvesting (CRN- RF-EH) functionalities are investigated. For the CRN-RF-EH, the cognitive radio (CR) node first harvests its required energy directly from the transmitter of spectrum licensed user for its data communication and consequently transmits its data on the licensed frequency of the legacy user using the underlay accessing technique. Thus, RF-EH is an exciting innovation for energizing low-powered next- generation wireless networks (NGWNs). Consequently, due to CRN-RFEH’d low power limitations, the resource allocation for CRN-RF-EH has to be optimized considering the trade-off among spectral efficiency, energy efficiency, and RF energy supply. Equal allocation of transmission time and/or transmission power may not be efficient for CRN-RF-EH with limited transmission time and power resources. A joint optimal time and power allocation (OTPA) strategy for CRN-RF-EH is proposed to maximise the total spectral efficiency of the CRN- RF-EH. The coupled variables in the formulated joint resource allocation problems create a non-convex optimization problem formulation. For analytical tractability, the non-convex optimization formulation is initially converted to its equivalent standard convex optimization formulation using proper variables and next, it is then solved using the convex optimization technique. The CONOPT solver, a powerful optimization-solving tool for solving convex optimization problems, is utilized to resolve the equivalent standard convex optimization problem formulation. When compared with the baseline biased random time optimum power allocation (BRTOPA) scheme, numerical simulation results show that the OTPA strategy dramatically improves the total spectral efficiency performance. In a severe radio propagation environment with a path loss exponent (PLE) equal to 3.5 such as in urban areas and less severe radio propagation environment with a path loss exponent (PLE) equal to 2.0, such as in rural areas, the OTPA outperformed the BROTPA with a mean performance improvement of approximately 23. 96% and 42.94% , respectively.
E. Obayiuwana, O. Ipinnimo, P. Ayodele et al.· Nigerian Journal of Technolo...· 1 citation
A more superior hybrid M/T-NOMA cognitive communication scheme is proposed, in which the system will adaptively select the one with higher system throughput between M-NOMA and T-NOMA as the final transmission scheme.
Yafang Zhang, Ye Tian, Haixia Li et al.· Scientific Reports· 0 citations
Integrated Sensing and Communication (ISAC) is emerging as a key technology for next-generation wireless networks, enabling simultaneous communication and sensing functionalities. This paper focuses a RIS-assisted full-duplex (FD) ISAC system, in which a multi-antenna base station (BS) concurrently performs multi-user uplink and downlink transmission while also carrying out radar sensing. To maximize the joint uplink–downlink sum rate, an optimization problem is formulated under practical constraints, such as radar detection SINR, self-interference, BS transmit power, user power budgets, and RIS unit-modulus conditions. To address the nonconvexity of this problem, a two-stage hybrid optimization approach is developed. In the first stage, the augmented Lagrangian technique decomposes the complex problem into simpler subproblems involving beamforming, power allocation, and RIS phase optimization, leading to a feasible initial solution. The second stage employs a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to refine this solution adaptively, enabling the system to respond effectively to variations in the channel environment, mobility patterns, and interference levels. The proposed hybrid framework achieves optimal resource allocation while maintaining feasibility, robustness, and adaptability. Analytical results confirm its convergence behavior, and extensive simulation results confirm that the proposed scheme consistently outperforms conventional optimization and single-agent DRL baselines in sum-rate maximization, interference mitigation, and sensing accuracy, confirming its effectiveness for RIS-assisted full-duplex ISAC systems.
S. Waqas, Fenghua Huang, Fakhar Abbas et al.· IEEE Transactions on Wireles...· 0 citations
This paper studies a cooperative wireless system in which a single-antenna base station (BS) communicates with a destination user (U) via a half-duplex energy-harvesting amplify-and-forward relay, while the direct BS–U link is unavailable. The destination (U) is equipped with a fluid antenna system (FAS) comprising multiple closely spaced receive ports, enabling spatial reconfigurability through instantaneous port selection. A power-splitting architecture is adopted at the relay to support simultaneous energy harvesting and information forwarding. All wireless links are modeled as flat Rayleigh fading, and the spatial correlation among the FAS ports is explicitly incorporated. To analytically characterize the impact of correlated port selection, a Gaussian copula framework is employed to model the joint distribution of the FAS-channel power gains. Exact integral expressions for the cumulative distribution function of the end-to-end signal-to-noise ratio are derived, from which the outage probability is obtained. For the special case of uncorrelated FAS ports, closed-form expressions are further developed using order statistics and special functions. In addition, asymptotic analysis is carried out to provide further insight into system performance in the high-signal-to-noise-ratio region. Numerical and Monte Carlo simulation results validate the analytical derivations and demonstrate that FAS-based receiver selection yields significant gains in outage performance, even in the presence of strong spatial correlation and energy-harvesting constraints.
This paper enhances the proposed system cell-edge user performance and derives analytical frameworks for outage probability, throughput, and ergodic capacity of PR, SR, respectively and determines the optimal power allocation scheme to enhance the performance of PR/SR signals.
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
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