The proposed MC-NOMA with 3D beamforming consistently outperforms baseline schemes that employ OFDMA with shared spectrum or uniform linear arrays, especially under high channel estimation errors, strong external interference, stringent coverage constraints, and increasing user densities.
Abstract
The dense deployment of Internet of Things (IoT) networks in smart cities poses severe challenges in spectral efficiency, energy consumption, and interference management. This paper addresses the joint optimization of three-dimensional (3D) beamforming, subcarrier assignment, and power allocation in a multi-carrier non-orthogonal multiple access (MC-NOMA) network supporting both device-to-infrastructure (D2I) and device-to-device (D2D) communications. A robust percentile-based channel model with spatial shadowing correlation is adopted to cope with urban propagation uncertainties, and an accurate elliptical footprint model derived from the 3-dB antenna pattern is used to evaluate coverage gaps and beam overlaps. The resulting mixed-integer nonlinear programming problem is solved by a three-layer memetic particle swarm optimization (Hybrid PSO) algorithm that combines a fixed-point Successive Interference Cancellation (SIC-aware) power solver, an iterative Hungarian method for subcarrier assignment, and an adaptive multi-phase local search. Simulation results demonstrate fast convergence, with the network power consumption stabilizing at 88 mW at a 600 MHz carrier frequency. The proposed MC-NOMA with 3D beamforming consistently outperforms baseline schemes that employ OFDMA with shared spectrum or uniform linear arrays, especially under high channel estimation errors, strong external interference, stringent coverage constraints, and increasing user densities. The findings confirm that the joint framework significantly enhances energy efficiency and robustness, making it a scalable solution for next-generation urban IoT networks.
Comparison 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.
Nikolaos Prodromos, Damianos Diasakos, V. Kokkinos et al.· Wireless personal communicat...· 0 citations
This paper investigates joint subcarrier and power allocation for a multi-user Orthogonal Frequency Division Multiplexing (OFDM)-based Integrated Sensing and Communication (ISAC) system in Vehicle-to-Everything (V2X) environments. The goal is to maximize a weighted sum of the capped effective radar Signal-to-Noise Ratio (SNR) and aggregate communication rate, under per-user constraints on minimum effective radar SNR, communication rate, and range resolution. The problem is formulated as a Mixed-Integer Nonlinear Programming (MINLP) model. To address its non-convexity, we develop a Block Coordinate Descent–based Joint Resource Allocation (BCD-JRA) algorithm that alternates between nonlinear power allocation and mixed-integer subcarrier assignment and is used as a benchmark in our study. To support real-time V2X operation, we further propose a low-complexity two-stage heuristic, termed Phased Constraint Satisfaction and Greedy Allocation (PSGA). PSGA first allocates the minimum resources needed to satisfy the Quality of Service (QoS) constraints, and then greedily assigns remaining resources based on marginal utility gains while accounting for effective radar SNR capping. The simulation results show that PSGA attains utility close to the BCD-JRA benchmark with millisecond-level latency and satisfies all QoS constraints in the reported experiments.
Jiahao Zheng, Xinhao Chen, Linyu Huang et al.· IEEE Transactions on Wireles...· 0 citations
A cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection is developed and a fundamental implementation trade-off is revealed: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Z. Behdad, Özlem Tuğfe Demir, Ki Won Sung et al.· 0 citations
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%.
Anselme Ndikumana, K. Nguyen, Oscar Delgado et al.· IEEE Transactions on Network...· 0 citations
Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are two emerging technologies envisioned for sixth-generation (6G) wireless systems. These technologies enhance conventional cellular networks by extending coverage and enabling ubiquitous connectivity. However, spectrum scarcity and interoperability challenges create a strong need for efficient spectrum sharing among cellular users supported by these technologies. In this context, this paper considers a dynamic spectrum-sharing method in which data rate-aware spectrum sharing plays a critical role in managing base station power consumption and mitigating interference. We investigate a UAV-RIS-assisted cellular system where legacy cellular users share the spectrum with cellular Internet-of-Things (IoT) devices. To enhance the overall system sum data rate, we propose a joint user pairing, spectrum, power allocation, and RIS phase shift optimization approach based on matching theory. Simulation results demonstrate the effectiveness of the proposed method in improving resource allocation efficiency and significantly enhancing the sum data rate performance of wireless communication systems.
Lilatul Ferdouse, Mashiwat Tabassum Waishy· International Conference on...· 0 citations
A maximum matching algorithm for channel allocation with a faster convergence rate that divides the entire set of cellular users and D2D groups into overlapping clusters based on channel gains and utilizes the Kuhn-Munkres algorithm for the best channel allocation to the D2D groups within the same cluster.