2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 8998-9016· 0 citations· 46 references
TL;DR
A cooperative resource-allocation framework for C-NOMA-enabled multi-uncrewed aerial vehicle (UAV) millimeter-wave (mmWave) networks with two-dimensional (2D) HB and an EE-maximization problem that jointly considers user subclustering, power allocation (PA), and subchannel assignment (SA) under transmit-power and SIC constraints is developed.
Abstract
Sixth-generation (6G) wireless networks require massive connectivity, high spectral efficiency (SE), and energy efficiency (EE). Although conventional non-orthogonal multiple access (NOMA) improves spectrum utilization by allowing multiple users to share the same subchannel through power-domain multiplexing, applying NOMA to large user groups significantly increases successive interference cancellation (SIC) complexity and intra-group interference. To address this limitation, clustered NOMA (C-NOMA) groups users into small subclusters where SIC is performed over fewer users, thereby improving scalability while reducing decoding complexity. Combining C-NOMA with hybrid beamforming (HB) further enhances SE and lowers power consumption by serving each subcluster through a dedicated analog beam with fewer radio-frequency chains. In this paper, we develop a cooperative resource-allocation framework for C-NOMA-enabled multi-uncrewed aerial vehicle (UAV) millimeter-wave (mmWave) networks with two-dimensional (2D) HB. UAVs equipped with 2D uniform planar array antennas serve as aerial base stations to enhance line-of-sight (LoS) connectivity. We formulate an EE-maximization problem that jointly considers user subclustering, power allocation (PA), and subchannel assignment (SA) under transmit-power and SIC constraints. User subclustering is first performed through head selection and channel-disparity-based pairing to enable efficient C-NOMA transmission. Given the resulting structure, the 2D HB and joint PA/SA optimization problem is solved via a two-stage approach: a heuristic 2D HB construction followed by a multi-agent deep deterministic policy gradient (MADDPG) algorithm for cooperative PA and SA optimization under centralized training and decentralized execution (CTDE). Simulation results demonstrate that the proposed framework consistently improves EE over representative benchmarks across different network configurations.
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
An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.
Shunxuan Wang, Qi Zhu· Italian National Conference...· 0 citations
This paper proposes a tri-hybrid beamforming (tri-HBF) scheme with antenna-selection (AS)-based reconfigurable sub-arrays for full-duplex (FD) massive multiple-input multiple-output (mMIMO) systems. A sub-connected HBF architecture is adopted, where AS is performed in a group-wise manner to avoid excessive switch-network and routing complexity. An alternating optimization (AO) algorithm is developed to jointly optimize the i) active antenna subsets considering a self-interference (SI)-aware utility, ii) analog beamformers through projected gradient ascent (PGA), iii) digital precoders/combiners via SI-aware regularized zero-forcing (RZF) and minimum mean-square error (MMSE) updates, and iv) DL/UL power allocation by successive convex approximation (SCA). To capture realistic electromagnetic coupling in FD mMIMO operation, experimental SI channels based on an 8x8 Tx-8x8 Rx FD array prototype are incorporated into the study. The proposed AS-aided tri-HBF optimization scheme exhibits robust convergence across various base station configurations and effectively balances desired-signal enhancement, SI mitigation, and multi-user interference suppression in FD mMIMO operation. Illustrative results show that selective activation can outperform full-array activation, achieving a 21.3% higher average sum-rate and a more consistent performance across user realizations, with power-efficiency benefits by reducing the active paths. A comprehensive study is conducted to characterize how the number of activated antennas affects the achievable rate, user-channel coherence, and SI suppression gain. Compared with various selection baselines, it achieves a 45.1% improvement in average sum-rate, with average DL and UL rate gains of 36.9% and 82.9%, respectively. In addition, beam-level isolation better than 63 dB is achieved, further confirming the effectiveness of the proposed SI-aware design.
Secure resource allocation in future sixth‐generation (6G) networks is a significant challenge due to the increasing demand for efficient spectrum utilization and advanced multimedia services. Emerging 6G technologies, including cognitive radios (CRs) for dynamic spectrum access, hybrid multiple access (H‐MA) that integrates orthogonal multiple access (OMA) and non‐orthogonal multiple access (NO‐MA), and clustering techniques for efficient user grouping, offer promising solutions to address these challenges. The collective integration of these technologies within cognitive radio networks (CRNs) is referred to as a 6G CRN framework. In this paper, a novel cluster‐assisted CR‐enabled downlink hybrid multiple access (CCRDHMA) scheme is proposed in the presence of eavesdroppers to maximize the sum secrecy rate (SSR). The formulated optimization problem incorporates minimum quality of service (QoS) requirements while satisfying false detection (FD) and missed detection (MD) constraints. To efficiently solve the resulting mixed‐integer nonlinear programming (MINLP) optimization problem, a low‐complexity ϵ$$ \epsilon $$ ‐optimal outer approximation algorithm (OAA) is employed. The performance of the proposed CCRDHMA scheme is evaluated against conventional OMA‐assisted CRN, NO‐MA‐assisted CRN, and other existing secure resource allocation approaches. Extensive simulation results demonstrate that the proposed CCRDHMA framework significantly improves SSR while achieving superior performance in Key Performance Measures (KPMs), including secondary mobile handsets (SMHs) admission within clusters, association with secondary tower (ST), QoS satisfaction, fair power allocation (PA), FD, and MD. Furthermore, the ϵ$$ \epsilon $$ ‐optimal OAA achieves near‐optimal solutions with ϵ=10−3$$ \epsilon =1{0}^{-3} $$ , highlighting its computational efficiency and practical applicability for secure resource allocation in future 6G CRNs.
Umar Ghafoor· International Journal of Com...· 0 citations
Simulation results demonstrate that the proposed GNN-RSMA interference management algorithm outperforms conventional multiple access schemes while achieving fairness and worst-user performance comparable to successive convex approximation (SCA)-based optimization at only a fraction of its computational cost.
Afsoon Alidadi Shamsabadi, Animesh Yadav, H. Yanikomeroglu· 0 citations
Due to their resilience and global coverage, satellite networks are poised to become a key component for non-terrestrial networks in the future. However, given the scarcity of spectrum resources, the dense deployment of low Earth orbit (LEO) satellites introduces significant interference challenges. Meanwhile, the limited computing power and backhaul capacity of satellites have become bottlenecks hindering the development of advanced interference mitigation techniques. This paper studies beamforming in GEO-LEO heterogeneous multi-satellite systems. For the GEO system, we develop a multicast beamforming approach based on a nonlinear eigenvalue problem (NEPv) for beam direction design and Lagrange dual decomposition (LDD) for power allocation. For the LEO system, we propose a general distributed beamforming framework and two distributed beamforming methods. Specifically, we first leverage equivalent multi-dimensional fractional programming (FP) to decompose the objective function. The resulting subproblems are then optimized in a distributed manner across multiple satellites via the parallel block coordinate descent (PBCD) method. For the distributed optimization subproblems, we derive semi-closed-form solutions using Lagrangian dual ascent (LDA) and alternating direction method of multipliers (ADMM) for scenarios without and with GEO-LEO interference avoidance, respectively. Simulation results show that the proposed NEPv-LDD method strictly satisfies the QoS constraints of users and achieves near-optimal performance with low complexity. For the LEO beamforming, the developed distributed FP (DiFP) framework exhibits strong scalability in large-scale constellations. Built upon the DiFP framework, the proposed DiFP-NoSIA incurs almost no performance loss, while DiFP-ADMM shows only an 8.58% performance degradation compared to the centralized benchmark.
Xin Chen, Zhiyong Luo· IEEE Transactions on Wireles...· 0 citations
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