Movable antenna (MA) enable adaptive reconfiguration of array geometry by dynamically adjusting antenna element positions, thereby providing new spatial degrees of freedom for unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) systems. However, since the UAV trajectory changes the time-varying link distances and spatial directions, it is inherently coupled with MA position optimization. Most existing studies separately design antenna positioning, beamforming, and UAV trajectory, which limits the achievable average sum rate under sensing constraints. To address this issue, this paper proposes an MA-enabled UAV-ISAC system, where MA element positions are dynamically adjusted to enhance channel reconfigurability and spatial coordination capability. Under the sensing performance constraint, transmit power constraint, and UAV propulsion energy budget constraint, a non-convex joint optimization problem is formulated to maximize the average sum rate of the system by jointly optimizing MA positions, beamforming, and UAV trajectory. To solve this problem, an alternating optimization algorithm is developed by integrating particle swarm optimization, successive convex approximation, and semidefinite relaxation. Simulation results demonstrate that the proposed scheme significantly outperforms benchmark schemes with fixed-position antennas, random-position antennas, and fixed trajectory in terms of average sum rate, thereby verifying its effectiveness in enhancing the communication-sensing synergy of UAV-ISAC systems.
This paper investigates hybrid reconfigurable intelligent surfaces (RIS) integrating sub-connected (SC) active partitions with fully-connected (FC) active, SC-active, or passive designs for multi-user multiple-input multiple-output dual-functional radar-communication (DFRC) systems. We formulate weighted DFRC optimization and energy efficiency maximization under DFRC constraints problems to jointly design DFRC waveforms and RIS beamforming. Efficient alternating optimization algorithms with proven convergence are developed for the proposed hybrid architectures. Numerical results demonstrate substantial performance gains: compared to conventional FC-active RIS, the hybrid SC-active/SC-active design achieves about 80% higher energy efficiency while preserving competitive sumrate and radar-detection performance. Relative to purely passive RIS, hybrid SC-active/passive configurations provide approximately 50–70% energy-efficiency gains and 25–35% higher sum-rate over the simulated distance range. Against no-RIS DFRC baselines, even minimal SC-active partitioning, with 25% of the RIS elements assigned to the active subsurface, yields roughly 30–50% sum-rate enhancement, depending on the base station–user distance. The proposed designs exhibit superior hardware efficiency, with SC-active-based architectures requiring substantially fewer RF chains than FC-active-based alternatives while delivering competitive or superior performance. Results validate that hybrid SC-active RIS-based architectures enable scalable, power-efficient DFRC systems suitable for sustainable next-generation wireless networks.
We consider the downlink of a multi-cell massive multi-input-multi-output wireless network with an intelligent reflecting surface (IRS) in each cell. Each base station (BS) serves its users via its IRS by using non-orthogonal multiple access (NOMA). We derive a closed form spectral efficiency (SE) lower bound for our system by considering spatially-correlated Rician channels. This lower bound is then used to maximize the non-concave global energy efficiency (GEE) metric in two steps. In the first step, we optimize the BS power by leveraging the minorization-maximization (MM) framework, which transforms a non-concave optimization into a sequence of surrogate concave problems. We construct a novel surrogate function to develop the MM framework. In the second step, we design a novel low-complexity accelerated gradient projection algorithm to jointly optimize phases of IRS in all the cells. The algorithm accelerates its convergence by using Nesterov extrapolation. We analytically prove the algorithm convergence. We also show that for uncorrelated Rayleigh channels, SE becomes independent of the IRS phase. It is, therefore, crucial to consider spatial correlation while analyzing and developing IRS-based NOMA systems. We numerically investigate multiple aspects which are crucial for realizing tangible SE and GEE gains while designing multi-cell multi-IRS NOMA systems.
Next-generation networks aiming to support ultra-reliable, low-latency, and high-throughput services often operate in the THz band where high absorption losses necessitate dense gNodeB (gNB) deployment. While this enhances coverage and Quality-of-Service (QoS), it also increases the network energy consumption. A commonly explored solution in this context is the gNB sleep scheduling, which, although effective in conserving energy, introduces additional latency, thereby creating an Energy-Delay Dilemma in time-sensitive, energy-aware communication scenarios. To address this, we propose EnRoute, an intelligent framework for unified routing and scheduling in heterogeneous networks, designed to optimize the route energy efficiency while simultaneously meeting the traffic-specific stringent latency and reliability constraints. We first formulate the Time-Sensitive Energy-Efficient Routing Problem (TSEE-RP), and prove its NP-hardness. Subsequently, EnRoute offers a solution that integrates Deep Q-Network (DQN), k-means clustering, and an adaptive fair scheduling policy to tackle the energy-delay dilemma while preserving the performance of throughput-intensive applications. Our performance analysis shows that EnRoute outperforms state-of-the-art methods with up to 98.4% fewer latency violations, 46.5% higher energy efficiency, and 72.8% lower model energy cost, while ensuring Key Performance Index (KPI) compliance across mixed traffic.
