Learning-Initialized Trajectory Planning in Unknown Environments

Autonomous flight in unknown environments requires precise planning for both the spatial and temporal profiles of trajectories, which generally involves nonconvex optimization, leading to high time costs and susceptibility to local optima. To address these limitations, we introduce the Learning-Initialized Trajectory Planner (LIT-Planner), a novel approach that guides optimization using a Neural Network (NN) Planner to provide initial values. We first leverage the spatial-temporal optimization with batch sampling to generate training cases, aiming to capture multimodality in trajectories. Based on these data, the NN-Planner maps visual and inertial observations to trajectory parameters for handling unknown environments. The network outputs are then optimized to enhance both reliability and explainability, ensuring robust performance. Furthermore, we propose a framework that supports robust online replanning with tolerance to planning latency. Comprehensive simulations validate the LIT-Planner's time efficiency without compromising trajectory quality compared to optimization-based methods. Real-world experiments further demonstrate its practical suitability for autonomous drone navigation

Estimation of MIMO transmit-antenna number using higher-order moments based hypothesis testing

This letter proposes a higher-order-moment based hypothesis testing algorithm to estimate the transmit-antenna number for multiple-input multiple-output (MIMO) systems. Exploiting the asymptotic normal distribution of the moments composed by noise eigenvalues, the proposed algorithm improves the estimation performance for low signal-to-noise ratios (SNRs). Moreover, since the empirical distribution of the moments converges quickly to the normal distribution when the number of samples increases, our algorithm can make a reliable estimation in a sample starved condition. Computer simulations are provided to demonstrate that the proposed algorithm outperforms the conventional algorithms

Joint Power Allocation and Beamforming for Active IRS-aided Directional Modulation Network

To boost the secrecy rate (SR) of the conventional directional modulation (DM) network and overcome the double fading effect of the cascaded channels of passive intelligent reflecting surface (IRS), a novel active IRS-assisted DM system with a power adjusting strategy between transmitter and active IRS is proposed in this paper. Then, a joint optimization of maximizing the SR is cast by alternately optimizing the power allocation (PA) factors, transmit beamforming at the BS, and reflect beamforming at the active IRS, subject to the power constraint at IRS. To tackle the formulated non-convex optimization problem, a high-performance scheme of maximizing SR based on fractional programming (FP) and successive convex approximation (SCA) (Max-SR-FS) is proposed, where the FP and SCA methods are employed to optimize the PA factor of confidential message and the PA factor of power allocated to the BS, and the SCA algorithm is also utilized to design the transmit beamforming and phase shift matrix of the IRS. To reduce the high complexity, a low-complexity scheme, named maximizing SR based on derivative operation (DO) and general power iterative (GPI) (Max-SR-DG), is developed, where the DO and methods of the equal amplitude reflecting (EAR) and GPI are adopted to derive the PA factors and IRS phase shift matrix, respectively. Simulation results show that with the same power constraint, both the proposed schemes harvest about 12 percent and 70 percent rate gains over the equal PA and passive IRS schemes, respectively

Outage Performance Enhancement for NOMA Based Cooperative Relay Sharing Networks

This letter considers a non-orthogonal multiple access (NOMA) based cooperative relay sharing (CRS) network, where two sources communicate with their corresponding users over the same time and frequency via a shared decode-and-forward relay. A novel transmission scheme using max-min criterion based dynamic decoding order strategy is proposed to minimize the outage probability of the network at the cost of lower complexity and overhead. The closed-form expression of the overall outage probability for the proposed scheme is derived. Both analytical and simulation results show that the proposed scheme can achieve non-zero diversity order and almost the same outage performance as the dynamic power allocation based transmission scheme for NOMA based CRS networks

Sum-Rate Maximization Based Relay Selection for Cooperative NOMA over Nakagami-m Fading

This paper proposes a new sum-rate maximization based relay selection (RS) scheme for cooperative non-orthogonal multiple access (NOMA) networks over Nakagami-$m$ fading, where one base station communicates with two mobile users by means of multiple relays. The outage probability of the proposed scheme is derived in a closed-form expression, and the diversity order is also obtained. Simulation results are shown to compare the outage performance of the proposed scheme with that of the existing RS schemes