Unmanned Aerial Vehicle (UAV) can enhance communication quality in wireless systems. It is helpful to address the blockage problem in mountainous regions. However, it has high energy consumption. In this paper, we propose a novel blockage model converted blockage region constraints into linear constraint. Then, the joint resource allocation and trajectory design scheme is proposed to maximize energy efficiency (EE). Specifically, we model the blockage effect caused by mountains. The Dinkelbach algorithm is designed to address the fractional objective function for EE. Then, we develop an iterative block coordinate descent (I-BCD) algorithm that alternately optimizes resource allocation and trajectory design subproblems. The resource allocation subproblem employs a dual-loop structure with Lagrangian duality to obtain closed-form solutions for power and bandwidth allocation. Meanwhile, the trajectory optimization uses successive convex approximation (SCA) to transform non-convex constraints into convex optimization problems. Simulation results demonstrate that the proposed scheme improves the EE compared with the benchmark schemes for UAV-assisted communications in mountainous regions.

Recently, the application of deep learning (DL) technologies to communication systems has garnered significant attention. The Mamba architecture can adaptively adjust the model parameters based on the dynamic changes of the sequence, effectively capturing complex dependencies in long sequences. Furthermore, Mamba provides similar modeling capabilities to Transformers while ensuring near-linear scalability with respect to sequence length. In this paper, we propose a Mambabased detector for orthogonal time frequency space with index modulation (OTFS-IM), termed as MambaIM, that can extract features and establish the global input-output relations from the received signal. Simulation results demonstrate that the proposed MambaIM detector outperforms current DL-based detectors in terms of bit error rate (BER) performance.

Abstract In the field of single image super‐resolution, the prevalent use of convolutional neural networks (CNN) typically assumes a simplistic bicubic downsampling model for image degradation. This assumption misaligns with the complex degradation processes encountered in medical imaging, leading to a performance gap when these algorithms are applied to real medical scenarios. Addressing this critical discrepancy, our study introduces a novel degradation comparative learning framework meticulously designed for the nuanced degradation characteristics of medical images within the Internet of Medical Things (IoMT). Unlike traditional CNN‐based super‐resolution approaches that homogeneously process image channels, our method acknowledges and leverages the disparity in informational content across channels. We present a blind image super‐resolution technique, underpinned by edge reconstruction and an innovative image feature supplement module. This approach not only preserves but enriches texture details, crucial for the accurate analysis of medical images in the IoMT. Comparative evaluations of our model against existing blind super‐resolution methods, utilizing both natural image testing datasets and medical images, demonstrate its superior performance. Notably, our approach exhibits remarkable proficiency in stably restoring various degraded super‐resolution images, a critical requirement in the IoMT context. Experimental results demonstrate that our method is superior to the current state‐of‐the‐art methods, marking a significant advancement in the field of medical image super‐resolution.

Licensed-Assisted Access Long-Term Evolution (LAA- LTE) framework can offload excessive data stream to the unlicensed spectrum. Devices from different operators will compete for the same band and have to endure a more complex and congested channel conditions. Therefore a rapid and efficient access mechanism should be introduced in the LAA framework. Based on the Type B LBT channel access scheme, this work proposes an Energy-Based Signal Assisted Access (EBSA) mechanism. The EBSA mechanism can realize the rapid information transfer between devices belonging to different operators. Then a Markov chain model is set up and used to analyze this mechanism theoretically. Simulation results verify the validity of the theoretical analysis, and in the ultra-dense employed LAA network, the proposed method is efficient compared with the existing access mechanisms.

We present optimization algorithm for relay (RLY) selection and, source (SRC) and RLY power allocations in a cooperative multicast millimeter-wave (mmWave) system with non-line-of-sight (non-LoS) sensing. We show that there is a significant benefit to the system outage performance by allocating powers for the SRC and the RLYs selected from the candidates availing of line-of-sight (LoS) paths. Specifically, we consider the joint optimization of RLY selection, and, SRC and RLY power allocations to minimize the largest outage probability among all the common-multicast group (co-MGroup) users. The joint optimization problem is non-convex and the complexity of finding the optimal solution is extremely high. Using the dual-step iterative optimization (DIO) algorithm, the joint problem is decomposed into a non-convex RLY selection, and, a convex SRC and RLY power allocation problem. By exploiting the search method and the Lagrange multipliers algorithm, we present efficient algorithms that yield the optimal solutions for both RLY selection (non-convex) and, SRC and RLY power allocation problems. The simulation results indicate significant improvements both on outage probability and power consumption performances over the traditional two-stage multicasting.

In this work, we consider outage performance for a 5G wireless cellular network consisting of multiple cooperative multicast groups (MGroups) under kinds of fading channels, which allows users adhering to different MGroups can be served simultaneously. Based on the law of total probability theory, we first propose a new analytical approach to derive a new probability density equality lemma, which can be used to upper bound average outage probability of multiple MGroups (MMGroups) systems with interference-aware spectrum sharing transmissions in the case that interference channel state information (CSI) is known. After then, based on the proposed probability density equality lemma, we also analyze average outage probability upper bound under kinds of fading channels with inter-MGroup interferences. The simulated results reveal that the derived upper bounds indeed provide upper bounds over the average outage probability of MMGroups systems operated on kinds of fading channels, and these bounds become tight when interferences decrease or outage thresholds drop.

This paper proposes a novel relay selection strategy based on the feedback beamforming (BF) information for 60GHz millimeter-wave (mmWave) wireless communication systems under non-line-of-sight (NLoS) condition. First, an approach of collecting instantaneous channel state information (CSI) through overheard during BF is devised. Second, with the aim of maximizing overall network throughput capacity, the optimal relay selection issue for NLoS links is formulated as a bipartite graph, and the Kuhn Munkres (KM) algorithm is provided to resolve it. Third, both theoretical analysis and simulation results show that the improvements achieved over the best harmonic mean selection scheme in terms of overall network throughput capacity and outage probability.

In this paper, a joint design of axis alignment and positioning under non-line-of-sight (NLoS) indoor conditions or 60 GHz millimeter-wave (mmWave) wireless local area networks/wireless personal area networks (WLANs/WPANs) is proposed. First, an axis alignment scheme called rotation vector method is proposed, in which the newly joining station (STA) rotates x- and y-axis based on the proposed two-step-wisely refinement principle to make its axis aligned with the personal basic service set (PBSS) control point/access point (PCP/AP). Second, the equipotential region (ER) based positioning approach is carried out for the newly joining STA in a conference room rich with obstacles. Finally, our proposed joint design is evaluated both through theoretical analysis and simulation. The results show that the number of rotation is reduced by the proposed axis alignment scheme without cost of the alignment accuracy under a same condition, and the positioning accuracy is increased by the proposed joint design.

This paper proposes a novel link scheduling strategy for concurrent transmission in millimeter-wave (mmWave) wireless personal area networks (WPANs) based on beamforming information. Firstly, we consider the signal-to-interference-plus noise ratio (SINR) as the links coexistence criterion, and derive the interference model with different antenna configurations. Then we describe how to use the beamforming information to calculate the SINR for concurrent transmission in a common service period (SP). A first-request-first-scheduling (FRFS) SINR based algorithm is applied to schedule links in SPs. Simulation results show that the proposed link scheduling scheme performs well and achieves a higher spatial multiplexing gain.