Exploiting Symmetric Non-Convexity for Multi-Objective Symbol-Level DFRC Signal Design

Symbol-level precoding (SLP) is a promising solution for addressing the inherent interference problem in dual-functional radar-communication (DFRC) signal designs. This paper considers an SLP-DFRC signal design problem which optimizes the radar performance under communication performance constraints. We show that a common phase modulation applied to the transmit signals from an antenna array does not affect the performance of different radar sensing metrics, including beampattern similarity, signal-to-interference-plus-noise ratio (SINR), and Cram\'er-Rao lower bound (CRLB). We refer to this as symmetric-rotation invariance, upon which we develop low-complexity yet efficient DFRC signal design algorithms. More specifically, we propose a symmetric non-convexity (SNC)-based DFRC algorithm that relies on the non-convexity of the radar sensing metrics to identify a set of radar-only solutions. Based on these solutions, we further exploit the symmetry property of the radar sensing metrics to efficiently design the DFRC signal. We show that the proposed SNC-based algorithm is versatile in the sense that it can be applied to the DFRC signal optimization of all three sensing metrics mentioned above (beampattern, SINR, and CRLB). In addition, since the radar sensing metrics are independent of the communication channel and data symbols, the set of radar-only solutions can be constructed offline, thereby reducing the computational complexity. We also develop an accelerated SNC-based algorithm that further reduces the complexity. Finally, we numerically demonstrate the superiority of the proposed algorithms compared to existing methods in terms of sensing and communication performance as well as computational requirements.

Grant-Free Random Access in Uplink LEO Satellite Communications with OFDM

This paper investigates joint device activity detection and channel estimation for grant-free random access in Low-earth orbit (LEO) satellite communications. We consider uplink communications from multiple single-antenna terrestrial users to a LEO satellite equipped with a uniform planar array of multiple antennas, where orthogonal frequency division multiplexing (OFDM) modulation is adopted. To combat the severe Doppler shift, a transmission scheme is proposed, where the discrete prolate spheroidal basis expansion model (DPS-BEM) is introduced to reduce the number of unknown channel parameters. Then the vector approximate message passing (VAMP) algorithm is employed to approximate the minimum mean square error estimation of the channel, and the Markov random field is combined to capture the channel sparsity. Meanwhile, the expectation-maximization (EM) approach is integrated to learn the hyperparameters in priors. Finally, active devices are detected by calculating energy of the estimated channel. Simulation results demonstrate that the proposed method outperforms conventional algorithms in terms of activity error rate and channel estimation precision.

UAV-Assisted 5G Networks: Mobility-Aware 3D Trajectory Optimization and Resource Allocation for Dynamic Environments

This paper proposes a framework for robust design of UAV-assisted wireless networks that combine 3D trajectory optimization with user mobility prediction to address dynamic resource allocation challenges. We proposed a sparse second-order prediction model for real-time user tracking coupled with heuristic user clustering to balance service quality and computational complexity. The joint optimization problem is formulated to maximize the minimum rate. It is then decomposed into user association, 3D trajectory design, and resource allocation subproblems, which are solved iteratively via successive convex approximation (SCA). Extensive simulations demonstrate: (1) near-optimal performance with $\epsilon \approx 0.67\%$ deviation from upper-bound solutions, (2) $16\%$ higher minimum rates for distant users compared to non-predictive 3D designs, and (3) $10-30\%$ faster outage mitigation than time-division benchmarks. The framework's adaptive speed control enables precise mobile user tracking while maintaining energy efficiency under constrained flight time. Results demonstrate superior robustness in edge-coverage scenarios, making it particularly suitable for $5G/6G$ networks.

LEO Satellite-Enabled Random Access with Large Differential Delay and Doppler Shift

This paper investigates joint device identification, channel estimation, and symbol detection for LEO satellite-enabled grant-free random access systems, specifically targeting scenarios where remote Internet-of-Things (IoT) devices operate without global navigation satellite system (GNSS) assistance. Considering the constrained power consumption of these devices, the large differential delay and Doppler shift are handled at the satellite receiver. We firstly propose a spreading-based multi-frame transmission scheme with orthogonal time-frequency space (OTFS) modulation to mitigate the doubly dispersive effect in time and frequency, and then analyze the input-output relationship of the system. Next, we propose a receiver structure based on three modules: a linear module for identifying active devices that leverages the generalized approximate message passing algorithm to eliminate inter-user and inter-carrier interference; a non-linear module that employs the message passing algorithm to jointly estimate the channel and detect the transmitted symbols; and a third module that aims to exploit the three dimensional block channel sparsity in the delay-Doppler-angle domain. Soft information is exchanged among the three modules by careful message scheduling. Furthermore, the expectation-maximization algorithm is integrated to adjust phase rotation caused by the fractional Doppler and to learn the hyperparameters in the priors. Finally, the convolutional neural network is incorporated to enhance the symbol detection. Simulation results demonstrate that the proposed transmission scheme boosts the system performance, and the designed algorithms outperform the conventional methods significantly in terms of the device identification, channel estimation, and symbol detection.

Massive MIMO-OTFS-Based Random Access for Cooperative LEO Satellite Constellations

This paper investigates joint device identification, channel estimation, and symbol detection for cooperative multi-satellite-enhanced random access, where orthogonal time-frequency space modulation with the large antenna array is utilized to combat the dynamics of the terrestrial-satellite links (TSLs). We introduce the generalized complex exponential basis expansion model to parameterize TSLs, thereby reducing the pilot overhead. By exploiting the block sparsity of the TSLs in the angular domain, a message passing algorithm is designed for initial channel estimation. Subsequently, we examine two cooperative modes to leverage the spatial diversity within satellite constellations: the centralized mode, where computations are performed at a high-power central server, and the distributed mode, where computations are offloaded to edge satellites with minimal signaling overhead. Specifically, in the centralized mode, device identification is achieved by aggregating backhaul information from edge satellites, and channel estimation and symbol detection are jointly enhanced through a structured approximate expectation propagation (AEP) algorithm. In the distributed mode, edge satellites share channel information and exchange soft information about data symbols, leading to a distributed version of AEP. The introduced basis expansion model for TSLs enables the efficient implementation of both centralized and distributed algorithms via fast Fourier transform. Simulation results demonstrate that proposed schemes significantly outperform conventional algorithms in terms of the activity error rate, the normalized mean squared error, and the symbol error rate. Notably, the distributed mode achieves performance comparable to the centralized mode with only two exchanges of soft information about data symbols within the constellation.

Ubiquitous Integrated Sensing and Communications for Massive MIMO LEO Satellite Systems

The next sixth generation (6G) networks are envisioned to integrate sensing and communications in a single system, thus greatly improving spectrum utilization and reducing hardware costs. Low earth orbit (LEO) satellite communications combined with massive multiple-input multiple-output (MIMO) technology holds significant promise in offering ubiquitous and seamless connectivity with high data rates. Existing integrated sensing and communications (ISAC) studies mainly focus on terrestrial systems, while operating ISAC in massive MIMO LEO satellite systems is promising to provide high-capacity communication and flexible sensing ubiquitously. In this paper, we first give an overview of LEO satellite systems and ISAC and consider adopting ISAC in the massive MIMO LEO satellite systems. Then, the recent research advances are presented. A discussion on related challenges and key enabling technologies follows. Finally, we point out some open issues and promising research directions.

Enhancing Indoor and Outdoor THz Communications with Beyond Diagonal-IRS: Optimization and Performance Analysis

This work investigates the application of Beyond Diagonal Intelligent Reflective Surface (BD-IRS) to enhance THz downlink communication systems, operating in a hybrid: reflective and transmissive mode, to simultaneously provide services to indoor and outdoor users. We propose an optimization framework that jointly optimizes the beamforming vectors and phase shifts in the hybrid reflective/transmissive mode, aiming to maximize the system sum rate. To tackle the challenges in solving the joint design problem, we employ the conjugate gradient method and propose an iterative algorithm that successively optimizes the hybrid beamforming vectors and the phase shifts. Through comprehensive numerical simulations, our findings demonstrate a significant improvement in rate when compared to existing benchmark schemes, including time- and frequency-divided approaches, by approximately $30.5\%$ and $70.28\%$ respectively. This underscores the significant influence of IRS elements on system performance relative to that of base station antennas, highlighting their pivotal role in advancing the communication system efficacy.

Integrated Communications and Localization for Massive MIMO LEO Satellite Systems

Integrated communications and localization (ICAL) will play an important part in future sixth generation (6G) networks for the realization of Internet of Everything (IoE) to support both global communications and seamless localization. Massive multiple-input multiple-output (MIMO) low earth orbit (LEO) satellite systems have great potential in providing wide coverage with enhanced gains, and thus are strong candidates for realizing ubiquitous ICAL. In this paper, we develop a wideband massive MIMO LEO satellite system to simultaneously support wireless communications and localization operations in the downlink. In particular, we first characterize the signal propagation properties and derive a localization performance bound. Based on these analyses, we focus on the hybrid analog/digital precoding design to achieve high communication capability and localization precision. Numerical results demonstrate that the proposed ICAL scheme supports both the wireless communication and localization operations for typical system setups.

Joint Computation and Communication Resource Optimization for Beyond Diagonal UAV-IRS Empowered MEC Networks

Intelligent Reconfigurable Surfaces (IRS) are crucial for overcoming challenges in coverage, capacity, and energy efficiency beyond 5G (B5G). The classical IRS architecture, employing a diagonal phase shift matrix, hampers effective passive beamforming manipulation. To unlock its full potential, Beyond Diagonal IRS (BD-IRS or IRS 2.0) emerges as a revolutionary member, transcending limitations of the diagonal IRS. This paper introduces BD-IRS deployed on unmanned aerial vehicles (BD-IRS-UAV) in Mobile Edge Computing (MEC) networks. Here, users offload tasks to the MEC server due to limited resources and finite battery life. The objective is to minimize worst-case system latency by optimizing BD-IRS-UAV deployment, local and edge computational resource allocation, task segmentation, power allocation, and received beamforming vector. The resulting non-convex/non-linear NP-hard optimization problem is intricate, prompting division into two subproblems: 1) BD-IRS-UAV deployment, local and edge computational resources, and task segmentation, and 2) power allocation, received beamforming, and phase shift design. Standard optimization methods efficiently solve each subproblem. Monte Carlo simulations provide numerical results, comparing the proposed BD-IRS-UAV-enabled MEC optimization framework with various benchmarks. Performance evaluations include comparisons with fully-connected and group-connected architectures, single-connected diagonal IRS, and binary offloading, edge computation, fixed computation, and local computation frameworks. Results show a 7.25% lower latency and a 17.77% improvement in data rate with BD-IRS compared to conventional diagonal IRS systems, demonstrating the effectiveness of the proposed optimization framework.

Deep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things

Backscatter communication (BC) technology offers sustainable solutions for next-generation Internet-of-Things (IoT) networks, where devices can transmit data by reflecting and adjusting incident radio frequency signals. In parallel to BC, deep reinforcement learning (DRL) has recently emerged as a promising tool to augment intelligence and optimize low-powered IoT devices. This article commences by elucidating the foundational principles underpinning BC systems, subsequently delving into the diverse array of DRL techniques and their respective practical implementations. Subsequently, it investigates potential domains and presents recent advancements in the realm of DRL-BC systems. A use case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is meticulously examined to highlight its potential. Lastly, this study identifies and investigates salient challenges and proffers prospective avenues for future research endeavors.