Holographic MIMO for Next Generation Non-Terrestrial Networks: Motivation, Opportunities, and Challenges

In this article, we propose the integration of the Holographic Multiple Input Multiple Output (HMIMO) as a transformative solution for next generation Non-Terrestrial Networks (NTNs), addressing key challenges, such as high hardware costs, launch expenses, and energy inefficiency. Traditional NTNs are constrained by the financial and operational limitations posed by bulky, costly antenna systems, alongside the complexities of maintaining effective communications in space. HMIMO offers a novel approach utilizing compact and lightweight arrays of densely packed radiating elements with real-time reconfiguration capabilities, thus, capable of optimizing system performance under dynamic conditions such as varying orbital dynamics and Doppler shifts. By replacing conventional antenna systems with HMIMO, the complexity and cost of satellite manufacturing and launch can be substantially reduced, enabling more streamlined and cost-effective satellite designs. This advancement holds significant potential to democratize space communications, making them accessible to a broader range of stakeholders, including smaller nations and commercial enterprises. Moreover, the inherent capabilities of HMIMO in enhancing energy efficiency, scalability, and adaptability position this technology as a key enabler of new use cases and sustainable satellite operations.

Integration of Beyond Diagonal RIS and UAVs in 6G NTNs: Enhancing Aerial Connectivity

The reconfigurable intelligent surface (RIS) technology shows great potential in sixth-generation (6G) terrestrial and non-terrestrial networks (NTNs) since it can effectively change wireless settings to improve connectivity. Extensive research has been conducted on traditional RIS systems with diagonal phase response matrices. The straightforward RIS architecture, while cost-effective, has restricted capabilities in manipulating the wireless channels. The beyond diagonal reconfigurable intelligent surface (BD-RIS) greatly improves control over the wireless environment by utilizing interconnected phase response elements. This work proposes the integration of unmanned aerial vehicle (UAV) communications and BD-RIS in 6G NTNs, which has the potential to further enhance wireless coverage and spectral efficiency. We begin with the preliminaries of UAV communications and then discuss the fundamentals of BD-RIS technology. Subsequently, we discuss the potential of BD-RIS and UAV communications integration. We then proposed a case study based on UAV-mounted transmissive BD-RIS communication. Finally, we highlight future research directions and conclude this work.

CR-Enabled NOMA Integrated Non-Terrestrial IoT Networks with Transmissive RIS

This work proposes a T-RIS-equipped LEO satellite communication in cognitive radio-enabled integrated NTNs. In the proposed system, a GEO satellite operates as a primary network, and a T-RIS-equipped LEO satellite operates as a secondary IoT network. The objective is to maximize the sum rate of T-RIS-equipped LEO satellite communication using downlink NOMA while ensuring the service quality of GEO cellular users. Our framework simultaneously optimizes the total transmit power of LEO, NOMA power allocation for LEO IoT (LIoT) and T-RIS phase shift design subject to the service quality of LIoT and interference temperature to the primary GEO network. To solve the non-convex sum rate maximization problem, we first adopt successive convex approximations to reduce the complexity of the formulated optimization. Then, we divide the problem into two parts, i.e., power allocation of LEO and phase shift design of T-RIS. The power allocation problem is solved using KKT conditions, while the phase shift problem is handled by Taylor approximation and semidefinite programming. Numerical results are provided to validate the proposed optimization framework.

Efficient Resource Allocation and User Association in NOMA-Enabled Vehicular-Aided HetNets with High Altitude Platforms

The increasing demand for massive connectivity and high data rates has made the efficient use of existing spectrum resources an increasingly challenging problem. Non-orthogonal multiple access (NOMA) is a potential solution for future heterogeneous networks (HetNets) due to its high capacity and spectrum efficiency. In this study, we analyze an uplink NOMA-enabled vehicular-aided HetNet, where multiple vehicular user equipment (VUEs) share the access link spectrum, and a high-altitude platform (HAP) communicates with roadside units (RSUs) through a backhaul communication link. We propose an improved algorithm for user association that selects VUEs for HAPs based on channel coefficient ratios and terrestrial VUEs based on a caching-state backhaul communication link. The joint optimization problems aim to maximize a utility function that considers VUE transmission rates and cross-tier interference while meeting the constraints of backhaul transmission rates and QoS requirements of each VUE. The joint resource allocation optimization problem consists of three sub-problems: bandwidth allocation, user association, and transmission power allocation. We derive a closed-form solution for bandwidth allocation and solve the transmission power allocation sub-problem iteratively using Taylor expansion to transform a non-convex term into a convex one. Our proposed three-stage iterative algorithm for resource allocation integrates all three sub-problems and is shown to be effective through simulation results. Specifically, the results demonstrate that our solution achieves performance improvements over existing approaches.

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.

Dynamic Resource Management in Integrated NOMA Terrestrial-Satellite Networks using Multi-Agent Reinforcement Learning

This study introduces a resource allocation framework for integrated satellite-terrestrial networks to address these challenges. The framework leverages local cache pool deployments and non-orthogonal multiple access (NOMA) to reduce time delays and improve energy efficiency. Our proposed approach utilizes a multi-agent enabled deep deterministic policy gradient algorithm (MADDPG) to optimize user association, cache design, and transmission power control, resulting in enhanced energy efficiency. The approach comprises two phases: User Association and Power Control, where users are treated as agents, and Cache Optimization, where the satellite (Bs) is considered the agent. Through extensive simulations, we demonstrate that our approach surpasses conventional single-agent deep reinforcement learning algorithms in addressing cache design and resource allocation challenges in integrated terrestrial-satellite networks. Specifically, our proposed approach achieves significantly higher energy efficiency and reduced time delays compared to existing methods.

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.

Enhancing Congestion Control to Improve User Experience in IoT Using LSTM Network

This study suggests a new strategy for improving congestion control by deploying Long Short-Term Memory (LSTM) networks. LSTMs are recurrent neural networks (RNN), that excel at capturing temporal relationships and patterns in data. IoT-specific data such as network traffic patterns, device interactions, and congestion occurrences are gathered and analyzed. The gathered data is used to create and train an LSTM network architecture specific to the IoT environment. Then, the LSTM model's predictive skills are incorporated into the congestion control methods. This work intends to optimize congestion management methods using LSTM networks, which results in increased user satisfaction and dependable IoT connectivity. Utilizing metrics like throughput, latency, packet loss, and user satisfaction, the success of the suggested strategy is evaluated. Evaluation of performance includes rigorous testing and comparison to conventional congestion control methods.

Reconfigurable Intelligent Surfaces for 6G Non-Terrestrial Networks: Assisting Connectivity from the Sky

This paper studies the potential of RIS-integrated NTNs to revolutionize the next-generation connectivity. First, it discusses the fundamentals of RIS technology. Secondly, it delves into reporting the recent advances in RIS-enabled NTNs. Subsequently, it presents a novel framework based on the current state-of-the-art for low earth orbit satellites (LEO) communications, wherein the signal received at the user terminal traverses both a direct link and an RIS link, and the RIS is mounted on a high-altitude platform (HAP) situated within the stratosphere. Finally, the paper concludes by highlighting open challenges and future research directions to revolutionize the realm of RIS-integrated NTNs.

Reconfigurable Intelligent Surfaces Enhanced NOMA D2D Communications Underlaying UAV Networks

Device-to-device (D2D) communications offers high spectral efficiency, low energy consumption and transmission latency. However, one of the main limitations of D2D communications is co-channel interference from underlaying wireless system. Reconfigurable intelligent surfaces (RIS) is a promising technology because it can manipulate the electromagnetic waves in their environment to overcome interference and enhance wireless communications. This paper considers RIS enhanced D2D communications underlaying unmanned aerial vehicle (UAV) networks with non-orthogonal multiple access (NOMA). The objective is to maximize the sum rate of NOMA D2D communications by simultaneously optimizing the power budget of D2D transmitter, NOMA power allocation coefficients of D2D receivers and passive beamforming of RIS while guaranteeing the quality of services of UAV user. Due to non-convexity, the optimization problem is intractable and challenging to handle. Therefore, it is solved in two parts using alternating optimization. Simulation results unviel the performance of the proposed RIS enhanced D2D communications scheme. Results demonstrate that the proposed scheme achieves 15\% and 27\% higher sum rates compared to the fixed power D2D and orthogonal D2D schemes.