Research

The rapid evolution of wireless communication technologies has profoundly shaped the way we interact, work, and access information. From the early days of voice-only communication to the era of high-speed data transfer and Internet of Things (IoT) connectivity, wireless communication has become an indispensable part of modern life. By considering the IoT-dominated communication networks in the future, which is also one of the 6G visions, the use of satellites (LEO, MEO, and GEO) is undoubtedly required. This is because billions of cellular IoT connections including all the types such as massive, critical, industrial, and broadband are expected. In this context, Non-Terrestrial Network (NTN) can support the upsurge in the number of IoT devices by providing reliable and ubiquitous connectivity. NTNs encompass an array of innovative communication systems that operate beyond traditional terrestrial infrastructures, offering unprecedented possibilities for global connectivity via the internet, IoT, navigation, disaster resilience, remote access, Earth observation, and other scientific explorations such as interplanetary communications. Although NTNs have shown promising results, there are several challenges associated with their usage, such as signal propagation delays, interference, security, etc. Fortunately, Reconfigurable Intelligent Surface (RIS) technology effectively addresses challenges such as multi-path fading, attenuation, and interference, while also enabling widespread IoT connectivity essential for Industry 4.0 and 5.0, as envisioned by the development of 6G technology. To inspire future research in NTN-based 6G IoT networks, here are some promising usage scenarios and applications:

1. RIS-Aided Satellite NTNs and Interference Management: The LEO within the served area can transmit the corresponding information to the RIS-deployed LEO to serve IoT users in unserved areas. This can be possible via an inter-satellite link between LEOs. This approach reduces the number of unnecessary costly terrestrial infrastructures thanks to the nearby RIS-LEO satellites. Another paramount use case is to exploit RIS nodes in a spectrum-sharing integrated satellite-terrestrial network wherein the satellites and terrestrial infrastructures have the opportunity to use the same spectrum resources.

2. RIS-Aided UAVs: One of the most extensively explored applications of UAVs is their integration as mobile BSs, functioning as complementary aerial platforms within the low-altitude airspace (below 150 m) of 5G/6G cellular networks to bolster communication services or establish a dynamic radar network. This utilization of UAVs as mobile BSs is particularly well-suited for serving massive machine-type communication (mMTC) and IoT links, given that IoT nodes are primarily stationary (with fixed positions over time) and typically exhibit predictable traffic demands. The possibility of mounting a RIS on UAVs to function as passive smart reflectors has been put forth in the literature. This approach offers several key advantages, including reduced weight, lower costs, negligible energy consumption, and efficient spectrum utilization (as it does not involve emitting additional radio frequency (RF) waves). However, several challenges must be addressed before UAV-RIS-aided wireless networks can become a viable solution, such as, poor link budget, fast channel estimation and tracking, anomalous reflections, interference, jamming, path optimization, and atmospheric turbulence.

3. RIS and AI-aided UAVs for Ubiquitous Connectivity and Coverage Hole Discovery: Two comprehensive scenarios illustrate the integration of flying platforms into future networks, with RIS and machine learning algorithms assuming a pivotal role in network densification: 1) UAV as a Coverage Hole detector, 2) UAV as an Access Network

RIS-Aided NTN IoT Future Prospect: So far, the literature has widely presented valuable research on Internet-of-Space-to-Ground-Things (IoSGT), a spatial expansion of IoT. Specifically, IoSGT consists of space networks (including LEO, MEO, and GEO satellites), air networks (including UAV, HAP, and electrically Vertical Take-Off Landing (eVTOL) vehicles), terrestrial networks, maritime/sea networks, and underwater networks. IoSGT supports both horizontal and vertical connectivity with a significant heterogeneity. One step ahead, to ensure ubiquitous 3D connectivity without backhauling issues, it is envisioned that the RIS-aided IoSGT concept will take place in the forthcoming 6G applications. In this regard, several futuristic cases can be considered for rural, urban, and sea/underwater areas, as illustrated in the figure given below.

While NOMA fully decodes interference and treat interference as noise. On the other hand, a more general and powerful multiple access framework, that is Rate Splitting Multiple Access (RSMA), based on linearly precoded rate splitting (RS) at the transmitter and SIC at the receivers has been proposed. This enables to decode part of the interference and treat the remaining part of the interference as noise. This capability of RSMA to partially decode interference and partially treat interference as noise enables to softly bridge the two extreme strategies of fully treating interference as noise and fully decoding interference. In order to partially decode interference and partially treat interference as noise, RS splits messages into common and private messages and relies on a superimposed transmission of common messages decoded by multiple users and private messages decoded by their corresponding users (and treated as noise by co-scheduled users). Users rely on SIC to first decode the common messages before accessing the private messages. By adjusting the message split and the power allocation to the common and private messages, RS has the ability to softly bridge the two extreme of fully treat interference as noise and fully decode interference. (Corresponding Reference-1)

Corresponding Reference-1: Y. Mao, B. Clerckx, and V. O. Li, “Rate-splitting multiple access for downlink communication systems: Bridging, generalizing, and outperforming SDMA and NOMA,” EURASIP J. Wireless Commun. Netw., vol. 2018, no. 1, pp. 1–54, May 2018.

UAVs can be served not only as users but also as flying or aerial base stations (ABSs). The deployment of ABSs enables supporting ubiquitous connectivity, particularly in disaster and rural areas, and also provides high data rates in urban and suburban areas with favorable line-of-sight (LOS) propagation conditions. Thanks to their ability to extend network coverage and ensure high data rates, ABSs have emerged as one of the key enabling technologies for 5G networks and beyond. However, ABSs suffer from interference much more than terrestrial base stations (BSs) due to the large and moving coverage areas while utilizing multiantenna technologies to serve multiple users over the same frequency/time resource. Moreover, there are rapid channel variations due to the relative movement of ABSs with respect to ground users, thus acquiring perfect instantaneous channel state information (CSI) at the transmitter (CSIT) or the receiver (CSIR) becomes a challenging issue. To overcome the limitation of imperfect CSI in practical multi-antenna systems, RSMA has been recognized as a promising interference management strategy in various networks and propagation conditions. It has been revealed that RSMA can embrace conventional multiple access techniques and thus outperform in the presence of perfect CSI.

The following study considers Energy Harvesting Problem in RSMA-based Aerial Communications. In the study, a novel deep reinforcement learning (DRL)-based power allocation framework in energy harvesting-enabled ABS networks with RSMA to maximize the average sum-rate. Furthermore, realistic constraints such as randomness of energy arrival, time-varying channels, imperfect CSI, and finite-sized batteries are considered. Moreover, it is assumed that ABSs cannot have any prior knowledge of future arrival energy and CSI.