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Signal processing techniques have played a pivotal role in the early development of joint sensing and communication systems [1]. These efforts were driven by the need to address spectrum scarcity and to reduce hardware size and cost. Initially focused on dual-function radar-communication systems, this field has since evolved into the broader paradigm of Integrated Sensing and Communication (ISAC). ISAC encompasses a wide range of interactions between sensing and communication, incorporating not just radar but also other sensors, and leveraging their capabilities for applications such as autonomous driving, drone-based services, radio-frequency identification, and weather monitoring. With wireless networks now operating at higher frequencies, their dual role as communication networks and environmental sensors has become increasingly significant, providing critical information for both user needs and network operations [2].

The applications of upcoming sixth-generation (6G) empowered vehicle-to-everything (V2X) communications depend heavily on large-scale data exchange with high throughput and ultra-low latency to ensure system reliability and passenger safety. However, in urban and remote areas, signals can be easily blocked by various objects. Moreover, the propagation of signals with ultra-high frequencies, such as millimetre waves and terahertz communications, is severely affected by obstacles. To address these issues, the Intelligent Reflecting Surface (IRS), which consists of nearly passive elements, has gained popularity because of its ability to intelligently reconfigure signal propagation in an energy-efficient manner. Due to the promise of ease of deployment and low cost, IRS has been widely acknowledged as a key technology for both terrestrial and non-terrestrial networks to improve wireless coverage signal strength, physical layer security, positioning accuracy, and reduce latency. This article first describes the introduction of 6G empowered V2X communications and IRS technology. Then, it discusses different use case scenarios of IRS enabled V2X communications and reports recent advances in the existing literature. Next, it focuses our attention on the scenario of vehicular edge computing involving IRS enabled drone communications in order to reduce vehicle computational time via optimal computational and communications resource allocation. Finally, this article highlights current challenges and discusses future perspectives of IRS enabled V2X communications in order to improve current work and spark new ideas.

Current trends in non-geostationary orbit (NGSO) satellite communications pose the challenge of attaining high spectral efficiency with low-complexity user terminals (UTs). In this context, this letter investigates the downlink/forward transmission of linear space-time block coded (STBC) signals through multiple geographically distributed NGSO satellites. As such, by combining the signals through multiple satellites, STBC provides a diversity advantage, which helps in combating the adverse effects of fading and interference at the UT. The main challenge of implementing such a scheme is the lack of time synchronization and the interference effect through a multi-satellite system toward the UT. To address this issue, we propose a digital receiver structure with multiple branches specifically designed to lock onto each satellite of the multi-satellite system, thus providing a solution for simple UT without requiring additional complexity in terms of radio-frequency components. Herein, we consider the zero-forcing (ZF) based diversity combining scheme while deriving the signal-to-interference-plus-noise ratio (SINR) expression at UT. Our results demonstrate the SINR improvement brought by the ZF combiner.

Sixth-generation (6G) non-terrestrial networks (NTNs) are advanced wireless communication systems that operate beyond traditional terrestrial networks. These networks utilize various technologies and platforms to provide flexible, enhanced connectivity and coverage. When operating at high frequency, ground user terminals require low-directional antennas, which experience poor link budgets from satellites and thus drive the quest for novel solutions. Reconfigurable Intelligent Surfaces (RISs) have recently emerged as a promising technology for 6G and beyond cellular systems. This article studies the potential of RIS-in-tegrated NTNs to revolutionize next-generation connectivity. First, it discusses the fundamentals of RIS technology. Secondly, it delves into reporting the recent advances in RIS-integrated NTNs. Subsequently, it presents a novel framework based on the current state-of-the-art for IRS-integrated NTNs with classical single connected diagonal RIS and fully connected beyond diagonal RIS architectures. Finally, the article highlights open challenges and future research directions to revolutionize the realm of RIS-integrated NTNs.

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 article, 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.

The low-Earth orbiting (LEO) satellite constellations in the 90s (such as Iridium and Globalstar), although representing a major technical breakthrough, were not able to achieve the initial goal of complementing the second-generation mobile terrestrial networks due to the rapid worldwide adoption of GSM and other standards. However, the decline of conventional linear television and the persisting need to mitigate the digital divide still affecting billions of people recently generated a renewed interest by private investors for Low-Earth Orbiting (LEO) satellite mega-constellations. The mega-constellations under deployment can provide lower delays versus geostationary (GEO) satellites, broadband access anywhere, anytime leveraging the low-cost series production of small satellites, and more affordable launch solutions. At the end of the last decade, key industrial players realized the potential complementary role satellites can play to extend the 5G terrestrial network coverage over low-density populated areas, oceans, or similar. This has led to great technological developments and a sharp reduction in LEO satellite volume, weight, and, ultimately, manufacturing and launching costs. Also, it has triggered the inclusion of a non terrestrial network (NTN) component in the latest 5G 3GPP standard releases. Yet, there are many technological challenges remaining to ensure that both the quality and cost of the NTN services are comparable to the terrestrial counterpart. This is mainly due to the constraints in satellite payload power, mass, and antenna size. This comes in addition to the stringent power flux density limitations on the ground, in particular in the below 6 GHz satellite bands. The required order of magnitude increase of the effective delivered throughput and service cost reduction can only be achieved by a mix of system architectures and innovative technologies for both the space and ground segments. Space communication systems and technologies will also play a key role in human return to the Moon planned for mid-2020, to prepare for human exploration of Mars in the more distant future and further cosmic exploration. As missions voyage further from Earth, it is important to consider how we can continue to reliably communicate with them and how they will accurately navigate through space when they are so far from home.

Beam prediction and tracking (BPT) are key technology for high-frequency communications. Typical techniques include Kalman filtering and Gaussian process regression (GPR). However, Kalman filter requires explicit models of system dynamics, which are challenging to obtain, especially for complicated environments. In contrast, as a data-driven approach, there is no need to derive the system dynamics model for GPR. However, the computational complexity of GPR is often prohibitive, which makes real-time application challenging. To tackle this issue, we propose a novel hybrid model and data driven approach in this paper, which can exploit simultaneously the advantages from the two techniques while overcoming their drawbacks. In particular, the system dynamics required can be obtained in a data-driven manner. Based on a characterization of the system dynamics, we further investigate the long-term behavior of system evolution and propose two more efficient algorithms - long-term prediction and beam width optimization. We demonstrate two advantages of the proposed BPT approach. First, the computational complexity is low due to the inherent Kalman filter. Second, system performance can be significantly improved thanks to the long-term prediction and beam width optimization.

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Automotive-Industry 5.0 will use beyond fifth-generation (B5G) technologies to provide robust, computationally intelligent, and energy-efficient data sharing among various onboard sensors, vehicles, and other devices. Recently, ambient backscatter communications (AmBC) have gained significant interest in the research community for providing battery-free communications. AmBC can modulate useful data and reflect it towards near devices using the energy and frequency of existing RF signals. However, obtaining channel state information (CSI) for AmBC systems would be very challenging due to no pilot sequences and limited power. As one of the latest members of multiple access technology, non-orthogonal multiple access (NOMA) has emerged as a promising solution for connecting large-scale devices over the same spectral resources in B5G wireless networks. Under imperfect CSI, this paper provides a new optimization framework for energy-efficient transmission in AmBC enhanced NOMA cooperative vehicle-to-everything (V2X) networks. We simultaneously minimize the total transmit power of the V2X network by optimizing the power allocation at BS and reflection coefficient at backscatter sensors while guaranteeing the individual quality of services. The problem of total power minimization is formulated as non-convex optimization and coupled on multiple variables, making it complex and challenging. Therefore, we first decouple the original problem into two sub-problems and convert the nonlinear rate constraints into linear constraints. Then, we adopt the iterative sub-gradient method to obtain an efficient solution. For comparison, we also present a conventional NOMA cooperative V2X network without AmBC. Simulation results show the benefits of our proposed AmBC enhanced NOMA cooperative V2X network in terms of total achievable energy efficiency.

Unmanned aerial vehicles (UAV) have emerged as a practical solution that provides on-demand services to users in areas where the terrestrial network is non-existent or temporarily unavailable, e.g., due to natural disasters or network congestion. In general, UAVs' user-serving capacity is typically constrained by their limited battery life and the finite communication resources that highly impact their performance. This work considers the orthogonal frequency division multiple access (OFDMA) enabled multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive an efficient technique for the allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: (i) 3D placement and user association and (ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after fewer than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, results showed that the optimal power and sub-carrier allocation help to mitigate the inter-cell interference that directly impacts the system's performance.