The evolution toward 6G networks introduces unprecedented challenges and opportunities, particularly in the realm of serving both aerial and ground users seamlessly. In this article, we propose a holistic adaptive combined airspace and non-terrestrial network (NTN) architecture designed to address the unique requirements of the 6G era. Three principle features - joint sensing, communication, and computation (JSCC) in three dimensions (3D), cloud-native and artificial intelligence (AI) native, and the flexibility of radio access network (RAN) and core functions of the proposed architecture - are presented. Next, two application scenarios are analyzed: one catering to aerial users and the other supporting ground users, each, in particular, supporting communication links. Finally, we look into the network management and control aspects of the proposed architecture and discuss challenges and future research directions.

In this paper, we introduce a neural network (NN)-based framework aimed at classifying sensing and communication signals at base stations, improving the efficiency of integrated sensing and communication (ISAC) systems in a bistatic configuration. The framework leverages a key mathematical insight: the Hankelized matrix formed from an equidistantly sampled signal of sparsely superimposed radio waves exhibits a low-rank property, whereas a frequency-modulated signal lacks this characteristic. It ensures that, even in practical environments, the Hankelized matrix of a sensing or communication channel statistically retains the relevant information. Hence, we use the singular values of the Hankelized matrix as the input to the neural NN, while the output is a one-hot encoded vector indicating whether the received signal is intended for sensing or communication. We investigate three scenarios where the communication and sensing signals either use the same or different waveforms in terms of the detection performance of the communication signals. The results demonstrate that the proposed method outperforms existing approaches in classification performance across all scenarios, regardless of whether the communication and sensing signals utilize the same waveform or not. The framework achieves a detection rate of over 95% even at an SNR of 0 dB. Notably, the network performs well in terms of a small number of pilot symbols, a small number of training dataset, and dynamic environments.

In recent years, drones, or unmanned aerial vehicles (UAVs), have become widely used across various applications, from aerial photography and videography to the delivery of packages and medical supplies. However, their increasing presence has raised concerns about physical safety and privacy, highlighting the need for effective drone detection and monitoring solutions. To address this, we utilize the fact that most commercial drones use the Zadoff-Chu (ZC) sequence as the synchronization sequence in their communications, making it a useful feature for detection. Yet, detecting the ZC sequence blindly is challenging, as the transmitter's frequency is unknown to the receiver. While existing studies on ZC sequence detection with different frequency offsets focus largely on Long Term Evolution (LTE) scenarios, the ZC sequence structure and length used by drones differ, leading to unique detection challenges. In this paper, we analyze the autocorrelation properties of the specific ZC sequence used by drones under various center frequency offsets. We further propose a blind detection and identification algorithm that can detect and identify multiple drones utilizing ZC sequences in their video transmission protocols and autocorrelation properties. We study the performance of the proposed algorithm with extensive simulations and field tests. Even in low signal-to-noise ratio (SNR) conditions, with an SNR as low as -14 dB, our algorithm achieves a detection rate exceeding 99%.

As Non-terrestrial Networks (NTNs) becomes integral to future 6G systems, ensuring seamless connectivity and service continuity over Low Earth Orbit (LEO) satellite constellations is essential. This work investigates the impact of Open Radio Access Network (RAN) functional splits on handover performance in NTNs, focusing on minimizing service interruptions. We propose Effective Service Time as a novel availability metric that accounts for end-to-end Conditional Handover (CHO) delay, Radio Link Failures (RLFs), coverage gaps, and constellation-specific propagation dynamics-factors often simplified or ignored. Unlike baseline models that assume ideal, instantaneous switching with no protocol delays or topology changes, our CHO model reflects 3GPP-compliant, real-world constraints. Leveraging a digital twin-based satellite handover framework, we evaluate availability across multiple constellations, geographic regions, and Open RAN architectures (gNB onboard, Split 2, and Split 7.2x). Results reveal that increasing satellite density beyond a threshold yields diminishing returns, as denser constellations suffer more frequent handovers and higher downtime. For instance, a medium-density constellation with lower altitude achieves an average of 11 minutes of daily downtime, which rises to 13-16 minutes under a denser deployment. In contrast, a higher-altitude but sparser constellation provides only 5-7 minutes of downtime, benefiting from fewer handovers. Our analysis revealed that the claim of 99.9% availability in LEO is impractical, where we demonstrated that maximum 99.2% can be achieved with lower-altitude constellations. Moreover, functional splits impact performance: transitioning from gNB onboard to Split 7.2x can reduce availability from say about 99% to 98.5%. Finally, we construct a four-dimensional suitability map to identify optimal constellation-architecture pairings across a variety of service requirements defined by delay, modulation, reliability, and availability. Notably, stringent 50 ms delay requirements are not supported by higher-altitude constellations despite their higher availability, whereas lower-altitude constellations can satisfy them. This study provides valuable insights into NTN design, highlighting the interplay between satellite constellation, network architecture, and service-level guarantees.

Ultra-reliable low-latency communication (URLLC) services are essential for real-time control applications, such as industrial automation and autonomous vehicles, where stringent performance and reliability are paramount. Traditional diversity techniques-employing time, frequency, or spatial domains-enhance communication service availability. In bandwidth-constrained systems, these techniques often result in redundant transmissions and excessive resource consumption, limiting the efficient utilization of available resources. This paper investigates the potential of dynamic spatial diversity to enhance the availability of URLLC services in multi-connectivity scenarios. To this end, we propose to employ an entropy-based deep reinforcement learning framework. This framework leverages the soft actor-critic algorithm to dynamically optimize spatial diversity by selecting transmission paths and determining the optimal number of packet instances for transmission. The proposed approach, implemented in a 3GPP-compliant simulator, is evaluated in a factory automation scenario employing dual connectivity and packet duplication. The experiments demonstrate that our framework significantly outperforms conventional single-path and static packet duplication strategies, achieving superior efficiency in packet duplication and load balancing.

Given the advancements in next-generation low Earth orbit (LEO) satellites, there is an expected shift from transparent architectures (acting as radio repeaters) to regenerative architectures (hosting a part or all of the gNodeB (gNB) onboard). Such regenerative architectures enable disaggregation and distribution of radio access network (RAN) functions between the ground and space. Open RAN is a promising approach for non-terrestrial networks and offers flexible function placement through open interfaces. The present study examines three open RAN-based regenerative architectures, namely, Split 7.2x (low-layer physical functions onboard), Split 2 (Layers 1 and 2 onboard), and a gNB onboard the satellite. Handover (HO) management becomes increasingly complex in this disaggregated RAN, particularly for LEO satellites, where the part of the gNB is constantly in motion. The choice of regenerative architecture and its dynamic topology influence the additional HO control signals required between the satellite and ground stations. Using a realistic dynamic LEO constellation model, we analyze the interplay among conditional handover (CHO) delay, computational complexity, and control signaling overhead under different network architectures. Our findings reveal that transitioning from a transparent architecture to Split 7.2x does not reduce CHO delay despite the introduction of additional onboard processing. The gNB onboard the satellite minimizes cumulative CHO delay but demands 55%-70% more computational resources than the Split 7.2x architecture. Conversely, although Split 7.2x is computationally more efficient, it increases the cumulative CHO delay by 25%-30%. Additionally, we observed that under limited onboard processing conditions, only the transparent and Split 7.2x architectures supported delay-sensitive services up to 100 ms. In contrast, under ample processing conditions, gNB was suitable for stringent 50 ms requirements, while Split 2 best supported delay-tolerant services with 200 ms requirements.

This paper addresses the challenge of optimizing handover (HO) performance in non-terrestrial networks (NTNs) to enhance user equipment (UE) effective service time, defined as the active service time excluding HO delays and radio link failure (RLF) periods. Availability is defined as the normalized effective service time which is effected by different HO scenarios: Intra-satellite HO is the HO from one beam to another within the same satellite; inter-satellite HO refers to the HO from one satellite to another where satellites can be connected to the same or different GSs. We investigate the impact of open radio access network (O-RAN) functional splits (FSs) between ground station (GS) and LEO satellites on HO delay and assess how beam configurations affect RLF rates and intra- and inter-satellite HO rates. This work focuses on three O-RAN FSs - split 7.2x (low layer 1 functions on the satellite), split 2 (layer 1 and layer 2 functions on the satellite), and gNB onboard the satellite - and two beam configurations (19-beam and 127-beam). In a realistic dynamic LEO satellite constellation where different types of HO scenarios are simulated, we maximize effective service time by tuning the time-to-trigger (TTT) and HO margin (HOM) parameters. Our findings reveal that the gNB onboard the satellite achieves the highest availability, approximately 95.4%, while the split 7.2x exhibits the lowest availability, around 92.8% due to higher intra-satellite HO delays.

Multi-connectivity (MC) for aerial users via a set of ground access points offers the potential for highly reliable communication. Within an open radio access network (O-RAN) architecture, edge clouds (ECs) enable MC with low latency for users within their coverage area. However, ensuring seamless service continuity for transitional users - those moving between the coverage areas of neighboring ECs - poses challenges due to centralized processing demands. To address this, we formulate a problem facilitating soft handovers between ECs, ensuring seamless transitions while maintaining service continuity for all users. We propose a hierarchical multi-agent reinforcement learning (HMARL) algorithm to dynamically determine the optimal functional split configuration for transitional and non-transitional users. Simulation results show that the proposed approach outperforms the conventional functional split in terms of the percentage of users maintaining service continuity, with at most 4% optimality gap. Additionally, HMARL achieves better scalability compared to the static baselines.

Despite increasing interest in cellular-connected unmanned aerial vehicles (UAVs), their integration into existing cellular networks poses substantial challenges, including intense interference from UAVs to terrestrial user equipments (UEs) and numerous redundant handovers. To jointly reduce the generated interference and redundant handovers of cellular-connected UAVs while keeping their low transmission delay, we define an optimization problem with total available bandwidth and quality of service (QoS) constraints. Then, we formulate the optimization problem as a partially observable Markov decision process (POMDP) within a cooperative game. We have further developed a collaborative trajectory and handover management scheme using a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, specifically the Q-learning with a MIXer network (QMIX) algorithm, to optimize the aforementioned three metrics jointly. To demonstrate the superiority of our proposed scheme, we compare it with two benchmarks, namely the conventional handover management (CHM) scheme and the independent dueling double deep recurrent Q-network (ID3RQN) scheme. Simulation results show that QMIX outperforms the other schemes. Compared with the CHM scheme, QMIX reduces the delay, interference, and number of handovers for UAVs by an average of 46.9%, 70.0% and 90.5%, respectively. Compared with the ID3RQN scheme, QMIX reduces the three metrics by an average of 90.0%, 43.0% and 41.7%, respectively. 

Despite increasing interest in cellular-connected unmanned aerial vehicles (UAVs), their integration into existing cellular networks poses substantial challenges, including intense interference from UAVs to terrestrial user equipments (UEs) and numerous redundant handovers. To jointly reduce the generated interference and redundant handovers of cellular-connected UAVs while keeping their low transmission delay, we define an optimization problem subject to constraints on total available bandwidth and quality of service (QoS). Then, we formulate the optimization problem as a decentralized partially observable Markov decision process (Dec-POMDP) in the context of a cooperative game. We further develope a collaborative trajectory and handover management scheme using a multi-agent deep reinforcement learning algorithm, specifically the Q-learning with a MIXer network (QMIX) algorithm, to jointly optimize the aforementioned three metrics. Simulation results demonstrate that QMIX significantly outperforms two benchmark schemes: the conventional handover management (CHM) scheme and the independent dueling double deep recurrent Q-network (ID3RQN) scheme. Compared with the CHM scheme, QMIX reduces the delay, interference, and number of handovers for UAVs by an average of 46.9%, 70.0% and 90.5%, respectively. Compared with the ID3RQN scheme, QMIX reduces the three metrics by an average of 90.0%, 43.0% and 41.7%, respectively.