Without requiring operational costs such as cabling and powering while maintaining reconfigurable phase-shift capability, self-sustainable reconfigurable intelligent surfaces (ssRISs) can be deployed in locations inaccessible to conventional relays or base stations, offering a novel approach to enhance wireless coverage. This study assesses the feasibility of ssRIS deployment by analyzing two harvest-and-reflect (HaR) schemes: element-splitting (ES) and time-splitting (TS). We examine how element requirements scale with key system parameters, transmit power, data rate demands, and outage constraints under both line-of-sight (LOS) and non-line-of-sight (NLOS) ssRIS-to-user equipment (UE) channels. Analytical and numerical results reveal distinct feasibility characteristics. The TS scheme demonstrates better channel hardening gain, maintaining stable element requirements across varying outage margins, making it advantageous for indoor deployments with favorable harvesting conditions and moderate data rates. However, TS exhibits an element requirement that exponentially scales to harvesting difficulty and data rate. Conversely, the ES scheme shows only linear growth with harvesting difficulty, providing better feasibility under challenging outdoor scenarios. These findings establish that TS excels in benign environments, prioritizing reliability, while ES is preferable for demanding conditions requiring operational robustness.

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.

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 present a study on energy-saving techniques for mobile networks by focusing on sector shut down. We propose a novel algorithm called the Neighbour-Based Expert System Solution, which is an alternative to the traditional carrier shutdown method. The proposed algorithm takes into account the coverage and capacity needs of the network, as well as the potential impact on the customer experience. We also compare the proposed algorithm with the vendor sleeping feature and show that it can improve energy savings by up to 10% without compromising network coverage or quality of service. The algorithm leverages real-world geolocated user measurement data to identify compensable sectors, enabling precise and risk-aware shutdown decisions. It is specifically designed to operate under live network conditions in dense urban areas where service continuity is critical. We present results from a live network trial, demonstrating the proposed algorithm's effectiveness in a real-world setting. Overall, our results show that the proposed algorithm can significantly reduce energy consumption in mobile networks while maintaining network performance.

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%.

Reconfigurable intelligent surfaces (RISs) can greatly improve the signal quality of future communication systems by reflecting transmitted signals toward the receiver. However, even when the base station (BS) has perfect channel knowledge and can compute the optimal RIS phase-shift configuration, implementing this configuration requires feedback signaling over a control channel from the BS to the RIS. This feedback must be kept minimal, as it is transmitted wirelessly every time the channel changes. In this paper, we examine how the feedback load, measured in bits, affects the performance of an RIS-aided system. Specifically, we investigate the trade-offs between codebook-based and element-wise feedback schemes, and how these influence the signal-to-noise ratio (SNR). We propose a novel quantization codebook tailored for line-of-sight (LoS) that guarantees a minimal SNR loss using a number of feedback bits that scale logarithmically with the number of RIS elements. We demonstrate the codebook's usefulness over Rician fading channels and how to extend it to handle a non-zero static path. Numerical simulations and analytical analysis are performed to quantify the performance degradation that results from a reduced feedback load, shedding light on how efficiently RIS configurations can be fed back in practical systems.

Integrated sensing and communication (ISAC) boosts network efficiency by using existing resources for diverse sensing applications. In this work, we propose a cell-free massive MIMO (multiple-input multiple-output)-ISAC framework to detect unauthorized drones while simultaneously ensuring communication requirements. We develop a detector to identify passive aerial targets by analyzing signals from distributed access points (APs). In addition to the precision of the sensing, timeliness of the sensing information is also crucial due to the risk of drones leaving the area before the sensing procedure is finished. We introduce the age of sensing (AoS) and sensing coverage as our sensing performance metrics and propose a joint sensing blocklength and power optimization algorithm to minimize AoS and maximize sensing coverage while meeting communication requirements. Moreover, we propose an adaptive weight selection algorithm based on concave-convex procedure to balance the inherent tradeoff between AoS and sensing coverage. Our numerical results show that increasing the communication requirements would significantly reduce both the sensing coverage and the timeliness of the sensing. Furthermore, the proposed adaptive weight selection algorithm can provide high sensing coverage and reduce the AoS by 45% compared to the fixed weights, demonstrating efficient utilization of both power and sensing blocklength.

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.

Massive multiple-input multiple-output (mMIMO) has been the core of 5G due to its ability to improve spectral efficiency and spatial multiplexing significantly; however, celledge users still experience performance degradation due to intercell interference and uneven signal distribution. While cell-free mMIMO (cfmMIMO) addresses this issue by providing uniform coverage through distributed antennas, it requires significantly more deployment cost due to the fronthaul and tight synchronization requirements. Alternatively, repeater-assisted massive MIMO (RA-MIMO) has recently been proposed to extend the coverage of cellular mMIMO by densely deploying low-cost single-antenna repeaters capable of amplifying and forwarding signals. In this work, we investigate amplification control for the repeaters for two different goals: (i) providing a fair performance among users, and (ii) reducing the extra energy consumption by the deployed repeaters. We propose a max-min amplification control algorithm using the convex-concave procedure for fairness and a joint sleep mode and amplification control algorithm for energy efficiency, comparing long- and short-term strategies. Numerical results show that RA-MIMO, with maximum amplification, improves signal-to-interference-plus-noise ratio (SINR) by over 20 dB compared to mMIMO and performs within 1 dB of cfmMIMO when deploying the same number of repeaters as access points in cfmMIMO. Additionally, our majority-rule-based long-term sleep mechanism reduces repeater power consumption by 70% while maintaining less than 1% spectral efficiency outage.

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.