Satellite communications (SatCom) are envisioned as a critical enabler of 6G networks, enabling seamless global coverage by integrating terrestrial infrastructures with multi-layered satellite constellations. Among these, the integration between geostationary (GEO) and low Earth orbit (LEO) satellite networks is particularly attractive, as they combine the broad coverage of GEO satellites with the low latency and high capacity of LEO systems. Within this context, we address the resource allocation problem for LEO satellite through a joint design of beam size and transmit power, while accounting for GEO interference constraints, residual Doppler frequency offsets, and frequency reuse strategies. The objective is to maximize the spectral efficiency of LEO system operating in multi-beam GEO-LEO networks. Motivated by the limitations of classical deep reinforcement learning (RL) in such dynamic orbital settings and the potential of quantum RL for accelerated convergence, we propose a hybrid solution that exploits quantum acceleration during offline training and subsequently exports the learned policy into a classical representational format for onboard LEO satellite deployment. A fully quantum deep deterministic policy gradient framework with variational quantum circuit-based actor and critic is developed, along with a neural network-based policy translator for classical inference. To the best of our knowledge, this is the first deployment-ready quantum RL framework in SatCom, offering efficient offline training, reduced retraining latency, and practical deployment compatibility with existing LEO satellite hardware.

The Mean Opinion Score (MOS) is a standard metric for assessing the Quality of Experience (QoE) in Voice over IP (VoIP) applications. Accurate prediction of how network conditions influence MOS is critical for network planning, operation, and optimization. This requires modeling traffic flows with application-level granularity, which significantly increases both the dimensionality and structural complexity of the learning task. The ability to achieve efficient, robust, and generalizable data-driven learning in the presence of such complexity depends critically on the careful design of model architectures. This paper presents NetMOS, a Graph Neural Network (GNN) architecture specifically crafted to model IP networks and predict VoIP MOS scores. NetMOS models IP networks as heterogeneous graphs and designs a two-stage Message Passing Neural Network (MPNN) to capture both permutation invariant and sequential dependencies in traffic flow and network interactions. It uses a Gated Recurrent Unit (GRU) layer to model the ordered influence of links along a traffic path and introduces a customized attention layer with Sigmoid activations to model the cumulative effects of multiple flows on the links. Simulations demonstrate that NetMOS consistently outperforms conventional GNN-based baselines across diverse network topologies in Mean Absolute Error (MAE), R² score, Pearson correlation, and Spearman correlation. NetMOS generalizes effectively beyond the training topology, maintaining high prediction accuracy on unseen network topologies and varying network activity durations without retraining. NetMOS also provides MOS predictions 44×–170× faster than packet-level simulations.

The advancement of sixth-generation (6G) technology significantly enhances the Internet of Things (IoT) applications, especially in remote areas where traditional cellular infrastructure is not feasible. Satellite communication, a crucial component of 6G, extends IoT connectivity to these underserved regions. In this context, the growing interest in low Earth orbit (LEO) satellite communication stems from its recent advancements in offering high data rate services and minimizing service latency. Next-generation LEO satellite systems, with regenerative capabilities, allow for adaptability in bandwidth management and on-board data processing. However, the scarcity of satellite spectrum presents a barrier to the expansion of LEO satellite networks and the development of integrated terrestrial-space infrastructures. To address this challenge, we propose constructing a radio environment map (REM) aboard LEO satellites to opportunistically tap into the unused spectrum of geostationary (GEO) satellites within a cognitive GEO-LEO satellite IoT network. This solution facilitates REM construction through collaboration among neighboring LEO satellites while also considering the frequency reuse scheme of GEO satellites. Our REM construction approach leverages cyclostationary-based sensing at LEO satellites, serving the dual purpose of REM construction and Doppler shift estimation to track multiple GEO frequency signals. Following REM construction, LEO satellites utilize deep learning techniques to predict GEO spectrum occupancy without further sensing, thereby optimizing secondary spectrum utilization of the IoT network. We propose a deep learning neural network architecture based on a sequence-to-sequence model tailored for spectrum prediction at LEO satellites. Simulations demonstrate superior performance in detection probability of the proposed deep learning network compared to convolutional long short-term memory networks, achieving this with lower computational complexity.

As demand for ubiquitous connectivity grows, integrating satellite communications into sixth-generation (6G) networks has emerged as a crucial strategy to enhance global coverage, especially in remote and underserved regions. However, achieving the stringent performance, reliability, and spectral efficiency required for 6G presents significant challenges. Coexisting geostationary (GEO) and low Earth orbit (LEO) satellite networks offer a promising solution by enabling complementary coverage and enhanced service capabilities. Nonetheless, a critical challenge is managing intersystem interference from the LEO satellite system on the GEO system when sharing spectral resources, all while maintaining the performance of both systems. To address this, this paper introduces a fast fuzzy deep reinforcement learning (DRL)-based approach for joint beam design and power allocation in multi-beam GEO-LEO coexisting satellite networks. A robust design problem of LEO beam size and power allocation is formulated to maximize the spectral efficiency of the LEO system, considering tolerable interference on the GEO system, frequency reuse schemes employed by both GEO and LEO systems, and Doppler frequency offset induced by LEO satellite movement. A fast DRL algorithm, integrating fuzzy logic, post-decision state, and deep deterministic policy gradient, is proposed to solve this problem. Numerical results demonstrate a faster learning convergence rate for the proposed DRL algorithm compared to benchmark algorithms and confirm that the proposed method enhances LEO spectral efficiency while maintaining tolerable intersystem interference on the GEO system.

This paper explores the significance of the timeliness of information in the context of fifth generation (5G) non-terrestrial networks (NTN). As 5G technology continues to evolve, its integration with non-terrestrial components such as satellites, high-altitude platforms, and unmanned aerial vehicles brings about new possibilities and challenges for ensuring the timely delivery of information. In this paper, we delve into the network structure of NTNs and emphasize the significance of timeliness in various applications, including 5G massive Internet of Things and enhanced Mobile Broadband. We conduct an in-depth review of the design technologies and methodologies that enhance the timeliness of information in these applications. These include network architecture design, resource allocation, protocol design, modulation design, trajectory planning, reconfigurable intelligent surfaces design, energy harvesting scheduling design, offloading strategy design, and caching strategy design. By exploring these technical aspects and solutions, we aim to provide valuable insights into ensuring timely information delivery in 5G NTN. Furthermore, we propose potential future research directions to further improve the timeliness of information in NTNs. Recognizing the importance of timeliness and addressing the related challenges will unlock the full potential of 5G NTN, enabling the successful deployment and operation of a wide range of applications and services that depend on real-time data exchange.

To meet the capacity needs of the next generation wireless communications, U.S. Federal Communications Commission has recently introduced Spectrum Access System. Spectrum is shared between three tiers - Incumbents, Priority Access Licensees (PAL) and General Authorised Access (GAA) Licensees. When the incumbents are absent, PAL and GAA share the spectrum under the constraint that GAA ensure the aggregate interference to PAL is no more than -80 dBm within the PAL protection area. Currently GAA users are required to report their geolocations. However, geolocation is private information that GAA may not be willing to share. We propose a distributed GAA power allocation algorithm that does not require centralised coordination on sharing locations with other GAA users via SAS. We analytically proved the critical point of the interference along the PAL protection area to avoid calculating the interference on every points of the area. We proposed exclusion zone, transitional zone and open zone for GAA users to calculate the self-determined transmit power. Simulation results show that our method meets the interference requirement and achieve more than 90% of capacity approximation to the optimal centralised method, while completely masking the GAA locations.

Unmanned Aerial Vehicles (UAVs) are fast becoming a popular choice in a variety of applications in wireless communication systems. UAV-mounted base stations (UAV-BSs) are an effective and cost-efficient solution for providing wireless connectivity where fixed infrastructure is not available or destroyed. We present a method of using UAV-BSs to provide coverage to mobile users in a fixed area. We propose an algorithm for predicting the user locations based on their mobility data and clustering the predicted locations, so that one UAV-BS would provide coverage to one user cluster. The proposed method, hence is similar to the UAV-BSs following the users to keep them under the coverage region. Simulation results show that the proposed method increases the user coverage by 47%-72% and increases the spectral efficiency by 43%-55% depending on the scenario and in addition, reduces the number of UAV-BSs required to provide coverage.

Network densification has been one of the principal causes of performance gain in cellular networks, and 5G networks will not be any different. As cell sizes shrink, handovers become more frequent incurring extra delays that bury all the prospective gains. Mobility in multi-tier dense cellular networks calls for a change in the way it has been traditionally handled in an always-on world, where users take universal data access for granted. Invisible to them, mobile network operators need to provision backhauling to include advanced interference mitigation techniques. In this paper, we propose a spectrum database-aided handover management technique that aims to mitigate the number of disconnections without overloading the backhaul unnecessarily. The proposed technique exploits a spectrum database that stores reception information along with geolocation data, commercially available on any handheld device. Moreover, we have benchmarked several state-of-the-art handover schemes for 5G networks against ours in a realistic urban environment with user mobility trace data. The results highlight that our method can deliver the same downstream traffic with 33% decrease in disconnections when compared to the conventional approach. At the same time, backhaul traffic is reduced up to 68% against our counterparts.

Unmanned Aerial Vehicles (UAV) are gaining popularity in a range of areas and are already being used for a wide variety of purposes. While UAVs have many desirable features, limited battery lifetime is identified as a key restriction in UAV applications. Typical UAVs being electric devices, powered by on-board batteries, this constrain has limited their capabilities to a considerable extent. Thus planning UAV missions in an energy efficient manner is of utmost importance. To achieve this, for prediction of power consumption, it is necessary to have a reliable power consumption model. In this paper, we present a consistent and complete power consumption model for UAVs based on empirical studies of battery usage for various UAV activities. The power consumption model presented in this paper can be readily used for energy efficient UAV mission planning.

Unmanned Aerial Vehicles (UAV) are becoming popular in a range of areas. This has given rise to the concept of UAV swarms, where multiple UAVs act together to achieve a common task. With multiple UAVs flying in close proximity to each other, sharing the same airspace, the risk of inter-UAV collisions increases. It's important to avoid these collisions while having minimal impact on the UAV system. We propose a novel Potential Field Method (PFM) based algorithm for inter-UAV collision avoidance which considerably reduces the total time taken by the UAV system to achieve its goal. We control the collision avoidance actions of the UAVs by virtually relocating their targets. The positions of the virtual targets are calculated to minimize the collision probability, based on a probability function we introduced. The proposed algorithm reduces the total system time approximately by 20\% as opposed to the traditional PFM.

Spectrum Access System (SAS) is a three-tier spectrum sharing framework proposed for 3.5 GHz by Federal Communication Commission (FCC) in the United States. General Authorized Access (GAA) users in SAS do not have an assigned channel and can opportunistically access the Priority Access Licensee (PAL) channel satisfying the interference constraint proposed by FCC. Coexistence among GAA users in SAS is a key problem to be solved to enhance the system capacity to meet the increasing traffic demand. In this work, we propose a method for fair and efficient spectrum utilisation for GAA users. To achieve the fairness among GAA users equal interference budget allocation scheme is proposed for each set of GAA users that can hear each other. Our proposed method decide the optimal channel switching schedule that maximises the average capacity of GAA users while satisfying the interference constraint at PAL protection area. This work jointly considers the fairness between GAA users and the average capacity maximisation of GAA network. Simulation result justifies the performance of our proposed method for average capacity maximisation of GAA users and fairness between GAA users by comparing with existing works.

The optimal use of spectrum is a key focus for all regulatory bodies. Federal Communications Commission has introduced Spectrum Access System (SAS) to maximise the spectrum utilisation in the US 3.5 GHz band. SAS is a three-tier spectrum sharing framework where Citizen Broadband Radio Service (CBRS) devices can access the channel when it is not used by Incumbent Access users. CBRS consists of Priority Access Licensee (PAL) and General Authorized Access (GAA). In this paper, we consider the problem of optimum transmit power allocation for GAA users using a carrier sensing range i.e. maximum distance a user can be sensed while guaranteeing the interference to PAL from GAA users is below the threshold. We use carrier sensing range to find the sets of GAA users that cannot transmit at the same time and adjust the interference budget of transmitting GAA users. We present an algorithm for transmit power allocation for GAA users in the SAS. The proposed algorithm uses the transmission characteristics and location information provided by Citizen Broadband Radio Service Devices to SAS to maximise the peak capacity of GAA users ensuring the interference constraint to PAL. Simulation results show that the proposed algorithm significantly increases the peak capacity of GAA users by considering the carrier sensing range and adjusted interference budget.

The strongest-cell criterion has been extensively used for handover algorithms during the last cellular-network generations. When network topologies become multi-layered, it results in abrupt behaviors such as the ping-pong effect as a consequence of the power gap between tiers and their irregular deployment. This effect not only affects users' quality of experience but also introduces a significant network overhead. Therefore, we propose an original handover algorithm based on predicted incomplete channel states from a Radio Environment Map to reduce this effect. The proposed algorithm is user triggered, network assisted, and fully backward compatible with LTE-A. Moreover, we evaluate the performance of our proposed algorithm against LTE-A in a two-tier cellular network for different user speeds following the guidelines outlined by the 3GPP on diverse matters (channel, mobility, wrapping, etc.). When applying realistic timing, our results reveal a highly substantial improvement in the number of ping-pong handovers regardless of the handover policy adopted in comparison to LTE-A without sacrificing users' experience; for instance, we obtain at least an order of magnitude decrease in the ping-pong rate at the expense of losing less than 9 percent in spectral efficiency.

The advent of 5G networks, where a plethora of spectrum-sharing schemes are expected to be adopted as an answer to the ever-growing users' need for data traffic, will require addressing mobility ubiquitously. The trend initiated with the deployment of heterogeneous networks and past standards will give way to a multitiered network where different services will coexist, such as device-to-device, vehicle-to-vehicle or massive-machine communications. Because of the high variability in the cell sizes given the different transmit powers, the classical handover process, which relies solely on measurements, will lead to an unbearable network overhead as a consequence of the high number of handovers. The use of spatial databases, also known as radio environment maps (REM), was first introduced as a tool to detect opportunistic spectrum access opportunities in cognitive radio applications. Since then, REM usage has been widely expanded to cover deployment optimization, interference management or resource allocation to name a few. In this paper, we introduce a handover algorithm that can predict the best network connection for the current user's trajectory from a radio environment map. We consider a geometric approach to derive the handover and handover-failure regions and compare the current handover algorithm used in Long-Term Evolution with our proposed one. Results show a drastic reduction in the number of handovers while maintaining a trade-off between the ping-pong shandover and the handover-failure probabilities.

Spectrum Access System (SAS) is a three tier spectrum sharing framework proposed by the FCC. In this framework the aggregate interference of tier-3 General Authorised Access (GAA) users should be below a predetermined threshold anywhere within the tier-2 Priority Access Licensee (PAL) exclusion zone. GAA are expected to use a diverse range of Radio Access Technologies (RATs) with different levels of loading. We propose an optimal transmit power and probability of spectrum utilisation allocation scheme for GAA users that meets the average aggregate interference constraint within the GAA network. Most of the capacity maximisation studies consider the instantaneous aggregated interference from secondary users. In this paper we present an average aggregated interference method to optimise the capacity of GAA users in a single channel. Simulation results suggest that we can significantly increase the capacity of the channel by considering the probability spectrum utilisation of GAA users.

Spectrum Access System (SAS) allows incumbent military systems to share spectrum in a hierarchical manner with tier-2 Priority Access License (PAL) users and tier-3 General Authorized Access (GAA) users. FCC has recently allowed PAL owners to sublicense their channels. Therefore, when GAA channels are congested they can request a sublicense to access the PAL channel on a coordinated basis, which provides interference protection from other GAA users. In this paper, we propose a grid map to measure and monitor the secondary spectrum market for the purpose of spectrum trading with QoS guarantee. This work provides the subsequent spectrum trading models with a reasonable and dedicated interference graph for further optimization of spectrum allocation. Compared with traditional longterm spectrum licensing policy, short-term licensing makes the spectrum allocated effectively. We find the optimal resolution of the discrete grid map that maximizes the profit from sublicensing. Simulation results are provided to demonstrate how fine to grid the region and let the PAL owner achieve monetary benefit, in a given number of sensors.

Licensed Shared Access is a European standardisation effort which promotes repository based quasi-static hierarchical spectrum sharing. In this scheme the sharing time base is in the order of months if not years. For widespread use of Licensed Shared Access, shrinking the sharing time base is crucial. In this paper we propose a scheme to reduce the sharing time base to seconds or minutes scale. We present a new technique named lightweight Radio Environment Map based on a Kalman Filter derived from geo-location aware spectrum measurements, which can be run at the shared access licensee end. Our objective is to determine the active area of a static or slowly moving incumbent. We consider a challenging scenario where a large fraction of measurements is missing and the available measurements are highly distorted. Performance of our incumbent active area detection approach is evaluated by simulating a low power incumbent in an urban cellular environment. Simulation results show a substantial improvement of missed detection area in comparison to the counterpart that does not use our lightweight Radio Environment Map.

Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. In our previous work we formulated spectrum cartography as a compressive sensing problem and we illustrated how cartography can be used in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. This paper investigates the performance of compressive sensing based approach to cartography in a fading environment where realtime channel estimation is not feasible. To accommodate for lack of channel information we take an iterative approach. We extend the well-known iteratively reweighted ℓ1minimisation approach by exploiting spatial correlation between two points in space. We evaluate the performance in an urban environment where Rayleigh fading is prominent. Our numerical results show a significant improvement in the probability of accurately making a spectrum sensing decision, in comparison to the well-known weighted approach and the traditional compressive sensing based method.

Licence-exempt secondary Long Term Evolution systems have been proposed recently, in attempt to meet the needs of rapidly growing wireless mobile applications. However, where the secondary network is spread over a large geographical area, traditional detect-and-avoid algorithms are less effective in providing interference protection to Primary Users while maximising the secondary throughput. Spectrum cartography is an emerging technique that can be used to discover spectrum holes in space. We propose a downlink power allocation algorithm using Kriging Spatial Interpolation and Compressive Sensing based spectrum cartography in an environment where large scale shadow fading is prominent. We evaluate the performance of our approach by simulating a secondary Urban Microcell network operating in TV White Space. Simulation results show a significant improvement in interference and throughput, in comparison to traditional detect-and-avoid algorithms.

Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. Multiple research groups have recently proposed to use spectrum cartography in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. In our novel approach, we exploit the sparsity of primary users in space to formulate the cartography process as a compressive sensing problem. Further, we present a novel algorithm for solving the cartography problem that builds on the well-known Orthogonal Matching Pursuit algorithm. We evaluate the performance of our approach by simulating a cognitive radio network where primary users are low power wireless microphones. Our simulation results show a significant improvement in reconstruction error, in comparison to two existing compressive sensing based methods.