This letter investigates dynamic routing in Next-Generation Satellite Orbit (NGSO) constellations and proposes a hybrid strategy that combines precomputed routing tables with a Deep Q-Learning (DQL) fallback mechanism. While fully RL-based schemes offer adaptability to topology dynamics, they often suffer from high complexity, long convergence times, and unstable performance under heavy traffic. In contrast, the proposed framework exploits deterministic table lookups under nominal conditions and selectively activates the DQL agent only when links become unavailable or congested. Simulation results in large-scale NGSO networks show that the hybrid approach consistently achieves higher packet delivery ratio, lower end-to-end delay, shorter average hop count, and improved throughput compared to a pure RL baseline. These findings highlight the effectiveness of hybrid routing as a scalable and resilient solution for delay-sensitive satellite broadband services

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peer reviewed

This paper presents an intelligent device selection framework for Federated Learning (FL) in Low Earth Orbit (LEO) satellite constellation networks. In particular, the high mobility, intermittent connectivity and long communication delays in LEO satellite networks significantly impact FL convergence and model performance. To address these challenges, we formulate a device selection optimization problem that determines the optimal subset of LEO satellites to participate in global model aggregation at each round. The objective is to minimize the average loss while satisfying latency constraints. To solve the formulated problem, we develop a deep reinforcement learning (DRL)-based solution that enables adaptive satellite selection considering the stochastic nature of satellite connectivity and channel variability. The proposed framework selects participating satellites dynamically based on their communication latency and computational capabilities to improve the overall efficiency of the FL process. Simulation results show that the proposed approach accelerates convergence compared to conventional synchronous FL while maintaining model accuracy.

In this paper, we study the effect of differential delay on the performance of a centralized and cooperative multi-gateway satellite system, where multiple spatially separated gateways transmit simultaneously in the feeder forward link, each serving a cluster of beams. Full frequency reuse is assumed in the user forward link and thus precoding is employed at the central gateway to mitigate the interference, before the precoded signals are distributed to the remote gateways to be sent to the satellite. Simulation results considering the DVB-S2X waveform show that channel state information (CSI) estimation, and as a result the signal-to-interference-plus-noise ratio (SINR), can be significantly degraded, when the inter-cluster interference originating from different gateways is high, emphasizing the importance of user scheduling in this architecture such that the inter-cluster interference is minimized.

Low Earth Orbit (LEO) satellite constellations consist of numerous satellites orbiting at different altitudes to serve diverse terrestrial users. Efficient resource management in such dynamic and large-scale networks presents a significant challenge. This paper studies Resource Blocks (RBs) and transmit power allocation at each LEO satellite, aiming at enhancing network performance while meeting Quality of Service (QoS) requirements. A stochastic optimization problem is formulated where the required QoS by each user is expressed as a chance constraint of the minimum data rate requirements considering network dynamics and uncertainties in channel conditions and traffic demands. The Conditional Value at Risk (CVaR) is employed to reformulate the chance constraint into a convex form and achieve a robust solution. Then, an alternating optimization approach is applied to solve the optimization problem. Through simulations using real data from the Starlink constellation, we demonstrate the efficacy of the proposed approach in improving network data rates while maintaining the required QoS levels.

In this paper, we investigate the performance of the rate-splitting multiple access (RSMA) framework in a mmWave cell-free massive multiple-input multiple-output (CF-mMIMO) system assisted by multiple reconfigurable intelligent surfaces (RISs). We consider the practical scenario of low-resolution digital-to-analog converters (DACs) at the distributed access points (APs) to reduce hardware complexity and power consumption. Our main objective is to maximize the minimum rate among the users by jointly optimizing the precoding vectors at each AP, the common rates, and the reflection coefficients of RISs. The resultant non-convex optimization problem is then solved using alternating optimization and successive convex approximation-based methods. Numerical results demonstrate the superior performance of the proposed RSMA-based scheme over traditional methods across several deployment scenarios, with performance gains from RIS deployment notably improved in hotspot scenarios.

Reconfigurable Intelligent Surface (RIS) technology has emerged as a transformative solution for enhancing satellite networks in next-generation wireless communication. The integration of RIS in satellite networks addresses critical challenges such as limited spectrum resources and high path loss, making it an ideal candidate for next-generation Internet of Things (IoT) networks. This paper provides a new framework based on transmissive beyond diagonal RIS (T-BD-RIS) mounted low earth orbit (LEO) satellite networks with non-orthogonal multiple access (NOMA). The NOMA power allocation at LEO and phase shift design at T-BD-RIS are optimized to maximize the system's spectral efficiency. The optimization problem is formulated as non-convex, which is first transformed using successive convex approximation and then divided into two problems. A closed-form solution is obtained for LEO satellite transmit power using KKT conditions, and a semi-definite relaxation approach is adopted for the T-BD-RIS phase shift design. Numerical results are obtained based on Monte Carlo simulations, which demonstrate the advantages of T-BD-RIS in satellite networks.

Network energy saving has received great attention from operators and vendors to reduce energy consumption and CO2 emissions to the environment as well as significantly reduce costs for mobile network operators. However, the design of energy-saving networks also needs to ensure that mobile users' (MUs) QoS requirements such as throughput requirements (TR). This work considers a mobile cellular network including many ground base stations (GBSs), and some GBSs are intentionally turned off due to network energy saving (NES) or crash, so the MUs located in these outage GBSs are not served in time. Based on this observation, we propose the problem of maximizing the total achievable throughput in the network by optimizing the GBSs' antenna tilt and adaptive transmission power with a given number of served MUs satisfied. Notice that, the MU is considered successfully served if its Reference Signal Received Power (RSRP) and throughput requirement are satisfied. The formulated optimization problem becomes difficult to solve with multiple binary variables and nonconvex constraints along with random throughput requirements and random placement of MUs. We propose a Deep Q-learning-based algorithm to help the network learn the uncertainty and dynamics of the transmission environment. Extensive simulation results show that our proposed algorithm achieves much better performance than the benchmark schemes.

Satellite communication (SatCom) systems play a vital role in providing global connectivity and enable a wide range of applications, including Internet of Things (IoT) connectivity for remote areas, such as forests and oceans. Two crucial resource allocation challenges in SatCom are beam placement (BP) and frequency assignment (FA) problems, which involve the clique covering (CC) and graph coloring (GC) problems, respectively. Conventional solutions for these problems incur excessive computational cost, which is intractable for classical computers. A promising approach is to formulate these problems using the Ising model, construct their Hamiltonians, and then solve them efficiently by a quantum computer. However, the current quantum computers have very limited hardware and can only handle rather small inputs. To overcome this limitation, we propose a hybrid-quantum-classical-computational pipeline where an efficient hamiltonian reduction method is the key for solving large CC/GC instances. Through experiments on real quantum computers, our reduction method outperforms commercial solutions, allowing quantum annealers to handle significantly larger BP/FA instances while maintaining high probability to achieve feasible solutions and near-optimal performance. Although the inherent hardness of the CC/GC problems cannot be overcome by quantum computing, our research contributes to the early exploration of quantum computing in the context of the complex optimization problems in SatCom systems, particularly in the realm of IoT connectivity for remote areas.

Satellite communications (SatComs) have recently been through a renaissance, both technologically and entrepreneurially. Ambitious plans have already come into fruition with the operation of low-Earth orbit (LEO) constellations including thousands of satellites and supported by state of the art but proprietary technologies, such as active antenna arrays and intersatellite links (ISLs). In this context, this article aims to provide a forward-looking vision of use cases and a deep dive into technological enablers that will be prominent in space communications beyond 2030. In parallel, it motivates how open standards can play a role in delivering affordable communication services in space. Starting from the 5G plans for nonterrestrial networks, we provide a survey and roadmap toward artificial intelligence (AI)-supported satellite systems, space-enabled quantum networks, and joint communications and positioning (JCAP) for space missions and interplanetary exploration.

This paper investigates downlink transmission in 5G Integrated Satellite-Terrestrial Networks (ISTNs) supporting automotive users (UEs) in urban environments, where base stations (BSs) and Low Earth Orbit (LEO) satellites (LSats) cooperate to serve moving UEs over shared C-band frequency carriers. Urban settings, characterized by dense obstructions, together with UE mobility, and the dynamic movement and coverage of LSats pose significant challenges to user association and resource allocation. To address these challenges, we formulate a multi-objective optimization problem designed to improve both throughput and seamless handover (HO). Particularly, the formulated problem balances sum-rate (SR) maximization and connection change (CC) minimization through a weighted trade-off by jointly optimizing power allocation and BS-UE/LSat-UE associations over a given time window. This is a mixed-integer and non-convex problem which is inherently difficult to solve. To solve this problem efficiently, we propose an iterative algorithm based on the Successive Convex Approximation (SCA) technique. Furthermore, we introduce a practical prediction-based algorithm capable of providing efficient solutions in real-world implementations. Especially, the simulations use arealistic 3D map of Londonand UE routes obtained from the Google Navigator application to ensure practical examination. Thanks to these realistic data, the simulation results can show valuable insights into the link budget assessment in urban areas due to the impact of buildings on transmission links under the blockage, reflection, and diffraction effects. Furthermore, the numerical results demonstrate the effectiveness of our proposed algorithms in terms of SR and the CC-number compared to the greedy and benchmark algorithms.

This paper proposes a joint optimization framework for energy-efficient linear precoding and feeder-link-beam matching design in a multi-gateway multi-beam bent-pipe satellite communication system. The proposed scheme jointly optimizes the precoding vectors at the gateway antennas and amplifying-and-matching mechanism at the satellite to maximize the system-weighted energy efficiency under the transmit power budget constraint. The technical designs are formulated into a non-convex sparsity problem consisting of a fractional-form objective function and sparsity-related constraints. To address these challenges, two iterative efficient designs are proposed by utilizing the concepts of Dinkelbach's method and the compressed-sensing approach. The simulation results demonstrate the effectiveness of the proposed scheme compared to another benchmark method.

Enhancing reliability in high-throughput satellites (HTS) operating in geostationary orbit (GEO) is critical, particularly under adverse channel conditions. This study investigates the potential of multi-connectivity (MC) enabled by carrier aggregation (CA) to improve the data rate, ensuring a high level of reliability of multi-band $\mathrm{K a} / \mathrm{Ku}$ GEO HTS systems. A system and channel model for the multi-band GEO satellite system is developed, and an inter-band CA algorithm is proposed. This algorithm dynamically adjusts the user link transmission scheme based on channel quality and user requirements, ranging from a single Ka-band connectivity to MC, utilizing both bands with CA via packet duplication or packet splitting. The numerical results in various weather scenarios validate the effectiveness of the algorithm, demonstrating significant improvements in system performance, reduced outage probability, and improved overall system reliability. These findings highlight the importance of MC and multi-band technologies in future $\mathrm{6 G}$ networks.

The terahertz communications have the potential to revolutionize data transfer with unmatched speed and facilitate the development of new high-bandwidth applications. This paper studies the performance of downlink terahertz system assisted by beyond diagonal intelligent reconfigurable surface (BD-IRS). For enhanced energy efficiency and low cost, a joint precoding and BD-IRS phase shift design satisfying the 1-bit resolution constraints to maximize the spectral efficiency is presented. The original problem is non-linear, NP-hard, and intricately coupled, and obtaining an optimal solution is challenging. To reduce the complexity, we first transform the optimization problem into two problems and then iteratively solve them to achieve an efficient solution. Numerical results demonstrate that the proposed approach for the BD-IRS assisted terahertz system significantly enhances the spectral efficiency compared to the conventional diagonal IRS assisted system.

The demand for improved satellite communication (SatCom)-based broadband connectivity has led to significant technological advancements, particularly in non-geostationary orbit (NGSO) satellites. The new SatCom systems are expected to have flexible beam footprints with fully adaptable payloads while being energy-efficient. With this in mind, this paper explores using neuromorphic processors (NPs) for the in-orbit receive digital beamforming design. We specifically address the beamsteering challenges of high-speed user mobility by means of beamforming adaptation. Inspired by thinned antenna arrays, the proposed beamforming solutions are based on the least absolute shrinkage and selection operator (LASSO) and are adapted to NPs using spiking locally competitive algorithms, namely S-LCA and S-LCA with graded spikes. The proposed approaches can benefit from the energy efficiency of NPs and further reduce the SatCom payload’s power consumption by turning off as many radio frequency chains as possible without compromising the beamforming performance. Numerical experiments conducted on a real-world aeronautical dataset demonstrate that the proposed NP-oriented solutions offer performance on par with conventional optimization algorithms, with the promise of a lower energy expenditure after future implementation on dedicated hardware.

Satellite constellation systems are becoming more attractive to provide communication services worldwide, especially in areas without network connectivity. While optimizing satellite gateway placement is crucial for operators to minimize deployment and operating costs, reducing the number of gate-ways may require more inter-satellite link hops to reach the ground network, thereby increasing latency. Therefore, it is of significant importance to develop a framework that optimizes gateway placement, dynamic routing, and flow management in inter-satellite links to enhance network performance. To this end, we model an optimization problem as a mixed-integer problem with a cost function combining the number of gateways, flow allocation, and traffic latency, allowing satellite operators to set priorities based on their policies. Our simulation results indicate that the proposed approach effectively reduces the number of active gateways by selecting their most appropriate locations while balancing the trade-off between the number of gateways and traffic latency. Furthermore, we demonstrate the impact of different weights in the cost function on performance through comparative analysis.

This paper examines integrated satellite-terrestrial networks (ISTNs) in urban environments, where terrestrial networks (TNs) and non-terrestrial networks (NTNs) share the same frequency band in the C-band which is considered the promising band for both systems. The dynamic issues in ISTNs, arising from the movement of low Earth orbit satellites (LEOSats) and the mobility of users (UEs), are addressed. The goal is to maximize the sum rate by optimizing link selection for UEs over time. To tackle this challenge, an efficient iterative algorithm is developed. Simulations using a realistic 3D map provide valuable insights into the impact of urban environments on ISTNs and also demonstrates the effectiveness of the proposed algorithm.

The third generation partnership project (3GPP) has recently defined two frequency bands for direct access with satellites, which is a concrete step toward realizing the anticipated space-air-ground integrated networks. In addition, given the rapid increase in the numbers of satellites orbiting the Earth and emerging satellites applications, non-terrestrial networks (NTNs) might soon need to operate with integrated access and backhaul (lAB), which has been standardized for terrestrial networks to enable low-cost, flexible and scalable network densification. Therefore, this work investigates the performance of satellite lAB, where the same spectrum resources at a low earth orbit (LEO) satellite are utilized to provide access to a handheld user (UE) and backhaul via inter-satellite links. The UE is assumed to operate with frequency division duplex (FDD) as specified by the 3GPP, while both FDD and time division duplex (TDD) are investigated for backhauling. Our analysis demonstrate that the interference between access and backhaul links can significantly affect the performance under TDD backhauling, especially when the access link comes with high quality-of-service demands.

Coexisting satellite and terrestrial networks present a unique set of challenges and opportunities when the two networks share the same spectrum. One of these challenges is the desired signal recovery in such interference-limited scenario. In this work, we design a signal recovery scheme in coexisting satellite and terrestrial networks. We formulate an optimization problem and propose a diffusion model to perform signal recovery. The proposed diffusion model leverages the denoising mechanism to recover the signals from noisy and distorted signals. The proposed diffusion model consists of encoder to encode the input to the latent space, U-Net for denoising, attention block to integrate different relevant feature to create better context for signal recovery, and decoder to deliver the recovered signal.

Very High Throughput Satellite (VHTS) systems are typically deployed in Geostationary (GEO) orbit to benefit from the ubiquitous coverage of such orbits. Although the satellites deployed in GEO orbits appear as a static point in the sky from the on-ground user perspective, in practice, the GEO satellite experiences a north-south drift due to the influence of the sun and moon. Such movement may cause a small Doppler effect in the signals sent from geographically distributed cooperative gateways, which may cause significant performance loss when exploiting DVB-S2X SF-Pilot fields and for precoding purposes. This paper presents the first work investigating the effect of GEO Doppler shift in precoded cooperative multi-gateway satellite systems. In addition, to compensate for the frequency variations produced by the GEO movement, we present and test a user-gateway closed-loop compensation procedure. Results using software-defined radio (SDR) in the Lab are provided to validate the proposed method.

Reflective Intelligent Surface (RIS)-assisted Ultra-Massive MIMO (Um-MIMO) systems in the terahertz (THz) spectrum are gaining attention for surpassing current wireless system limitations. However, limited diffraction at these frequen-cies typically results in low-rank Um-MIMO channels, completely reducing the potential for spatial multiplexing gains. In this work, we aim at promoting a new research direction towards the strategies for achieving high-rank Um-MIMO for RIS-assisted THz communications. The maximum achievable spatial multiplexing gain over the RIS-assisted channel is analyzed, and the optimal antennas and RIS elements placement strategy is proposed for achieving extremely high-rank Um-MIMO channels. Simulation results demonstrate that while conventional Um-MIMO THz systems display low-rank, the proposed approach enables achieving a rank on the order of hundreds for the Um-MIMO THz systems.

With the commercial deployment of low earth orbit (LEO) satellites, the future integrated 6G-satellite system represents an excellent solution for ubiquitous connectivity and high-throughput data service to massive users. Due to the heterogeneity of users’ traffic profiles, uneven traffic distribution among beams or users often occurs in LEO satellite systems. Conventional satellite payloads with fixed beam radiation patterns may result in large gaps between requested and allocated capacity. The advances of flexible satellite payloads with dynamic beamforming capabilities enable spot beams to adjust their coverage and adaptively schedule users, thus offering spatial-temporal domain flexibility. Motivated by this, as an early attempt, we investigate how adaptive beam patterns with flexible user scheduling schemes can help alleviate mismatches of requested-transmitted data in uneven-traffic and full-frequency reuse LEO systems. We formulate an optimization problem to jointly determine beam patterns, power allocation, user-LEO association, and user-slot scheduling. The problem is identified as mixed-integer nonconvex programming. We propose an efficient iterative algorithm to solve the problem by first determining beam patterns and user associations at the frame scale, followed by optimizing power allocation and user scheduling at the timeslot scale. The four-decision components are iteratively updated to improve the overall performance. Numerical results demonstrate the benefits brought by adaptive beam patterns and their effectiveness in reducing the mismatch effect in uneven-traffic LEO systems.

The advent of Satellite as a Service (SaaS) platforms has empowered satellite service providers (SPs) to rent portions of satellite capacity from infrastructure providers (IPs) to cater to the diverse demands of their users across multiple satellite services. To effectively manage costs and maintain a high Quality of Experience (QoE) for numerous concurrent connections, SPs should secure flexible capacity from IPs. However, the irregular and unpredictable nature of traffic demands from various applications complicates the capacity-renting framework. This study presents a dynamic capacity allocation framework that efficiently handles diverse traffic flows with varying arrival rates, aiming to minimize rental costs while meeting blocking probability and QoE requirements. Utilizing theMt/Mt/1 queuing model and a continuous-time Markov chain, the technical designs are framed as a statistical optimization problem. In this context, the system waiting-queue lengths are estimated using the transient probabilities of Kolmogorov equations. Subsequently, cumulative distribution functions are employed to re-formulate this stochastic optimization problem into a convex form, which can be tackled through the Lagrangian duality method.Through extensive simulations and numerical assessments, we illustrate our method’s efficacy, with the proposed algorithm outperforming benchmarks by reducing costs by up to 9.85% and 3.1%.

Next-generation (NextG) cellular networks are expected to evolve towards virtualization and openness, incorporating reprogrammable components that facilitate intelligence and real-time analytics. This paper builds on these innovations to address the network slicing problem in multi-cell open radio access wireless networks, focusing on two key services: enhanced Mobile BroadBand (eMBB) and Ultra-Reliable Low Latency Communications (URLLC). A stochastic resource allocation problem is formulated with the goal of balancing the average eMBB data rate and its variance, while ensuring URLLC constraints. A distributed learning framework based on the Deep Reinforcement Learning (DRL) technique is developed following the Open Radio Access Networks (O-RAN) architectures to solve the formulated optimization problem. The proposed learning approach enables training a global machine learning model at a central cloud server and sharing it with edge servers for executions. Specifically, deep learning agents are distributed at network edge servers and embedded within the Near-Real-Time Radio access network Intelligent Controller (Near-RT RIC) to collect network information and perform online executions. A global deep learning model is trained by a central training engine embedded within the Non-Real-Time RIC (Non-RT RIC) at the central server using received data from edge servers. The performed simulation results validate the efficacy of the proposed algorithm in achieving URLLC constraints while maintaining the eMBB Quality of Service (QoS).

Beam-hopping (BH) technology, integral to multi-beam satellite systems, adapts beam activation to the variable communication demands of terrestrial users. The optimization of power allocation and beam illumination scheduling constitutes the core design challenge in BH systems, especially under the constraint on a limited number of simultaneously active beams due to restricted radio frequency chain availability. This paper proposes a two-stage BH design solution, which minimizes energy consumption in BH satellite communications while accommodating the heterogeneous demands of users. The first stage addresses the coupling variables of power and beam status by recasting the allocation and scheduling problem through a statistical lens, thus breaking down the intricate relationship between variables. To manage the resulting non-convex challenge, we propose an iterative method that capitalizes on the optimality conditions inherent to this problem. This method is designed to procure a statistically-informed solution that aligns with our reformulated interpretation. Subsequently, the second stage maps this solution into a concrete beam illumination schedule, employing binary quadratic programming techniques. A penalty-based iterative method is applied, ensuring convergence to a locally optimal solution. Through numerical simulations, the proposed framework has been validated for its efficacy in improving energy efficiency and accurately matching demands.

This paper investigates the uplink transmission of an integrated satellite-terrestrial network, wherein the low-earth-orbit (LEO) satellites provide backhaul services to isolated cellular base stations (BSs) for forwarding mobile user (UE) data to the core network. In this integrated system, the high mobility of LEO satellites (LEOSats) introduces significant challenges in managing radio resource allocation (RA), as well as the associations between UEs, BSs, and LEOSats for supporting users’ demands efficiently, while also dynamically balancing the capacity of UE-BS access and BS-LEO backhaul links. Regarding these critical issues, the paper aims to jointly optimize the two-tier UE-BS and BS-LEOSat association, sub-channel assignment, bandwidth allocation, and power control to meet users’ demands in the shortest transmission time. This optimization problem, however, falls into the category of mixed-integer non-convex programming, making it very challenging and requiring advanced solution techniques to find optimal solutions. To tackle this complex problem efficiently, we first develop an iterative centralized algorithm by utilizing convex approximation and compressed-sensing-based methods to deal with binary variables. Furthermore, for practical implementation and to offload computation from the central processing node, we propose a Dec-Alg that can be implemented in parallel at local controllers and achieve efficient solutions. Numerical results are also illustrated to strengthen the effectiveness of our proposed algorithms compared to traditional greedy and benchmark algorithms.

This paper proposes a robust beamforming algorithm for massive multiple-input multiple-output (MIMO) low earth-orbit (LEO) satellite communications under uncertain channel conditions. Specifically, a Conditional Value at Risk (CVaR)-based stochastic optimization problem is formulated to optimize the hybrid digital and analog precoding aiming at maximizing the network data rate while considering the required Quality-of-Service (QoS) by each ground user. In particular, the CVaR is used as a risk measure of the downlink data rate to capture the high dynamic and random channel variations of the satellite network, achieving the required QoS under the worst-case scenario. Utilizing the decomposition and relaxation optimization techniques, an alternating optimization algorithm is developed to solve the formulated problem. Simulation results demonstrate the efficacy of the proposed approach in achieving the QoS requirements under uncertain satellite channel conditions.

The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate. This paper considers problem of efficient downlink communication of multi-spectral satellite images for Earth observation using change detection. The proposed method for image processing consists of the joint design of cloud removal and change encoding, which can be seen as an instance of semantic communication, as it encodes important information, such as changed multi-spectral pixels (MPs), while aiming to minimize energy consumption. It comprises a three-stage end-to-end scoring mechanism that determines the importance of each MP before deciding its transmission. Specifically, the sensing image is (1) standardized and passed through a high-performance cloud filtering via the Cloud-Net model, (2) passed to the proposed scoring algorithm that uses Change-Net to identify MPs that have a high likelihood of being changed, compress them and forward the result to the ground station, and (3) reconstructed at ground gateway based on reference image and received data. The experimental results indicate that the proposed framework is effective in optimizing energy usage while preserving high-quality data transmission in satellite-based Earth observation applications.

Beam Placement (BP) is a well-known problem in Low-Earth Orbit (LEO) satellite communication (SatCom) systems, which can be modelled as an NP-hard clique cover problem. Recently, quantum computing has emerged as a novel technology which revolutionizes how to solve challenging optimization problems by formulating Quadratic Unconstrained Binary Optimization (QUBO), then preparing Hamiltonians as inputs for quantum computers. In this paper, we study how to use quantum computing to solve BP problems. However, due to limited hardware resources, existing quantum computers are unable to tackle large optimization spaces. Therefore, we propose an efficient Hamiltonian Reduction method that allows quantum processors to solve large BP instances encountered in LEO systems. We conduct our simulations on real quantum computers (D-Wave Advantage) using a real dataset of vessel locations in the US. Numerical results show that our algorithm outperforms commercialized solutions of D-Wave by allowing existing quantum annealers to solve 17.5 times larger BP instances while maintaining high solution quality. Although quantum computing cannot theoretically overcome the hardness of BP problems, this work contributes early efforts to applying quantum computing in satellite optimization problems, especially applications formulated as clique cover/graph coloring problems.

To allow flexible and cost-efficient network densification and deployment, the integrated access and backhaul (IAB) was recently standardized by the third generation partnership project (3GPP) as part of the fifth-generation new radio (5G-NR) networks. However, the current standardization only defines the IAB for the terrestrial domain, while non-terrestrial networks (NTNs) are yet to be considered for such standardization efforts. In this work, we motivate the use of IAB in NTNs, and we discuss the compatibility issues between the 3GPP specifications on IAB in 5G-NR and the satellite radio regulations. In addition, we identify the required adaptation from the 3GPP and/or satellite operators for realizing an NTN-enabled IAB operation. A case study is provided for a low earth orbit (LEO) satellite-enabled in-band IAB operation with orthogonal and non-orthogonal bandwidth allocation between access and backhauling, and under both time- and frequency-division duplex (TDD/FDD) transmission modes. Numerical results demonstrate the feasibility of IAB through satellites, and illustrate the superiority of FDD over TDD transmission. It is also shown that in the absence of precoding, non-orthogonal bandwidth allocation between the access and the backhaul can largely degrades the network throughput.

Recently, non-geostationary orbit (NGSO) satellite communication constellations have regained popularity due to their ability to provide global coverage and lower-latency connectivity. However, the new wave of Low Earth Orbit (LEO) satellite constellations operate on the same spectral bands as legacy satellites in geosynchronous orbit (GSO), which concurrently access the electromagnetic spectrum. Even if international regulations are in place, such increased spectral congestion will result in interference events. Therefore, both regulator entities and GSO operators have a high interest in detecting illegal or unlicensed NGSO interference sources. In this work, we simulate a realistic downlink interference scenario by emulating an actual commercial NGSO orbit whose signal is eventually received in a GSO receiver that is pointed toward a specific GSO satellite. We design an autoencoder deep neural network and we evaluate its performance considering both time-series and frequency-domain series of the overall received samples. Extensive numerical results are presented, validating the interference detection accuracy and comparing both domains of inputs at the autoencoder.

Reconfigurable Intelligent surfaces (RIS) have the potential to significantly improve the performance of future 6G LEO satellite networks. In particular, RIS can improve the signal quality of ground terminal, reduce power consumption of satellite and increase spectral efficiency of overall network. This paper proposes an energy-efficient RIS-enabled NOMA communication for LEO satellite networks. The proposed framework simultaneously optimizes the transmit power of ground terminals at LEO satellite and passive beamforming at RIS while ensuring the quality of services. Due to the nature of the considered system and optimization variables, the problem of energy efficiency maximization is formulated as non-convex. In practice, it is very challenging to obtain the optimal solution for such problems. Therefore, we adopt alternating optimization methods to handle the joint optimization in two steps. In step 1, for any given phase shift vector, we calculate efficient power for ground terminals at satellite using Lagrangian dual method. Then, in step 2, given the transmit power, we design passive beamforming for RIS by solving the semi-definite programming. To validate the proposed solution, numerical results are also provided to demonstrate the benefits of the proposed optimization framework.

A whole suite of innovative technologies and architectures have emerged in response to the rapid growth of wireless traffic. This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite for enhancing the network’s spectral efficiency.As for our analytical contributions, upon coherently combing the signals received by the central processing unit (CPU) from the users through the space and terrestrial links, we first mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. Our generic achievable throughput expression is applicable for arbitrary received signal detection techniques employed at the APs and the satellite under realistic imperfect channel estimates. A closed-form expression is then obtained for the ergodic UL data throughput, when maximum ratio combining is utilized for detecting the desired signals.As for our resource allocation contributions, we formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics for performing power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated relying on a closed-form expression. Our integrated hybrid network concept allows users to be served that may not otherwise be accommodated due to the excessive data demands. The algorithms proposed allow us to address the congestion issues appearing when at least one user is served at a rate below his/her target. The mathematical analysis is also illustrated with the aid of our numerical results that show the added benefits of considering the space links in terms of improving the ergodic data throughput. Furthermore, the proposed algorithms smoothly circumvent any potential congestion, especially in face of high rate requirements and weak channel conditions.

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.

Beam Hopping (BH) is a popular technique considered for next-generation multi-beam satellite communication system which allows a satellite focusing its resources on where they are needed by selectively illuminating beams. While beam illumination plan can be adjusted according to its needs, the main limitation of convectional BH is the adjacent beam avoidance requirement needed to maintain acceptable levels of interference. With the recent maturity of precoding technique, a natural way forward is to consider a dynamic beam illumination scheme with selective precoding, where large areas with high-demand can be covered by multiple active precoded beams. In this paper, we mathematically model such beam illumination design problem employing an interference-based penalty function whose goal is to avoid precoding whenever possible subject to beam demand satisfaction constraints. The problem can be written as a binary quadratic programming (BQP). Next, two convexification frameworks are considered namely: (i) A Semi-Definition Programming (SDP) approach particularly targeting BQP type of problems, and (ii) Multiplier Penalty and Majorization-Minimization (MPMM) based method which guarantees to converge to a local optimum. Finally, a greedy algorithm is proposed to alleviate complexity with minimal impact on the final performance. Supporting results based on numerical simulations show that the proposed schemes outperform the relevant benchmarks in terms of demand matching performance while minimizing the use of precoding.

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.

This paper proposes a distributed learning framework for network slicing in multi-cell open radio access networks providing two services: Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). In particular, a resource allocation optimization problem is formulated with an objective to maximize the average eMBB data rate while considering URLLC constraints and the data rate variance among eMBB users. A multi-agent Deep Reinforcement Learning (DRL) based algorithm is developed to solve the formulated problem, where network components collaboratively train a global machine learning model and then share learning parameters for distributed executions at network edges. Specifically, DRL agents are installed at Near-Real-Time Radio access network Intelligent Controllers (Near-RT RICs) located in the network edge servers to provide online resource allocation decisions while the training process is performed offline at the Non-Real-Time RIC (Non-RT RIC) located in a regional cloud server. The achieved simulation results show that the proposed algorithm can ensure the required URLLC reliability while keeping the Quality-of-Service (QoS) requirements of the eMBB service.

This paper studies an integrated network design that boosts system capacity through cooperation between wireless access points (APs) and a satellite. By coherently combing the signals received by the central processing unit from the users through the space and terrestrial links, we mathematically derive an achievable throughput expression for the uplink (UL) data transmission over spatially correlated Rician channels. A closed-form expression is obtained when maximum ratio combining is employed to detect the desired signals. We formulate the max-min fairness and total transmit power optimization problems relying on the channel statistics to perform power allocation. The solution of each optimization problem is derived in form of a low-complexity iterative design, in which each data power variable is updated based on a closed-form expression. The mathematical analysis is validated with numerical results showing the added benefits of considering a satellite link in terms of improving the ergodic data throughput.

This paper aims to determine linear precoding (LP) vectors, beam hopping (BH), and discrete DVB-S2X transmission rates jointly for the GEO satellite communication systems to minimize the payload power consumption and satisfy ground users' demands within a time window. Regarding constraint on the maximum number of illuminated beams per time slot, the technical requirement is formulated as a sparse optimization problem in which the hardware-related beam illumination energy is modeled in a sparsity form of the LP vectors. To cope with this problem, the compressed sensing method is employed to transform the sparsity parts into the quadratic form of pre-coders. Then, an iterative window-based algorithm is developed to update the LP vectors sequentially to an efficient solution. Additionally, two other two-phase frameworks are also proposed for comparison purposes. In the first phase, these methods aim to determine the MODCOD transmission schemes for users to meet their demands by using a heuristic approach or DNN tool. In the second phase, the LP vectors of each time slot will be optimized separately based on the determined MODCOD schemes.

Low Earth orbit (LEO) satellite communication has drawn particular attention recently due to its high data rate services and low round-trip latency. It is low-cost to launch and can provide global coverage. However, the spectrum scarcity might be one of the critical challenges in the growth of LEO satellites, impacting severe restrictions on the development of ground-space integrated networks. To address this issue, we propose rate splitting multiple access (RSMA) for cognitive radio (CR) enabled nongeostationary orbit (GEO)-LEO coexisting satellite network. In particular, this work aims to maximize the system's sum rate by simultaneously optimizing the power allocation and sub carrier beam assignment of LEO satellite communication while restricting the interference temperature to GEO satellite users. The problem of sum rate maximization is formulated as non-convex and a Global optimal solution is challenging to obtain. Therefore, we first employ the successive convex approximation technique to reduce the complexity and make the problem more tractable. Then for the power allocation, we exploit Karush-Kuhn-Tucker (KKT) condition and adopt an efficient algorithm based on the greedy approach for subcarrier beam assignment. We also propose two suboptimal schemes with fixed power allocation and random sub carrier beam assignment as the benchmark. Results demonstrate the benefits of the proposed scheme compared to the benchmark schemes.

The low earth orbit (LEO) satellite system is one of the promising solutions to provide broadband services to a wide-coverage area for future integrated LEO-6G networks, where users' demands vary with time and geographical locations. Conventional satellites with fixed beam pattern and footprint planning may not be capable of meeting such dynamic requests and irregular traffic distributions. As the development of flexible satellite payload with beamforming capabilities, spot beams with flexible size and shape are considered potential solutions to this issue. As an early investigation, in this paper, we consider the scenarios where satellite payloads are equipped with multiple beam patterns and study the optimal beam pattern selection. We exploit the potential synergies of joint resource optimization between adaptive beam patterns and non-orthogonal multiple access (NOMA) in a LEO satellite system, where NOMA is employed to reduce intra-beam interference and flexible beam pattern is adopted to mitigate inter-satellite interference. The formulated problem is to minimize the capacity-demand gap of terminals, which falls into mixed-integer nonconvex pro-gramming (MINCP). To tackle the discrete variables and non-convexity, we design a joint approach to allocate power and select beam patterns. Numerical results show that the proposed scheme achieves capacity-demand gap reduction of 37.8% over conventional orthogonal multiple access (OMA) and 42.5% over the fixed-beam-pattern scheme.

The commercial low earth orbiting (LEO) satellite constellations have shown unprecedented growth. Accordingly, the risk of generating harmful interference to the geostationary orbit (GSO) satellite services increases with the number of satellites in such mega-constellations. As the GSO arc encompasses the primary and existing satellite assets providing essential fixed and broadcasting satellite services, the interference avoidance for this area is of the utmost importance. In particular, non-geostationary orbit (NGSO) operators should comply with the regulations set up both by their national regulators and by the International Telecommunications Union (ITU) to minimize the impact of emissions on existing GSO and non-GSO systems. In this paper, we first provide an overview of the most recent radio regulations that dictate the NGSO-GSO spectral co-existence. Next, we analyze the NGSO-GSO radio frequency interference for the downlink scenario, following the so-called time-simulation methodology introduced by ITU. The probability distribution of aggregated power flux-density for NGSO co-channel interference is evaluated and assessed, adopting different degrees of exclusion angle strategy for interference avoidance. We conclude the paper by discussing the resulting implications for the continuity of operation and service provision and we provide remarks for future work.

Through precoding, the spectral efficiency of the system can be improved; thus, more users can benefit from 5G and beyond broadband services. However, complete precoding (using all precoding coefficients) may not be possible in practice due to the high signal processing complexity involved in calculating a large number of precoding coefficients and combining them with symbols for transmission. In this paper, we propose an energy-efficient sparse precoding design, where only a few precoding coefficients are used with lower transmit power consumption depending on the demand. In this context, we formulate an optimization problem that minimizes the number of in-use precoding coefficients and the system power consumption while matching the per beam demand. This problem is non-convex. Hence, we apply Lagrangian relaxation and successive convex approximation to convexify it. The proposed solution outperforms the benchmark schemes in energy efficiency and demand satisfaction with the additional advantage of sparse precoding design.

Satellite communications industry is currently going through a rapid and profound transformation to adapt to the recent innovations and developments in the realm of non-geostationary orbit (NGSO) satellites. The growing popularity of NGSO systems, with cheap manufacturing and launching costs, has set to revolutionize the internet market. In this context, accurate channel characterization is crucial for the performance optimization and designing efficient NGSO communications, especially considering the dynamic propagation environment. While the Third Generation Partnership Project (3GPP) has provided some guidelines in Release 15, we observed certain divergence on the channel models considered in the literature, each with different assumptions and peculiarities. This paper provides an extensive review of the existing methods proposed for NGSO channel modeling that consider different orbits, frequency bands, user equipment, use-case and scenario peculiarities. The provided review discusses the channel modeling efforts from a contemporary perspective through trade-off analyses, classifications, and highlighting their advantages and pitfalls. The main goal is to provide a comprehensive overview of NGSO channel models to facilitate the selection of the most appropriate channel based on the scenario requirements to be evaluated and/or analysed.

To ensure quality of service to the users within the coverage area, time-flexible satellite system needs to design a beam illumination strategy, i.e. a time-space transmission pattern that is periodically repeated. The beam activation dwells just long enough to satisfy the traffic demand. The beam illumination pattern design is typically a combinatorial problem with a non-convex structure due to the presence of inter-beam interference. The computational complexity of existing solutions addressing this problem are unbearable for practical systems. In this paper, we propose a low-complexity beam illumination design which splits the task into two sequential sub-problems: (i) Estimation of number of time-slots to be allocated to each geographical area in order to satisfy its demand; (ii) Assignment of illumination slots over the time domain. Note that the outcome of step (ii) determines the resulting interference environment and, as a consequence, the resulting offered capacity. The latter is, at the same time, an input needed for step (i). For this reason, we propose an adaptive system where the two steps are iteratively executed until convergence. Furthermore, we show that a random assignment for step (ii) significantly reduces the complexity without a major impact on the performance. The proposed design is validated and compared with existing schemes using numerical results.

Efficiently utilizing the network resources to minimize the operation costs while satisfying customer’s Quality-of-Experience (QoE) related requirement as well as dynamic demands is a challenging task of all network operators. This paper aims to develop a stationary capacity allocation method that anticipates time-varying demand and keeps the network operating under constraints on a stochastic blocking probability. Queuing delay requirement is also regarded as an QoE-oriented practical design. Employing an approximation of time-varying queuing model and continuous time Markov chain (CTMC) for queue length, the technical designs are stated as a convex stochastic optimization based on which a dynamic capacity allocation is proposed by using Lagrangian and gradient descent searching method. Numerical studies confirm that our proposed framework can efficiently and dynamically allocate optimal capacity for a blocking probability of less than 1% and the probability of violating the queuing-delay requirement is less than 5%.

Backscatter communications (BC) has emerged as a promising technology for providing low-powered transmissions in nextG (i.e., beyond 5G) wireless networks. The fundamental idea of BC is the possibility of communications among wireless devices by using the existing ambient radio frequency signals. Non-orthogonal multiple access (NOMA) has recently attracted significant attention due to its high spectral efficiency and massive connectivity. This paper proposes a new optimization framework to minimize total transmit power of BC-NOMA cooperative vehicle-to-everything networks (V2XneT) while ensuring the quality of services. More specifically, the base station (BS) transmits a superimposed signal to its associated roadside units (RSUs) in the first time slot. Then the RSUs transmit the superimposed signal to their serving vehicles in the second time slot exploiting decode and forward protocol. A backscatter device (BD) in the coverage area of RSU also receives the superimposed signal and reflect it towards vehicles by modulating own information. Thus, the objective is to simultaneously optimize the transmit power of BS and RSUs along with reflection coefficient of BDs under perfect and imperfect channel state information. The problem of energy efficiency is formulated as non-convex and coupled on multiple optimization variables which makes it very complex and hard to solve. Therefore, we first transform and decouple the original problem into two sub-problems and then employ iterative sub-gradient method to obtain an efficient solution. Simulation results demonstrate that the proposed BC-NOMA V2XneT provides high energy efficiency than the conventional NOMA V2XneT without BC.

Reflecting intelligent surfaces (RIS) is a low-cost and energy-efficient solution to achieve high spectral efficiency in sixth-generation (6G) networks. The basic idea of RIS is to smartly reconfigure the signal propagation by using passive reflecting elements. On the other side, the demand of high throughput geostationary (GEO) satellite communications (SatCom) is rapidly growing to deliver broadband services in inaccessible/insufficient covered areas of terrestrial networks. This paper proposes a GEO SatCom network, where a satellite transmits the signal to a ground mobile terminal using multicarrier communications. To enhance the effective gain, the signal delivery from satellite to the ground mobile terminal is also assisted by RIS which smartly shift the phase of the signal towards ground terminal. We consider that RIS is mounted on a high building and equipped With multiple re-configurable passive elements along with smart controller. We jointly optimize the power allocation and phase shift design to maximize the channel capacity of the system. The joint optimization problem is formulated as nonconvex due to coupled variables which is hard to solve through traditional convex optimization methods. Thus, we propose a new $\epsilon-$ optimal algorithm which is based on Mesh Adaptive Direct Search to obtain an efficient solution. Simulation results unveil the benefits of RIS-assisted SatCom in terms of system channel capacity.

Very High Throughput Satellite (VHTS) systems are characterized by a multi-beam footprint covering wide areas and providing service to large numbers of users. Multicasting comes naturally to exploit the multiuser diversity in VHTS systems, where the data of different users are multiplexed in a single PHY frame. Following the DVB-S2(X) standard, the resulting PHY frame is encoded using a single codeword. The latter brings some practical implementation challenges when precoding is considered, as the precoder can no longer be designed on a user-by-user basis. Avoiding overambitious and impractical precoding designs, our work focuses on the low-complexity MMSE-based precoding, which has been considered as the baseline for early satellite over-the-air precoding tests. While the multicast scheduling has been widely investigated in the literature, we will show in this work that its performance is significantly impacted by the equivalent multicast channel calculation. Therefore, in this paper, we analyze and report the impact of the user scheduling (i.e., selection of users to be multiplexed together in a single PHY frame) as well as the methodology employed for the equivalent multicast channel considered for the precoding computation.

High-throughput satellite communication systems are growing in strategic importance thanks to their role in delivering broadband services to mobile platforms and residences and/or businesses in rural and remote regions globally. Although precoding has emerged as a prominent technique to meet ever-increasing user demands, there is a lack of studies dealing with congestion control. This paper enhances the performance of multi-beam high throughput geostationary satellite systems under congestion, where the users’ quality of service (QoS) demands cannot be fully satisfied with limited resources. In particular, we propose congestion control strategies, relying on simple power control schemes. We formulate a multi-objective optimization framework balancing the system sum-rate and the number of users satisfying their QoS requirements. Next, we propose two novel approaches that effectively handle the proposed multi-objective optimization problem. The former is a model-based approach that relies on the weighted sum method to enrich the number of satisfied users by solving a series of the sum-rate optimization problems in an iterative manner. The latter is a data-driven approach that offers a low-cost solution by utilizing supervised learning and exploiting the optimization structures as continuous mappings. The proposed general framework is evaluated for different linear precoding techniques, for which the low computational complexity algorithms are designed. Numerical results manifest that our proposed framework effectively handles the congestion issue and brings superior improvements of rate satisfaction to many users than previous works. Furthermore, the proposed algorithms show low run-time and make them realistic for practical systems.

Next-generation satellite systems require more flexibility in resource management such that available radio resources can be dynamically allocated to meet time-varying and non-uniform traffic demands. Considering potential benefits of beam hopping (BH) and non-orthogonal multiple access (NOMA), we exploit the time-domain flexibility in multi-beam satellite systems by optimizing BH design, and enhance the power-domain flexibility via NOMA. In this paper, we investigate the synergy and mutual influence of beam hopping and NOMA. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the gap between requested traffic demand and offered capacity. In the solution development, we formally prove the NP-hardness of the optimization problem. Next, we develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the benefits of combining NOMA and BH, and validate the superiority of the proposed BH-NOMA schemes over benchmarks.

Smart radio resource allocation combined with the recent advances of digital payloads will allow to control the transmit power and bandwidth of the satellites depending on the demand and the channel conditions of users. The system flexibility is important not only to handle divergent demand requirements but also to efficiently utilize the limited and expensive satellite resources. In this paper, we propose a demand-aware smart radio resource allocation technique, where the transmit power and the bandwidth of the GEO satellite are minimized while satisfying the user demand. The formulated optimization problem is non-convex mixed-integer nonlinear program which is difficult to solve. Hence, we apply a quadratic transform to solve the problem iteratively. The numerical results showed that the proposed scheme outperforms the benchmark schemes in terms of bandwidth utilization while accurately providing capacity-on-demand.

In this paper, we apply non-orthogonal multiple access (NOMA) in satellite systems to assist data transmission for services with latency constraints. We investigate a problem to minimize the transmission time by jointly optimizing power allocation and terminal-timeslot assignment for accomplishing a transmission task in NOMA-enabled satellite systems. The problem appears non-linear/non-convex with integer variables and can be equivalently reformulated in the format of mixed-integer convex programming (MICP). Conventional iterative methods may apply but at the expenses of high computational complexity in approaching the optimum or near-optimum. We propose a combined learning and optimization scheme to tackle the problem, where the primal MICP is decomposed into two learning-suited classification tasks and a power allocation problem. In the proposed scheme, the first learning task is to predict the integer variables while the second task is to guarantee the feasibility of the solutions. Numerical results show that the proposed algorithm outperforms benchmarks in terms of average computational time, transmission time performance, and feasibility guarantee.

Satellite Communications offer complementary benefits to terrestrial 5G/6G infrastructure, covering a wide range of use cases in need of ubiquitous coverage and reliability. However, to be as competitive as the terrestrial counterpart in terms of supplied throughput, satellite communications require a highly efficient use of the limited available spectrum. Linear precoding has demonstrated the ability to boost the spectral efficiency in the satellite domain, but raising a new issue: the bandwidth requirements of the feeder link. Deployment of several gateways, each of which precoding an independent cluster of beams causes performance degradation. Therefore, in this paper, we investigate the centralized gateway concept, where all digital baseband processes (including precoding) are implemented in a remote server connected via high speed fibers to the distributed remote gateways responsible for the downlink and uplink of the satellite radio frequency signals. In particular, we highlight the main technical challenges and provide a preliminary vision of potential solutions.

This paper addresses the radio access network (RAN) resource slicing problem in the context of the joint allocation of transmit powers and time-frequency resource blocks (RBs) in the 5G system consisting of ultra-reliable and low-latency communication (URLLC) and enhanced mobile broadband (eMBB) users. Specifically, we formulate a modulation and coding scheme (MCS) based optimization problem to maximize the sum goodput of eMBB users while satisfying URLLC and eMBB users' QoS requirements. The proposed scheme considers the impact of imperfect channel state information (CSI) and the active user's queue status for the dynamic assignment of radio resources to the heterogeneous users according to its demand. The resulting mixed-integer non-convex problem is first transformed into a tractable form by exploiting the probabilistic to non-probabilistic conversion, Big-M theory, and difference-of-convex (DC) programming. Later, the transformed problem is solved using the successive convex approximation (SCA) based iterative algorithm. Our simulation results illustrate the superiority of the proposed algorithm compared to the baseline methods in terms of eMBB rate, latency in delivering the URLLC packets, and total power consumption.

In this paper, the limits in terms of offered capacity for a non-precoded geostationary (GEO) satellite communication system is investigated. In particular, we focus on the smart radio resource assignment as a technique to manage the interference across the multi-beam pattern of the GEO system. In this context, a joint power and carrier allocation problem is formulated to maximize the capacity of the system. The formulated optimization problem is non-convex and difficult to solve. Hence, we propose to address the frequency allocation first by assuming an equal power distribution, followed by the optimal power assignment to maximize the sum-capacity. Numerical evaluations are presented comparing the proposed method with a precoded-based system and with benchmark resource allocation schemes, showing the benefits of the proposed technique and identifying the limits of a non-precoded GEO satellite communications system.

The growing demand for broadband applications has driven the satellite communication service providers to investigate High Throughput Satellite (HTS) solutions. While precoding has been identified as the most promising technique to boost the satellite spectral efficiency, new advanced solutions focus on re-configurable demand-driven systems, where throughput delivered aligns with the time and geographical variations of the traffic demand. For such goal, conventional user scheduling algorithms fail to meet the uneven user traffic demand. In this paper, we propose a novel unicast scheduling algorithm that takes into account both the channel orthogonality required for precoding along with the particular user demands. We name such technique as Weighted Semi-Orthogonal Scheduling (WSOS) methodology. Supporting numerical results are provided that validate the effectiveness of the proposed scheduling and quantify the benefits over conventional scheduling techniques.

In this paper, we investigate potential synergies of non-orthogonal multiple access (NOMA) and beam hopping (BH) for multi-beam satellite systems. The coexistence of BH and NOMA provides time-power-domain flexibilities in mitigating a practical mismatch effect between offered capacity and requested traffic per beam. We formulate the joint BH scheduling and NOMA-based power allocation problem as mixed-integer non-convex programming. We reveal the exponential-conic structure for the original problem, and reformulate the problem to the format of mixed-integer conic programming (MICP), where the optimum can be obtained by exponential-complexity algorithms. A greedy scheme is proposed to solve the problem on a timeslot-by-timeslot basis with polynomial-time complexity. Numerical results show the effectiveness of the proposed efficient suboptimal algorithm in reducing the matching error by 62.57% in average over the OMA scheme and achieving a good trade-off between computational complexity and performance compared to the optimal solution.

Conventional GEO satellite communication systems rely on a multibeam foot-print with a uniform resource allocation to provide connectivity to users. However, applying uniform resource allocation is inefficient in presence of non-uniform demand distribution. To overcome this limitation, the next generation of broadband GEO satellite systems will enable flexibility in terms of power and bandwidth assignment, enabling on-demand resource allocation. In this paper, we propose a novel satellite resource assignment design whose goal is to satisfy the beam traffic demand by making use of the minimum transmit power and utilized bandwidth. The motivation behind the proposed design is to maximize the satellite spectrum utilization by pushing the spectrum reuse to affordable limits in terms of tolerable interference. The proposed problem formulation results in a non-convex optimization structure, for which we propose an efficient tractable solution. We validate the proposed method with extensive numerical results, which demonstrate the efficiency of the proposed approach with respect to benchmark schemes.

The use of multi-antenna transmitters is emerging as an essential technology of the future wireless communication systems. While Zero-Forcing Beamforming (ZFB) has become the most popular low-complexity transmit beamforming design, it has some drawbacks basically related to the effort of "trying" to invert the channel coefficients towards the interfered users. In particular, ZFB performs poorly in the low Signal-to-Noise Ratio (SNR) regime and does not work when the interfered users outnumber the transmit antennas. In this paper, we study in detail an alternative transmit beamforming design framework, where we allow some residual received-interference power instead of trying to null it completely out. Subsequently, we provide a close-form non-iterative optimal solution that avoids the use of sophisticated convex optimization techniques that compromise its applicability onto practical systems. Supporting results based on numerical simulations show that the proposed transmit beamforming is able to perform close to the optimal with much lower computational complexity.

Carrier Aggregation is one of the essential approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in terrestrial wireless systems, its application to satellite has not been substantially explored. Carrier aggregation can be a prominent solution to address the issue of the spatially-heterogeneous satellite data traffic demand. This paper studies introducing carrier aggregation into satellite systems from a link layer perspective. The proposed modifications at the link layer have been carefully designed to make carrier aggregation transparent to the other layers. However, deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. Since channel awareness is indispensable for any efficient resource allocation schemes, we have proposed a perceptive scheduling algorithm that takes into account channel properties along with the instantaneous available resources to ensure that the received data packets are delivered without perturbing the original transmission order. Simulation results are given to validate our analysis and demonstrate the design tradeoffs, and thus, our results provide useful insights to practical scheduler design.

We propose a novel technique for total transmit power minimization and optimal precoder design in wireless multi-group (MG) multicasting (MC) systems. The considered framework consists of three different systems capable of handling heterogeneous user types viz., information decoding (ID) specific users with conventional receiver architectures, energy harvesting (EH) only users with non-linear EH module, and users with joint ID and EH capabilities having separate units for the two operations, respectively. Each user is categorized under unique group(s), which can be of MC type specifically meant for ID users, and/or an energy group consisting of EH explicit users. The joint ID and EH users are a part of the (last) EH group as well as any one of the MC groups distinctly. In this regard, we formulate an optimization problem to minimize the total transmit power with optimal precoder designs for the three aforementioned scenarios, under constraints on minimum signal-to-interference-plus-noise ratio and harvested energy by the users with respective demands. The problem may be adapted to the well-known semi-definite program, which can be typically solved via relaxation of rank-l constraint. However, the relaxation of this constraint may in some cases lead to performance degradation, which increases with the rank of the solution obtained from the relaxed problem. Hence, we develop a novel technique motivated by the feasible-point pursuit and successive convex approximation method in order to address the rank-related issue. The benefits of the proposed method are illustrated under various operating conditions and parameter values, with comparison between the three above-mentioned scenarios.

Current multi-beam GEO satellite systems operate under a limited frequency reuse configuration and considering uniform power assignment across beams. The latter has been shown to be inefficient in matching the geographic distribution of the traffic demand. In this context, next generation of broadband GEO satellite systems will be equipped with more flexible and reconfigurable payloads, facilitating on-demand resource allocation. In this paper, we consider both carrier and power assignment to match the requested beam demands while minimizing the total transmit power and the total utilized bandwidth. A novel optimization problem is formulated and, given its non-convex structure, we divide the problem into two tractable sub-problems. First, we estimate the number of adjacent frequency carriers required for each beam to satisfy its demand and, subsequently, we optimize the power allocation based on the previously assigned carriers. We validate the proposed method with extensive numerical results, which demonstrate its efficiency with respect to benchmark strategies.

Data-driven approaches, e.g., deep learning (DL),have been widely studied in terrestrial wireless communications fields, proving the benefits and potentials of such techniques. In comparison, DL for satellite networks is studied to a limited extent in the literature. In this paper, we develop a DL assisted approach to facilitate efficient beam hopping (BH) in multibeam satellite systems. BH is adopted to provide a high level of flexibility to manage irregular and time variant traffic requests in the satellite coverage area. Conventional iterative optimization approaches and typical data-driven techniques may have their respective limitations in achieving timely and satisfactory performance. We herein explore a combined learning-and-optimization approach to provide a fast, feasible, and near-optimal solution for BH scheduling. Numerical study shows that in the proposed solution, the learning component is able to largely accelerate the procedure of BH pattern selection and allocation, while the optimization component can guarantee the solution's feasibility and improve the overall performance.

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) multiuser systems, in which a base station serves a set of users with both information and energy simultaneously via a power splitting (PS) mechanism. To capture realistic scenarios, a nonlinear energy harvesting (EH) model is considered. In particular, we jointly design the PS factors and the beamforming vectors in order to maximize the total harvested energy, subjected to rate requirements and a total transmit power budget. To deal with the inherent non-convexity of the formulated problem, an iterative optimization algorithm is proposed based on the inner approximation method and semide-finite relaxation (SDR), whose convergence is theoretically guaranteed. Numerical results show that the proposed scheme significantly outperforms the baseline max-min based SWIPT multicast and fixed-power PS designs.

This paper designs dynamic onboard signal processing schemes in a multiple gateway multi-beam satellite system where full frequency reuse pattern is considered among the beams and feeds. In particular, we deploy on-board Joint Precoding, Feed selection and Signal switching mechanism (JPFS) so that the following advantages are realized, I) No need of Channel State Information (CSI) exchange among the gateways and satellite, since the performance of precoding is highly sensitive to the quality of CSI, II) In case one gateway fails, rerouting signals through other gateways can be applied without any extra signal processing, III) Properly selecting on-board feed/s to serve each user which generates maximum gain toward corresponding user, IV) Flexibly switching the signals received from the gateways to requested users where each user can dynamically request traffic from any gateway, and V) Multiple users with multiple traffic streams can be dynamically served at each beam. However, deploying such JPFS architecture imposes high complexity to the satellite payload. To tackle this issue, this study aims at deploying JPFS that can provide affordable complexity at the payload. In addition, while increasing the data demand imposes extensive bandwidth resources requirement in the feeder link, the proposed JPFS design works efficiently with available feeder link resources even if the data demand increases. The proposed design is evaluated with a close-to-real beam pattern and the latest broadband communication standard for satellite communications.

Carrier Aggregation (CA) is an integral part of current terrestrial networks. Its ability to enhance the peak data rate, to efficiently utilize the limited available spectrum resources and to satisfy the demand for data-hungry applications has drawn large attention from different wireless network communities. Given the benefits of CA in the terrestrial wireless environment, it is of great interest to analyze and evaluate the potential impact of CA in the satellite domain. In this paper, we study CA in multibeam high throughput satellite systems. We consider both inter-transponder and intra-transponder CA at the satellite payload level of the communication stack, and we address the problem of carrier-user assignment assuming that multiple users can be multiplexed in each carrier. The transmission parameters of different carriers are generated considering the transmission characteristics of carriers in different transponders. In particular, we propose a flexible carrier allocation approach for a CA-enabled multibeam satellite system targeting a proportionally fair user demand satisfaction. Simulation results and analysis shed some light on this rather unexplored scenario and demonstrate the feasibility of the CA in satellite communication systems.

Beam-Hopping (BH) and precoding are two trending technologies for the satellite community. While BH enables flexibility to adapt the offered capacity to the heterogeneous demand, precoding aims at boosting the spectral efficiency. In this paper, we consider a high throughput satellite (HTS) system that employs BH in conjunction with precoding. In particular, we propose the concept of Cluster-Hopping (CH) that seamlessly combines the BH and precoding paradigms and utilize their individual competencies. The cluster is defined as a set of adjacent beams that are simultaneously illuminated. In addition, we propose an efficient time-space illumination pattern design, where we determine the set of clusters that can be illuminated simultaneously at each hopping event along with the illumination duration. We model the CH time-space illumination pattern design as an integer programming problem which can be efficiently solved. Supporting results based on numerical simulations are provided which validate the effectiveness of the proposed CH concept and time-space illumination pattern design.

In a multi-beam satellite communication system, traffic requests are typically asymmetric across beams and highly heterogeneous among terminals. In practical operations, it is important to achieve a good match between the offered and requested traffic, i.e., to improve the performance of Offered Capacity to requested Traffic Ratio (OCTR). Due to satellites’ payload constraints and limited flexibilities, it is a challenging task for resource optimization. In this paper, we tackle this issue by formulating a max-min resource allocation problem, taking fairness into account such that the lowest OCTR can be maximized. To exploit the potential synergies, we introduce Non-Orthogonal Multiple Access (NOMA) to enable aggressive frequency reuse and mitigate intra-beam interference. Although NOMA has proven its capabilities in improving throughput and fairness in 5G terrestrial networks, for multi-beam satellite systems it is unclear if NOMA can help to enhance the OCTR performance, and hence is worth quantifying how much gain it can bring. To solve the problem, we design a suboptimal algorithm to firstly decompose the original problem into multiple convex subproblems by fixing power allocation for each beam, and secondly adjust beam power to improve the minimum OCTR in iterations. Numerical results show the convergence of the proposed algorithm and the superiority of the proposed NOMA scheme in max-min OCTR.

We propose a novel formulation for joint maximization of total weighted sum-spectral efficiency and weighted sum-harvested energy to study Simultaneous Wireless Information and Power Transfer (SWIPT) from a pricing perspective. Specifically, we consider that a transmit source communicates with multiple destinations using Orthogonal Frequency Division Multiplexing (OFDM) system within a dual-hop relay-assisted network, where the destination nodes are capable of jointly decoding information and harvesting energy from the same radiofrequency (RF) signal using either the time-switching (TS) or power-splitting (PS) based SWIPT receiver architectures. Computation of the optimal solution for the aforementioned problem is an extremely challenging task as joint optimization of several network resources introduce intractability at high numeric values of relays, destination nodes and OFDM sub-carriers. Therefore, we present a suitable algorithm with sub-optimal results and good performance to compute the performance of joint data processing and harvesting energy under fixed pricing methods by adjusting the respective weight factors, motivated by practical statistics. Furthermore, by exploiting the binary options of the weights, we show that the proposed formulation can be regulated purely as a sum-spectral efficiency maximization or solely as a sum-harvested energy maximization problem. Numerical results illustrate the benefits of the proposed design under several operating conditions and parameter values.

The optimal flow assignment is strongly dependent on the network link capacities, which in turn are determined by the allocation of the available radio resources. In this paper, we consider the holistic design of joint power and flow assignment in the context of Integrated Terrestrial-Satellite Backhaul (ITSB) networks. Aiming for an spectral efficient system, we focus on the scenario where the satellite links operate in the non- exclusive Ka band, which is shared with the terrestrial microwave backhaul links. We focus on the maximization of the network throughput considering a penalizing term to restrict the use of the satellite links in order to avoid the expensive cost of satellite bandwidth. The interference resulting from the spectrum sharing assumption makes the joint power and flow assignment a very challenging problem. We propose a convex relaxation approach which eases the formulation and allows the implementation of efficiency convex optimization tools to achieve a feasible solution to the original problem. Supporting results based on numerical simulations validate the proposed approach.

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user orthogonal frequency division multiple access (OFDMA) network, where the end-nodes support the simultaneous wireless information and power transfer (SWIPT) employing a power splitting (PS) technique. Our goal is to optimize the end-nodes’ PS ratios as well as the relay, carrier, and power assignment so that the sum-rate of the system is maximized subject to harvested energy and transmitted power constraints. Such joint optimization with mixed-integer non-linear programming structure is combinatorial in nature. Due to the complexity of this problem, we propose to solve its dual problem, which guarantees asymptotic optimality and less execution time compared to a highly-complex exhaustive search approach. Furthermore, we also present a heuristic method to solve this problem with lower computational complexity. The simulation results reveal that the proposed algorithms provide significant performance gains compared to a semi-random resource allocation and relay selection approach and is close to the optimal solution when the number of OFDMA sub-carriers is sufficiently large.

In this paper, we investigate resource allocation and relay selection in a dual-hop orthogonal frequency division multiplexing (OFDM)-based multi-user network where amplify-and-forward (AF) enabled relays facilitate simultaneous wireless information and power transfer (SWIPT) to the end- users. In this context, we address an optimization problem to maximize the end-users' sum-rate subjected to transmit power and harvested energy constraints. Furthermore, the problem is formulated for both time-switching (TS) and power- splitting (PS) SWIPT schemes.We aim at optimizing the users' SWIPT splitting factors as well as sub-carrier-destination assignment, sub-carrier pairing, and relay-destination coupling metrics. This kind of joint evaluation is combinatorial in nature with non-linear structure involving mixed-integer programming. In this vein, we propose a sub-optimal low complex sequential resource distribution (SRD) method to solve the aforementioned problem. The performance of the proposed SRD technique is compared with a semi- random resource allocation and relay selection approach. Simulation results reveal the benefits of the proposed design under several parameter values with various operating conditions to illustrate the efficiency of SWIPT schemes for the proposed techniques.

In this paper, we study a problem of relay selection in a two-hop relaying network where the destination is equipped with Simultaneous Wireless Information and Power Transmission (SWIPT) capabilities. In contrast to conventional cooperative networks, the destination node is considered to be capable of simultaneously decoding information and harvesting energy from both the source and the relay transmissions. In this context, we formulate two optimization problems for both time switching (TS) and power splitting (PS) based SWIPT schemes. The first problem is the maximization of the overall user data rate while ensuring a minimum harvested power. The second problem focuses on the maximization of the overall harvested power at the user under the constraint on the minimum achievable rate. Assuming an amplify-and-forward (AF) relay protocol, closed-form solutions are obtained for the selection of an optimal relay, relay amplification coefficient and the optimal time or power splitting factor. The performance of the proposed relay selection strategies with the aforementioned objectives is evaluated and compared with the case of random relay selection. Furthermore, the Rate-Energy (R-E) tradeoff performance of the scenario with both the direct and indirect relay-assisted links is compared to the case where only a relay-assisted link is available. Our simulation results demonstrate the significant benefits of combining direct and indirect links in SWIPT-enabled cooperative networks in terms of the R-E tradeoff.

This paper investigates closed-form expressions to evaluate the performance of the Compressive Sensing (CS) based Energy Detector (ED). The conventional way to approximate the probability density function of the ED test statistic invokes the central limit theorem and considers the decision variable as Gaussian. This approach, however, provides good approximation only if the number of samples is large enough. This is not usually the case in CS framework, where the goal is to keep the sample size low. Moreover, working with a reduced number of measurements is of practical interest for general spectrum sensing in cognitive radio applications, where the sensing time should be sufficiently short since any time spent for sensing cannot be used for data transmission on the detected idle channels. In this paper, we make use of low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. More precisely, this paper provides new closed-form expressions for accurate evaluation of the CS-based ED performance as a function of the compressive ratio and the Signal-to-Noise Ratio (SNR). Simulation results demonstrate the increased accuracy of the proposed equations compared to existing works.

One of the fundamental issues in Wireless Sensor Networks (WSN) is to count and localize multiple targets accurately. In this context, there has been an increasing interest in the literature in using Compressive Sensing (CS) based techniques by exploiting the sparse nature of spatially distributed targets within the monitored area. However, most existing works aim to count and localize the sparse targets utilizing a Single Measurement Vector (SMV) model. In this paper, we consider the problem of counting and localizing multiple targets exploiting the joint sparsity feature of a Multiple Measurement Vector (MMV) model. Furthermore, the conventional MMV formulation in which the same measurement matrix is used for all sensors is not valid any more in practical time-varying wireless environments. To overcome this issue, we reformulate the MMV problem into a conventional SMV in which MMVs are vectorized. Subsequently, we propose a novel reconstruction algorithm which does not need the prior knowledge of the sparsity level unlike the most existing CS-based approaches. Finally, we evaluate the performance of the proposed algorithm and demonstrate the superiority of the proposed MMV approach over its SMV counterpart in terms of target counting and localization accuracies.

In this paper, we consider the cognitive satellite uplink where satellite terminals reuse frequency bands of Fixed-Service (FS) terrestrial microwave links which are the incumbent users in the Ka 27.5–29.5 GHz band. In this scenario, the transmitted power of the cognitive satellite terminals has to be controlled so as to satisfy the interference constraints imposed by the incumbent FS receivers. We investigate and analyze a set of optimization frameworks for the power and rate allocation problem in the considered cognitive satellite scenario. The main objective is to shed some light on this rather unexplored scenario and demonstrate feasibility of the terrestrial-satellite co-existence. In particular, we formulate a multi-objective optimization problem where the rates of the satellite terminals form the objective vector and derive a general iterative framework which provides a Pareto-optimal solution. Next, we transform the multi-objective optimization problem into different single-objective optimization problems, focusing on popular figures of merit such as the sum-rate or the rate fairness. Supporting results based on numerical simulations are provided which compare the different proposed approaches.

A fundamental problem facing the next generation of Satellite Communications (SatComs) is the spectrum congestion and how the scarce spectral resources are allocated to meet the demand for higher rate and reliable broadband communications. In this context, this paper addresses the cognitive satellite uplink where satellite terminals reuse frequency bands of Fixed-Service (FS) terrestrial microwave links which are the incumbent users in the Ka band. In this scenario, the transmit power of the satellite terminals has to be controlled such that the total aggregated interference at the FS system is kept below some acceptable threshold. In this paper, we review simple and efficient power allocation techniques available in the literature and, with slight adaptations, we evaluate them to the proposed satellite uplink and terrestrial FS co-existence scenario. The presented numerical results highlight the tradeoff between the level of channel state information and the rates that can be achieved at the satellite network.

Compressive sensing (CS) has been successfully applied to alleviate the sampling bottleneck in wideband spectrum sensing leveraging the sparsity described by the low spectral occupancy of the licensed radios. However, the existence of interferences emanating from low-regulated transmissions, which cannot be taken into account in the CS model because of their nonregulated nature, greatly degrade the identification of licensed activity. This paper presents a feature-based technique for primary user's spectrum identification with interference immunity which works with a reduced amount of data. The proposed method not only detects which frequencies are occupied by primary users' but also identifies the primary users' transmitted power. The basic strategy is to compare the apriori known spectral shape of the primary user with the power spectral density of the received signal. This comparison is made in terms of autocorrelation by means of a correlation matching, thus avoiding the computation of the power spectral density of the received signal. The essence of the novel interference rejection mechanism lies in preserving the positive semidefinite character of the residual correlation, which is inserted by means of a weighted formulation of the l1-minimization. Simulation results show the effectiveness of the technique for interference suppression and primary user detection.

In cognitive radio (CR), the problem of limited spectral resources is solved by enabling unlicensed systems to opportunistically utilize the unused licensed bands. Compressive Sensing (CS) has been successfully applied to alleviate the sampling bottleneck in wideband spectrum sensing leveraging the sparseness of the signal spectrum in open-access networks. This has inspired the design of a number of techniques that identify spectrum holes from sub-Nyquist samples. However, the existence of interference emanating from low-regulated transmissions, which cannot be taken into account in the CS model because of their non-regulated nature, greatly degrades the identification of licensed activity. Capitalizing on the sparsity described by licensed users, this paper introduces a feature-based technique for primary user's spectrum identification with interference immunity which works with a reduced amount of data. The proposed method detects which channels are occupied by primary users' and also identify the primary users transmission powers without ever reconstructing the signals involved. Simulation results show the effectiveness of the proposed technique for interference suppression and primary user detection.

We address the problem of detecting building dominant scatterers using a reduced number of measurements with applications to through-the-wall radar (TWR) and urban sensing. We consider oblique illumination, which specially enhances the radar returns from the corners formed by the orthogonal intersection of two walls. This letter uses a novel type of image descriptor, named correlogram, which encodes information about spatial correlation of complex amplitudes of each TWR image pixel. The proposed technique compares the known correlogram of the scattering response of an isolated canonical corner reflector with the correlogram of the received radar signal. The feature-based nature of the proposed detector enables corner separation from other indoor scatterers, such as humans.

Compressive sensing (CS) for urban operations and through-the-wall radar imaging has been shown to be successful in fast data acquisition and moving target localizations. The research in this area thus far has assumed effective removal of wall electromagnetic backscatterings prior to CS application. Wall clutter mitigation can be achieved using full data volume which is, however, in contradiction with the underlying premise of CS. In this paper, we enable joint wall clutter mitigation and CS application using a reduced set of spatial-frequency observations in stepped frequency radar platforms. Specifically, we demonstrate that wall mitigation techniques, such as spatial filtering and subspace projection, can proceed using fewer measurements. We consider both cases of having the same reduced set of frequencies at each of the available antenna locations and also when different frequency measurements are employed at different antenna locations. The latter casts a more challenging problem, as it is not amenable to wall removal using direct implementation of filtering or projection techniques. In this case, we apply CS at each antenna individually to recover the corresponding range profile and estimate the scene response at all frequencies. In applying CS, we use prior knowledge of the wall standoff distance to speed up the convergence of the orthogonal matching pursuit for sparse data reconstruction. Real data are used for validation of the proposed approach.