Differential Phase Compensation in Over-the-air Precoding Test-bed for a Multi-beam Satellite

Scientific Conference (2022, April 10)

This article presents a closed-loop differential phase compensation system for a precoding-enabled multibeam satellite forward link and its validation by live experiments on a GEO satellite scenario. The ... [more ▼]

This article presents a closed-loop differential phase compensation system for a precoding-enabled multibeam satellite forward link and its validation by live experiments on a GEO satellite scenario. The precoding operation avoids inter-beam interference and maximizes the spectrum efficiency by full frequency reuse as an alternative to the traditional two-color or four-color reuse methods proposed in the DVB-S2 standard. However, the satellite payload introduces differential phase and frequency impairments, which can degrade the precoding performance. This work describes the implementation of the differential phase and frequency tracking and compensation loop in an end-to-end testbed over a multibeam satellite system with independent local oscillators. The developed system performs end-to-end real-time communication over the satellite link, including channel measurements and precompensation. Results are validated by an over-the-air demonstration using two beams of the SES-14 multibeam satellite. Each beam is transmitted by independent transponders, which results in differential frequency and phase offsets due to the transponder undisciplined local oscillators. This phase offset makes it impossible to use precoding without the phase compensation loop. We prove that the implemented system can successfully track and compensate the differential phase and frequency to improve precoding performance. [less ▲]

Machine Learning for Radio Resource Management in Multibeam GEO Satellite Systems

in Electronics (2022), 11(7), 992

Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and ... [more ▼]

Satellite communications (SatComs) systems are facing a massive increase in traffic demand. However, this increase is not uniform across the service area due to the uneven distribution of users and changes in traffic demand diurnal. This problem is addressed by using flexible payload architectures, which allow payload resources to be flexibly allocated to meet the traffic demand of each beam. While optimization-based radio resource management (RRM) has shown significant performance gains, its intense computational complexity limits its practical implementation in real systems. In this paper, we discuss the architecture, implementation and applications of Machine Learning (ML) for resource management in multibeam GEO satellite systems. We mainly focus on two systems, one with power, bandwidth, and/or beamwidth flexibility, and the second with time flexibility, i.e., beam hopping. We analyze and compare different ML techniques that have been proposed for these architectures, emphasizing the use of Supervised Learning (SL) and Reinforcement Learning (RL). To this end, we define whether training should be conducted online or offline based on the characteristics and requirements of each proposed ML technique and discuss the most appropriate system architecture and the advantages and disadvantages of each approach. [less ▲]

Bayesian Poisson Factorization with SideInformation for User Interest Prediction inHierarchical Edge-Caching Systems

in IEEE Open Journal of the Communications Society (2022), 3

Edge-caching is an effective solution to cope withthe unprecedented data traffic growth by storing contents inthe vicinity of end-users. In this paper, we formulate a hier-archical caching policy where ... [more ▼]

Edge-caching is an effective solution to cope withthe unprecedented data traffic growth by storing contents inthe vicinity of end-users. In this paper, we formulate a hier-archical caching policy where the end-users and cellular basestation (BS) are equipped with limited cache capacity with theobjective of minimizing the total data traffic load in the network.The caching policy is a nonlinear combinatorial programmingproblem and difficult to solve. To tackle the issue, we design aheuristic algorithm as an approximate solution which can besolved efficiently. Moreover, to proactively serve the users, itis of high importance to extract useful information from datarequests and predict user interest about contents. In practice,the data often containimplicit feedbackfrom users which isquite noisy and complicates the reliable prediction of userinterest. In this regard, we introduce a Bayesian Poisson matrixfactorization model which utilizes the available side informationabout contents to effectively filter out the noise in the data andprovide accurate prediction. Subsequently, we design an efficientMarkov chain Monte Carlo (MCMC) method to perform theposterior approximation. Finally, a real-world dataset is appliedto the proposed proactive caching-prediction scheme and ourresults show significant improvement over several commonly-used methods. For example, when the BS and the users havecaches with storage of25%and10%of the total contents sizerespectively, our approach yields around8%improvement withrespect to the state-of-the-art approach in terms of cachingperformance. [less ▲]

Dynamic Bandwidth Allocation and Precoding Design for Highly-Loaded Multiuser MISO in Beyond 5G Networks

in IEEE Transactions on Wireless Communications (2022), 21(3), 1794-1805

Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency ... [more ▼]

Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency resource. It is well known that a multi-antenna base station (BS) can efficiently serve a number of users not exceeding the number of antennas at the BS via precoding design. However, when there are more users than the number of antennas at the BS, conventional precoding design methods perform poorly because inter-user interference cannot be efficiently eliminated. In this paper, we investigate the performance of a highly-loaded multiuser system in which a BS simultaneously serves a number of users that is larger than the number of antennas. We propose a dynamic bandwidth allocation and precoding design framework and apply it to two important problems in multiuser systems: i) User fairness maximization and ii) Transmit power minimization, both subject to predefined quality of service (QoS) requirements. The premise of the proposed framework is to dynamically assign orthogonal frequency channels to different user groups and carefully design the precoding vectors within every user group. Since the formulated problems are non-convex, we propose two iterative algorithms based on successive convex approximations (SCA), whose convergence is theoretically guaranteed. Furthermore, we propose a low-complexity user grouping policy based on the singular value decomposition (SVD) to further improve the system performance. Finally, we demonstrate via numerical results that the proposed framework significantly outperforms existing designs in the literature. [less ▲]

Intelligent Reflecting Surface-assisted MU-MISOSystems with Imperfect Hardware: ChannelEstimation and Beamforming Design

in IEEE Transactions on Wireless Communications (2022), 21(3), 2077-2092

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improvingthe spectral and energy efficiency of the fifth-generation (5G)and beyond networks. It ... [more ▼]

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improvingthe spectral and energy efficiency of the fifth-generation (5G)and beyond networks. It is also noteworthy that an IRS canshape the reflected signal propagation. Most works in IRS-assisted systems have ignored the impact of the inevitable residualhardware impairments (HWIs) at both the transceiver hardwareand the IRS while any relevant works have addressed only simplescenarios, e.g., with single-antenna network nodes and/or withouttaking the randomness of phase noise at the IRS into account.In this work, we aim at filling up this gap by considering ageneral IRS-assisted multi-user (MU) multiple-input single-output(MISO) system with imperfect channel state information (CSI)and correlated Rayleigh fading. In parallel, we present a generalcomputationally efficient methodology for IRS reflect beamforming(RB) optimization. Specifically, we introduce an advantageouschannel estimation (CE) method for such systems accounting forthe HWIs. Moreover, we derive the uplink achievable spectralefficiency (SE) with maximal-ratio combining (MRC) receiver,displaying three significant advantages being: 1) its closed-formexpression, 2) its dependence only on large-scale statistics, and3) its low training overhead. Notably, by exploiting the first twobenefits, we achieve to perform optimization with respect to thethat can take place only at every several coherence intervals,and thus, reduces significantly the computational cost comparedto other methods which require frequent phase optimization.Among the insightful observations, we highlight that uncorrelatedRayleigh fading does not allow optimization of the SE, whichmakes the application of an IRS ineffective. Also, in the case thatthe phase drifts, describing the distortion of the phases in theRBM, are uniformly distributed, the presence of an IRS providesno advantage. The analytical results outperform previous worksand are verified by Monte-Carlo (MC) simulations. [less ▲]

Refracting RIS-Aided Hybrid Satellite-Terrestrial Relay Networks: Joint Beamforming Design and Optimization

in IEEE Transactions on Aerospace and Electronic Systems (2022)

Reconfigurable intelligent surface (RIS) has been viewed as a promising solution in constructing reconfigurable radio environment of the propagation channel and boosting the received signal power by ... [more ▼]

Reconfigurable intelligent surface (RIS) has been viewed as a promising solution in constructing reconfigurable radio environment of the propagation channel and boosting the received signal power by smartly coordinating the passive elements’ phase shifts at the RIS. Inspired by this emerging technique, this article focuses on joint beamforming design and optimization for RIS-aided hybrid satellite-terrestrial relay networks, where the links from the satellite and base station (BS) to multiple users are blocked. Specifically, a refracting RIS cooperates with a BS, where the latter operates as a half-duplex decode-and-forward relay, in order to strengthen the desired satellite signals at the blocked users. Considering the limited onboard power resource, the design objective is to minimize the total transmit power of both the satellite and BS while guaranteeing the rate requirements of users. Since the optimized beamforming weight vectors at the satellite and BS, and phase shifters at the RIS are coupled, leading to a mathematically intractable optimization problem, we propose an alternating optimization scheme by utilizing singular value decomposition and uplink–downlink duality to optimize beamforming weight vectors, and using Taylor expansion and penalty function methods to optimize phase shifters iteratively. Finally, simulation results are provided to verify the superiority of the proposed scheme compared to the benchmark schemes [less ▲]

Deep Convolutional Self-Attention Network forEnergy-Efficient Power Control in NOMA Networks

in IEEE Transactions on Vehicular Technology (2022), 71(5), 5540-5545

In this letter, we propose an end-to-end multi-modalbased convolutional self-attention network to perform powercontrol in non-orthogonal multiple access (NOMA) networks. Weformulate an energy efficiency ... [more ▼]

In this letter, we propose an end-to-end multi-modalbased convolutional self-attention network to perform powercontrol in non-orthogonal multiple access (NOMA) networks. Weformulate an energy efficiency (EE) maximization problem wedesign an iterative solution to handle the optimization problem.This solution can provides an offline benchmark but might notbe suitable for online power control therefore, we employ ourproposed deep learning model. The proposed deep learning modelconsists of two main pipelines, one for the deep feature mappingwhere we stack our self-attention block on top of a ResNet toextract high quality features and focus on specific regions in thedata to extract the patterns of the influential factors (interference,quality of service (QoS) and the corresponding power allocation).The second pipeline is to extract the shallow modality features.Those features are combined and passed to a dense layer toperform the final power prediction. The proposed deep learningframework achieves near optimal performance and outperformstraditional solutions and other strong deep learning models suchas PowerNet and the conventional convolutional neural network(CNN). [less ▲]

Throughput Maximization for Backscatter- and Cache-Assisted Wireless Powered UAV Technology

in IEEE Transactions on Vehicular Technology (2022), 71(5), 5187-5202

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory ... [more ▼]

This paper investigates a wireless powered unmanned aerial vehicle (UAV) communication network with backscatter and caching technologies. Specifically, we assume a self-energized UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source’s signals to the destination. Whereas the source S can act as a wireless charging station or a base station to supply power or transmit information to the UB using the dynamic time splitting (DTS) method. The UAV utilizes its harvested energy for backscattering (i.e., passive communication) and transmit information (i.e., active communication) to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the DTS ratio and the UB’s trajectory with caching capability at the UB. The formulation is troublesome to solve since it is a non-convex problem. To find solutions, we decompose the original problem into two sub-problems, whereas we first optimize the DTS ratio for a given UB’s trajectory and the UB’s trajectory optimization for a given DTS ratio. By using the KKT conditions, a closed-form expression for the optimal value of the DTS ratio is obtained, greatly reducing the computation time. Moreover, the solution of the second sub-problem can be acquired by adopting the successive convex approximation (SCA) technique. Consequently, an efficient alternating algorithm is proposed by leveraging the block coordinate descent (BCD) method. To show the advantages of the proposed BCD-based algorithm, we also provide the solution of the original problem applying the inner approximation (IA) method. Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes. [less ▲]

A Survey on Non-Geostationary Satellite Systems: The Communication Perspective

in IEEE Communications Surveys & Tutorials (2022)

The next phase of satellite technology is being characterized by a new evolution in non-geostationary orbit (NGSO) satellites, which conveys exciting new communication capabilities to provide non ... [more ▼]

The next phase of satellite technology is being characterized by a new evolution in non-geostationary orbit (NGSO) satellites, which conveys exciting new communication capabilities to provide non-terrestrial connectivity solutions and to support a wide range of digital technologies from various industries. NGSO communication systems are known for a number of key features such as lower propagation delay, smaller size, and lower signal losses in comparison to the conventional geostationary orbit (GSO) satellites, which can potentially enable latency-critical applications to be provided through satellites. NGSO promises a substantial boost in communication speed and energy efficiency, and thus, tackling the main inhibiting factors of commercializing GSO satellites for broader utilization. The promised improvements of NGSO systems have motivated this paper to provide a comprehensive survey of the state-of-the-art NGSO research focusing on the communication prospects, including physical layer and radio access technologies along with the networking aspects and the overall system features and architectures. Beyond this, there are still many NGSO deployment challenges to be addressed to ensure seamless integration not only with GSO systems but also with terrestrial networks. These unprecedented challenges are also discussed in this paper, including coexistence with GSO systems in terms of spectrum access and regulatory issues, satellite constellation and architecture designs, resource management problems, and user equipment requirements. Finally, we outline a set of innovative research directions and new opportunities for future NGSO research. [less ▲]

Controlling Smart Propagation Environments: Long-Term Versus Short-Term Phase Shift Optimization

in ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment ... [more ▼]

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to validate the obtained analytical framework. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks. [less ▲]