Demand and Interference Aware Adaptive Resource Management for High Throughput GEO Satellite Systems

The design problem associated with robust downlink beamforming in multicast, multigroup, multicell wireless systems is addressed. The channel state information (CSI) of users is assumed to be imperfect and the uncertainty of CSI is modelled using the Frobenius norm. The objective is to optimise the signal-to-interference-plus-noise ratio over all users with a constraint on the maximum total transmitted power. This was achieved through a robust solution using the successive convex approximation (SCA) method. The beamforming problem is treated as a bi-convex problem, which is solved using the iterate-alternative convex technique. Here, the CSI uncertainty is addressed using a convex package through the non-monotone spectral projected gradient method and the beamforming vector is extracted using the SCA method. Also, the authors offer the required condition to extract the beamform vector using the SCA method through a suboptimal solution that always addressed before using different beamforming methods. Their simulation results examine all proposed system parameters in order to show convergence and feasibility of the solution. They also compare the solution with a suboptimal solution and the quality of service method for imperfect CSI in downlink beamforming. Numerical results show that the robust solution achieves the best power efficiency for practical solutions.

Special Issue: Smart Satellite Systems

This paper studies the data rate that a High Throughput Satellite (HTS) system with fully-regenerative payload can achieve when using an intensity modulation/direct detection optical feeder link. A low-order M-ary Pulse Amplitude Modulation (M-PAM) with time-packing is used to modulate the intensity of the laser diode beam, making use of an external Mach-Zehnder modulator. These M-PAM symbols are recovered on-board the satellite with the aid of a photodetector, and are then encapsulated into the 5G radio frame of the access link. The M-PAM modulation order and the overlapping factor of timepacking are jointly selected to tackle the impact of slowly-varying weather conditions. Moreover, the inter-symbol interference that time-packing introduces is mitigated in reception using a Viterbi equalizer. As expected, time-packing enables a finer granularity on the link adaptation capability of the optical feeder link, enabling to adjust its spectral efficiency according to the moderate attenuation that thin cloud layers introduce.

SummaryThis paper introduces architectures for next‐generation high throughput satellite (HTS) systems comprising various satellite payload options, ground terminal advances, and scalable system‐level software control and management techniques. It describes a model to estimate aggregate system capacity as a function of radio band, available spectrum, spot beams, waveforms, and payload capability, including antenna size, power, and digital/analog connectivity across various links and availability objectives. This system model has been used to evaluate aggregate capacity of representative Ka‐Band low earth orbit and geosynchronous orbit systems. A system implementation approach is described for next‐generation HTS systems based on widely used Industry standards. Modulation and coding techniques are based on Digital Video Broadcasting – S2 extensions (DVB‐S2X), which comprises spectrally efficient modulation schemes combined with low‐rate codes. Several implementation technologies are analyzed related to configurable onboard payload and ground‐based, software‐defined resource control and management, key enablers of next‐generation HTS systems. Basic architectural building blocks are introduced for design of end‐to‐end systems across low earth orbit, medium earth orbit, and geosynchronous orbit satellite constellations, with and without onboard processing and inter‐satellite links, and including several efficient scenarios to achieve lossless handovers. Copyright © 2016 John Wiley & Sons, Ltd.

This paper contains two parts. The first part presents a novel framework for the successive convex approximation (SCA) method to solve a general optimization problem, as well as its properties. This framework starts with making change of variables (COV), motivated by the fact that it might be easier to construct convex approximations for the problem after making the COV. Furthermore, a general method is proposed to construct a convex upper bound approximation (CUBA) for a nonconvex function that satisfies tightness and differentiation conditions. Moreover, a way is introduced to generalize that CUBA by incorporating a convex function. These methods lead to plenty of degrees of freedom for using the SCA method to solve a problem. The second part revisits state-of-the-art dynamic spectrum management (DSM) algorithms, namely the successive convex approximations for low-complexity (SCALE) algorithm, the convex approximation for distributed spectrum balancing (CA-DSB) algorithm and the difference-of-convex-functions algorithm based DSM (DCA-DSM) method, to show how they can be derived from the SCA and CUBA construction methods. Numerical experiments are shown to compare them.

Hybrid terrestrial-satellite transmission systems have been among hottest spots in the wireless communication industry due to the high demand for global high data rates. In such systems, free-space optical (FSO) technology is advocated for assessing the feeder links (ground-satellite) in order to fulfill high data rates requirements and provide a larger coverage in terms of the connected number of users. This paper gives an overview of various components of very-high throughput hybrid terrestrial-satellite communication system with an FSO feeder link, and Ka-band multibeam RF channels for the satellite ground link. In this survey paper, we depict the main communication challenges in the FSO side such as pointing error, turbulence, cloud blockage, and also the multibeam RF side such as spectrum scarcity, inter-beam interference, and security. Based on these challenges we point out some potential research directions.

Non-terrestrial network (NTN) communication is included in the 3GPP standard because of its excellent features such as resistance to ground physical attacks and wide coverage. Since the available power and storage resources are limited on high throughput satellites (HTSs), optimizing the resource allocation can greatly improve the performance of the HTS based communication system. Notably, weather and other factors make the channel status between the satellite and the terrestrial base stations constantly change. In this paper, we first exploit the model-free feature of reinforcement learning to formulate the aforementioned dynamic and unpredictable channel conditions into the power allocation problem in HTS systems. Due to the complexity of environment state and power allocation, a deep neural network is introduced to replace the Q table, and a deep reinforcement learning framework is built. Furthermore, a power allocation algorithm based on a deep reinforcement learning framework is presented. Finally, the simulation results show that the proposed algorithm is superior to existing algorithms in terms of the long-term system throughput performance, and has better adaptability to dynamic channel environment, thereby improving network performance.

Due to recent advances in laser satellite communications technology, free-space optical (FSO) links are presented as an ideal alternative to the conventional radio frequency (RF) feeder links of the geostationary satellite for next generation very high throughput satellite (VHTS) systems. In this paper, we investigate the performance of multibeam VHTS systems that account for nonlinear high power amplifiers at the transparent fixed gain satellite transponder. Specifically, we consider the forward link of such systems, where the RF user link is assumed to follow the shadowed Rician model and the FSO feeder link is modeled by the Gamma-Gamma distribution in the presence of beam wander and pointing errors where it operates under either the intensity modulation with direct detection or the heterodyne detection. Moreover, zero-forcing precoder is employed to mitigate the effect of inter-beam interference caused by the aggressive frequency reuse in the user link. The performance of the system under study is evaluated in terms of the outage probability, the average bit-error rate (BER), and the ergodic capacity that are derived in exact closed-forms in terms of the bivariate Meijer’s G function. Simple asymptotic results for the outage probability and the average BER are also obtained at high signal-to-noise ratio.

Low earth orbit (LEO) multi-beam satellites have the characteristics of high capacity and high spectral efficiency, providing geographically independent services to ground users. However, the use of frequency reuse technologies leads to increasing inter-beam interference. Rate-splitting multiple access (RSMA), a novel multiple access technology, decomposes messages into common and private parts, effectively reducing interference. This makes RSMA highly promising for multibeam satellite scenarios. Therefore, this paper investigates the resource allocation problem of RSMA in the beam-hopping multicast LEO satellite network. The aim is to minimize the gap between traffic demand and achievable rates while meeting user latency requirements. To solve this problem, it is decomposed into two subproblems. First, considering user latency constraints, the beam scheduling problem is modeled as a potential game to determine the beam hopping pattern (BHP). Then, for the power allocation and rate matching problem, auxiliary variables are introduced to convert it into a convex problem, which is solved using the successive convex approximation (SCA) method. The simulation results confirm that this method performs better than the baseline approaches of Spatial Division Multiple Access (SDMA) and Orthogonal Frequency Division Multiple Access (OFDMA).

High Throughput Satellite (HTS) systems still offer much less bandwidth per user with respect terrestrial broadband networks. In order to reach a very high throughput towards “Tbit/s connectivity”, an important breakthrough is needed in terms of bandwidth availability which can be offered by the use of Extremely High Frequency (EHF) bands (30–300 GHz). In particular, Q-V band (30–50 GHz) and W-band (70–110 GHz) offer very promising perspectives, being not used for commercial systems and offering a large part of the spectrum allocated for satellite services. However, it is well known that EHF band transmission through the atmosphere is subject to absorption and dispersive effects in amplitude and phase that have to be carefully characterized. In this framework, some mitigation techniques have to be analyzed and properly tuned in order to realize an efficient transmission. In this paper, the current experimental activities to support future EHF satellite communication systems are reported, with reference to the Italian Space Agency Q/V-band satellite communication experimental campaign. In particular, a new error model for QPSK modulation scheme has been developed from the experimental data. This error model can be used for the design of optimized channel coding and adaptive transmission schemes for Q/V band satellite channels.

Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term maximum task queue by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and successive convex approximation (SCA) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme.

The next-generation fully regenerative payload-enabled low Earth orbit (LEO) satellites enable Satellite-as-a-Service (SaaS) capabilities. In this work, we propose a three-tier SaaS platform for on-demand computing services such as disaster forecast, 2D/3D scene observation, route finding, and rescue operations based on satellite images/videos, taking into account different beam management techniques. Unlike existing works where LEO satellites collaborate with ground users, in the considered system, users only request a service index, while all computation is fully handled within the satellite network and/or its connected gateway (GW). The primary objective is to minimize the worst-case service completion time by optimizing the computation and communication allocation among the satellites and the GW. The formulated multi-objective optimization problem is characterized as non-linear, non-convex, and non-deterministic polynomial-time (NP)-hard. To tackle this challenge, we propose an iterative algorithm based on the alternating optimization (AO) framework. This approach solves two sub-problems in sequence during each iteration: i) optimizing the link selection and task offloading decisions via a successive convex approximation (SCA) method, and ii) optimizing computational resources and downlink communication bandwidth via a convex formulation. Furthermore, we propose a delivery strategy during handover (HO) periods for returning the task results under both Earth-fixed beam and Earth-moving beam configurations. Simulation results based on realistic system parameters and datasets show that our proposed design reduces the task completion time by at least 16% compared to the reference strategies. The impact of computational cycles, inter-satellite link rates, feeder link rates, and HO decisions under different beam configurations is illustrated in terms of mean task completion time.

This paper studies a joint uplink and downlink mobile edge computing (MEC) system assisted by reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT), aiming to minimize delay by jointly optimizing the offloading transmit power, the local computing tasks, the power splitting (PS) ratio, and the uplink and downlink phase shift matrices of RIS. To solve this problem, an alternating optimization (AO) algorithm is proposed, which decomposes the problem into two subproblems and solves them using the successive convex approximation (SCA) method. Simulation results validate the effectiveness of the proposed algorithm.

Reconfigurable intelligent surface (RIS) has recently been regarded as a potential technique to enhance the performance of wireless communication systems by creating additional communication links. However, it is almost impossible to get the perfect channel state information (CSI) from the base station (BS) to the Internet of Things Devices (IoTDs) and the RIS-related channels. Furthermore, residual transceiver hardware impairments inevitably affect the performance of wireless communication systems. Hence, we study the robust design for an RIS-aided wireless communication system based on the imperfect CSI and hardware impairments. Minimizing the power consumption of BS is formulated by ensuring the minimum signal-to-interference-plus-noise ratio (SINR) demands of the IoTDs and the unit-modulus constraints of the RIS. Specifically, after approximating the nonconvex constraints by using the S-procedure and the successive convex approximation (SCA) methods, we adopt the block coordinate descent (BCD) technique to iteratively optimize one set of variables while keeping the other variables fixed in various channel uncertainty scenarios. Simulation results demonstrate that the influence of transceiver hardware impairments can be effectively decreased by deploying RIS even with channel uncertainty, which is more advantageous than increasing the number of BS’s antennas.

This letter investigates an active reconfigurable intelligent surface (RIS)-aided mobile edge computing (MEC) system. Compared with passive RIS, the active RIS is equipped with active reflective amplifier, which can effectively circumvent the “double path loss” attenuation. We propose a joint computing and communication design to minimize the maximum computational latency (MCL), subject to both the phase shift constraints and the edge computing capability constraints. Specifically, the original problem is decoupled into four subproblems, and then the block coordinate descent (BCD) method and the successive convex approximation (SCA) method are applied to alternately optimize the subproblems. The simulation results show that with the same power budget, the performance gain achieved by the active RIS is much larger than that by the passive RIS.