International audience

International audience

Interference cancellation based on spectrum sharing is a key solution to improve the network performance of multi-satellite systems. In this paper, we investigate a user-centric multi-satellite communication system where users are collaboratively served by multiple satellites. In this system, the integrated access and backhaul (IAB) networks, i.e., the satellite-to-user access network and the gateway-to-satellite backhaul network, are jointly considered. Our objective is to maximize the weighted sum rates (WSR) of all users by jointly optimizing the beamformers of multiple satellites and the gateway while considering the backhaul link constraints. To solve this non-convex optimization problem, a centralized algorithm is developed using the block coordinate update (BCU) method, assuming that global channel state information (CSI) is perfectly known. In addition, a low-complexity distributed algorithm based on multi-agent deep reinforcement learning (MA-DRL) is further proposed. This approach offers enhanced flexibility for implementation in multi-satellite systems and adaptability to dynamic environments. Simulation results demonstrate that the proposed distributed algorithm based on MA-DRL can achieve a performance almost equivalent to that of the centralized algorithm. It also reveals that the spatial diversity can be fully exploited through the joint beamforming of a multi-satellite system compared with a single-satellite system, while the growing number of satellites makes the backhaul constraint a more critical bottleneck for system capacity.

In satellite-assisted maritime communications, wave-induced rotational motions of unmanned surface vessels (USVs) cause severe beam misalignment with satellites, significantly degrading transmission performance. To overcome this challenge, we propose equipping the USV with a smart metasurface-based antenna to enable adaptive beamforming that dynamically compensates for USV rolling in harsh sea conditions. To facilitate effective beam tracking under long feedback delays, we design a transmission framework that ensures accurate channel state information (CSI) acquisition. Within this framework, a BLTNet-based model is developed to predict the instantaneous rolling angle of the USV, which is then used to infer the USV-to-satellite CSI for beamforming optimization. We further formulate a stochastic optimization problem to maximize the ergodic achievable rate of the uplink transmission and design a robust beamformer accordingly. Simulation results demonstrate the high accuracy of the proposed rolling angle prediction model under various settings and sea states, confirming that the corresponding robust beamforming design substantially enhances the USV-to-satellite transmission performance.

In satellite-assisted maritime communications, unmanned surface vessels (USVs) suffer from intensive rotational motions due to wave fluctuations, resulting in beam misalignment with the satellite and thus deteriorating the transmission performance severely. This paper initially proposes to leverage the smart metasurface at the USV to combat the rolling motion of the USV in hostile sea environments. Specifically, we design an uplink transmission framework for beam tracking and propose a rolling angle prediction model based on triple-layer long shortterm memory (TL-LSTM) to predict the instantaneous rolling angle of the USV. Then the USV-to-satellite uplink transmission rate is maximized accordingly through the joint beamforming design of the USV and the satellite based on the predicted channel state information (CSI). Simulation results demonstrate the robust accuracy of the proposed rolling angle prediction scheme under various sea conditions, while the corresponding beamforming optimization scheme is also significantly efficient in improving the USV-to-satellite transmission performance.

Software-defined Low Earth Orbit (LEO) satellite networks enable efficient and centralized inter-satellite communication management by leveraging real-time network data, supporting high-speed, global communication services. However, due to the necessity of deploying extensive constellations in LEO systems to achieve global geographical coverage, massive data transmission and processing may lead to a shortage in communication throughput and computational resources. This paper proposes a Selective Traffic State Collection and Prediction (STS-CP) scheme to reduce ISL data collection in LEO satellite systems. We first introduce a metric to quantify ISL importance, allowing data collection to focus on the most essential links. To address data incompleteness, we propose a matrix completion module based on a GAT-based autoencoder, which summarizes local dependencies and global patterns to effectively infer uncollected traffic states. A spatio-temporal residual network is proposed for traffic prediction, capable of comprehensively capturing both spatial and temporal correlations. Experimental results validate that the proposed scheme can reduce data collection while preserving the accuracy and robustness of traffic prediction.

This letter focuses on greedy approaches to select the most informative$k$sensors from$N$candidates to form a measurement submatrix that minimizes the estimation error. It is a submatrix selection problem. We refine conventional greedy sensor selection algorithms based on the square maximum-volume (SMV) submatrices finding method, particularly at their$n$th step, with$n$being the problem dimension. Our main idea is to increase the volume of the square measurement submatrix associated with the$n$sensors by iteratively swapping the selected and unselected sensors based on the dominant property of the maximum-volume submatrix. This simple refinement method ensures a square measurement matrix with increased volume, facilitating the subsequent greedy steps. It can be easily applied to existing greedy algorithms for performance improvement without increasing their complexity order. Numerical results demonstrate the effectiveness of the proposed refinement method in improving several popular greedy algorithms.

Orthogonal chirp division multiplexing (OCDM) has emerged as an attractive modulation scheme due to its double-spreading property in both the time and frequency domains. Meanwhile, it is well known that multiple-input multiple-output (MIMO) techniques enhance spatial diversity to combat channel fading. Thus, MIMO-OCDM is promising for reliable high rate wireless communications. When deploying MIMO-OCDM systems, most studies assume perfect channel knowledge at the receivers. However, channel estimation is crucial and should be considered in the system design. In this paper, we apply Alamouti code for MIMO-OCDM over frequency-selective fading channels. In order to facilitate the channel estimation, three pilot-aided transmission (PAT) schemes are proposed, where the pilots are inserted in the frequency, time, and Fresnel domains, respectively. The first two schemes enjoy higher bandwidth efficiency, while the third one is more resistant to burst interference. The channel estimation methods and the optimal PAT designs are developed accordingly for these PAT schemes. Simulation results corroborate the superior performance of the proposed MIMO-OCDM system and channel estimation methods.

Wireless communications involving unmanned aerial vehicles (UAVs) are more vulnerable to the malicious jamming. As a promising solution, intelligent reflecting surface (IRS) can be equipped on the UAVs to achieve anti-jamming transmissions by leveraging the reconfigurable passive beamforming technique. In this paper, we study a wireless communication system where a UAV carries an IRS and acts as a mobile relay for a multi-antenna transmitter and receiver pair in the presence of a smart jammer that transmits jamming signals to the receiver and the IRS simultaneously. By jointly designing the transmit beamformer, IRS reflection phase, and UAV trajectory, we aim to maximize the average achievable rate over the entire flight with effective resistance to jamming attacks. To solve the non-convex problem, we adopt the alternating optimization (AO) algorithm and decompose the problem into two subproblems, i.e., joint optimization of the transmit beamformer and reflection phase for a specific time slot and optimization of the UAV trajectory. Simulation results show that the proposed joint optimization framework can well combat the jammer under various network settings such as changing the position of the jammer and the initial position of the UAV. The proposed algorithm has good convergence and achieves better performance than other benchmark schemes.

Orthogonal chirp division multiplexing (OCDM) is a novel modulation scheme with increased spectral efficiency compared with conventional chirp spectrum spread systems. Meanwhile, it is well known that multiple-input multiple-output (MIMO) techniques can enhance diversity to combat channel fading. Thus, MIMO-OCDM is promising for reliable high-rate wireless communications. In MIMO-OCDM systems, channel estimation is crucial and challenging. In this paper, we apply the Alamouti technique to develop a MIMO-OCDM system and enable its spatial diversity under frequency-selective channels. In order to facilitate the channel estimation, a pilot-aided transmission (PAT) scheme is proposed. The pilots are designed in the Fresnel domain to inherit the double-spreading property of OCDM and be robust to burst interference. The corresponding channel estimator is provided to achieve the lower bound of the mean square error (MSE) of channel estimation. Simulation results verify the superior performance of the proposed channel estimation method for the pilot-aided MIMO-OCDM system.

The frame-based precoding oriented from the frame structure under the DVB-S2 standard for satellite communications leads to the multicast transmission in each user frame. This paper investigates the multicast-aware user framing/grouping problem to facilitate the frame-based precoding that demands users of high channel similarity in each group. We propose two alternative approaches to increase the intra-group channel similarity by taking into account the channels of all users already in the group when selecting the parallel users for it. One approach extracts the first principle component vector from the channel matrix constituted by current group members and uses it to measure the similarity to the ungrouped users for the selection of the next group member. The other one adds up the projections of the ungrouped user's channel to the channels of the current group members to measure the similarity. Numerical results demonstrate that the proposed two algorithms outperform a benchmark algorithm in various scenarios, verifying the effectiveness of exploiting the channel information of all group members to constitute multicast groups with high intra-group similarity.

Spectrum sharing and interference cancellation are key solutions for multi-satellite systems to improve network performance. In this paper, we investigate a user-centric multi-satellite cell-free communication system where users are collabo-ratively served by multiple satellites. Our objective is to maximize the weighted sum rates (WSR) of all users by jointly optimizing the beamformers of multiple satellites. To solve this non-convex optimization problem, we first assume that the global channel state information (CSI) can be perfectly obtained and propose a centralized algorithm named per-satellite power constraints weighted minimum mean-square error (PSPC- WMMSE). To address practical implementation issues, we further propose a low-complexity distributed algorithm based on multi-agent deep reinforcement learning (MA-DRL). It is more flexible to be ap-plied to the multi-satellite system and is adaptive to the dynamic environment. Simulation results demonstrate that the proposed distributed algorithm can achieve almost similar performance to the centralized algorithm. Moreover, it is verified that the spatial diversity can be fully exploited via joint beamforming of the multi-satellite system compared to the single satellite system.

This paper investigates an intelligent reflecting surfaces (IRS) aided anti-jamming communication strategy in the integrated terrestrial-satellite network (ITSN), where the IRS is exploited to mitigate jamming interference and enhance the integrated system communication performance. In such a network, the terrestrial network and satellite network are co-existing with a spectrum-sharing scheme in the presence of a multi-antenna jammer. We aim at maximizing the weighted sum rate (WSR) of all users by jointly optimizing the terrestrial beamformers and IRS phase shifts while considering the signal-to-interference-plus-noise ratio (SINR) requirements of legitimate users. Different from the non-convex optimization techniques utilized in the IRS-related problem, a novel optimization-driven deep reinforcement learning (DRL) algorithm is proposed, which leverages both the robustness of model-free learning approaches and the efficiency of model-based optimization methods. In the optimization module of the proposed algorithm, we analyze the smart jammer under the unknown jamming model and derive a lower bound of the anti-jamming uncertainty, such that the IRS-aided anti-jamming problem can be solved by alteration method with second-order cone programming (SOCP) algorithm and semidefinite relaxation (SDR) technique. Simulation results demonstrate that the IRS can enhance the anti-jamming performance efficiently, and the proposed optimization-driven DRL algorithm can improve both the learning rate and the system performance compared with existing solutions.

The integrated terrestrial satellite networks (ITSNs) have been deemed as a promising solution to ubiquitous Internet access anytime and anywhere. In this paper, we investigate the spectrum sharing problem in ITSN, in particular focusing on the downlink transmission of the satellite network, which adopts the framing structure for the satellite users (SUs). We model the interference from both terrestrial downlink and uplink transmissions to SUs according to the beamforming techniques. For the terrestrial downlink transmission, we assume that terrestrial users (TUs) are served cooperatively by multiple small base stations (SBSs) via joint beamforming, while the virtual multiple access channel (VMAC) scheme is adopted for the terrestrial uplink transmission. We propose the optimization framework by jointly considering the terrestrial beamformer design and satellite user scheduling to maximize the sum rate of all users. The optimization problems are decomposed into three sub-problems: satellite user scheduling, terrestrial beamformer design, and time slot allocation, which are solved by deep clustering, second-order cone programming (SOCP) (or fractional programming (FP)), and linear programming, respectively. Then, an alternating iterative algorithm is designed to obtain the optimal solution. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm in multiple cases.

Intelligent reflecting surface (IRS) is a novel technology to manipulate wireless propagation channels via smart and controllable signal reflection. In this paper, we investigate an IRS-aided integrated terrestrial-satellite network (ITSN) system, where the IRS is deployed to assist the co-existing transmissions of the terrestrial small base stations (SBSs) and the satellite. Because of the spectrum sharing in the ITSN, the interference between the two systems should be carefully mitigated. Our objective is to maximize the weighted sum rate (WSR) of all users by jointly optimizing the frame-based coordinated transmit beamforming vectors at the SBSs, the phase shift matrix at the IRS, and the frame user scheduling, subject to SBSs’ individual power constraints and unit modulus constraints of phase shifters. To this end, we first adopt the agglomerative hierarchical clustering (AHC) method to schedule the satellite users to different frames. Then the block coordinate descent (BCD) algorithm is proposed, which alternately optimizes the transmit beamforming vectors and the reflective phase shift matrix. In particular, the optimal transmit beamforming vectors are obtained via the fractional programming (FP) technique. Meanwhile, two efficient algorithms, i.e., the Riemannian manifold (RM) and the successive convex approximation (SCA), are proposed for the phase shift optimization. Finally, simulation results are provided to demonstrate the performance gain of our schemes over other benchmark schemes.

This paper proposes to implement the wireless blockchain in a cellular system and focuses on the communication demanding consensus processes. The consensus processes within the cellular network based on three typical consensus protocols, i.e., PoW, Raft and PBFT, are first analyzed. The delay optimal transmission problems subject to consensus traffic demands and power constraints are formulated for uplinks and downlinks respectively. The goal is to minimize the transmission airtime by joint user scheduling and power control of the multi-cell network. We adopt the column generation (CG) and fractional programming (FP) methods to improve transmission efficiency in consensus process. The original problem is first decomposed by the CG method into a restricted master problem and a pricing problem. Then, the FP method is adaptively adapted to optimize the pricing problem for potential improvement of the current solution. The two sub-problems are iteratively processed until the optimal solution is obtained. Numerical results demonstrate that the proposed CGFP algorithm efficiently mitigates the inter-user interference, attributing to the joint spatial-temporal optimization, and notably improves the transmission delay performance.

Jointdetection and communication systems demonstrate their unique efficiencies in both spectrum and cost. In this letter, we propose a sonar-communication (SonarCom) system for underwater scenarios based on two multicarrier (MC) waveforms, which are orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM) waveforms. Different types of waveforms provide flexibilities to deal with various underwater environments. Furthermore, a generalized likelihood ratio test (GLRT) is proposed for detection to deal with multipath channels. Time aliasing techniques are applied to leverage the signal structure. Moreover, a minimum mean square error (MMSE) equalizer is developed for communication to counter frequency-selective fading. Simulation results show that the GLRT detector for the SonarCom system outperforms the existing matched filter (MF). The OCDM scheme maintains better communication performance than the OFDM one.

The problem of single-group multicast beamforming (SMBF) is well-known NP-hard. It motivates the pursuit of computationally efficient near-optimal solutions. Due to multicasting, the multicast group is bottlenecked by the user(s) with the minimum received signal-to-noise ratio (SNR). This paper provides an in-depth interpretation of the SMBF problem from the multicasting point of view and proposes to solve it in two steps: i) select the bottlenecking users by a machine learning approach based on determinantal point process (DPP), and ii) design the beamformer for the selected users. The DPP model jointly considers the magnitudes and directions of users’ channel vectors, and thus enables an efficient selection of the bottlenecking users. Moreover, for a specific channel model, the DPP model is only associated with network size and each takes a one-off training cost, thus can be used as a codebook. The proposed DPP-based subset selection is incorporated adaptively into two fast beamforming algorithms, i.e., the QR decomposition algorithm and the successive beamforming (SB) algorithm. They specifically design the beamformers for the selected users by leveraging channel orthogonalization therein. Numerical results demonstrate the superiority of the proposed QR-DPP and SB-DPP algorithms in terms of the performance-complexity compromise and their robustness to different scenarios.

This paper investigates a multiple intelligent reflecting surfaces (IRSs) aided integrated terrestrial-satellite network (ITSN), where the IRSs are deployed to cooperatively assist the low channel gain users in the co-existing transmission system. In such a network, the coordinated beamforming and frame based transmission scheme are considered for the terrestrial network and the satellite network, respectively. We aim at maximizing the weighted sum rate (WSR) of all users by jointly designing the frame based beamforming at the small base stations (SBSs) and the phase shifts at the IRSs, subject to the individual maximum SBS's transmit power constraints and the IRSs' reflection constraints. This non-convex problem is firstly decomposed via fractional programming (FP) technique in the objective function, then transmit beamforming vectors and reflective phase shifts matrix are optimized alternatingly. A block coordinate descent (BCD) method is proposed to obtain the stationary solution. Simulation results verify the effectiveness of the proposed algorithm compared with different benchmark schemes.

This paper investigates an intelligent reflecting surface (IRS)-aided integrated terrestrial-satellite network (ITSN) system for the low channel gain users, where an IRS is deployed to assist the co-existing transmissions of the terrestrial small base stations (SBSs) and the satellite. Because of the spectrum sharing in the ITSN, the interference between two systems should be carefully mitigated. We aim for maximizing the weighted sum rate (WSR) of all users through jointly optimizing the frame based coordinated transmit beamforming vectors at the SBSs and the phase shift matrix at the IRS, and the frame user scheduling subject to each SBS's power and unit modulus. To this end, we propose efficient algorithms based on alternating optimization, in which the transmit beamforming vectors and reflective phase shifts matrix are optimized in an alternating manner. In particular, we develop the second-order-cone programming (SOCP) for optimizing the coordinated transmit beamforming and propose the Riemannian conjugate gradient (RCG) for updating the reflecting shifts. For frame user scheduling, we propose the chordal distance measure method to improve the intra-fame correlation. Simulation results verify the effectiveness of the proposed algorithm compared with different benchmark schemes.

In this paper, we investigate the downlink transmission in the integrated terrestrial satellite networks, whereby the same spectrum is shared between two systems, and thus interference to each other should be carefully mitigated. We address this challenging issue by unifying the terrestrial beamformer design and satellite user scheduling into the same optimization framework. This nontrivial problem is decomposed into two subproblems, one deals with the terrestrial beamformer design to control the interference from the terrestrial base stations to the satellite users, the other tries to optimize the scheduling of the satellite users following the framing structure the DVB-S2X standards for satellite communication systems. A deep clustering user scheduling scheme is developed to group suitable satellite users to the same frame using the channel state information as the input feature. Finally, a joint iterative algorithm is designed to maximize the sum rate of all users in the integrated systems. We conduct extensive simulation results to show the effectiveness of the proposed scheme.

The noncooperative target tracking is an important issue for the Internet of Underwater Things (IoUT). Autonomous underwater vehicles (AUVs) are preferred options to achieve the target tracking especially in anchor-free environments, where no equipments with known positions, named anchors, are deployed. The self-organized mobile network of multiple AUVs can localize and continuously monitor the target. Thus, in this article, we investigate the problem of the noncooperative target tracking using multiple AUVs in anchor-free environments. In the target tracking, AUVs play as references and their positions need to be estimated first. We propose a multi-AUV cooperative localization and target tracking (MCLTT) framework based on belief propagation (BP). Under MCLTT, BP-based underwater cooperative localization (BPUCL) and noncooperative mobile target tracking (NcMTT) algorithms are designed. Gaussian approximations are used to reduce communication costs among AUVs. The designed BPUCL alleviates the impact of the accumulated errors in the inertial measurements of AUVs and slows down the growth of the localization error. In NcMTT, model-free position prediction processes are proposed and a novel form of the particle-based BP message is designed using time-difference-of-arrival (TDOA) measurements. The simulation results validate the proposed algorithms by comparing with state-of-the-art methods.

State estimation, which computes the best possible approximation for the system state based on the perceived information transmitted from sensors to the estimators, is vital for control system performance in industrial cyber-physical systems (ICPSs) with the integrated techniques of control, communication and computing. Thus the performance of state estimation relies on the communication reliability. In order to improve the reliability, redundant channels/slots are reserved for the data transmission in industrial wireless techniques, such as WirelessHART. However, the redundancy scheme burdens the increasingly over-crowded ISM spectrum band due to the envisioned emerging ubiquitous industrial wireless monitoring in the architecture of ICPS in the near future. The cognitive radio (CR) technology can intelligently explore the available spectrum opportunities on licensed channels, and it motivates this paper to consider the redundant transmission through the opportunistically available licensed channels to guarantee the transmission reliability for state estimation. Unfortunately, spectrum sensing takes extra energy consumption, thus it is necessary to take into account the energy efficiency for the battery-powered IWSN. Then a CR enabled energy- efficiency maximization problem is formulated by regarding the convergence of state estimation as a constraint of the resource allocation problem. In order to solve the non-convex and mixed integer programming, the Dinkelbach and Lagrangian relaxation techniques are adopted to transform the problem into a convex programming and furthermore reduce the computational complexity. Numerical results demonstrate that the CR technology can significantly release the spectrum for the redundancy design from the ISM band while guarantee the reliability for the effective state estimation.

In this paper, we consider the relay selection and power allocation problem in an amplify-and-forward relaying network, the objective is to minimize the outage probability given that only mean channel gain information is known. We firstly show that relay selection is important in achieving the global optimal solution in addition to the power allocation. Based on this argument, we then propose to decompose the problem into two parts: relay selection and power allocation. For the relay selection problem, a novel scheme is designed to incrementally select relays according to their ordering of mean channel gain. Then for the given set of relays, the optimal power allocation for the source and relays are obtained by exploiting the special structure of the problem. Simulation results show that the proposed scheme outperforms existing schemes without relay selection, which is also very efficient since the results obtained by this scheme are very close to the optimal results achieved by exhaustive search.

In the conventional dual-hop cooperative communications, relays with imbalanced channel condition to the source or the destination may become the bottleneck of the overall cooperation. Therefore, in this paper, a Three-Stage Relaying (TSR) framework is proposed for clustered networks to extend the dual-hop cooperation to three stages by dividing relays into two groups. The long-haul communication between two groups form a virtual multi-input multi-output (MIMO) link with which the bottleneck between the relay and the source (or the destination) can be removed. We focus on the power allocation problem based on this framework, with the objective of minimizing the outage probability at the destination under the total power constraint. To address the computational complexity, the problem is decomposed into two subproblems, one deals with the power allocation of the source and the first-hop relays, the other deals with the power allocation of the second-hop relays. We design the algorithms for each subproblem by exploiting their special structure, and then develop a master procedure to handle the power allocation across these subproblems. The performance of the proposed scheme is evaluated through simulation study, which shows that the TSR framework achieves significant improvement on the outage probability compared with the dual-hop cooperation scheme, and the power consumption is more fairly distributed across relays.

In the conventional dual-hop cooperative communications, the relays with imbalanced channels to the source and the destination become the bottleneck of the overall cooperation. In this paper, a Three-Stage Relaying (TSR) scheme is presented to extend the dual-hop cooperation to three stages by dividing relays into two groups. The relay-to-relay cooperation is introduced with which the bottleneck link between the relay and the source (or the destination) can be efficiently broken. We focus on the power allocation based on this framework, with the objective of minimizing the outage probability at the destination under the total power constraint. To address the computational complexity, the problem is decomposed into two subproblems, one deals with the power allocation of the source and the first-hop relays, the other deals with the power allocation of the second-hop relays. We design the algorithms for each subproblems by exploiting the special structure of the problems, and develop a master procedure to handle the power allocation across these subproblems. The performance of the proposed scheme is evaluated through simulation study, which shows that the TSR framework achieves significant improvement on the outage probability compared with the dual-hop protocol, and the power consumption is more balanced across relays.