N-step model predictive control with persistent bounded disturbances under AF cooperation protocol.

Optimizing IRS placement and element configuration in B5G: A novel cooperative hybrid communication system

ABSTRACT A set of spanning trees that do not share any edges with each other are called edge-disjoint spanning trees (EDSTs for short). EDSTs can effectively improve the fault tolerance and transmission efficiency of networks, and play a key role in the fields such as efficient broadcasting and secure information distribution. As a highly scalable interconnection network, the dragonfly network D ( n , h , g ) is widely used in large-scale parallel computing systems. Research on the construction of EDSTs in this network is significant for optimizing network communication performance. We propose algorithms for constructing EDSTs in the dragonfly network, which can generate n + h − 1 2 EDSTs, and this number has reached the theoretical optimum. In addition, a simulated broadcasting experiment was carried out based on the constructed EDSTs. The results show that compared with a single tree structure, the cooperative transmission of multiple EDSTs can significantly improve broadcasting efficiency, further verifying the practicality of the proposed algorithm.

In this paper, we investigate stochastic beamforming approaches to maximize the ergodic sum-rate (ESR) for cooperative transmission systems, including both coherent and non coherent joint transmission. To this end, by utilizing the seminal weighted minimum mean square error (WMMSE) transform and adopting the stochastic successive upper-bound minimization (SSUM) framework, we first propose a stochastic WMMSE (SWMMSE) algorithm that maximizes the network’s ESR via analytically updating beamformers in an online manner. This algorithm can operate without any prior knowledge on the channel statistics and is applicable to very generic channel models with arbitrary probability distributions. Based on that, by substituting the SWMMSE update with the determinant equivalents (DEs) derived from large-dimensional random matrix theory, we further develop a DE-WMMSE algorithm. This method requires only the first- and second-order statistics of channel state information (CSI) and obviates the need for sampling channel. As will be proved, unlike the restrictions in the existing literature, our proposed DE-WMMSE algorithm can be applied to rather generic channel models beyond Rayleigh. Extensive numerical results verify the effectiveness and benefit of our proposed algorithms. Especially, the SWMMSE algorithm is highly computationally efficient for large-scale networks, while the DE-WMMSE algorithm is fast and convenient for modest-scale networks.

Cell-free (CF) networks have attracted increasing attention for their effectiveness in mitigating inter-cell interference through cooperative transmission among distributed access points (APs). However, conventional terrestrial CF networks often lack spatial flexibility and struggle to adapt to dynamic environments. To overcome these limitations, we propose a new CF network served by unmanned aerial vehicles (UAVs) equipped with a three-dimensional (3D) rotatable antenna array. Combined with the UAV’s controllable 3D position, the resulting six-dimensional (6D) spatial reconfigurability enables the active beam steering of such aerial APs, thereby enhancing interference mitigation and dynamic user association. However, this design, referred to as 6D aerial rotatable antenna arrays (6DARAs), faces several critical challenges, such as high-dimensional coupled control variables, time-varying user positions, and increased channel state information (CSI) estimation overhead. To address these issues, we develop a two-timescale optimization framework that separates large-timescale 6DARA control (i.e., clustering, position, and rotation) from small-timescale signal processing. At the small-timescale, a closed-form team minimum mean-squared error decoder is derived using local and statistical CSI. At the large-timescale, 6DARA clustering is modeled as a local altruistic game and solved via a concurrent update algorithm, while 6DARA mobility is managed by an enhanced multi-agent reinforcement learning algorithm for efficient position and rotation adaptation under partial observability. Simulation results demonstrate that the proposed network and optimization framework significantly outperform existing baselines in terms of throughput, scalability, and robustness in dynamic environments.

Multi-satellite cooperative transmission (MSCT) is a promising paradigm to enhance spectral efficiency in Low Earth Orbit constellations. However, heterogeneous Doppler shifts and coupled multi-satellite interference hinder the capacity improvement of conventional precoding in multiple-input multiple-output (MIMO). Although orthogonal time-frequency space (OTFS) modulation exhibits robustness against doubly-selective channels, its direct application in massive MIMO faces prohibitive computational complexity. To address these challenges, this paper proposes a joint precoding and link scheduling (JPL) design for OTFS-based MSCT systems. Specifically, we formulate a sum-rate maximization problem that couples continuous precoding matrices with discrete link indicators, and decompose it into two tractable subproblems. For precoder design, based on regularized zero-forcing (RZF)-criterion, we present single-satellite precoding (SSP) and multi-satellite precoding (MSP) in the delay-Doppler domain for different MSCT modes, and optimize per-satellite regularization coefficients to maximize the sum-rate. Leveraging the quasi-banded MIMO-OTFS structure, a low-complexity RZF algorithm is developed to reduce the cubic complexity to quadratic order without performance loss. Based on random matrix theory, we conduct asymptotic analysis and derive the deterministic equivalents of sum-rate for SSP and MSP. For link scheduling, a two-stage heuristic algorithm is designed based on tabu search to iteratively optimize link indicators for maximizing the sum-rate. By alternately optimizing precoding matrices with each scheduling iteration, the JPL algorithm is proposed to achieve near-optimal sum-rate performance with polynomial computational complexity. Numerical results demonstrate the proposed schemes significantly improve the sum-rate performance compared to existing works.

User-centric cell-free massive MIMO is emerging as a key architecture for next-generation wireless networks, offering improved spectral efficiency, seamless connectivity, and enhanced fairness by eliminating cell boundaries and leveraging cooperative transmission from distributed access points (APs). However, to fully exploit its potential, especially in dense deployments, effective uplink power control (UPC) mechanisms are essential to manage interference while balancing throughput and user fairness. This paper compares three UPC strategies: Full power control, fractional power control (FPC) with exponent υ = 0.5, and a fixed-point algorithm (FPA) designed for max-min fairness. Simulation results under varying network scales reveal critical trade-offs. In per-user spectral efficiency (SE) distribution, Full yields the best median (3.089 bit/s/Hz) but poor fairness. FPA maximizes fairness with a minimum SE of 2.1 bit/s/Hz, outperforming Full (1.585) and FPC (0.47). In total SE, Full achieves 34.81 bit/s/Hz (cumulative distribution function=0.5), higher than FPC (28.566) and FPA (21.05). Scalability analysis shows Full and FPC improve total SE with more APs (up to 47.23 and 40.56 bit/s/Hz at 100 APs), while FPA leads in minimum SE (3.347). In larger user equipment scenarios, FPA maintains fairness (min SE drops mildly from 2.36 to 1.63), while Full suffers (1.9 to 0.98). Computation-wise, FPA runs up to 10× faster than FPC (0.017 ms vs. 0.153-0.493 ms). Overall, Full suits throughput-focused designs; FPA excels in fairness; and FPC offers a scalable, tunable balance between the two.

Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framework for semantic communication to address these challenges. We introduce a knowledge graph-based approach to represent information as semantic triples (structured entity-relation-attribute representations), whose importance is quantified using a Zipf distribution, enabling prioritized transmission. At the physical layer, a semantic-aware cooperative communication scheme is proposed to combat fading and enhance transmission reliability. The joint optimization of the number of transmitted triples and node power allocation is formulated as a cross-layer problem. To tackle this Mixed-Integer Nonlinear Programming (MINLP) problem with a hybrid action space, we employ the Multi-Pass Deep Q-Network (MP-DQN) algorithm, which is specifically designed for problems with hybrid discrete-continuous action spaces. Simulation results demonstrate that our framework dynamically adapts to channel states and semantic value, achieving up to 85% end-to-end success rate and improving convergence speed by approximately 40% compared to conventional methods.

ABSTRACT With the continuous growth of satellite communication traffic and user access demands, the limited bandwidth capacity of a single satellite is insufficient to support the scalable broadband access demands of video services. To address this challenge, this paper proposes a multisatellite cooperative distributed orthogonal carrier aggregation (CA). By dynamically allocating satellite spectrum resources to orthogonal subcarriers and combining signals at the ground terminal, the system bandwidth is significantly enhanced. Employing a guard‐interval‐free design, the scheme relies on precise synchronization and Doppler precompensation to achieve seamless orthogonal CA, effectively avoiding spectrum waste, suppressing interference, and maximizing spectral efficiency. In response to the key technical challenges in cooperative transmission, this paper focuses on studying symbol timing synchronization and frequency synchronization algorithms for multilink scenarios. To validate the practical performance of the scheme, a dual‐satellite cooperative transmission simulation system with a signal bandwidth of 5 MHz was established, and QPSK modulation was adopted for testing. Simulation results demonstrate that at a bit error rate (BER) of 10 −3 , the signal‐to‐noise ratio (SNR) gains under ideal synchronization conditions (without timing and frequency impairments) reach 3 and 2.7 dB in AWGN and NTN‐TDL‐D channels, respectively. Even when channel impairments such as Doppler frequency offset and link delay are introduced, the system still maintains SNR gains of 2.75 and 2.5 dB after precompensation at the transmitter based on the proposed algorithm. These results significantly validate the effectiveness and robustness of the proposed scheme in complex satellite‐terrestrial communication scenarios.

This paper investigates the downlink performance of Cooperative Rate-Splitting Multiple Access (C-RSMA) in a multi-cell wireless network employing Joint-Transmission Coordinated Multipoint (JT-CoMP). Each cell comprises a multi-antenna base station (BS), multiple cell-center users (CCUs), and multiple cell-edge users (CEUs). Leveraging JT-CoMP, all BSs jointly transmit data to both CCUs and CEUs. To enhance signal quality at the cell edges, CCUs assist by relaying the common stream to CEUs via full-duplex (FD) decode-and-forward relaying. We aim to jointly optimize the beamforming vectors at the BSs, the allocation of common stream rates, and the transmit power at relaying users, i.e., CCUs, aiming to maximize the minimum achievable data rate. To address the non-convex challenge, we employ change-of-variables, first-order Taylor approximations, and a low-complexity algorithm based on Successive Convex Approximation (SCA). In this context, we also evaluate our proposed system model considering imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC). The results show that the proposed FD C-RSMA can achieve 25% over FD C-NOMA with BS transmit power of 20 dBm.

Device-to-Device (D2D) content sharing supports real-time applications but still faces challenges of large data and limited resources. With the growing computing capabilities of terminal devices, semantic content sharing has emerged as a promising solution. In this paper, a cooperative transmission D2D semantic content sharing based on probabilistic graphs is investigated to enhance user quality of experience (QoE). Specifically, D2D is divided into requesters and helpers. It is noteworthy that if both matching entities have knowledge bases, smaller-sized semantic information can be obtained by further compressing the semantic data. To encourage cooperation between D2D, we design utility functions for helpers and requesters, and formulate an optimization problem to maximize system utility by optimizing compression ratio, transmit power, and D2D pairing. In order to solve this problem, we introduce a matching game framework. First, we use an Nelder-Mead (NM)-based heuristic algorithm to solve the optimization problem for the compression ratio and transmit power. Then, a distributed cooperative semantic content sharing matching algorithm is proposed to achieve one-to-one matching, ultimately resulting in a stable strategy. The simulation results validate the optimality and convergence of the proposed algorithm. Compared to classical distributed algorithms, the proposed algorithm improves QoE performance by over 4.8%.

Unmanned aerial vehicles (UAVs) have been widely applied as aerial relays for satellite-to-terrestrial transmission so that data packets can be delivered either directly from satellites to user equipments (UEs) or relayed via UAVs. To exploit the advantages of such space-aerial cooperative transmission, data transmission, and resource allocation should be jointly optimized under constrained resources in dynamic space-air-ground integrated networks (SAGINs). However, the tight coupling between discrete transmission strategies and continuous resource allocation among users leads to a challenging long-term, large-scale, and non-convex optimization problem. While existing multi-agent reinforcement learning (MARL) techniques have exhibited potential in addressing such problems, their limitations in handling discrete-continuous decisions and ensuring robust multi-agent cooperation often result in significant performance deterioration. In this paper, we propose a new MARL-based Cooperative Transmission Strategy (MCTS) to overcome these limitations. In MCTS, each agent employs a hybrid Actor-Critic (H-AC) algorithm, where the optimal discrete transmission strategy with its associated resource allocation is modeled as parameterized actions and determined by an improved AC structure. To enhance cooperation among agents, a multi-agent cooperation framework is designed based on QMIX, employing a centralized Critic to efficiently guide convergence toward optimal solutions while reducing computational complexity. Simulation results demonstrate the superiority of MCTS over a number of benchmark algorithms and efficiency under various environments, in terms of overall transmitted data and resource utilization.

A cislunar relay cooperative transmission scheme was proposed based on the Queqiao communication，navigation，and remote sensing satellite constellation system. To address unreliable data transmission in ultra-long-distance cislunar communication environments caused by highly dynamic channels and significant path loss，using the in-orbit information processing and multi-source data fusion capabilities of Queqiao communication, navigation and remote sensing satellite constellation system. By introducing a symbol-level forwarding strategy and a multi-source redundancy sharing mechanism，a distributed RaptorQ transmission mechanism for heterogeneous data fusion was designed，enhancing the reliability of image data transmission. By adopting a block-based cyclic decoding algorithm，the computational complexity was reduced by 40% compared to the standard Gaussian elimination decoding algorithm，while still ensuring strong error correction performance. Simulation results demonstrate that in typical cislunar space communication scenarios，compared to traditional RS coding，the proposed distributed RaptorQ scheme achieved a 2 dB coding gain under equivalent coding redundancy. Compared to conventional relay storage-and-forward schemes，this approach increased decoding success rate by 20%，thereby improving image PSNR by over 5 dB，enabling highly reliable transmission of multi-source image data in cislunar space. <inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M1"> <mml:mo>∙</mml:mo> </mml:math> </inline-formula> Distributed RaptorQ coding achieves a 20% improvement in decoding success rate compared to traditional store-and-forward strategies through multi-relay symbol-level fusion and redundant sharing mechanisms. Under identical redundancy levels，it delivers higher image recovery quality than RS（255，223） codes. <inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M2"> <mml:mo>∙</mml:mo> </mml:math> </inline-formula> Blockwise cyclic decoding algorithm reduces computational complexity by 40% through optimized matrix partitioning and parallel processing，while maintaining equivalent error correction capability. <inline-formula> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M3"> <mml:mo>∙</mml:mo> </mml:math> </inline-formula> The proposed scheme achieves an improvement of over 5 dB in image PSNR in lunar communication scenarios through adaptive redundancy allocation and priority protection mechanisms.

The hybrid active–passive radar (HAPR) can effectively reduce the radiation power of active nodes, thus improving the radar system's electromagnetic environmental friendliness and anti-interception capability. Meanwhile, active nodes in the HAPR can provide cooperative transmitters with known signals for passive nodes, overcoming performance losses caused by noncooperative illuminators of opportunity (IOs). Although the advantages of HAPR have been gradually recognized in recent years, there is currently almost no systematic solution to the joint localization problem involving active and passive nodes. To address this problem, we propose two target localization algorithms for distributed HAPRs based on two-step localization and direct localization, respectively. In the two-step localization, we establish two geometric equation sets with consistent structures to handle different geometric relationships among the target, transmitters/IOs, and receivers associated with the active and passive measurements. In direct localization, we reduce the high-dimensional joint likelihood function constructed from active and passive received signals into a low-dimensional one through two dimensionality reductions, significantly reducing the complexity. These algorithms combine active and passive received signals, enhancing localization performance. We also derive the Cramér–Rao lower bound (CRLB), which provides a useful performance indicator. The results indicate that the two-step localization algorithm yields suboptimal performance, with localization errors approaching the CRLB only under specific conditions. The direct localization algorithm approaches the CRLB and outperforms systems using only active or passive nodes, thereby confirming its effectiveness and robustness.

In this paper, we consider a cooperative transmission model for video applications and services (VASs) in dense device-to-device (D2D) networks. The model enables the mobile users (MUs) to flexibly receive the videos from macro base station (MBS) and D2D networks with mobile edge caching. Particularly, we formulate a multi-rate selection and power allocation assisted probabilistic edge caching (MPC) optimisation problem under the resource constraints on storage, bandwidth, and power. This problem is solved for the optimal caching probabilities of requested videos corresponding to proper encoding rates selected. The optimal powers of caching MUs and MBS for transmitting the videos are also found to maximise the playback quality, while utilising the system resources. The MPC optimisation problem, which is complicated due to the presence of binary and real variables and various constraints, is feasibly solved by genetic algorithms (GA) with penalty function and truncated chromosome. Simulation results are shown to demonstrate the benefits of both GA and MPC methods compared to other benchmarks. Detailed analyses and interesting findings provide useful insights into the mobile edge caching design of dense D2D networks for VASs.

This paper proposes a highly reliable multilayer encryption scheme with constellation shaping based on joint index modulation. Multilayer encryption is used to ensure the security of the scheme. We utilize the 3D-Zhang chaotic system to realize the multidimensional perturbation of bits, subcarriers, and symbols of the original signal. The key information is masked with the signal using joint index modulation (JIM). The cooperative transmission of the initial value of the chaotic system is realized through the position information of the silent subcarriers and the constellation modes of the corresponding subcarriers. The scheme interleaves the initial values of the two chaotic systems to encode them as index bits, realizing the first layer of masked keys. Additional constellation shaping is achieved by optimizing the index mapping rules and changing the distribution of constellation points in the center and in the dual mode. The rotational encryption of the constellation is realized using a one-dimensional sinusoidal chaotic system after the completion of JIM, and the second layer of spherical shell masking of the key information is completed. The experimental implementation demonstrates the successful transmission of a 14 Gb/s 3D-JIM -8QAM signal over a 2 km weakly coupled seven-core fiber. Results confirm that the multi-layer encryption approach ensures secure signal transmission even under partial key leakage, without introducing degradation to the system's transmission performance. Compared to a random constellation distribution, the optimized index mapping scheme provides a coding gain of 0.75 dB at an FEC (Forward Error Correction) threshold of 3.8×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup>. The proposed scheme achieves error-free key transmission, with a bit error rate (BER) consistently maintained at 0. Notably, even minor key misalignments result in a BER of 0.5, facilitating timely key updates. The scheme boasts a vast key space of 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">150</sup>, effectively resisting brute-force attacks. Due to its high reliability and robust security features, the proposed scheme demonstrates strong potential for applications in future optical access networks.

In multicell downlink vehicular visible light communication (VLC) systems, vehicles at a cell edge experience lower achievable data rates compared with those at a cell center, primarily due to the weaker channel gain and intercell interference, leading to unfairness among vehicles. In this article, a cooperative transmission approach is proposed for a vehicular VLC system with optical intelligent reflecting surface (OIRS) to enhance the max-min fairness of the system by leveraging the additional OIRS-reflected channels and the interference-mitigating capabilities of cooperative transmission. To this end, the system model is established, followed by the formulation of a resource allocation problem aiming at enhancing max-min fairness, in which the minimum achievable data rate among vehicles is optimized. Then, an effective resource algorithm is proposed, transforming the original problem into an equivalent form and subsequently decomposing it into three subproblems, focusing on OIRS assignment, subchannel allocation, and power adjustment, respectively. By employing a block coordinate descent algorithm, the three subproblems are solved iteratively until convergence. In addition, simulation results confirm the improvement in max-min fairness brought by OIRS and the proposed cooperative transmission approach. Moreover, compared with various baselines, the proposed resource allocation algorithm significantly enhances in the max-min fairness for the vehicular VLC system, crucial for this application.

Users’ connection is pivotal for advancing meta-computing in Industrial Internet of Things (IIoT), where efficient data transmission can help overcome the barriers of computing resources distributed among billions of devices from diverse IIoT networks. Cooperative device-to-device (D2D) transmission, which is underlaid with cellular networks, plays an important role in enhancing the users’ connections for IIoT. However, since serving as a content provider in cooperative transmission needs consuming resources, IoT devices are typically willing to participate in cooperative transmission only when the content requesters are within the same IIoT networks. To encourage the cooperation across different IIoT networks, we in this article introduce social credit as a universal virtual concurrency to stimulate users’ willingness to assist content requesters via D2D transmission. Specifically, we first establish the relationship between social credit and data transmission, and model the process of credit acquisition and expenditure using a queue, which is initially unstable. Then, the inversely queuing technique is adopted to construct an equivalent stable queue, and a statistical credit guarantee mechanism is proposed to maintain cooperative transmission. Based on this mechanism, we explore the throughput maximization problem for cooperative D2D transmission in IIoT underlaying cellular networks, considering constraints on average and peak transmit power as well as interference to cellular communications, for obtaining the optimal credit-aware power control scheme. When multiple content providers are available, we prioritize the provider located at the shortest distance and examine the corresponding credit-aware power minimization problem, where the optimal power control scheme is obtained. Simulation results demonstrate that the proposal offers more stable credit guarantees than baseline methods, encouraging greater participation in cooperative transmission while achieving higher throughput and reducing transmit power consumption.

The large bandwidth of 5G wireless networks results in a discontinuous optimal spectrum. This study leverages cognitive radio networks and collaborative spectrum sensing to improve the transmission performance in 5G communication. Energy limitations for each secondary user (SU) and potential errors in secondary transmission within cognitive nodes during cooperative transmissions and spectrum sensing contribute to the dynamic energy efficiency. This paper details an Electronic Energy Relay Selection (EERS) system. The weighted average function determines the optimal relays when the network communication power consumption and spectrum-detection levels are equal. The EERS system examines the correlation between energy efficiency and detection precision. The proposed EERS system surpasses the performance of the compressed sensing collaborative detection (CSCD) system. MATLAB was used to evaluate and compare performance metrics such as weighted energy consumption, number of collaborative SU relays, and probability of missing detection with those of compressed sensing-based collaborative detection.

Cell-free massive multiple-input multiple-output (MIMO) networks eliminate cell boundaries and enhance uniform quality of service by enabling cooperative transmission among access points (APs). In conventional cellular networks, user equipment located at the cell edge experiences severe interference and unbalanced resource allocation. However, in cell-free massive MIMO networks, multiple access points cooperatively serve user equipment (UEs), effectively mitigating these issues. Beamforming and cooperative transmission among APs are essential in massive MIMO environments, making efficient power allocation a critical factor in determining overall network performance. In particular, considering power allocation from the central processing unit (CPU) to the APs enables optimal power utilization across the entire network. Traditional power allocation methods such as equal power allocation and max–min power allocation fail to fully exploit the cooperative characteristics of APs, leading to suboptimal network performance. To address this limitation, in this study we propose a convolutional neural network (CNN)-based power allocation model that optimizes both CPU-to-AP power allocation and AP-to-UE power distribution. The proposed model learns the optimal power allocation strategy by utilizing the channel state information, AP-UE distance, interference levels, and signal-to-interference-plus-noise ratio as input features. Simulation results demonstrate that the proposed CNN-based power allocation method significantly improves spectral efficiency compared to conventional power allocation techniques while also enhancing energy efficiency. This confirms that deep learning-based power allocation can effectively enhance network performance in cell-free massive MIMO environments.