Cooperative Communication Networks Research Articles

Robust joint multiple resources allocation algorithm for cooperative underwater acoustic communication networks

AbstractIn this article, a joint relay selection, power control and time allocation problem is studied to maximize the energy efficiency for the cooperative underwater acoustic communication networks. The joint optimization problem is full of challenges due to the strong coupling of multiple resources and the uncertain characteristics of the underwater acoustic communication scenario. To address this issue, the worst‐case method is employed to transform an original uncertain problem into a deterministic problem. Furthermore, we propose the block coordinate descent‐based method to decouple the strongly coupling multi‐resource allocation problem into three relatively independent sub‐problems. The coupling of multiple resources is completely decoupled, thereby greatly reducing the solving difficulty. In addition, given that the sub‐problems with the fractional objective function are still non‐convex and hard to solve, the Dinkelbach‐based method is proposed to transform the fractional objective function into a subtractive form. At last, the relay selection sub‐problem is transformed into a integer programming problem, and the time allocation sub‐problem is transformed into a linear programming problem, whose optimal solutions can be obtained by some well‐established solution methods. The power allocation problem is transformed into a convex optimization problem, which can be solved by the Lagrangian dual method. Finally, in the proposed iteration structure, the three sub‐problems are alternatingly solved until convergence. Simulation results are presented to demonstrate the efficiency and robustness of the proposed algorithm.

Fair resource optimization for cooperative non-terrestrial vehicular networks

Non-terrestrial networks aim to extend wireless communications capabilities beyond traditional terrestrial infrastructure by utilizing various platforms such as satellites, high-altitude platform stations (HAPS), and unmanned aerial vehicles to provide global or localized coverage. However, improving data rates in non-terrestrial cooperative communications (NTCC) networks presents unique challenges, necessitating innovative solutions. This paper presents a novel approach to enhance the request–response ratio (RRR) of NTCC in time asynchrony. By leveraging the advantages of HAPS, such as wide coverage, high altitude, and flexible deployment, we aim to optimize the performance of NTCC networks. We formulate an optimization problem considering the simultaneous connection between the HAPS and ground users, power distribution, and decoding order. The joint optimization problem is formulated as non-convex-non-linear and is also NP-hard, making it very challenging to obtain a globally optimal solution. To reduce the complexity of the problem and make it more tractable, we decouple it into subproblems and achieve an efficient solution in two steps. In the first step, we use the Gale–Shapley algorithm to solve the many-to-many two-sided matching problem of HAPS user terminal association, given the fixed decoding and power allocation. Then, we solve the decoding order and power allocation problem using the Dinkeback-like algorithm, given the optimal association in the second step. Our proposed method iteratively updates the preference lists, mitigating interference among HAPS and enhancing overall system performance. Through extensive simulations, we demonstrate that implementing our proposed schemes achieves high RRR compared to benchmark schemes. Additionally, when temporal asynchrony is employed alongside our approach, there is an increase in the overall performance obtained from the process.

Performance Analysis of UAV RF/FSO Co-Operative Communication Network with Co-Channel Interference

The unmanned aerial vehicle (UAV) communication network has emerged as a promising paradigm capable of independent operation and as a relay to enhance communication coverage and efficiency. However, densely distributed terrestrial base stations with shared communication frequencies inevitably generate co-channel interference (CCI). The interference effect can be effectively eliminated by implementing free-space optical (FSO) communication in the UAV communication network. This paper proposes a solution for the UAV communication network to address interference effectively, specifically by employing a hybrid millimeter-wave radio frequency (RF)/FSO communication system. The RF links serve as the primary means of communication, while the FSO links act as a backup means of communication in the case of CCI. The exact outage probability (OP) and average symbol error rate (SER) expressions are derived for the hybrid RF/FSO communication network. The decision to switch between them depends on the signal-to-interference-plus-noise ratio (SINR). Furthermore, the SINR switching threshold value, which satisfies the target SER, has been calculated numerically for the proposed model. Simulation results indicate that the proposed network notably enhances the OP and attains a signal-to-noise ratio gain of approximately 4.6 dB in the average SER, particularly in scenarios where the RF links are subjected to severe interference or adverse weather conditions, as opposed to a pure RF communication network.

Swarm Algorithm-based Power Optimization in Cooperative Communication Network

Background: Due to the environmental effects, the signal fades abruptly and is sometimes lost in the transmission path, which results in weak signal reception at the destination node. The Cooperative Communication Network (CCN) overcomes this problem and provides better spectral efficiency. The source node and the helper node both send the information to the receiver. In spite of many advantages, there are some limitations to such networks. Improving the system efficiency by power, energy, or relay selection optimization is quite desirable as multiple helper nodes are used in the network. Considering this crucial requirement of optimization, the proposed work presents optimal power allocation of the system. Methods: Here, tunicate-swarm optimization is implemented to improve the system parameters, symbol error rate (SER), outage probability, and bit error rate (BER). Two relaying protocols are used for analysis, i.e., Amplify-and-Forward (AF) and Differential AF (Diff-AF), with two combining schemes: Selection-Combining (SC) and Maximal-Ratio-Combining (MRC). Results: The results obtained are further compared with other metaheuristics algorithms, such as Particle- Swarm-Algorithm (PSO), Black-Widow-Optimization (BWO), and the traditional method of Equal-Power-Allocation (EPA).Equal-Power-Allocation (EPA). Conclusion: The simulation result shows that the proposed algorithm improves the system parameters compared to algorithms with less time.

Retracted: Communication Audio and Video Coding and Decoding Methods in Wireless Cooperative Communication Network

Retracted: Communication Audio and Video Coding and Decoding Methods in Wireless Cooperative Communication Network

Physical Layer Security for Wireless-Powered Ambient Backscatter Cooperative Communication Networks

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where the BD with nonlinear energy harvesting model cooperates with decode-and-forward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secrecy energy efficiency (SEE). Specifically, the closed-form and asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmit power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks.

Doubly coupled nested tensor decompositions with application to multirelay multicarrier MIMO communication networks

Coupled tensor decompositions have emerged as a promising approach to analyze large dimensional datasets in the context of signal processing applications. In this paper, the general concept of doubly coupled decomposition (DCD) for high-order tensors is first proposed, extending the idea of coupled decompositions to doubly coupled nested structures which result from the contraction of two sets of tensors, each set depending on a specific mode. Two new decompositions are defined, the so-called doubly coupled nested Tucker decomposition (DCNTD) and doubly coupled nested PARAFAC decomposition (DCNPD). Uniqueness of these DCDs is analyzed. In a second part, we show how these DCDs can be used to model multirelay multicarrier MIMO cooperative communication networks with two different tensor codings at the source and relay nodes. Exploiting the multilinear structure of the received signals and assuming the coding tensors are known at the destination, semi-blind closed-form receivers are developed for jointly estimating the channels and transmitted symbols. The proposed receivers use Khatri-Rao and Kronecker product factorization algorithms. Identifiability conditions for system parameter estimation and design of the tensor codes are also addressed. Monte Carlo simulation results illustrate the performance improvement of the proposed DCD-based systems over existing state-of-the-art ones.

Machine learning assisted delay minimization in full duplex energy constrained cooperative communication network

Cooperative communication is key in attaining high network throughput and better coverage area for a wireless powered communication network. The conventional solution methodologies proposed in the literature for these networks suffer from high run-time complexity. This paper presents an efficient polynomial-time machine learning approach for solving the total transmission time minimization problem through relay selection and scheduling of the users in a full duplex wireless powered cooperative communication network. Exploiting the simplicity offered by the tree ensemble models, a distributed gradient-boosted decision tree based model (XGBoost) is incorporated. The inputs of the model are the uplink channel coefficients and energy harvesting rates of the relays and the users, and outputs are the optimal completion time of the relays and users, relay selection and schedule of the users. Simulation results indicate that the proposed model very well approximates the true outputs, i.e., performs very close to the optimal solution of the optimization problem, while maintaining a very low run-time computational complexity. Specifically, for a network of 9 users, the run-time of the proposed model is just 25% of the optimal solution, and this ratio grows as the number of users increases in the network.

Robustness of higher-order interdependent networks

In recent years, the research of multilayer interdependent networks has become a hotspot in complex networks. However, most of the study is limited to describing pairwise interactions. The systems in the real world are usually networks with higher-order interactions consisting of three or more units, such as epidemic transmission and cooperative communication networks. To better reflect the complex networks in the real world, this paper introduces the higher-order structures in the network, that is, simplicial complexes. In this paper, we construct a theoretical model of a two-layer partial dependence network with simplicial complexes in which failures between nodes occur through the synergistic effects of pairwise and higher-order interactions. In this model, removing a node will cause all other nodes in the same simplex to be removed, and due to the dependency between the two networks, the failure of the node will spread through dependency links between the two networks. This process will occur recursively and eventually lead to the cascading process. We introduce percolation theory to study the robustness of the network and give the theoretical solutions of different properties of the network, such as the size of the mutually connected giant component (MCGC), percolation threshold, etc. We find that the density of the triangle and the dependent strength between the two networks affect the percolation behaviours of the network together. When the density of the triangle exceeds a certain value, the network shows a double transition.

Opportunistic Routing-Aided Cooperative Communication Network With Energy Harvesting

In this article, a cooperative communication network based on energy-harvesting (EH) decode-and-forward (DF) relays that harvest energy from the ambience using buffers with the harvest-store-use (HSU) architecture is considered. An opportunistic routing (OR) protocol, which selects the transmission path of packet based on the node transmission priority, is proposed to improve data delivery in this network. Additionally, an algorithm based on the state transition matrix (STM) is proposed to obtain the probability distribution of the candidate broadcast node set. Based on the probability distribution, the existence conditions and the theoretical expressions for the limiting distribution of energy in energy buffers using a discrete-time continuous-state space Markov chain (DCSMC) model are derived. Furthermore, the closed-form expressions for network outage probability and throughput are obtained with the help of the limiting distributions of energy stored in buffers. Numerous experiments have been performed to validate the derived theoretical expressions of the performance of this cooperative communication network.

FedRelay: Federated Relay Learning for 6G Mobile Edge Intelligence

Federated learning (FL) is a promising training paradigm to achieve ubiquitous intelligence for future 6G communication systems. However, it is challenging to apply FL in 6G-enabled edge system since decentralized training consumes considerable energy and mobile devices are mostly battery-powered and resource-constrained. The intensive computation and communication cost of local updates accumulated by hundreds of global rounds bring about the energy bottleneck, which is exacerbated when the data is non identically and independently distributed (non-IID). To address this issue, we propose FedRelay, a generic multi-flow relay learning framework in which local updates are performed relay-by-relay in the training flow via model propagation. We also present a decentralized relay selection protocol that takes advantage of the diversity of cooperative communication networks. Following that, we investigate a FedRelay optimization problem to simultaneously minimize the energy consumption of local updates and alleviate the global non-IIDness. Technically, an approximation algorithm is proposed to jointly optimize computation frequency and transmission power, thus reducing the local training overhead. We further regulate the training topology of each flow by proposing a greedy relay policy that encourages effective information exchange among devices. Experiment results show that, compared to state-of-the-art federated learning algorithms, our learning framework can save up to 5 times the total energy required to achieve a reasonable global test accuracy.

A New Distributed Robust Power Control for Two-Layer Cooperative Communication Networks in Smart Grids with Reduced Utility Costs

The packet loss during transmission of load control commands can lead to regulation errors in the smart grid and increase the cost of utility agencies due to the purchase of additional automatic generation control (AGC) services. In this paper, a two-layer cooperative communication network between the utility company and relays is presented. The utility company rents the relay to assist with the downlink transmission to improve the reliability of communication and reduce the data transmission cost due to packet loss. Furthermore, the uncertainty of channel gain is considered, and a two-tier game model is established. A distributed robust power control algorithm based on the continuous convex approximation method is proposed to obtain the optimal relay power allocation and price. Through the simulation analysis of the proposed scheme and the two comparison schemes, the cost of the utility company was reduced by 6% and 21%, and the standard deviation of income value between the relays was reduced by 40% and 48%, respectively.

Wireless Powered mmWave Cooperative Communication Network

Wireless Powered mmWave Cooperative Communication Network

On the Performance of Coded Cooperative Communication with Multiple Energy-Harvesting Relays and Error-Prone Forwarding

In this paper, we consider a coded cooperative communication network with multiple energy-harvesting (EH) relays. In order to adequately address the problem of error propagation due to the erroneous decoding at the relays, as in the case of conventional decode and forward (DF) relaying protocol, we propose coded cooperative schemes with hard information relaying (HIR) and soft information relaying (SIR) strategies. The performance of the relayed communication with EH relay depends crucially on the channel decoding capability at the relay, channel gains at the source–relay and relay–destination links, and ultimately on the power-splitting ratio of the relay EH receiver. The exact closed-form expression for the outage probability performance of the coded cooperative scheme with HIR strategy and relay selection (CC-HIR-RS) is derived for both cases, namely for constant and optimal power-splitting ratios. Concerning the coded cooperative scheme with SIR strategy, a Rayleigh Gaussian log likelihood ratio-based model is used to describe the soft estimated symbols at the output of the relay soft encoder. Directives are provided to determine the model parameters, and, accordingly, the signal-to-noise ratio (SNR) of the equivalent one-hop relaying channel is derived. A closed-form expression for the outage probability performance of the proposed coded cooperative scheme with SIR and relay selection (CC-SIR-RS) is derived. In addition, a fuzzy logic-based power-splitting scheme in EH relay applying SIR is proposed. The fading coefficients of the source–relay and relay–destination links and distance between source and relay node are considered as input parameters of the fuzzy logic system to obtain an appropriate power-splitting ratio that leads to a quasi-optimal SNR of the equivalent end-to-end channel. Monte Carlo simulations are presented to demonstrate the validity of the analytical results, and a comparison between the performance of the CC-HIR-RS scheme with constant and optimized power-splitting ratios and that of the CC-SIR-RS scheme with constant and fuzzy logic-based power-splitting ratios is provided.

Buffer-aided Relay Selection Technique in a Cooperative Communication Network

The goal of cooperative communication is to increase spatial diversity and wireless network coverage. However, in a classical relaying setup, where a single best relay is chosen to receive the source's data in the first time slot and retransmit in the second time slot regardless of channel circumstances, channel mismatch occurs. In this paper, we have developed an enhanced relay selection technique at the relay nodes that incorporates buffers to address the aforementioned problem. The outage probability and throughput computations are derived using closed-form formulas. The results obtained show that the enhanced relay selection technique gives lower values in outage probability and higher values in throughput when compared with the conventional technique, providing it a desirable strategy for use in technical communication.

D2D Cooperative Communication Network Resource Allocation Algorithm Based on Improved Monte Carlo Tree Search

In recent years, with the rapid development of mobile communication, D2D (Device-to-Device, D2D) cooperative communication network has become the main component of future communication network, which greatly improves the spectrum efficiency of the network and the quality of user communication. However, the existing D2D network resource allocation schemes have some problems, such as weak dynamic resource allocation capability and low user communication quality. In view of this challenge, this paper proposes a resource allocation algorithm for D2D cooperative communication networks based on improved Monte Carlo tree search. First, a double-chain deep deciduous Monte Carlo tree search (Dcdd-MCTS) resource allocation model is established, Then, the loss function composed of deciduous MCTS and parallel convolution network is used to update the parameters of the deep neural network model of Dcdd-MCTS. Then, the theory of optimal classification is used to solve the user’s transmit power. Finally, the optimal scheme of dynamic output resource allocation is output. The simulation results show that Dcdd-MCTS has good convergence. In the research on the distance between devices, compared with single-chain deep MCTS and joint optimization algorithm [52], the proposed algorithm in this paper increases the system throughput by 5%, 2%, respectively, and reduces the outage probability by 33%,18%.

Wireless Powered Cooperative Communication Network for Dual-Hop Uplink NOMA With IQI and SIC Imperfections

Wireless Powered Cooperative Communication Network for Dual-Hop Uplink NOMA With IQI and SIC Imperfections

Opportunistic Routing Aided Cooperative Communication MRC Network With Energy-Harvesting Nodes

In this research, we present a cooperative communication network based on two energy-harvesting (EH) decode-and-forward (DF) relays that contain the harvest-store-use (HSU) architecture and may harvest energy from the surrounding environment by utilizing energy buffers. To improve the performance of this network, an opportunistic routing (OR) algorithm is presented that takes into account channel status information, relay position, and energy buffer status, as well as using the maximal ratio combining (MRC) at the destination to combine the signals received from the source and relays. The theoretical expressions for limiting distribution of energy stored in infinite-size buffers are derived from the discrete-time continuous-state space Markov chain model (DCSMC). In addition, the theoretical expressions for outage probability, throughput, and per-packet time slot cost in the network are obtained utilizing both the limiting distributions of energy buffers and the probabilities of transmitter candidates set. Through numerous simulation results, it is demonstrated that simulation results match with corresponding theoretical results.

An Hybrid Heuristic optimal relay selection strategy for energy efficient multi hop cooperative cellular communication

One of the major constraints for mobile relay networks is energy-efficient communication owing to the restricted battery power of user terminals. Among them, the production method is cooperative cellular networks that focus on enhancing the data rate, network reliability, and network system capacity and also improving the spectrum resources in the Fifth Generation (5 G) networks. Thus, recent research works investigate the selection of mobile relays in vehicular networks and also the relay selection in cooperative communication. Here, the mobile relay is selected by the base station (operator) for every mobile user for reducing the cost with improved data rates and reducing the total transmission power. The selection of relay is performed by considering the benefits or utilities offered to three players such as mobile relay, mobile user, and operator. Conventional relay node selection approaches face challenges regarding reduced network lifetime, delay, increased energy consumption, and collusion attacks. For next-generation wireless networks, both energy efficiency and capacity are vital. To cope with these problems, a new multi-objective derived energy efficient accurate selection of relay in multi-hop cooperative cellular communication in the vehicular network through a hybrid heuristic strategy is implemented. The conventional approaches are utilized for probability-based multi-hop broadcasting for low latency. On the other hand, the major demerits of cellular communication are developing an effective multi-hop communication strategy that generates hidden node problems, and also it decreases the speed of the vehicles and the lack of infrastructure in network topology. Thus, this paper aims to propose a hybrid optimization model to choose the lowest count of multi-hops among the source and destination for the cooperative cellular vehicular communication network. The major issue well thought-out here is accurately choosing the count of hops or relays for appropriate communication, which is carried out through the multi-objective function regarding link reliability, energy consumption, outage probability, mobility factor, and transmission delay of the system. The adoption of the new hybrid algorithm with the aid of the Best Fitness-derived Crow Tunicate Swarm Optimization (BF-CTSO) algorithm is designed as the major contribution for optimally selecting the multi-hops between source and destination in a cooperative cellular communication network. Numerical results show that the proposed energy-efficient cooperative communication system outperforms traditional methods.

Evolutionary modeling and robustness analysis of multi-domain cooperative communication network under the environment of Internet of things

In the future, the Internet of things will reduce the cell radius and increase the number of low-power nodes to support thousands of times of traffic growth under 5G. As a virtual multiple input multiple output technology, cooperative communication technology can solve these problems effectively. According to the evolution characteristics of cooperative communication networks, a multi-domain cooperative communication network evolution model with preferential attachment and random attachment is constructed in this article. And then, the network properties and robustness are analyzed using the mean-field method and different attacks. Aiming at the resource constraints and resource allocation problems of communication nodes, a relay selection strategy based on the combination of maximum degree and minimum clustering coefficient is proposed. The simulation results show that the relay node selection strategy based on the combination of maximum degree and minimum clustering coefficient has significant advantages in selection steps and selection time, which greatly enhanced the performance of relay selection in multi-domain cooperative communication networks. Through real-time monitoring and updating of the performance and security indicators of the multi-domain cooperative communication networks, it provides a strong guarantee for the node deployment and security management of the Internet of things cooperative communication system.