Cooperative Communication Technology Research Articles

Outage performance prediction of cooperative vehicle network based on sparrow search algorithm based on back‐propagation neural network

AbstractWith the support of the sixth‐generation mobile networks (6G) technology, the Internet‐of‐Vehicle (IoV) can realize the perception and monitoring of vehicle road information. However, due to the change of network topology and various environment, the reliable performance of the communication link is facing challenges. For the sake of improving communication quality, a cooperative vehicular network (CVN) system is established, which adopts cooperative communication and multiple input multiple output (MIMO) technology. According to the signal‐to‐noise ratio (SNR) threshold of relay vehicles, using hybrid decode‐amplify‐forward (HDAF) protocol and combining with antenna selection, the analytical expression of outage probability (OP) with Meijer‐G function is obtained. For predicting the OP accurately, the sparrow search algorithm based on back‐propagation neural network (SSA‐BPNN) is put forward. The simulation results show that the cascade order of the channels has a negative effect on the OP. Meanwhile, the prediction accuracy of SSA‐BPNN is 64.8% higher than that of BPNN, and 98.96% greater than that of general regression neural network, and the convergence rate is faster than ICS‐BPNN.

A 3D Non-Stationarity and Consistency Model for Cooperative Multi-Vehicle Channels

In this paper, based on the irregular shaped geometry-based stochastic modeling (IS-GBSM) approach, a novel three-dimensional (3D) model for cooperative multi-vehicle communication channels is developed. By employing the cooperative communication technology, multi-vehicles can form a platoon to cooperatively transmit data streams, which can achieve distributed multiple-input multiple-output (MIMO) system performance. The proposed cooperative IS-GBSM distinguishes static and dynamic clusters, where continuously arbitrary vehicular movement trajectories (VMTs) of dynamic clusters and multi-vehicles are imitated. A new method, which combines frequency-dependent path gain and spectral clustering algorithm in machine learning (ML), is proposed to model cooperative space-time-frequency (S-T-F) non-stationarity with cooperative time-space (T-S) consistency. Based upon Poisson and Bernoulli processes, the developed method can also model the disappearance of links/sub-channels due to the destruction of transceivers by missiles in an optional manner. Therefore, the proposed cooperative IS-GBSM can further mimic an actual military cooperative communication scenario, i.e., cooperative multiple-unmanned ground vehicle (multi-UGV) communications for Mosaic Warfare. From the proposed cooperative IS-GBSM, typical channel statistics are derived. Simulation results show that cooperative S-T-F non-stationarity with cooperative T-S consistency is mimicked and the influences of vehicular traffic density (VTD) and VMT on channel statistics are investigated. Finally, simulation results fit well with the ray-tracing-based results, confirming the accuracy of the proposed cooperative IS-GBSM.

Decision-Based Routing for Unmanned Aerial Vehicles and Internet of Things Networks

New technologies and communication standards have changed traditional network processes. Internet of Things (IoT) is one of the emerging technologies where devices are connected to facilitate the users. When the networks are more congested due to a large number of users then the existing routing protocol and communication channels suffer from congestion, disconnection, overhead, and packet drop issues. Unmanned Aerial Vehicles (UAVs) are adopted to support the ground networks for more feasible data communication. These networks provide coverage facilities to IoT networks and provide smooth data dissemination services. Through the use of relay and cooperative communication technologies, UAVs could enlarge the communication space for IoT networks. Traditional network routing protocols have been adopted for data communication in these networks. However, the adopted protocols are not able to handle mobility and uncertain network conditions. This paper proposes a Decision-based Routing for Unmanned Aerial Vehicles and Internet of Things (DR-UAVIoT) network. The proposed protocol is useful for UAV-to-IoT and UAV-to-UAV data communication. The performance of the proposed solution is evaluated with the existing protocols in terms of data delivery, delay, and network overhead. The experimental results indicate the better performance of the proposed protocol in terms of less delay, less overhead, and better data delivery ratio as compared with existing routing protocols.

Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

In recent years, cooperative communication (CC) technology has emerged as a hotspot for testing wireless communication networks (WCNs), and it will play an important role in the spectrum utilization of future wireless communication systems. Instead of running node transmissions at full capacity, this design will distribute available paths across multiple relay nodes to increase the overall throughput. The modeling WCNs coordination processes, as a recurrent mechanism and recommending a deep learning-based transfer choice, propose a recurrent neural network (RNN) process-based relay selection in this research article. This network is trained according to the joint receiver and transmitter outage likelihood and shared knowledge, and without the use of a model or prior data, the best relay is picked from a set of relay nodes. In this study, we make use of the RNN to do superdimensional (high-layered) processing and increase the rate of learning and also have a neural network (NN) selection testing to study the communication device, find out whether or not it can be used, find out how much the system is capable of, and look at how much energy the network needs. In these simulations, it has been shown that the RNN scheme is more effective on these targets and allows the design to keep converged over a longer period of time. We will compare the accuracy and efficiency of our RNN processed-based relay selection methods with long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BLSTM),which are all acronyms for long short-term memory 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.

A novel power allocation strategy for cooperative PDMA systems

Cooperative communication technology is of great importance for increasing the user reachable rate, further improving throughput and reducing the outage probability of non-orthogonal multiple access (NOMA) systems. This paper mainly studies the power allocation optimization method based on amplify-and-forward (AF) pattern division multiple access (PDMA) to obtain the maximum achievable throughput. We formulate an optimization problem of user power allocation in a downlink PDMA system with cooperative relaying, the exact expressions of system throughput and user outage probability of the AF-PDMA system are derived, and a novel power allocation optimization method based on uniform distribution and restricted constraints is proposed. The effectiveness of the restricted constraints and optimization method is verified by theoretical analysis and simulation. The studies we have performed showed that the proposed scheme with uniform distribution and restricted constraints can be significantly improved in terms of the system throughput in comparison to the case with a genetic algorithm (GA) and fixed power allocation scheme. Concerning the proposed method, the search space is reduced to 1/3 of the original feasible region, and the runtime of the algorithm accounts for only 20% of the GA runtime.

A CSMA/CA based MAC protocol for hybrid Power-line/Visible-light communication networks: Design and analysis

Hybrid Power-line/Visible-light Communication (HPVC) network has been one of the most promising Cooperative Communication (CC) technologies for constructing Smart Home due to its superior communication reliability and hardware efficiency. Current research on HPVC networks focuses on the performance analysis and optimization of the Physical (PHY) layer, where the Power Line Communication (PLC) component only serves as the backbone to provide power to light Emitting Diode (LED) devices. So designing a Media Access Control（MAC) protocol remains a great challenge because it allows both PLC and Visible Light Communication (VLC) components to operate data transmission, i.e., to achieve a true HPVC network CC. To solve this problem, we propose a new HPC network MAC protocol (HPVC MAC) based on Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) by combining IEEE 802.15.7 and IEEE 1901 standards. Firstly, we add an Additional Assistance (AA) layer to provide the channel selection strategies for sensor stations, so that they can complete data transmission on the selected channel via the specified CSMA/CA mechanism, respectively. Based on this, we give a detailed working principle of the HPVC MAC, followed by the construction of a joint analytical model for mathematical-mathematical validation of the HPVC MAC. In the modeling process, the impacts of PHY layer settings (including channel fading types and additive noise feature), CSMA/CA mechanisms of 802.15.7 and 1901, and practical configurations (such as traffic rate, transit buffer size) are comprehensively taken into consideration. Moreover, we prove the proposed analytical model has the solvability. Finally, through extensive simulations, we characterize the HPVC MAC performance under different system parameters and verify the correctness of the corresponding analytical model with an average error rate of 4.62% between the simulation and analytical results.

Resource Allocation in Multi-Carrier Multiplexed NOMA Cooperative System

Non-orthogonal multiple access (NOMA) cooperative communication technology can combine the advantages of NOMA and cooperative communication, providing high spectrum efficiency and increasing user coverage for next-generation wireless systems. However, the research on NOMA cooperative communication technology is still in a preliminary stage and has mainly concentrated on the scenario of fewer users. This paper focuses on a user-centered NOMA collaboration system in an ultra-dense network, and it constructs a resource allocation optimization problem to meet the demands of each user. Then, this paper decomposes the optimization problem into two subproblems; one is the grouping match among multiple relays and users, and the other is jointly allocating power and subcarrier resources. Accordingly, a dynamic packet matching algorithm based on Gale–Shapley and an iterative algorithm based on the difference of convex functions programing are proposed. Compared with existing schemes, the proposed algorithms can improve system throughput while ensuring the quality of service of users.

Relay selection scheme based on deep reinforcement learning in wireless sensor networks

Cooperative communication technology has realized the enhancement in the wireless communication system’s spectrum utilization rate without resorting to any additional equipment; additionally, it ensures system reliability in transmission, increasingly becoming a research focus within the sphere of wireless sensor networks (WSNs). Since the selection of relay is crucial to cooperative communication technology, this paper proposes two different relay selection schemes subject to deep reinforcement learning (DRL), in response to the issues in WSNs with relay selection in cooperative communications, which can be summarized as the Deep-Q-Network Based Relay Selection Scheme (DQN-RSS), as well as the Proximal Policy Optimization Based Relay Selection Scheme (PPO-RSS); it further compared the commonly used Q-learning relay selection scheme (Q-RSS) with random relay selection scheme. First, the cooperative communication process in WSNs is modeled as a Markov decision process, and DRL algorithm is trained in accordance with the outage probability, as well as mutual information (MI). Under the condition of unknown instantaneous channel state information (CSI), the best relay is adaptively selected from multiple candidate relays. Thereafter, in view of the slow convergence speed of Q-RSS in high-dimensional state space, the DRL algorithm is used to accelerate the convergence. In particular, we employ DRL algorithm to deal with high-dimensional state space while speeding up learning. The experimental results reveal that under the same conditions, the random relay selection scheme always has the worst performance. And compared to Q-RSS, the two relay selection schemes designed in this paper greatly reduce the number of iterations and speed up the convergence speed, thereby reducing the computational complexity and overhead of the source node selecting the best relay strategy. In addition, the two relay selection schemes designed and raised in this paper are featured by lower-level outage probability with lower-level energy consumption and larger system capacity. In particular, PPO-RSS has higher reliability and practicability.

Optimization Analysis of Max-Link Secure Relay Selection in Buffer-Aided Cooperative Networks

Cooperative communication technology has been proved to be an useful approach to accomplish physical layer security. In recent years, more and more attention has been paid to the collaboration network with buffer relay. This paper studies the safety performance of the maximum link level options. Compared with the existing buffer-assisted cooperative security schemes, max-Link secure relay selection (Max-Link-SRS) does not require the eavesdropper’s channel state information (CSI) and has a lower complexity. Based on markov chain theory, a new closed form expression of secure interrupt probability (SOP) is derived. Numerical simulation results confirm our analysis and demonstrate that the functioning of max-Link-SRS is superior to the previously reported buffer-less aided cooperative security scheme. It provides a significant coding obtain at a smaller buffer size, while ensuring diversity gain is close to the upper limit of the double number of relays at a bigger buffer zone.

Research on Cooperative Communication Technology to Improve Capacity and Reliability of Integrated Energy System

The integrated energy system makes full use of renewable resources such as local photovoltaics, fully considers the difference and complementarity of load types in the region, and realizes the efficient, economic and clean utilization of regional energy from the dimensions of time and space. The existing communication system of the integrated energy system has become a constraint on business development. First, the communication rate is low and the system capacity is limited. After the terminal collects various energy information, a large amount of data needs to be uploaded, which puts huge pressure on the communication bandwidth. The second is poor reliability and high operation and maintenance costs. The current communication technology is single, equipment and networking costs are high. Aiming at the abovementioned pain points, an opportunistic relay cooperation technology and scheme based on signaling measurement is proposed. Theoretical analysis and simulation results show that the above scheme can improve the performance of the cooperative diversity system while reducing conflicts.

Optimal Energy-Efficient Source and Relay Precoder Design for Two-Way MIMO-AF Relay Systems

Energy efficiency (EE) is a key figure of merit for designing the next generation of communication systems. Meanwhile, relay-based cooperative communication, through machine-to-machine and other related technologies, is also playing an important part in the development of these systems. This paper designs an energy efficient precoding method for optimizing the EE/energy consumption of two-way multi-input multi-output (MIMO)-amplify-and-forward (AF) relay systems by using pseudo-convexity analysis to design EE-optimal precoding matrices. More precisely, we derive an EE-optimal source precoding matrix in closed-form, design a numerical approach for obtaining an optimal relay precoding matrix, prove the optimality of these matrices, when treated separately, and provide low-complexity bespoke algorithms to generate them. These matrices are then jointly optimized through an alternating optimization process that is proved to be systematically convergent. Performance evaluation indicates that our method can be globally optimal in some scenarios and that it is significantly more energy efficient (i.e., up to 60% more energy efficient) than existing EE-based one-way or two-way MIMO-AF precoding methods.

Online Optimal Control for Wireless Cooperative Transmission by Ambient RF Powered Sensors

Ambient radio frequency (RF) energy harvesting technology provides a hopeful solution to power the low-power electronic system. Nevertheless, the extremely low power density and randomness of ambient RF energy pose a significant obstacle to the application of ambient RF energy harvesting technology. Aiming at wireless transmission by ambient RF-powered sensors, this paper presents a cooperative communication technology by combining ambient backscattering and energy beamforming to significantly decrease the transmission energy consumption of each sensor. For wireless transmission via this cooperative communication technology, a novel optimization problem is formulated to maximize the long-term time-averaged sensed data size, which jointly takes into account sensing scheduling, transmission power allocation and the control of sensed data size. Moreover, we employ techniques of Lyapunov drift and Lyapunov optimization to propose an online optimal control algorithm, which achieves the optimal solution to the proposed problem without requiring a-priori knowledge of harvested energy and wireless channels. By exploiting mixed integer programming (MIP) and signomial geometric programming (SGP) that cause the proposed problem difficult to solve, a novel Sequential Convex Approximation algorithm is further introduced into the online optimal control algorithm. Simulation results indicate the proposed online algorithm offers a dramatic performance gain over alternative online algorithms.

Research on Cooperative Communication Strategy and Intelligent Agent Directional Source Grouping Algorithms for Internet of Things

In order to improve the network layer of the Internet of things to improve transmission reliability, save time delay and energy consumption, the Internet of things cooperative communication and intelligent agent technology were studied in this paper. In cooperative communication, a new cooperative communication algorithm KCN (k-cooperative node), and a reliability prediction model are proposed. The k value is determined by the end-to-end reliability. After k cooperative nodes are selected, other nodes enter dormancy. In aggregate traffic allocation, game theory is used to model the traffic equilibrium and end-to-end delay optimization scenarios. In practice, the optimal duty cycle can be calculated, which makes some cooperative nodes enter an idle state to save energy. Under the premise of guaranteeing end-to-end delay, it is shown that the reliability of the proposed KCN algorithm is better than that of the other existing routing protocols. In the aspect of intelligent agent, a directional source grouping algorithm D-MIP is proposed. This algorithm studies the routing problem of multi-agent parallel access to multiple source nodes. A directed source packet multi-agent routing planning algorithm is proposed. The iterative algorithm of each source node is limited to a sector, and the optimal intelligent agent route is obtained by selecting an appropriate angle. Compared with other algorithms, it is shown through a lot of simulated results that energy consumption and time delay has been saved by the proposed D-MIP algorithm.

Cooperative Communications With Relay Selection Based on Deep Reinforcement Learning in Wireless Sensor Networks

Cooperative communication technology has become a research hotspot in wireless sensor networks (WSNs) in recent years, and will become one of the key technologies for improving spectrum utilization in wireless communication systems in the future. It leverages cooperation among multiple relay nodes in the wireless network to realize path transmission sharing, thereby improving the system throughput. In this paper, we model the process of cooperative communications with relay selection in WSNs as a Markov decision process and propose DQ-RSS, a deep-reinforcement-learning-based relay selection scheme, in WSNs. In DQ-RSS, a deep-Q-network (DQN) is trained according to the outage probability and mutual information, and the optimal relay is selected from a plurality of relay nodes without the need for a network model or prior data. More specifically, we use DQN to process high-dimensional state spaces and accelerate the learning rate. We compare DQ-RSS with the Q-learning-based relay selection scheme and evaluate the network performance on the basis of three aspects: outage probability, system capacity, and energy consumption. Simulation results indicate that DQ-RSS can achieve better performance on these elements and save the convergence time compared with existing schemes.

Performance Analysis of a Novel Distributed C-ARQ Scheme for IEEE 802.11 Wireless Networks

It is well-known that the cooperative communication and error control technology can improve the network performance, but most existing cooperative MAC protocols have not focused on how to cope with the contention process caused by cooperation and how to reduce the bad influence of channel packet error rate on the system performance. Inspired by this, this paper first modifies and improves the basic rules of the IEEE 802.11 Medium Access Control (MAC) protocol to optimize the contention among the multi-relay in a cooperative ARQ scheme. Secondly, a hybrid ARQ protocol with soft combining is adopted to make full use of the effective information in the error data packet and hence improve the ability of the receiver to decode the data packet correctly. The closed expressions of network performance including throughput and average packet transmission delay in a saturated network are then analyzed and derived by establishing a dedicated two-dimensional Markov model and solving its steady-state distribution. Finally, the performance evaluation and superiority of the proposed protocol are validated in different representative study cases through MATLAB simulations.

IoT Enabled UAV: Network Architecture and Routing Algorithm

Unmanned aerial vehicles (UAVs) can be deployed efficiently to provide high quality of service for Internet of Things (IoT). By using cooperative communication and relay technologies, a large swarm of UAVs can enlarge the effective coverage area of IoT services via multiple relay nodes. However, the low latency service requirement and the dynamic topology of UAV network bring in new challenges for the effective routing optimization among UAVs. In this paper, a layered UAV swarm network architecture is proposed and an optimal number of UAVs is analyzed. Furthermore, a low latency routing algorithm (LLRA) is designed based on the partial location information and the connectivity of the network architecture. Finally, the performance of the proposed LLRA is verified by numerical results, which can decrease the link average delay and improve the packet delivery ratio in contrast to traditional routing algorithms without layered architecture.

Energy-efficient multi-UAV coverage deployment in UAV networks: A game-theoretic framework

UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games (EPG) and have Nash equilibrium (NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play (MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.

Mapping Digital Co-Creation for Urban Communities and Public Places

Increasingly digital communication, social media and computing networks put the end-users at the center of innovation processes, thus shifting the emphasis from technologies to people. In the private sector, this shift to user-centricity has been conceptualized under such approaches as Service-Dominant Logic and Open Innovation 2.0. Public sector conceptualizes the change through the New Public Governance and Open Government paradigms and suggest that the public value is no longer created by the governments alone but in collaboration between the public entities, private sector, civil society organizations and citizens. While traditional approaches to public engagement and governmental transformations remain relevant, this article focuses on the growing potential of networked urban communities to solve the social problems. It expands the co-creation research field and suggests a typology discerning co-creation patterns when enhancing the public spaces with a community-wide participation with the use of creative, innovative and cooperative Information and Communication Technologies’ applications. The sample for web-based monitoring consists of 10 digital applications linked with design and improvement of public spaces in Vilnius, Lithuania. The proposed typology framework gives an overview of the state-of-art in the interaction between people, places and technology. The research helps to discern how different technological, organizational and other social factors influence and shape the patterns of co-creative initiatives.

Cooperative Vehicular Networking: A Survey.

With the remarkable progress of cooperative communication technology in recent years, its transformation to vehicular networking is gaining momentum. Such a transformation has brought a new research challenge in facing the realization of cooperative vehicular networking (CVN). This paper presents a comprehensive survey of recent advances in the field of CVN. We cover important aspects of CVN research, including physical, medium access control, and routing protocols, as well as link scheduling and security. We also classify these research efforts in a taxonomy of cooperative vehicular networks. A set of key requirements for realizing the vision of cooperative vehicular networks is then identified and discussed. We also discuss open research challenges in enabling CVN. Lastly, the paper concludes by highlighting key points of research and future directions in the domain of CVN.