Relay Networks Research Articles

Wireless sensor network relay transmitting using dual buffer pools

Wireless sensor network relay transmitting using dual buffer pools

The multi-dimensional power big data mining based on improved grey clustering algorithm

In order to overcome the problems of the traditional power big data mining methods, such as the low integrity of data mining and the long time-consuming of data mining, this paper realizes multi-dimensional power big data mining by improving the grey clustering algorithm. Firstly, a relay multi hop network is established to collect power big data through the collector; Secondly, Lagrange interpolation method is used to fill the missing data of power data mining; Standardized processing of power consumption data; Finally, according to the grey theory and FCM clustering algorithm, the multi-dimensional power big data mining is realized. The experimental results show that the integrity of power big data mining in this method is up to 0.996, the mining time is not more than 3.05 s, and the mining integrity is up to 0.992, which indicates that this method can effectively improve the effect of power big data mining.

Dynamic rate and channel use allocation for cooperative wireless networks

In this paper, we investigate the performance of link adaptation (LA) algorithms for cooperative multiple access multiple relay networks. In the first transmission phase, the sources transmit in turn in consecutive time slots. In the second cooperative retransmission phase, the destination schedules one relaying node to send redundancies. LA is a fundamental mechanism allowing the source nodes to adapt to the radio channel conditions. In this paper, we investigate the problem of rate allocation using a centralized method, where the destination is the central node that determines the source rates. Furthermore, and in sharp contrast with existing cooperative transmission schemes, we consider the packet size to be time-varying. In other words, we propose adapting the slot duration of each of the sources during the transmission phase as a new degree of freedom. Accordingly, the LA process determines the rate and the time slot duration of each source. We consider a practical performance metric namely, the network spectral efficiency. This metric being difficult to be optimized (given the complex multi-variable optimization problem), we propose best-response dynamic (BRD) algorithms to solve the rate and time duration allocation. Then, we conduct a thorough performance analysis using Monte-Carlo simulations. Our numerical results validate the effectiveness of the proposed BRD algorithms as they yield performance close to the corresponding exhaustive search approach. Furthermore, the results ensure the gain of exploiting the new degree of freedom of the variable slot duration.

Relay network design with direct shipment and multi-relay assignment

Relay network design with direct shipment and multi-relay assignment

UAV relay network deployment through the area with barriers

Flying ad hoc networks composed of Unmanned Aerial Vehicles (UAVs) are flexible, inexpensive, and fast to deploy. This makes them a very attractive technology for many civilian and military applications. Due to the high mobility of the nodes, maintaining a multi-hop communication link between the UAVs is a challenging task. The existing network deployment method designed for relay network may fail to guarantee the connectivity due to the limited wireless transmission and dynamic topology. Designing and implementing a cost-effective UAV relay network is still a problem when there are barriers between the source node and the ground control station. In this paper, we first build a general UAV relay network model; then we propose a UAV deployment algorithm based on Q-learning to construct a UAV relay network with the minimum number of nodes, and we use an Equidistant Node Control algorithm to construct a dynamic relay path. We conduct a series of experiments on the OMNeT++ simulation platform to evaluate the performance of our proposed algorithm. The experimental results show that our UAV relay network has the lower cost and reliable communication performance.

Cooperative encryption over backscatter: Secure green communication for two-way energy harvesting relay networks

This paper focuses on the design of a novel cooperative jamming strategy and resource allocation scheme in two-way energy harvesting (EH) relay networks. For solving the security vulnerability challenges, a backscatter jamming strategy is proposed by leveraging the information signals as the carrier, which can degrade the eavesdropping link without extra energy consumption, delay and strict synchronization requirement. Based on the above design, we formulate the secure energy efficiency (SEE) maximization problem by jointly optimizing the transmission powers of two sources, two different power splitting (PS) factors and time allocation factor. Although the resulting optimization problem is highly non-convex due to fractional objective function and multivariate coupling, an iterative algorithm is developed to convert the original problem into a tractable version and obtain the resource allocation solutions. Simulation results show that the achievable SEE of the proposed scheme is better than other conventional schemes.

Computation Efficiency Maximization for UAV-Assisted Relaying and MEC Networks in Urban Environment

Unmanned aerial vehicle (UAV)-assisted relaying and mobile edge computing (MEC) networks is a popular and promising technology to provide high-quality computation service to ground users (GUs). However, prior works on UAV-assisted relaying and MEC networks do not consider the complicated channel conditions in urban environment. Furthermore, the optimization of computation efficiency, defined as the ratio of computation resources (in terms of CPU frequency) and energy consumption, is not studied in prior works. In this work, we consider an UAV-assisted relaying and MEC networks composed of GUs, two MEC servers carried by one rotary-wing UAV and one base station (BS), respectively. Meanwhile, we consider the probabilistic Line-of-Sight (LoS) channel model and the Rician fading model in urban environments to make the channel model more practical and accurate to urban environments. Further, we aim to solve the maximization problem of computation efficiency by jointly optimizing computation resources, computation offloading, bandwidth and UAV trajectory. For the nonconvex formulated problem, we proposed an algorithm based on the Dinkelbachs method, the block coordinate descent (BCD) method and successive convex approximate (SCA) technique. Numerical results demonstrate that the proposed scheme can efficiently improve the computation efficiency compared to other traditional schemes in the practical simulation environment.

Phase-Aligned Complex Linear Physical-Layer Network Coding in OFDM-VLC Two-Way Relay Network

We propose and experimentally demonstrate a phase-aligned complex linear physical-layer network coding (PA-CLPNC) scheme in orthogonal frequency division multiplexing (OFDM) visible light communication (VLC) two-way relay network (TWRN). The proposed scheme, based on the finite field of the Gaussian integer, achieves PNC of high-order QAMs and reduces the constellation size of the superposed signal at the relay before broadcasting it back to users. We further adopt a phase alignment strategy to enhance the minimum Euclidean distance between classes, into which the constellation points of the superposed signal at the relay are categorized. VLC-TWRN experiments with throughput of 600Mbit/s-1.47Gbit/s, using either uniform 8QAM/16QAM OFDM or bit-and-power loading, show that the proposed PA-CLPNC achieves one to two orders of magnitude reduction in the bit error ratio compared to the conventional amplify-and-forward PNC scheme.

Implementation of DNN-Based Physical-Layer Network Coding

Physical-layer network coding (PNC) has been proposed to double the throughput of a wireless two-way relay network (TWRN) and has been adapted so that it can be applied in vehicular ad-hoc networks (VANETs). Deep neural network (DNN)-based physical-layer network coding (PNC) for the TWRN system has been studied in recent years, under the assumption that the channel state information (CSI) is available to the end nodes and the relay node. However, the state-of-the-art DNN-based PNC system was only feasible for theoretical simulations, and no practical system design has been studied. When moving from theory to practical wireless systems, there are two critical issues to tackle: 1) unlike conventional regularly shaped quadrature amplitude modulations (QAMs), the constellation points of a DNN-based PNC system's end nodes are irregular and unpredictable to the relay node; and 2) other than in high-precision simulations, the practical DNN-based PNC system's constellation points are not fixed, that is, there is usually a cluster of points that must be grouped together so that the transceiver's digital-to-analog converter (DAC) and analog-to-digital converter (ADC) can work properly. To solve the above issues, we proposed the DNN-PNC implementation that can be realized using the Universal Software Radio Peripheral (USRP) platform. The computational complexity of our simulated DNN-PNC is one order of magnitude smaller than that given in the literature, while maintaining a good bit error rate (BER). In the implementation phase, we find that the naturally-happened power imbalance between the two end nodes' signals in our implementation can significantly boost the PNC constellation recovery, and hence reduce the system BER. Our experimental results showed that the BER of DNN-PNC can be lower than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-4}$</tex-math></inline-formula> when the signal-to-noise ratio (SNR) is 16 dB for the 2-QAM case and 20 dB for the 4-QAM case, respectively. These results indicate that DNN-based PNC is feasible for VANET applications, where the CSI changes and is difficult to obtain.

Coverage Analysis of Hybrid RF/THz Networks With Best Relay Selection

Utilizing terahertz (THz) transmission to enhance coverage has proven various benefits compared to traditional radio frequency (RF) counterparts. This letter proposes a dual-hop decode-and-forward (DF) routing protocol in a hybrid RF and THz relay network named hybrid relay selection (HRS). The coverage probability of the HRS protocol is derived. The HRS protocol prioritizes THz relays for higher data rates or short source-destination distances; and RF relays for lower data rates or large source-destination distances. The proposed HRS protocol offers nearly the same performance as the optimal selection protocol, which requires complete instantaneous channel state information (CSI) of all the nodes.

Differential Modulation-Based Buffer-Aided Cooperative Relaying Network

In this article, we consider a multirelay cooperative network using a differential modulation technique. The given set-up consists of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> decode-and-forward (DF) relays, each equipped with a buffer of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L$</tex-math></inline-formula> . All transceiver nodes in the considered cooperative network apply differential modulation for the transmission of data, hence, channel-state information is not required for decoding of the data at the receiving nodes. A priority-based max link selection approach is used for the selection of the best links for the transmission and reception of data. The Markov chain approach is used to derive the state-transition matrix of the system, which is then further used to model the evolution of buffer status. Analytical expressions of the outage probability and average bit error rate are obtained with the help of the state-transition matrix. The performance of the considered system is analyzed for different buffer sizes and a number of relays. The performance is then further compared with that of a coherent buffer-aided network and also with the nonbuffer-aided differential amplify-and-forward and DF systems. As compared to the coherent buffer-aided network, the considered buffer-aided differential system suffers a negligible signal-to-noise ratio penalty in the scenario when more than one buffer-aided relay is present in the network, which is a significant improvement when compared with the nonbuffer-aided differential cooperative systems, which suffer a performance penalty of approximately 3 dB as compared to their coherent counterpart.

Age‐aware relay strategy with simultaneous wireless information and power transfer in remote monitoring systems

AbstractRemote monitoring systems has emerged to automate public services, where Internet of Things (IoT) devices are deployed to collect and relay streams of status updates to a remote control centre. The freshness of collected data is of critical importance to system performance, which can be quantified by the concept of age of information (AoI). To obtain timely perception of the monitored area, each sensor tends to sample status updates at the maximum frequency, which may lead to device drop‐outs due to limited battery capacity. Simultaneous wireless information and power transfer (SWIPT) is considered as a promising technology to prolong network lifetime. In this paper, a SWIPT‐enabled two‐hop relay network, where a relay node uses power splitting protocol to assist data transmissions from multiple source nodes to the destination is considered. The impact of power splitting coefficients, data arrival pattern and time‐varying channel conditions on the averaged age performance and expected lifetime of the system is theoretically analysed. Moreover, a lightweight relay strategy which selectively forward packets based on a reward function consisting of energy level at relay node and expected age reduction is developed. Simulation results show that the proposed strategy outperforms existing schemes in collecting status updates in a timely and energy‐efficient manner.

Research on non-smooth signal identification algorithm for electronic communication in a two-way relay network

Abstract To better allow accurate identification of non-smooth signals in two-way relay networks. In this paper, an algorithm based on the two-way relay network system model, the RLS algorithm, is proposed. The algorithm introduces the recursive least squares principle algorithm to the basic cost function of nonsmooth signal identification, optimizes the signal identification model by exponential weighting, and uses the time-varying variance matrix and the optimal identification data separation matrix to make real-time estimates of the new cost function for obtaining nonsmooth signals. And a new index, the PI index, is introduced to define whether the non-stationary signal is stable and reliable. Through two sets of comparison tests, we can learn that the RLS algorithm based on the two-way relay network system is better than other algorithms in the identification of non-stationary signals. It shows that the RLS algorithm can better identify and manage the information flow in a two-way relay network, make the non-stationary signal more stable, and further improve the convergence and convergence speed of the non-stationary signal to provide a better information interaction experience for both sides of electronic communication.

Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks

Satellite network is considered a prominent architecture for future space-air-ground integrated networks, where the relay satellite network (RSN) plays an essential role in data transmission. However, the relay satellite network faces practical challenges. First, due to the dynamic network topology and limited resources such as storage capacity, antennas, and bandwidth, optimizing data transmission and resource allocation in a resource-constrained stochastic system is challenging. Second, satellites intermittently enter the eclipse zone, which leads to an unstable energy supply. Therefore, optimal energy management is required to balance energy supply and consumption. In this paper, we proposed an online optimal control algorithm, based on Lyapunov stability theory, to optimize data transmission and energy management jointly. Furthermore, the performance of our proposal is analyzed comprehensively, including the reasonable bounds for data and energy and the optimality of our proposed algorithm. Finally, extensive performance evaluation simulations are performed based on real-world satellite parameters. The simulation results indicate that the proposed method can achieve long-term network stability and energy sustainability. Moreover, the proposed algorithm shows an improvement of 9.6% and 20.3% in system utility compared to the non-optimized transmission method and non-optimized energy management algorithm, respectively.

Minimum secrecy rate maximization for multiple peer-to-peer communications in two-way relaying networks

We investigate physical layer security for multi-user peer-to-peer relay networks, where a secure user pair and other unclassified users exchange information only through a multi-antenna relay in the presence of a multi-antenna eavesdropper. Our aim is to obtain the optimal values for variables of user transmit power and the relay beamforming matrix by solving the minimum secrecy rate maximization problem under specific constraints. We assume scenarios in which the eavesdropper’s channel state information is and is not available. Mathematically, the optimization problem raised in the first scenario is nonlinear and non-convex. The relay adopts a null space beamforming technique to facilitate the problem and guarantee secrecy. Also, we decouple it into two subproblems and solve them using inequality semi-definite programming and nonlinear optimization techniques to find solutions for variables. Also, the complexity of the proposed algorithms is analyzed and its convergency is proved by simulation. In the second scenario, we utilize an artificial noise-aided secure relay beamforming scheme. The problem is equivalent to the problem of assigning maximum power to the artificial noise evaluated by the secrecy outage probability concept. Finally, simulations demonstrate the validity of the proposed methods.

Deep Q-Learning-Based Buffer-Aided Relay Selection for Reliable and Secure Communications in Two-Hop Wireless Relay Networks

This paper investigates the problem of buffer-aided relay selection to achieve reliable and secure communications in a two-hop amplify-and-forward (AF) network with an eavesdropper. Due to the fading of wireless signals and the broadcast nature of wireless channels, transmitted signals over the network may be undecodable at the receiver end or have been eavesdropped by eavesdroppers. Most available buffer-aided relay selection schemes consider either reliability or security issues in wireless communications; rarely is work conducted on both reliability and security issues. This paper proposes a buffer-aided relay selection scheme based on deep Q-learning (DQL) that considers both reliability and security. By conducting Monte Carlo simulations, we then verify the reliability and security performances of the proposed scheme in terms of the connection outage probability (COP) and secrecy outage probability (SOP), respectively. The simulation results show that two-hop wireless relay network can achieve reliable and secure communications by using our proposed scheme. We also performed comparison experiments between our proposed scheme and two benchmark schemes. The comparison results indicate that our proposed scheme outperforms the max-ratio scheme in terms of the SOP.

Performance analysis of MRT/RAS scheme in dual-hop NOMA energy harvesting relay networks

This paper investigates the outage probability (OP) performance of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) relaying networks. In the investigated network, a source simultaneously communicates multiple NOMA users with the assistance of an amplify-and-forward energy harvesting relay. We derive each user’s OP expression in closed form over the generic channel model Nakagami-m fading. Moreover, we carry out asymptotic analysis to have clear insights into the OP behavior in the high signal-to-noise ratio region. The theoretical analysis’s correctness is finally verified via Monte Carlo simulations. The results show that the OP performance improves with the increased number of antennas and enhancement in the channel conditions. Furthermore, the relay has to be near the source in order to have better OP performance. Moreover, each user has a different optimum power division ratio.

An analytical model for multihop LoRaWAN networks

LoRaWAN is one of the most popular LPWAN technology due to the facility to implement applications that require a low cost, low energy consumption, low data rate, and large coverage area. LoRaWAN is a single-hop star-of-stars topology composed of end-devices that transmit data to be collected by gateways in the coverage area. Multihop networks are receiving the attention of the LoRaWAN community since it allows to extend the coverage area and improve energy efficiency, thus prolonging the network lifetime. In this paper, we propose an analytical model for multihop LoRaWAN networks. The objective of the analytical model is to provide a general and theoretical definition of a multihop LoRaWAN network, considering different device characteristics and network topologies. The analytical model gives new insights into the performance of multihop LoRaWAN networks. We investigate the energy consumption, throughput, delay, and packet delivery ratio of single-hop and multihop relay networks. The results showed that the relay network increases the network performance compared to the regular LoRaWAN network, although there are some limitations on the number of hops and relay characteristics. The development and deployment of multihop LoRaWAN networks are creating new opportunities, and the analytical model presented could be used before real deployments for a better knowledge of limitations and network needs.

Asymmetric Successive Compute-and-Forward and the Capacity Gap for the Gaussian Two-way Relay Channel

Background: The compute-and-forward strategy is one of the outstanding methods which is used for interference management in wireless relay networks where decoding linear combinations of code words is required. Recently, many efforts have been made for decoding integer and noninteger combinations. The difference between the methods is the manner of handling different conditions of networks, such as equal or unequal power constraints and equal or unequal channel gains. Objective: In this work, we present a modified n-step asymmetric successive compute-and-forward strategy for the communication network where we have both unequal power constraints and unequal channel gains conditions. Methods: In the proposed method, we scale channel gains and coefficients with the square root of power constraints. In this way, despite previous methods, without the need for scaling factors in our formulation, it is still able to solve the problem of general Gaussian relay networks with unequal power constraints and unequal channel gains. We also use scaling factors in our method in order to have the ability to divide the rates between users fairly. Results and Conclusion: We evaluate the ability of the modified strategy for the uplink communication of the two-way relay channel, where one relay can help communication between the two users. At the relay, we decode the linear combinations of the messages of the two users and obtain 1/2 bit/sec/Hz per user capacity gap from the cut-set bound. Through some theoretical and simulation results, we show that by appropriately adjusting parameters, different points and areas of rate regions are achievable.

Task Scheduling Under a Novel Framework for Data Relay Satellite Network via Deep Reinforcement Learning

Data relay satellite networks (DRSNs) face the challenge of increasing relay mission demands in space networks. To improve the task scheduling efficiency of DRSN further, we propose a novel task scheduling framework, wherein a scheduling sequence is generated by selecting one antenna and selecting one task for the antenna in each step. Subsequently, the task scheduling problem of DRSN (TSPD) is regarded as a sequential decision-making problem and is optimized using a method based on deep reinforcement learning (DRL), which overcomes the difficulty of designing heuristics with massive efforts. In this study, a mathematical model and corresponding Markov decision model based on our proposed scheduling framework are constructed, and for the first time, a policy network that includes one encoder based on the attention mechanism and two decoders is designed to solve the TSPD. In addtion, extensive experiments are conducted to verify the effectiveness of our scheduling framework and demonstrate that the DRL method can obtain a scheduling scheme with the highest profits with decent generalization to different task scales, number of user spacecrafts and execution duration of tasks.