Network coding-based channel quality indicator reporting for two-way multi-relay networks

This paper considers channel quality indicator (CQI) reporting for data exchange in a two-way multi-relay network. We first propose an efficient CQI reporting scheme based on network coding, where two terminals are allowed to simultaneously estimate the CQI of the distant terminal-relay link without suffering from additional overhead. In addition, the transmission time for CQI feedback at the relays is reduced by half while the increase in complexity and the loss of performance are negligible. This results in a system throughput improvement of 16.7% with our proposed CQI reporting. Upper and lower bounds of the mean square error (MSE) of the estimated CQI are derived to study performance behaviour of our proposed scheme. It is found that the MSE of the estimated CQI increases proportionally with the square of the cardinality of CQI level sets although an increased number of CQI levels would eventually lead to a higher data rate transmission. On the basis of the derived bounds, a low-complexity relay selection (RS) scheme is then proposed. Simulation results show that, in comparison with optimal methods, our suboptimal bound-based RS scheme achieves satisfactory performance while reducing the complexity at least three times in case of large number of relays

A secure network coding based image communications in two-hop wireless relay networks

This paper investigates the image communications in two-hop wireless relay networks (TH-WRNs) where a source node sends images to a destination node with the assistance of a relay node via two hops, i.e. source-to-relay and relay-to-destination links. Due to the broadcast nature of wireless media, there exists an eavesdropper who tries to overhear and recover the images. Aiming to enhance the security and also to save transmission bandwidth of the image communications, we propose a secure relaying transmission (SRT) protocol by exploiting both random linear network coding (RLNC) and image super-resolution (ISR) techniques. In the proposed protocol, the original high-resolution (HR) images are downscaled at the source node and the RLNC is employed at both the source and relay nodes to conceal the original images from the eavesdropper. The RLNC decoding and ISR are adopted at the destination node to decode and recover the HR images, while the eavesdropper cannot decode the images due to the unawareness of the coefficient matrices and the reference images in the RLNC. It is shown that the proposed SRT protocol achieves a significantly higher performance at the destination than at the eavesdropper. Furthermore, with high-quality relaying hops, the SRT protocol outperforms the secure direct transmission (SDT) protocol with only a direct link between the source and the destination nodes. Finally, simulation results are provided to verify the findings

CQI reporting strategies for nonregenerative two-way relay networks

This paper considers data exchange between two terminals in a nonregenerative two-way relay network. We first propose two efficient channel quality indicator (CQI) reporting schemes based on XOR and superposition coding for single-relay networks. These schemes allow two terminals to simultaneously estimate the CQI of the distant link without incurring additional overhead. In addition, the transmission time for CQI feedback is reduced by half while the loss of performance is negligible. Upper and lower bounds of the mean square error (MSE) of the estimated CQI are derived to analyze various effects on the performance of the proposed schemes. We then extend our MSE analysis to multi-relay networks where a low-complexity relay selection scheme is proposed based on the derived bounds. Simulation results show that, in comparison with conventional methods, this suboptimal bound-based scheme achieves satisfac- tory performance while reducing the complexity at least three times in case of large number of relays

Deep-NC: a secure image transmission using deep learning and network coding

Visual communications have played an important part in our daily life as a non-verbal way of conveying information using symbols, gestures and images. With the advances of technology, people can visually communicate with each other in a number of forms via digital communications. Recently Image Super-Resolution (ISR) with Deep Learning (DL) has been developed to reproduce the original image from its low-resolution version, which allows us to reduce the image size for saving transmission bandwidth. Although many benefits can be realised, the image transmission over wireless media experiences inevitable loss due to environment noise and inherent hardware issues. Moreover, data privacy is of vital importance, especially when the eavesdropper can easily overhear the communications over the air. To this end, this paper proposes a secure ISR protocol, namely Deep-NC, for the image communications based on the DL and Network Coding (NC). Specifically, two schemes, namely Per-Image Coding (PIC) and Per-Pixel Coding (PPC), are designed so as to protect the sharing of private image from the eavesdropper. Although the PPC scheme achieves a better performance than the PIC scheme for the entire image, it requires a higher computational complexity on every pixel of the image. In the proposed Deep-NC, the intended user can easily recover the original image achieving a much higher performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) than those at the eavesdropper. Simulation results show that an improvement of up to 32 dB in the PSNR can be obtained when the eavesdropper does not have any knowledge of the parameters and the reference image used in the mixing schemes. Furthermore, the original image can be downscaled to a much lower resolution for saving significantly the transmission bandwidth with negligible performance loss

An efficient pest classification in smart agriculture using transfer learning

To this day, agriculture still remains very important and plays considerable role to support our daily life and economy in most countries. It is the source of not only food supply, but also providing raw materials for other industries, e.g. plastic, fuel. Currently, farmers are facing the challenge to produce sufficient crops for expanding human population and growing in economy, while maintaining the quality of agriculture products. Pest invasions, however, are a big threat to the growth crops which cause the crop loss and economic consequences. If they are left untreated even in a small area, they can quickly spread out other healthy area or nearby countries. A pest control is therefore crucial to reduce the crop loss. In this paper, we introduce an efficient method basing on deep learning approach to classify pests from images captured from the crops. The proposed method is implemented on various EfficientNet and shown to achieve a considerably high accuracy in a complex dataset, but only a few iterations are required in the training process

Cooperative spectrum sensing with secondary user selection for cognitive radio networks over Nakagami-m fading channels

This paper investigates cooperative spectrum sensing (CSS) in cognitive wireless radio networks (CWRNs). A practical system is considered where all channels experience Nakagami-$m$ fading and suffer from background noise. The realisation of the CSS can follow two approaches where the final spectrum decision is based on either only the global decision at fusion centre (FC) or both decisions from the FC and secondary user (SU). By deriving closed-form expressions and bounds of missed detection probability (MDP) and false alarm probability (FAP), we are able to not only demonstrate the impacts of the $m$-parameter on the sensing performance but also evaluate and compare the effectiveness of the two CSS schemes with respect to various fading parameters and the number of SUs. It is interestingly noticed that a smaller number of SUs could be selected to achieve the lower bound of the MDP rather using all the available SUs while still maintaining a low FAP. As a second contribution, we propose a secondary user selection algorithm for the CSS to find the optimised number of SUs for lower complexity and reduced power consumption. Finally, numerical results are provided to demonstrate the findings

Editorial: Emerging techniques and applications for 5G networks and beyond

Editoria

Efficient ARQ retransmission schemes for two-way relay networks.

In this paper, we investigate different practical automatic repeat request (ARQ) retransmission protocols for two-way wireless relay networks based on network coding (NC). The idea of NC is applied to increase the achievable throughput for the exchange of information between two terminals through one relay. Using NC, throughput efficiency is significantly improved due to the reduction of the number of retransmissions. Particularly, two improved NC-based ARQ schemes are designed based on go-back-N and selective-repeat (SR) protocols. The analysis of throughput efficiency is then carried out to find the best retransmission strategy for different scenarios. It is shown that the combination of improved NC-based SR ARQ scheme in the broadcast phase and the traditional SR ARQ scheme in the multiple access phase achieves the highest throughput efficiency compared to the other combinations of ARQ schemes. Finally, simulation results are provided to verify the theoretical analysis

Internet traffic prediction using recurrent neural networks

Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are in general unpredictable and must adapt to unforeseen circumstances. In small to medium-size networks, the administrator can anticipate the fluctuations in traffic without the need of using forecasting tools, but in the scenario of large-scale networks where hundreds of new users can be added in a matter of weeks, more efficient forecasting tools are required to avoid congestion and over provisioning. Network and hardware resources are however limited; and hence resource allocation is critical for the NTP with scalable solutions. To this end, in this paper, we propose an efficient NTP by optimizing recurrent neural networks (RNNs) to analyse the traffic patterns that occur inside flow time series, and predict future samples based on the history of the traffic that was used for training. The predicted traffic with the proposed RNNs is compared with the real values that are stored in the database in terms of mean squared error, mean absolute error and categorical cross entropy. Furthermore, the real traffic samples for NTP training are compared with those from other techniques such as auto-regressive moving average (ARIMA) and AdaBoost regressor to validate the effectiveness of the proposed method. It is shown that the proposed RNN achieves a better performance than both the ARIMA and AdaBoost regressor when more samples are employed