Internode Distance-Based Redundancy Reliable Transport in Underwater Sensor Networks

Underwater communication is a very challenging topic. Protocols used in terrestrial sensor networks cannot be directly applied in the underwater world. High-bit error rate and large propagation delay make the design of transport protocols especially awkward. ARQ-based reliable transport schemes are not appropriate in underwater environments due to large propagation delay, low communication bandwidth, and high error probability. Thus, we focus on redundancy-based transport schemes in this paper. We first investigate three schemes that employ redundancy mechanisms at the bit and/or packet level to increase the reliability in a direct link scenario. Then, we show that the broadcast property of the underwater channel allows us to extend those schemes to a case with node cooperative communication. Based on our analysis, an adaptive redundancy transport protocol (ARRTP) for underwater sensor networks is proposed. We suggest an architecture for implementation. For two kinds of topologies, namely, regular and random, we show that ARRTP presents a better transmission success probability and energy efficiency tradeoff for single- and multihop transmissions. We also offer an integrated case study to show that ARRTP is not only supplying reliability but also has some positive effect in guiding the deployment of underwater sensor nodes

Efficient Transmission in Multiantenna Two-Way AF Relaying Networks

In this paper, an efficient transmission scheme, termed the joint antenna selection and data exchange (AS-DE) scheme, is proposed for a two-way amplify-and-forward relaying network, where two single-antenna source terminals exchange information via a multiantenna relay station. For the proposed scheme, the best antenna at the relay for each source terminal is first selected separately, following the max-max scheme. Then, from the set of the previously selected antennas, either one antenna is selected, in a similar fashion as well-known max-min and max-sum schemes, or two antennas exchange their respective received signals, which are then coded, amplified, and broadcasted to the source and destination terminals. Tight lower and upper bounds on the outage probability (OP) for the proposed scheme have been derived assuming independent and identically distributed Rayleigh fading channels. Furthermore, our analysis reveals that the proposed joint AS-DE scheme can achieve full diversity. Finally, it is shown that under the same resource constraints, i.e., in terms of the number of the utilized time slots and transmit power, the proposed joint AS-DE scheme outperforms the max-min, the max-sum, and the max-max schemes. Extensive numerical results accompanied with computer simulations, are further provided to validate the developed analytical results

Two-Timescale Transmission Design for Wireless Communication Systems Aided by Active RIS

This paper considers an active reconfigurable intelligent surface (RIS)-aided communication system, where an M-antenna base station (BS) transmits data symbols to a single-antenna user via an N-element active RIS. We use two-timescale channel state information (CSI) in our system, so that the channel estimation overhead and feedback overhead can be decreased dramatically. A closed-form approximate expression of the achievable rate (AR) is derived and the phase shift at the active RIS is optimized. In addition, we compare the performance of the active RIS system with that of the passive RIS system. The conclusion shows that the active RIS system achieves a lager AR than the passive RIS system

Impact of factor graph on average sum rate for uplink sparse code multiple access systems

In this paper, we first study the average sum rate of sparse code multiple access (SCMA) systems, where a general scenario is considered under the assumption that the distances between the mobile users and the base station are not necessarily identical. Closed-form analytical results are derived for the average sum rate based on which an optimal factor graph matrix is designed for maximizing the capacity of the SCMA systems. Moreover, we propose a low-complexity iterative algorithm to facilitate the design of the optimal graph matrix. Finally, Monte Carlo simulations are provided to corroborate the accuracy of the theoretical results and the efficiency of the proposed iterative algorithm

Contrastive Learning based Semantic Communication for Wireless Image Transmission

Recently, semantic communication has been widely applied in wireless image transmission systems as it can prioritize the preservation of meaningful semantic information in images over the accuracy of transmitted symbols, leading to improved communication efficiency. However, existing semantic communication approaches still face limitations in achieving considerable inference performance in downstream AI tasks like image recognition, or balancing the inference performance with the quality of the reconstructed image at the receiver. Therefore, this paper proposes a contrastive learning (CL)-based semantic communication approach to overcome these limitations. Specifically, we regard the image corruption during transmission as a form of data augmentation in CL and leverage CL to reduce the semantic distance between the original and the corrupted reconstruction while maintaining the semantic distance among irrelevant images for better discrimination in downstream tasks. Moreover, we design a two-stage training procedure and the corresponding loss functions for jointly optimizing the semantic encoder and decoder to achieve a good trade-off between the performance of image recognition in the downstream task and reconstructed quality. Simulations are finally conducted to demonstrate the superiority of the proposed method over the competitive approaches. In particular, the proposed method can achieve up to 56\% accuracy gain on the CIFAR10 dataset when the bandwidth compression ratio is 1/48