A Novel Cross Entropy Approach for Offloading Learning in Mobile Edge Computing

In this letter, we propose a novel offloading learning approach to compromise energy consumption and latency in a multi-tier network with mobile edge computing. In order to solve this integer programming problem, instead of using conventional optimization tools, we apply a cross entropy approach with iterative learning of the probability of elite solution samples. Compared to existing methods, the proposed one in this network permits a parallel computing architecture and is verified to be computationally very efficient. Specifically, it achieves performance close to the optimal and performs well with different choices of the values of hyperparameters in the proposed learning approach

Secure Multiple Amplify-and-Forward Relaying Over Correlated Fading Channels

This paper quantifies the impact of correlated fading on secure communication of multiple amplify-and-forward (AF) relaying networks. In such a network, the base station (BS) is equipped with multiple antennas and communicates with the destination through multiple AF relays, while the message from the relays can be overheard by an eavesdropper. We focus on the practical communication scenario, where the main and eavesdropper’s channels are correlated. In order to enhance the transmission security, transmit antenna selection (TAS) is performed at the BS, and the best relay is chosen according to the full or partial relay selection criterion, which relies on the dualhop relay channels or the second-hop relay channels, respectively. For these criteria, we study the impact of correlated fading on the network secrecy performance, by deriving an analytical approximation for the secrecy outage probability (SOP) and an asymptotic expression for the high main-to-eavesdropper ratio (MER). From these results, it is concluded that the channel correlation is always beneficial to the secrecy performance of full relay selection. However, it deteriorates the secrecy performance if partial relay selection is used, when the number of antennas at the BS is less than the number of relays.ARC Discovery Projects Grant DP150103905

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

Privacy preservation via beamforming for NOMA

Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access approach for 5G mobile systems because of its superior spectrum efficiency. However, the privacy between the NOMA users may be compromised due to the transmission of a superposition of all users’ signals to successive interference cancellation (SIC) receivers. In this paper, we propose two schemes based on beamforming optimization for NOMA that can enhance the security of a specific private user while guaranteeing the other users’ quality of service (QoS). Specifically, in the first scheme, when the transmit antennas are inadequate, we intend to maximize the secrecy rate of the private user, under the constraint that the other users’ QoS is satisfied. In the second scheme, the private user’s signal is zero-forced at the other users when redundant antennas are available. In this case, the transmission rate of the private user is also maximized while satisfying the QoS of the other users. Due to the nonconvexity of optimization in these two schemes, we first convert them into convex forms and then, an iterative algorithm based on the ConCave-Convex Procedure is proposed to obtain their solutions. Extensive simulation results are presented to evaluate the effectiveness of the proposed scheme