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Owing to the recent advancements of meta-materials and meta-surfaces, the concept of reconfigurable intelligent surface (RIS) has been embraced to meet the spectral- and energy-efficient, and yet cost-effective solutions for the sixth-generation (6G) wireless networks. From an operational standpoint, RISs can be easily deployed on the facades of buildings and indoor walls. Albeit promising, in the actual network operation, the deployment of RISs may face challenges because of the willingness and benefits of RIS holders from the aspect of installing RISs on their properties. Accordingly, RIS-aided wireless networks are faced with a formidable mission: how to balance the wireless service providers (WSPs) and RIS holders in terms of their respective interests. To alleviate this deadlock, we focus on the application of pricing models in RIS-aided wireless networks in pursuit of a win-win solution for both sides. Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs. In addition, a Stackelberg game-based model is exemplified to illustrate the operation of utility-maximization pricing. Finally, we highlight open issues and future research directions of applying pricing models to the RIS-aided wireless networks.

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.

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.

In this paper, a novel jointly adaptive transmission strategy for buffer-aided multiple-relay non-orthogonal multiple access (NOMA) networks is proposed. By leveraging time diversity brought by the data storage buffer, and multiple relays' beamforming gains, the source node broadcasts data in the first hop and all relays perform distributed beamforming to transmit information to users with NOMA in the second hop. Our focus is on maximizing the average network throughput (ANT) by jointly optimizing the mode selection, power and rate allocation at the source node, as well as the rate allocation and distributed beamforming coefficients at the relays along the time. As a solution to this complex problem, we propose an online scheduling scheme, which first adopts the Lyapunov optimization methodology to convert the ANT maximization problem into the drift-plus-penalty function minimization problem at each time slot. Secondly, focusing on the non-convexity of the transformed problem at each time slot, a novel two-loop algorithm is proposed as an effective solution. Extensive performance evaluation results have revealed the superiority of the proposed scheme as compared to other benchmark access schemes, namely, buffer-aided single relay NOMA, maximum ratio transmission based NOMA and buffer-aided time division multiple access schemes.

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the decision tree. Unfortunately, the existing optimal search algorithms often involve prohibitively high complexities, which indicates that they are infeasible in large-scale MIMO systems. To address this issue, we propose a general heuristic search algorithm, namely, hyper-accelerated tree search (HATS) algorithm. The proposed algorithm employs a deep neural network (DNN) to estimate the optimal heuristic, and then use the estimated heuristic to speed up the underlying memory-bounded search algorithm. This idea is inspired by the fact that the underlying heuristic search algorithm reaches the optimal efficiency with the optimal heuristic function. Simulation results show that the proposed algorithm reaches almost the optimal bit error rate (BER) performance in large-scale systems, while the memory size can be bounded. In the meanwhile, it visits nearly the fewest tree nodes. This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems. Besides, the code for this paper is available at \url{https://github.com/skypitcher/hats}.

Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the content of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

This paper presents a thorough performance analysis of dual-hop cognitive amplify-and-forward (AF) relaying networks under spectrum-sharing mechanism over independent non-identically distributed (i.n.i.d.) 􀀀 fading channels. In order to guarantee the quality-of-service (QoS) of primary networks, both maximum tolerable peak interference power Q at the primary users (PUs) and maximum allowable transmit power P at secondary users (SUs) are considered to constrain transmit power at the cognitive transmitters. For integer-valued fading parameters, a closed-form lower bound for the outage probability (OP) of the considered networks is obtained. Moreover, assuming arbitrary-valued fading parameters, the lower bound in integral form for the OP is derived. In order to obtain further insights on the OP performance, asymptotic expressions for the OP at high SNRs are derived, from which the diversity/coding gains and the diversity-multiplexing gain tradeoff (DMT) of the secondary network can be readily deduced. It is shown that the diversity gain and also the DMT are solely determined by the fading parameters of the secondary network whereas the primary network only affects the coding gain. The derived results include several others available in previously published works as special cases, such as those for Nakagami-m fading channels. In addition, performance evaluation results have been obtained by Monte Carlo computer simulations which have verified the accuracy of the theoretical analysis.