Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a recently developed technique which modulates part of the information bits using the indices of OFDM subcarriers. In this letter, a simple and efficient subcarrier-level interleaving scheme is introduced to improve the performance of conventional OFDM-IM through enlarging the Euclidean distances among the modulated symbols. Both theoretical analysis and simulation results are presented to show that the proposed OFDM with interleaved sucarrier-index modulation (OFDM-ISIM) can achieve better system performance than conventional OFDM-IM and OFDM with low-order modulation schemes such as binary phase shift keying, quadrature phase shift keying and 16 quadrature amplitude modulation.

Novel communication technology based on large intelligent surface (LIS) [1] has arisen recently, with the aim to enhance the signal quality at the receiver. In this paper, a practical structure of LIS-based spatial modulation (LIS-SM) is proposed, in order to utilize both transmit and receive antenna indices. Meanwhile, the theoretical average bit error rate (ABER) performance bound of the developed LIS-SM scheme is investigated. For the sake of achieving further spatial diversity gain, we extend its employment to the antenna selection (AS) scenario, and a low-complexity selection algorithm is designed on the basis of minimum squared Euclidian distance and signal-to-leakage-and-noise ratio as well as the idea of greedy elimination algorithm. Performance analysis shows that AS-aided LIS-SM is more robust in terms of ABER compared with conventional LIS-SM. Moreover, complexity analysis also depicts that the proposed fast selection algorithm achieves much lower complexity yet a comparable ABER performance, compared to the traditional exhaustive search.

Recently, reconfigurable intelligent surface (RIS), which operates with the aim to manipulate multi-path signals, has been widely considered for wireless communications. In this letter, we extend the employment of RIS to indoor positioning, with the aid of ultra-wideband (UWB) technique. Moreover, the Cramér-Rao lower bound of the developed positioning scheme is quantified. Both theoretical analysis and simulation results demonstrate that RIS has the potential to realize accurate positioning with a single access point, due to its ability to mark the channel and replace traditional active positioning anchors. Moreover, it is also depicted that when the number of receive antennas is limited, time-of-arrival based positioning has a higher accuracy than that based on angle-of-arrival, due to the high multipath resolution of UWB signals.

As an aerial base station, unmanned aerial vehicle (UAV) has been considered as a promising technology to assist future wireless communications due to its flexible, swift and low cost features, where resource allocation is the basis for ensuring energy-efficient UAV-assisted networks. This paper formulates a joint optimization problem of user association, UAV trajectory design and power control to maximize the channel capacity among all ground users at a limited power level in a downlink transmission. To tackle the mixed-integer non-linear programming problem, this paper proposes a clustering-aided reinforcement learning approach consisting of three consecutive stages. Firstly, modified expectation-maximization unsupervised learning algorithm is investigated to cluster the ground users, which reduces the dimensions and hence, the association complexity is reduced as well. Then, Kuhn-Munkres algorithm is incorporated for user association, which associates a UAV with the ground users via matching to the cluster, and assigns the UAVs to the centroid of the matching cluster for pre-placement, with the aim of speeding up the convergence of the following deep reinforcement learning algorithm. Finally, a multi-agent twin delayed deep deterministic (MATD3) policy gradient is proposed to solve the non-convex sub-problem, which determines the transmit power and designs the fine-tuned trajectory of UAVs. By incorporating low-bias value estimation technique, the reward of the proposed MATD3 algorithm is improved compared with existing multi-agent reinforcement learning algorithms. Simulation results have demonstrated that our proposed approach achieves higher reward as well as converging faster than existing reinforcement algorithms. Besides, the clustering-aided reinforcement learning has lower computational complexity than the benchmark schemes.

Owing to the breakthrough of artificial meta-materials, the emergence and evolution of reconfigurable intelligent surface (RIS) have drawn a novel blueprint for the development of the sixth-generation (6G) wireless networks, through the creation of smart radio environment to meet the requirement of ubiquitous connectivity. Meanwhile, the vision for the 6G illuminates the integration of sensing and communication, bringing out a demand for both high-quality communication and high-precision localization. Therefore, regarding the future wireless localization, the potential of RIS to tag the signals provides additional degrees of freedom. To this end, to fulfil the increasing demands for positioning, RIS has drawn a growing amount of research both from academia and industry. In this paper, we first introduce the emerging investigations and development of RIS, and we give a concise summary of the localization principles. Then we explain the potential of RIS in localization, and provide a comprehensive survey of the current state of research on RIS-assisted localization. Finally, we discuss the most significant research challenges to tackle for RIS-aided localization in the future.

Due to the broadcast nature of wireless communications, physical-layer security has attracted increasing attention from both academia and industry. Artificial noise (AN), as one of the promising physical-layer security techniques, leverages the spatial degree-of-freedom of channels to effectively enhance the security of wireless communications. In contrast to other physical-layer security techniques, the key distinguishing feature of AN lies in its ability to generate specific interfering signals according to channel characteristics, thereby increasing the secrecy capacity by reducing the wiretap channel capacity without affecting the legitimate channel capacity. Hence, this paper provides a comprehensive and up-to-date survey of AN, including its evolution, modeling, background, applications, and future trends. Initially, we introduce the development, fundamentals, and technical background of AN. Subsequently, we review the current state of research across various AN-empowered scenarios and AN-combined technologies. Finally, we discuss open challenges and outline potential future directions for AN-aided wireless security.