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With the dramatic increase in the number of mobile users and wireless devices accessing the network, the performance of fifth generation (5G) wireless communication systems has been severely challenged. Reconfigurable intelligent surface (RIS) has received much attention as one of the promising technologies for the sixth generation (6G) due to its ease of deployment, low power consumption, and low price. RIS is an electromagnetic metamaterial that serves to reconfigure the wireless environment by adjusting the phase, amplitude, and frequency of the wireless signal. To maximize channel transmission efficiency and improve the reliability of communication systems, the acquisition of channel state information (CSI) is essential. Therefore, an effective channel estimation method guarantees the achievement of excellent RIS performance. This survey presents a comprehensive study of existing channel estimation methods for RIS. Firstly, channel estimation methods in high and low frequency bands are overviewed and compared. We focus on channel estimation in the high frequency band and analyze the system model. Then, the comprehensive description of the different channel estimation methods is given, with a focus on the application of deep learning. Finally, we conclude the paper and provide an outlook on the future development of RIS channel estimation.

In this paper, we investigate covert communications for an unmanned aerial vehicle (UAV)-aided rate-splitting multiple access (RSMA) system in which the UAV transmits to the covert and public users separately while shielding covert transmissions from a warden. Under the RSMA principles, the messages of the covert and public users are converted to common and private streams for air-to-ground transmissions. Subject to the quality of service (QoS) requirements of the public user and covertness constraint of the UAV-aid RSMA (UAV-RSMA) system, the aim of covert communication design is to maximize the covert rate by jointly optimizing the transmit power allocation, common rate allocation, and UAV hovering height, which each contribute to the uncertainty of the warden’s binary decision and attempts to enhance communication performance. To address the non-convex covert rate maximization problem in addition to the highly coupled system parameters, we decouple the original problem into three subproblems of transmit power allocation, common rate allocation, and UAV hovering height. We derive the optimal solutions for each of the subproblems of the transmit power and rate allocations and formulate a signomial programming problem to tackle UAV hovering height optimization. The simulation results indicate the superior covert rate performance achieved by the proposed AO algorithm and demonstrate that the proposed UAV-RSMA scheme achieves a higher covert rate than the benchmark schemes.

We propose a sustainability-driven resource allocation algorithm for energy harvesting powered device-to-device (D2D) communication underlaying cellular networks. In this setup, D2D transmitters harvest energy from ambient energy sources and reuse the uplink cellular channels to perform transmission to the desired D2D receivers. Considering the time-varying and unpredictable nature of the harvested energy, the proposed algorithm ensures the energy sustainability-related quality-of-service with improved system capacity. To reach this goal, a statistical model based on the effective bandwidth/capacity is adopted to formulate the energy-harvesting/-consuming processes and evaluates the sustainability assurance in terms of the statistical outage exponent. Subsequently, we formulate the resource allocation problem with a view to maximizing the sum rate of the D2D links by jointly optimizing the power allocation and spectrum resource matching, meanwhile guaranteeing the energy sustainability requirements. By employing the Lagrangian dual method, we derive an analytical expression for the transmission power allocation and present two rules to match the cellular users with the D2D links for spectrum resource reuse. Our results reveal that both the power allocation and spectrum resource matching closely depend on the statistical outage exponent. With these theoretical analyses, we put forward a distributed subgradient-based iterative resource allocation algorithm with polynomial complexity. Finally, the simulation results demonstrate that the proposed resource allocation algorithm can acquire a substantial sum-rate improvement.

Due to the high-mobility of unmanned aerial vehicles (UAVs), actual UAV channel measurements show that low-altitude air-to-ground (A2G) channels in UAV communications illustrate non-stationary properties. This fact motivates us to develop a non-stationary channel model for UAV-to-ground links. In this paper, we propose a three-dimensional (3D) wideband non-stationary A2G vehicle-to-vehicle (V2V) geometry-based stochastic channel model (GBSM). In the proposed model, both UAVs and ground terminals can be moving, which makes the model more general. In order to mimic the non-stationary channel characteristics, parameters like the number of clusters, power, time delays, angels of departure (AoDs), and angles of arrival (AoAs) are all time-variant. The proposed model combining a line-of-sight (LoS) component, a ground reflection component, a cylinder model, and multiple confocal truncated ellipsoid models has the ability to investigate the impact of UAV heights and transceivers' movements on channel characteristics in diverse environments. Statistical properties like temporal autocorrelation function (ACF), spatial cross-correlation function (CCF), Doppler power spectral density (PSD), and stationary interval of A2G channels are derived and analyzed in detail. In addition, derived root mean square delay spread (RMS-DS), temporal ACF and spatial CCF are validated against channel measurement results. Furthermore, by adjusting channel parameters, the proposed GBSM is sufficiently generic and adaptable to model various UAV-to-ground communication scenarios.

The heterogeneous network composed of small cell base stations (SCBSs) and macro base stations (MBSs) has been widely deployed to alleviate the load of conventional MBSs, which may also cause severe interference and large energy consumption. The existing literature generally focus on the heterogeneous networks either without cooperation or with cross-tier cooperation. In this paper, we introduce a central controller and mobile edge cloud (MEC) servers and propose a heterogeneous network with SCBSs cooperation, i.e., the intra-tier cooperation. A novel load balancing metric instead of the simple access probability ratio is introduced. Based on the utility theory, we comprehensively evaluate several important performance metrics such as connectivity probability, load balancing, and energy efficiency to optimize the overall performance of the network. Moreover, in the special case of allowing partial connectivity, the discrete stochastic optimization algorithm is proposed so that the software-defined networks (SDNs) controller can adjust network parameters with the help of MEC servers to maximize the overall energy efficiency. The simulation results demonstrate that our proposed approach is valid to optimize the network in accordance with the user requirements. This paper provides a useful reference for the practical deployment of software-defined heterogeneous networks where the energy efficiency is increasingly becoming a key concern.

In this paper, a joint bit-to-symbol mapping scheme in a cooperative spatial modulation (SM) system with amplify-and-forward (AF) and decode-and-forward (DF) protocols is proposed to improve the system performance, such as the transmission bit rate and the average bit error rate (BER). The system model and the corresponding signal processing with maximum likelihood (ML) detection for the cooperative SM system without a direct link are presented first. With the consideration of both the cosine similarity among the relay nodes and the channel state information (CSI) of each transmit antenna, the bit-to-symbol mapping scheme that jointly selects the cooperative relay node, the transmit antenna, and the signal constellation is proposed. Then, its average BER performance is derived based on the moment generation function (MGF). For the proposed mapping scheme, a detailed realization process has been given as an example. Furthermore, the simulation results verify that the proposed mapping scheme effectively increases the system transmission bit rate and also improves the system average BER performance compared with the traditional natural mapping scheme.

Compared to orthogonal multiple access (OMA), non-orthogonal multiple access (NOMA) can achieve higher spectral efficiency by exploiting the power domain multiplexing. In this paper, we investigate the performance of NOMA in a two-tier heterogeneous network (HetNet) with non-uniform small cell deployment, where critical performance metrics like coverage probability and achievable rate are analyzed. First, a NOMA-based HetNet model is established, where users are paired based on the proposed user pairing scheme. Then the distribution of the order statistics for the distances between different NOMA users and the serving base station (BS) is presented considering the channel qualities from the NOMA users to the BSs. On this basis, we analytically demonstrate the impact of various network parameters on the coverage probability and achievable rate of NOMA users, such as signal-to-interference-plus-noise ratio threshold and BS density. Furthermore, an analysis is presented to provide insight into the energy efficiency of the considered system. Finally, extensive simulation and comparisons are conducted, which validate the advantages of NOMA over OMA in the considered HetNet environment.

Device-to-device (D2D) communication as a feasible technique can improve the system efficiency without fixed infrastructures. In this paper, we focus on the fair resource allocation of D2D communication underlaying cellular system with channel uncertainty and propose a joint resource allocation policy about the resource block assignment and power allocation among the D2D and cellular users (CUs). We formulate a nominal optimization problem to improve the sum rate of the D2D system and guarantee the quality of service of the CUs. Moreover, taking the uncertainty of the channel state information into consideration, we employ the robust optimization theory to define the uncertainty and construct a robust optimization problem with uncertain parameters in both objective function and constraints. To facilitate the solution, we segment the robust optimization problem into two subproblems. Furthermore, we utilize the chance constraint approach to cut down the cost of the robustness and achieve a tradeoff between the robustness and the optimality. An insight into the influence of the protection function to the achievable D2D sum rate is obtained by sensitivity analysis. In addition, the effect of the robustness on the D2D sum rate with different channel uncertainty is presented in the simulation, and a rate tradeoff between the D2D users and CUs is achieved by the proposed policy.

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.