The reconfigurable intelligent surface (RIS) aided index modulation (IM) is a promising technology for next-generation wireless communications. However, acquiring channel state information (CSI) for RIS-based IM requires expensive pilot overhead, especially for the IM in multiple domains. In this paper, a novel rectangular differential reflecting spatial modulation (RDRSM) system is proposed for the RIS-aided multiple-input multiple-output (MIMO) system. Specifically, the proposed multi-antenna-activated RDRSM (M-RDRSM) scheme utilizes a rectangular dispersion matrix (DM) to jointly map a digital beamforming (DBF) weight vector and a RIS reflection pattern to perform the rectangular differential modulation. A thorough analysis presents that the proposed M-RDRSM scheme can achieve high-spectral efficiency, low complexity of the system, and noncoherent decoding without prior knowledge of CSI in the space-reflection dual-domain IM. Further, the proposed M-RDRSM scheme is simplified to a single-antenna-activated RDRSM (S-RDRSM) scheme, which can reduce the hardware cost and avoid the inter-antenna synchronization problem more effectively. Simulation results demonstrate that the proposed RDRSM scheme performs better in terms of bit error rate performance and achieve a lower decoding complexity than the existing correlated differential IM schemes with the same achievable spectral efficiency.

Cooperative localization has emerged as an appealing technology since it can improve the localization performance without any infrastructure change compared with non-cooperative localization. However, some well-localized agents may not be willing to sacrifice additional power to improve the others' localization accuracy. This paper proposes an incentive mechanism from an economic perspective for cooperative localization, whereby a pricing scheme is designed to lead each agent to the optimal state. A game-theoretic algorithm is proposed where each player (agent) can obtain the optimal budget strategy to minimize its individual utility. To make profits in the game, the relationship between the agent's network condition and its budget strategy is derived. Furthermore, a fairness-aware price allocation rule (PAR) is developed to distribute the budget among the reference agents proportional to each node's contribution. Analytical and numerical results show that agents with better network conditions are more likely to join the cooperation under the proposed incentive mechanism, leading to an improved localization performance.

In order to achieve high throughput and low-power consumption of the transmitter and improve the error performance of the receiver, a novel reconfigurable intelligent surface (RIS)-aided code index modulation (RIS-CIM) is proposed in wireless communications. RIS-CIM utilizes the index of a spreading code to convey additional information, which further improves the spectral efficiency (SE) and error performance of the receiver by adding and adjusting the reflected phase shift of low-cost reflective elements at the RIS-aided relay. To reduce the computational complexity incurred by the maximum likelihood (ML) detection, we propose two low-complexity detections, namely the match correlation (MC) detection and the optimal low complexity (OLC) detection. MC and OLC approaches separate the dispreading from the demodulation in the detection, which achieves a significant reduction of the computational complexity over ML detection demonstrated by the analysis of the computational complexity measurements. We present a thorough analysis for the upper bound on the average bit error probability (ABEP) of the ML detection and the bit error probability (BEP) of the OLC detection of RIS-CIM, which is verified by numerical simulations. Numerical simulations also demonstrate that the proposed RIS-CIM performs better in terms of the error performance of ML detection than several existing CIM and RIS-related schemes at a given SE setting. In addition, the proposed ML and OLC detections achieve an error performance close to the ML detection under various measures of signal-to-noise ratio (SNR) and the increasing SE in the simulations. Moreover, RIS-CIM can utilize a large number of reflective elements at the RIS-aided relay to offset the performance degradation caused by the improvement of SE.

In this paper, we propose an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and wireless energy transfer (WET) network in disaster scenarios, where both the data-processing UAVs (DP-UAVs) and the energy-transferring UAVs (ET-UAVs) are deployed. To ensure the freshness of data, the age of information (AoI) is utilized on both user equipment (UE) and the data center (DC). An optimization problem is formulated to minimize the AoI and the energy consumption of the UAVs by jointly optimizing the UAVs’ trajectories and offloading strategies. To find the optimal solution, the problem is decomposed into two optimization subproblems: the trajectory planning and offloading policy design. Then, the modified multi-agent deep deterministic policy gradient (M2DDPG) algorithm and the traversal-based greedy (TG) algorithm are proposed to solve the two subproblems respectively, where the cooperation of the UAVs and the greedy-choice property of the problem are fully utilized. Simulation results validate the effectiveness of the proposed M2DDPG-TG algorithm and demonstrate that it performs better than the baseline algorithms and the traditional MADDPG algorithm. The scalability of the proposed algorithms is also verified by extensive simulations.

This work investigates the problem of simultaneously positioning and orientating (SPAO) the user equipment (UE) with reconfigurable intelligent surface (RIS)-assisted visible light communications (VLCs). In the proposed system model, the utilization of RISs effectively helps to address the SPAO problem of the UE in situations when there are insufficient light-emitting diode (LED) transmitters. Firstly, the channel gain between LED and UE and the one between RIS and UE are obtained through signal separation, followed by formulating an objective optimization function for the SPAO problem. A closed-form Cramer-Rao lower bound (CRLB) is then derived, representing the theoretical lower limit of positioning and orientating errors. Consequently, using CRLB as a benchmark for the SPAO performance, a fuzzy distance-based particle swarm optimization (FDPSO) is proposed to minimize the deviations between SPAO estimation errors and CRLBs. The proposed FDPSO algorithm adaptively adjusts the parameters and mutation operations to balance local exploitation and global exploration. Simulation results demonstrate that the proposed FDPSO algorithm achieves an asymptotic tightness with CRLB and performs better than the other five related iterative algorithms in terms of the cumulative distribution functions (CDFs) of positioning errors (PEs) and orientating errors (OEs).