When the phase shift variation of a reconfigurable intelligent surface (RIS) transitions from a quasi-static pattern to a rapidly changing pattern, it engenders a novel capability to support concurrent communications. This paper proposes a novel grouped RIS-enabled multi-user parallel communication framework, where each RIS group is assigned a specified spread spectrum (SS) sequence, e.g., Zadoff-Chu (ZC) code, to tag a distinct user. Although the RIS group establishes a customized virtual channel for its served user through the specified SS sequence, the received signal is still contaminated by other users’ signals. Thus, the quasi-static phase shift of the RIS is optimized to amplify the desired signal while suppressing inter-user interference. However, the transmission delays hinder the perfect detection of the SS sequences. Moreover, the optimization of phase shift necessitates the availability of channel state information (CSI) and exhibits a high sensitivity to the channel phase. Therefore, we propose a pilot-free subspace-based method combined with scalar ambiguity estimation to jointly estimate direct and cascaded channels without phase ambiguity, along with propagation delays. After obtaining the CSI and delays, the phase shift optimization problem could be reformulated as a generalized Rayleigh quotient maximization problem with closed-form solutions. Simulation results validate the accuracy of the blind channel estimation method. Meanwhile, the sum rate of the RIS-enabled parallel communications is ten times higher than that of the conventional RIS-assisted communication, attributed to the SS sequence design of the RIS.

The design of the dual-function signal is critical to integrated sensing and communication (ISAC). The communication and sensing signals share the channel estimation techniques since both communication and sensing capabilities rely on channel state information (CSI). A communication system typically estimate channels with specific preambles can be reused for sensing, which facilitates the integration of sensing with communication. With augmented sensing capability within existing communication frameworks, the ISAC systems based on preamble sharing achieves improved spectral efficiency and reduced costs. However, in multi-target scenarios, echoes from different targets cannot be accurately distinguished at the receiver, leading to severe sensing ambiguity. To address this issue, we propose a PArallel Multi-target Sensing and Echo Separation (PAMSES) scheme for ISAC systems operating in multi-user and multi-target environments. The receiver exploits the waveform diversity to discriminate echoes from distinct targets, effectively resolving multi-target sensing ambiguity. Furthermore, since perfect CSI is usually unavailable at the access point, we formulate a robust optimization problem to ensure reliable communication and sensing performance in the worst-case scenario. The proposed problem can be formulated as a semidefinite program (SDP) and solved through semidefinite relaxation (SDR) techniques. Simulation results demonstrate that the proposed method can achieve 0.01 m/s velocity estimation accuracy and 5 bps/Hz spectrum efficiency, while maintaining an energy efficiency of 6.27 bit/J/Hz.

Reconfigurable intelligent surfaces (RISs) can support concurrent transmissions through tailoring desired disparate channel conditions for multiple users. However, detection error probability of concurrent transmissions deteriorates as the channel gain becomes indistinguishable with growing number of users. Therefore, we investigate a practical group-level successive interference cancellation (GSIC)-based RIS-enabled multi-group parallel reception scheme in the presence of imperfect channel state information (CSI), in which GSIC is applied to distinguish user signals in different groups. In the same group, the signals from all users are tuned by RISs to form a higher-order meta-constellation to achieve parallel decoding. To maximize the sum throughput, the transmission power and phase shifts of RISs are jointly optimized subject to the detection error probability requirement. Particularly, transmission power is optimized with successive convex approximation (SCA) method, while a sequential optimization method is proposed for the design of the phase shift iteratively. Numerical results show that the sum throughput of the proposed algorithm increases by about 50% compared to hybrid GSIC and minimum mean square error (MMSE) scheme without RISs, and demonstrate the necessity of taking imperfect CSI into account.

Thanks to the autonomy of unmanned aerial vehicle (UAV), UAV-enabled data collection in Internet of Things (IoT) networks has become a key application for the next generation communication network. In this paper, we consider a scenario where an UAV is responsible for collecting data from sensor equipments (SEs) one by one and finally carrying the collected data to the computation center for processing. Different from the commonly used assumption that SEs always generate data at the beginning of each time slot, it is assumed that SEs can generate data at any instant during one time slot, which is more practical. Since SEs are energy limited, they upload data to the UAV by adopting backscatter communication technology to reduce energy consumption. Meanwhile, the updated information usually contains a small number of information bits but requires low latency and high reliability, thus the finite blocklength regime in ultra-reliable and low-latency communication is adopted for SEs’ data transmission to the UAV. To keep the freshness of the updated information, a joint resource allocation problem including data collection time allocation, transmission power and trajectory design of the UAV is formulated as an optimization problem to minimize the average age of information (AoI) of all SEs. The formulated problem mixes discrete and continuous variables, which makes it difficult to solve. Thereby, we decompose the optimization problem into data collection time minimization subproblem and UAV trajectory design subproblem, which are solved by the successive convex approximation method, and the backtracking algorithm and the genetic algorithm , respectively. Numerical results show that the backtracking-based algorithm that can obtain the optimal trajectory of the UAV gains the minimal average AoI, and the genetic-based algorithm achieves sub-optimal average AoI with much lower computational complexity. The results also demonstrate that the average AoI of the backtracking-based algorithm and the genetic-based algorithm is reduced by up to 54% and 46% compared with the greedy-based benchmark algorithm, respectively.

This article studies the data collection problem in an Internet-of-Things (IoT) network with multiple unmanned aerial vehicles (UAVs) where UAVs first power multiple IoT devices by wireless power transfer, and then IoT devices utilize the harvested energy to transmit data to UAVs. Different from most of the existing works that often assume the channel between the UAV and the IoT device is a simplified Line-of-Sight (LoS) channel, a more practical and accurate probabilistic LoS channel model is adopted, in which both the elevation angle and the distance between the UAV and the IoT device determine the channel gain. Our objective is to maximize the UAV's minimum data collection rate among all IoT devices by jointly optimizing time allocation and 3-D trajectory of UAVs within a limited time duration. This results in a nonconvex optimization problem, which is challenge to solve. To tackle this difficulty, we transform the nonconvex problem to a difference of convex (D.C.) optimization problem by subtly using several methods. To solve the D.C. optimization problem, an efficient iterative algorithm is designed via a successive convex approximation method. Numerical simulation results are provided to verify the performance of the proposed algorithm compared to two benchmark algorithms, the algorithm with simplified LoS model and that with 2-D trajectory optimization, under various conditions.