Abstracts:This article presents a unified framework analysis for the performance of reconfigurable intelligent surface (RIS)-aided coordinated non-orthogonal multiple access (C-NOMA) systems where the discrete phase-shift alignment and imperfect successive interference cancellation (SIC) are investigated. This framework yields double opportunities to enhance users' performance via RIS and dedicated relay transmission. To assess the system characteristic, the users' outage probability (OP) and ergodic capacity (EC) expressions are evaluated and derived in terms of approximate closed-form and asymptotic expressions. Numerical results validate our derivations and show that: i) expanding the number of RISs element implementations can significantly improve the users' OP, and ii) the RIS-aided C-NOMA system achieves a superior EC improvement compared to RIS-orthogonal multiple access (OMA) and conventional C-NOMA ones.

Abstracts:In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted wireless communications system with multi-user scheduling, where a pilot-embedded data transmission is carried out from a selected user to an access point (AP) with the aid of an RIS. Specifically, by taking into account a transmit energy constraint, we propose a joint passive beamforming and power allocation optimization (JPB-PAO) scheme for the RIS-assisted wireless system, in which a problem of joint optimization on the pilot power, data power, and RIS's phase shifts is formulated for maximizing the achievable rate of the user-AP transmission. To this end, we propose a Dinkelbach and majorization-minimization (DMM) aided JPB-PAO (termed as DMM-JPB-PAO) scheme for the sake of obtaining a semi-closed form expression of the optimal RIS phase shifts, based on which a closed-form solution of power allocation is derived. Moreover, we adopt the widely used semidefinite relaxation (SDR) algorithm as a baseline to solve the JPB-PAO problem, which is referred to as the SDR-JPB-PAO. Numerical results show that our proposed DMM-JPB-PAO achieves almost the same achievable rate as the SDR-JPB-PAO at a much lower computational complexity. Meanwhile, DMM-JPB-PAO scheme yields a higher achievable rate than the passive beamforming optimization with fixed power allocation (PBO-FPA) scheme and the random passive beamforming with fixed power allocation (RPB-FPA) scheme.

Abstracts:In this correspondence, we present a reconfigurable intelligent surface (RIS) that reflects an incident signal to the desired direction, depending on the pulse width. This unique reconfigurability emerges by virtue of the transient response of the embedded nonlinear circuits within the RIS unit cells. The RIS does not need any control lines connected to the unit cells or precise synchronization with the base station, leading to reduced complexity of the RIS system yet allowing automated beamforming characteristics in accordance with the incident pulse width. To verify this scheme, an RIS system model using binary phase shift keying is considered, in which the RIS wireless link is connected to a single transmitter at the input and two receivers at the output. Numerical demonstrations show that modulated signals are received with contrasting bit-error rates in the two receivers, consistent with the aforementioned pulse-width-based beam control.

Abstracts:The direction of arrival (DOA) based multiple-input multiple-output (MIMO) radar technique has been widely utilized for ubiquitous positioning due to its advantage of simple implementability. On the other hand, reconfigurable intelligent surface (RIS) has received considerable attention, which can be deployed on the walls and objects to strengthen the positioning performance. However, RIS is usually not equipped with a perception module, which results in the tremendous challenge for RIS-assisted positioning. To tackle this challenge, this article propose the fundamental problem of DOA-based target positioning in RIS-assisted MIMO radar system. Unlike conventional DOA estimation systems, the beneficial role of RIS is investigated in MIMO radar system, where a nonconvex $\ell _{p}$ promoting function is exploited to estimate DOA task. By adjusting the reflecting elements of the RIS, the proximal projection iterative strategy is developed to obtain the feasible solution. Both theoretical analysis and simulation results illustrate that the proposed scheme can achieve remarkable positioning performance and shed light on the benefits offered by the adoption of the RIS in terms of positioning performance.

Abstracts:The use of antenna arrays is pivotal in array signal processing, with the different antenna arrays having their own structural advantages. The uniform linear arrays (ULAs) are more robust against antenna failures, but their degrees of freedom (DOFs) and array aperture, which are crucial for the direction of arrival (DOA) estimation, are limited. On the other hand, the sparse arrays can offer more DOFs and a larger array aperture but may not necessarily be as robust as ULAs. Hence depending on the given requirement, the properties of certain antenna arrays need to be exploited for direction finding. However, most of the existing antenna array configurations are deprived of a universal design approach. This manuscript introduces a universal array designing framework (UADF), which can employ different antenna arrays and exhibit multiple desirable properties. The UADF is based on effectively cascading three identical levels that can be nested to another M-times, with only its initial level to be configured by the antenna array. Thus, it inherits the characteristics of configuration used in the initial level and simultaneously embeds a good balance between key features in terms of low cost, easier construction and extension, reliability, robustness, more DOFs, larger continuous virtual aperture, and higher spatial efficiency. Moreover, the UADF enjoys closed-form expressions for the array configuration and DOFs. We employed different antenna arrays to demonstrate the usefulness of UDAF, which is also confirmed via numerical experiments.

Abstracts:Intelligent reflecting surface (IRS) can effectively improve the performance of wireless communications and target sensing by passive beamforming/searching. In this correspondence, we consider IRS-aided target sensing in cellular networks by deploying an IRS near each base station (BS) and leveraging two IRSs' cooperative passive beam searching. Specifically, we propose a new three-stage sensing protocol by controlling the ON/OFF state of each IRS, thereby efficiently acquiring the essential angle information between the IRSs and the target. Based on the obtained angle information, the location of the target is estimated by applying the least-square method. Simulation results are provided to show the efficacy of the proposed cellular sensing scheme with practical half-duplex BSs as compared to other baseline schemes requiring full-duplex BSs.

Abstracts:In this article, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS). The following persisting challenges motivated our research: (i) User and vehicular mobility, as well as redundant beam-reselections in mmWave networks, degrade the efficiency; (ii) Due to the large beamforming dimension at the BS, the beamforming weights predicted by the cutting-edge DL-based methods often do not suit the channel distributions; (iii) Co-located user devices may cause a severe beam conflict, thus deteriorating system performance. To address the aforementioned challenges, we exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation. In the first step, we propose a method for beam-quality prediction. It is based on deep learning and explores the relationship between high- and low-resolution beam images (energy). Afterward, we develop a DL-based allocation approach, which enables high-accuracy beam and power allocation with only a portion of the available time-sequential low-resolution images. Theoretical and numerical results verify the effectiveness of our proposed framework.

Abstracts:Ultra reliable low latency communication (URLLC) is playing an important role in future wireless networks, such as industrial Internet of things (IIoT). Enormous industrial devices are requiring instant and highly-frequent control services, namely delay sensitive control, in order to improve the human safety or the operation accuracy. In this paper, a joint communication and delay sensitive control (JCDSC) system with URLLC is studied, where the control intervals are much shorter than the transmission latency. The control performance, known as the mean square error (MSE) of the plant states, are analysed in theory. The optimal blocklength of the control codewords, the data rate, as well as the minimum required control periods are then obtained, in order to minimize the control latency. Simulation results validate our theoretical analysis, while also demonstrating that an optimal blocklength of codewords should be selected in order to reduce the control latency.

Abstracts:Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has attracted extensive attentions due to its prominent advantages to future wireless networks. This correspondence considers a downlink multi-input single-output non-orthogonal multiple access system, in which the STAR-RIS is utilized to improve the secrecy performance. To restrain the internal eavesdropping, we formulate a secrecy rate maximization problem and propose a secure transmission design via the joint active and passive beamforming optimization with optimum power allocation. To decouple optimization variables, the original non-convex problem is divided into two subproblems, which are transformed into convex forms via successive convex approximation and solved by alternating optimization. Simulation results demonstrate the superiority of the proposed scheme in the security enhancement.

Abstracts:In this paper, we proposed a deep neural network (DNN) based fractional Doppler channel estimation scheme for orthogonal time frequency space (OTFS) modulation in the air-to-ground communication scenario with high-dynamic Doppler. Based on the zero-padded OTFS structure, the traditional pilot pattern with guard symbols is adopted. The received pilots in the OTFS domain are used as the inputs of the network to estimate the channel parameters which are used in the MRC algorithm to demodulate the signal. In our proposed method, it can achieve the similar performance with 14 dB boost of pilot energy comparing with the ideal channel estimation case, while the conventional method requires 30 dB higher. Both the accuracy and generalization ability of the DNN network are validated.