Digital twin (DT) can help create a digital representation of a physical system, thereby reflecting its real-time status. The digital object, often called cyber twin (CT), facilitates real-time monitoring and control of the physical object, i.e., the so-called physical twin (PT). Owing to this ability, CTs can optimize the PTs and simulate their status, without interrupting the physical world. Given the various CT use cases, one can identify two distinct types of DT tasks: 1) update tasks for PT-CT synchronization and 2) inference tasks for obtaining real-time testing responses. The diverse real-time requirements for update/inference tasks raise the task scheduling problem that has been neglected in previous studies. In this article, the real-time DT task scheduling problem is investigated. In particular, a new approach for evaluating the performance of real-time scheduling of DT tasks is introduced considering the relationship between update/inference tasks and fairness among CTs. Moreover, offline and online DT task scheduling schemes are proposed with the goals of maximizing the DT freshness ratio and minimizing task rejections. In particular, the DT freshness ratio maximization problem is formulated as an offline task scheduling scheme. The proposed offline solution can significantly reduce the solution space without losing optimality. Furthermore, the scheduling policies for achieving the maximal DT freshness ratio are established using which an online scheduling algorithm is designed. Simulation results show that the proposed offline/online schemes increase the DT freshness ratio by at least 16% and 11%, respectively, compared to benchmarks. The results also show that the task rejection ratio of the proposed online algorithm is within 8% of the lower bound.

Since its introduction for long term evolution (LTE), vehicle-to-everything (V2X) communications have evolved to the recent new radio (NR)-based V2X with enhanced reliability, latency, capacity, and flexibility. One of the key features of NR V2X is the enabling of sidelink (SL) communications between user equipments (UEs) without any assistance from a base station (i.e. support of the out-of-coverage scenario). To this end, NR V2X supports SL resource allocation (RA) mode 2, in which a UE autonomously determines the subset of resources to use for data transmission while avoiding resource collision due to other UEs. This paper summarizes the resource sensing and selection (RSS) mechanisms for SL RA mode 2 specified in releases 16 and 17 of NR V2X. The critical aspect for RSS is the minimization of UE power consumption during resource sensing, which is caused by multiple blind decodings on the physical SL control channel. To address this issue, the effective number of blind decodings for conventional RSS is quantitatively analyzed to determine the potential enhancements required. Furthermore, an enhanced RA mode 2 procedure based on an intelligent partial-sensing (IPS) scheme is proposed to minimize the number of blind decodings. The proposed IPS utilizes a convolutional neural network-based physical channel-type classification model. Simulation and numerical results show that the throughput obtained with the proposed IPS scheme approximates that obtained via full sensing-based RA mode 2, while reducing the number of blind decodings by 90%.

Reconfigurable intelligent surface (RIS) is a promising solution to support a large volume of data traffic and massive connectivity for future wireless mobile networks. Especially, RIS can mitigate the drawbacks in massive multiple-input multiple-output (MIMO) systems, such as the blockage caused by obstacles and the signal processing overhead by constructively and passively reflecting the incident wave toward the destination. In this paper, we provide an asymptotic analysis of the distribution of sum rate (SR) in RIS-aided massive MIMO systems. Using the asymptotic distribution of SR, the achievable scheduling gain and the optimal number of users are determined. In addition, we examined the channel hardening effect and outage probability through the achievable scheduling gain, and the optimal number of users is utilized to develop a low-complexity scheduling algorithm. Simulation results reveal that the SR obtained from our analysis closely aligns with the actual SR. The results also show that the channel hardening effect can vanish with many users thereby achieving the multiuser diversity gain, and an RIS-aided system is more reliable than a conventional massive MIMO system in terms of the outage probability. Furthermore, the proposed scheduling algorithm is shown to reduce computational complexity compared to the conventional scheduling algorithm.

The deployment of digital twins (DTs) at the edge of a wireless network can facilitate low-latency and high-throughput DT autonomous and real-time Internet of everything (IoE) applications. In such DT edge networks (DTENs), each DT has two types of real-time tasks that require timely processing: DT update tasks and DT inference tasks. However, the joint scheduling of these two types of tasks has been overlooked in prior works. In this paper, the first joint real-time scheduling scheme for DT update and inference tasks in a DTEN is proposed. Moreover, a novel performance metric called freshness is introduced to capture the effectiveness and synchronization performance of scheduling. Also, a new scheduling scheme is proposed to efficiently solve a freshness maximization problem for DTENs. Simulation results show that the performance of the proposed scheme is within 4% of the upper bound for DTENs with 20 physical objects, and within 12% of the upper bound in worst cases for DTENs with more than 30 physical objects. The results also show that the proposed approach reduces the maximum de-synchronization time by 63% compared to existing real-time scheduling algorithms.

In the Internet of Things (IoT) environment, edge computing can be initiated at anytime and anywhere. However, in an IoT environment, edge computing sessions are often ephemeral, i.e., they last for a short period of time and can often be discontinued once the current application usage is completed or the edge devices leave the system due to factors such as mobility. Therefore, in this paper, the problem of ephemeral edge computing in an IoT is studied by considering scenarios in which edge computing operates within a limited time period. To this end, a novel online framework is proposed in which a source edge node offloads its computing tasks from sensors within an area to neighboring edge nodes for distributed task computing, within the limited period of time of an ephemeral edge computing system. The online nature of the framework allows the edge nodes to optimize their task allocation and decide on which neighbors to use for task processing, even when the tasks are revealed to the source edge node in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the joint optimization, an online greedy algorithm is proposed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio of the online approach is analytically derived as a function of the task sizes and the data rates of the edge nodes. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.1% compared to the offline, optimal solution with complete knowledge of all tasks.

In this paper, the achievable sum rate of multiuser scheduling in reconfigurable intelligent surface (RIS)-based mas-sive multiple-input multiple-output systems is investigated. Using asymptotic analysis under the generic condition of large numbers of base station antennas, RISs, and users, the RIS-based sum rate is proven to follow a Gaussian distribution. In addition, based on the characteristics of Gaussian distribution, the conditions for the occurrence of the channel hardening phenomenon and achievable scheduling gain are derived as a function of the number of RISs and users. Numerical results show that the derived RIS-based sum rate and the Monte Carlo simulation results are in close agreement as well as that the proposed achievable sum rate constitutes a meaningful bound to verify the performance of various multiuser scheduling algorithms.

Channel acquisition is one of the main challenges in a reconfigurable intelligent surface (RIS) system due to the passive nature of an RIS. In order to accurately estimate RIS channels, a large number of pilot symbols are required, which could yield a severe performance degradation in terms of the spectral efficiency (SE). In this paper, a practical channel acquisition and passive beamforming technique is proposed using a limited number of pilot symbols in an RIS-assisted cellular network. In particular, the proposed technique relies on a meta-learning framework. To address practical RIS challenges, the problem of maximizing the instantaneous SE is formulated and a novel approach to solve this optimization problem is developed. The proposed algorithm enables an RIS to select the optimal phase shift matrix without the need for perfect channel state information. Also, the trained parameter resulting from the proposed meta-learning algorithm can be guaranteed to converge to an optimal solution. Simulation results show a comparable performance to an exhaustive search method with a few training symbols which validates the advantages of meta-learning for an RIS system.

Large intelligent surfaces (LISs) constitute a new and promising wireless communication paradigm that relies on the integration of a massive number of antenna elements over the entire surfaces of man-made structures. The LIS concept provides many advantages, such as the capability to provide reliable and space-intensive communications by effectively establishing line-of-sight (LOS) channels. In this paper, the system spectral efficiency (SSE) of an active LIS system is asymptotically analyzed under a practical LIS environment with a well-defined uplink frame structure. In order to verify the impact on the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically studied and a theoretical bound on its performance is derived. Given this performance bound, an optimal pilot training length for multi-LIS systems subjected to pilot contamination is characterized and, subsequently, the number of devices that need to be serviced by the LIS in order to maximize the performance is derived. Simulation results show that the derived analyses are in close agreement with the exact mutual information in presence of a large number of antennas, and the achievable SSE is limited by the effect of pilot contamination and intra/inter-LIS interference through the LOS path, even if the LIS is equipped with an infinite number of antennas. Additionally, the SSE obtained with the proposed pilot training length and number of scheduled devices is shown to reach the one obtained via a brute-force search for the optimal solution.

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can achieve high spectrum and energy efficiency for future wireless networks by integrating a massive number of low-cost and passive reflecting elements. An RIS can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, and, then, reflect this manipulated wave to a desired destination, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under a limited RIS control link and practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Moreover, a new resource allocation algorithm that jointly considers user scheduling and power control is designed, under consideration of the proposed passive beamforming and modulation schemes. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

In this paper the problem of recognizing waveform and modulation is addressed in radar-communications coexistence and shared spectrum scenarios. We propose a deep learning method for waveform classification. A hierarchical recognition approach is employed. The received complex-valued signal is first classified to single carrier radar, communication or multicarrier waveforms. Fourier synchrosqueezing transformation (FSST) time-frequency representation is computed and used as an input to a convolutional neural network (CNN). For multicarrier signals, key waveform parameters including the cyclic prefix (CP) duration, number of subcarriers and subcarrier spacing are estimated. The modulation type used for subcarriers is recognized. Independent component analysis (ICA) is used to enforce independence of I- and Q-components, and consequently significantly improving the classification performance. Simulation results demonstrate the high classification performance of the proposed method even for orthogonal frequency division multiplexing (OFDM) signals with high-order quadrature amplitude modulation (QAM).

Reconfigurable intelligent surfaces (RISs) have recently emerged as a promising technology that can manipulate the properties of an incident wave, such as the frequency, amplitude, and phase, without the need for complex signal processing. In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations. In particular, a passive beamformer that can achieve the asymptotic optimal performance by controlling the incident wave properties is designed, under practical reflection coefficients. In order to increase the achievable system sum-rate, a modulation scheme that can be used in an RIS without interfering with existing users is proposed and its average symbol error rate is asymptotically derived. Simulation results show that the proposed schemes are in close agreement with their upper bounds in presence of a large number of RIS reflecting elements thereby verifying that the achievable rate in practical RISs satisfies the asymptotic optimality.

When deployed as reflectors for existing wireless base stations (BSs), reconfigurable intelligent surfaces (RISs) can be a promising approach to achieve high spectrum and energy efficiency. However, due to the large number of RIS elements, the joint optimization of the BS and reflector RIS configuration is challenging. In essence, the BS transmit power and RIS's reflecting configuration must be optimized so as to improve users' data rates and reduce the BS power consumption. In this paper, the problem of energy efficiency optimization is studied in an RIS-assisted cellular network endowed with an RIS reflector powered via energy harvesting technologies. The goal of this proposed framework is to maximize the average energy efficiency by enabling a BS to determine the transmit power and RIS configuration, under uncertainty on the wireless channel and harvested energy of the RIS system. To solve this problem, a novel approach based on deep reinforcement learning is proposed, in which the BS receives the state information, consisting of the users' channel state information feedback and the available energy reported by the RIS. Then, the BS optimizes its action composed of the BS transmit power allocation and RIS phase shift configuration using a neural network. Due to the intractability of the formulated problem under uncertainty, a case study is conducted to analyze the performance of the studied RIS-assisted downlink system by asymptotically deriving the upper bound of the energy efficiency. Simulation results show that the proposed framework improves energy efficiency up to 77.3% when the number of RIS elements increases from 9 to 25.

The concept of a large intelligent surface (LIS) has recently emerged as a promising wireless communication paradigm that can exploit the entire surface of man-made structures for transmitting and receiving information. An LIS is expected to go beyond massive multiple-input multiple-output (MIMO) system, insofar as the desired channel can be modeled as a perfect line-of-sight. To understand the fundamental performance benefits, it is imperative to analyze its achievable data rate, under practical LIS environments and limitations. In this paper, an asymptotic analysis of the uplink data rate in an LIS-based large antenna-array system is presented. In particular, the asymptotic LIS rate is derived in a practical wireless environment where the estimated channel on LIS is subject to estimation errors, interference channels are spatially correlated Rician fading channels, and the LIS experiences hardware impairments. Moreover, the occurrence of the channel hardening effect is analyzed and the performance bound is asymptotically derived for the considered LIS system. The analytical asymptotic results are then shown to be in close agreement with the exact mutual information as the number of antennas and devices increase without bounds. Moreover, the derived ergodic rates show that hardware impairments, noise, and interference from estimation errors and the non-line-of-sight path become negligible as the number of antennas increases. Simulation results show that an LIS can achieve a performance that is comparable to conventional massive MIMO with improved reliability and a significantly reduced area for antenna deployment.

Large intelligent surfaces (LISs) have emerged as a new and promising wireless communication paradigm that relies on equipping man-made structures such as walls with a massive number of antennas. However, despite their potential benefits, a fundamental analysis on the performance limits of LIS systems is lacking. In this paper, the system spectral efficiency (SSE) of an uplink LIS system is asymptotically analyzed under a practical frame structure and LIS environment. In order to quantify the impact on the SSE of pilot contamination, the SSE of a multi-LIS system is asymptotically studied and a theoretical bound on its performance is derived. Simulation results show that the derived analyses are in close agreement with the exact mutual information in presence of a large number of antennas. Moreover, the results show that the achievable SSE is limited by the effect of pilot contamination and intra/inter-LIS interference through the line-of-sight path, even if the LIS is equipped with an infinite number of antennas.

We considered a precoder used in wireless information transfer (WIT) and wireless power transfer (WPT) systems for multi-user multiple-input multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) systems. We compare two representative linear precoders which are minimum mean square error (MMSE) and signal-to-leakage-and-noise ratio (SLNR) based precoders for data users and energy beamformer for energy users. The simulation results show that the rate-energy region of the considered precoder is weighed towards one side which is data rate side or harvested energy side according to power allocation between data users and energy users. Besides, when there are many energy users, controlling the direction of the data signal is important in the aspect of energy harvesting.

To support tens of Giga-bits/second (Gbps) data rate, 5G mobile communication systems consider the integration of backhaul and fronthaul links into Xhaul networks. This paper introduces the system-level simulation for mmWave based mobile Xhaul networks. Network scenarios, hybrid beamforming, with large antenna elements, and link-level models are discussed based on the results of the system-level simulation for the mobile Xhaul network. System-level simulation results show that the mobile Xhau network using a 40 GHz carrier frequency band can support a 20 Gbps data rate.

In this paper, an asymptotic distribution for the sum rate capacity is derived in zero-forcing beamforming (ZF-BF) based multiuser MIMO systems with a large number of base station (BS)antennas. By utilizing the characteristics of a large number of antennas and the central limit theorem, we prove that the asymptotic distribution for the sum rate capacity follows Gaussian distribution. Also we derive the mean and variance values for the asymptotic Gaussian distribution. Based on the asymptotic Gaussian distribution, we also obtain the outage probability for the sum rate capacity as a closed form. Simulation results show that the asymptotic Gaussian distribution is well follows the actual sum rate capacity, and the both asymptotic mean and variance value are also very accurate.

This paper analyzes the pilot power ratio (PPR) in multiuser multiple-input multiple-output (MU-MIMO) systems with a large number of receive antennas (M) at the base station (BS). We consider zero-forcing based MU-MIMO orthogonal frequency division multiplexing (OFDM) systems. Based on the deterministic uplink sum-rate approximation for imperfect channel state information, we can formulate the optimization problems in terms of the PPR to maximize the ergodic uplink sum-rate subject to the per-slot or per-symbol power constraint. Under the per-slot power constraint, the optimal PPR can be obtained in a closed form while under the per-symbol power constraint, we propose an iterative algorithm which generates a suboptimal PPR. Simulation results show that the proposed PPRs perform close to the optimal performance in terms of the sum-rate. Also, it is shown that the proposed PPRs outperform the equal power allocation. In particular, in the ZF-R based MU-MIMO OFDM system with 8 users and M=32 under the per-slot power constraint, the proposed PPR can achieve about 8bps/Hz performance gain compared to the equal power allocation.

This paper proposes a power-efficient mode selection and power allocation scheme in device-to-device (D2D) communication system as an underlay coexistence with cellular networks. The proposed scheme is performed based on the exhaustive search of all possible mode combinations of the devices which consist of the mode indices for all devices in the system. Specifically, the proposed scheme consists of two steps. First, we calculate the optimal power with respect to the maximum power-efficiency for all possible modes of each device. Since the power-efficiency is not a concave function for the transmission power, we obtain the suboptimal solution by using the concavity of the lower and upper bound for the power-efficiency. The powerefficiencies for all possible modes of each device are obtained by the suboptimal power allocation in the first step. In the second step, we select a mode sequence which has the maximal power-efficiency among all possible mode combinations of the devices based on the obtained power-efficiencies in the first step. Then we can jointly obtain the suboptimal transmission power and the mode maximizing the power-efficiency. The proposed suboptimal scheme for the power allocation and mode selection performs close to the upper bound with respect to the power-efficiency. The simulation results also show that the proposed scheme outperforms the conventional schemes with respect to the power-efficiency and system capacity.