Cross-Domain Waveform Design for 6G Integrated Sensing and Communication

Orthogonal frequency division multiplexing (OFDM) is one of the representative integrated sensing and communication (ISAC) waveforms, where sensing and communications tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for OFDM-based ISAC systems, following communication-centric and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communications according to prior knowledge of the communication channel. The remaining REs are then employed for sensing, where the sidelobe level and peak to average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics. For the sensing-centric design, a `locally' perfect auto-correlation property is ensured by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed communication-centric and sensing-centric waveform designs for ISAC applications.

Two Enhanced-rate Power Allocation Strategies for Active IRS-assisted Wireless Network

Due to its ability of overcoming the impact of double-fading effect, active intelligent reflecting surface (IRS) has attracted a lot of attention. Unlike passive IRS, active IRS should be supplied by power, thus adjusting power between base station (BS) and IRS having a direct impact on the system rate performance. In this paper, the active IRS-aided network under a total power constraint is modeled with an ability of adjusting power between BS and IRS. Given the transmit beamforming at BS and reflecting beamforming at IRS, the SNR expression is derived to be a function of power allocation (PA) factor, and the optimization of maximizing the SNR is given. Subsequently, two high-performance PA strategies, enhanced multiple random initialization Newton's (EMRIN) and Taylor polynomial approximation (TPA), are proposed. The former is to improve the rate performance of classic Netwon's method to avoid involving a local optimal point by using multiple random initializations. To reduce its high computational complexity, the latter provides a closed-form solution by making use of the first-order Taylor polynomial approximation to the original SNR function. Actually, using TPA, the original optimization problem is transformed into a problem of finding a root for a third-order polynomial.Simulation results are as follows: the first-order TPA of SNR fit its exact expression well, the proposed two PA methods performs much better than fixed PA in accordance with rate, and appoaches exhaustive search as the number of IRS reflecting elements goes to large-scale.

Intelligent Reflecting Surface Aided Multi-Tier Hybrid Computing

The Digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This paper investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid computing system that can achieve mutual benefits for DT and MEC in the DITEN. For the first time, this paper presents the opportunity to realize the network-wide convergence of DT and MEC. In the considered system, specifically, over-the-air computation (AirComp) is employed to monitor the status of the DT system, while MEC is performed with the assistance of DT to provide low-latency computing services. Besides, the IRS is utilized to enhance signal transmission and mitigate interference among heterogeneous nodes. We propose a framework for designing the hybrid computing system, aiming to maximize the sum computation rate under communication and computation resources constraints. To tackle the non-convex optimization problem, alternative optimization and successive convex approximation techniques are leveraged to decouple variables and then transform the problem into a more tractable form. Simulation results verify the effectiveness of the proposed algorithm and demonstrate the IRS can significantly improve the system performance with appropriate phase shift configurations. Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT. This paper proposes the network-wide integration of DT and MEC, then demonstrates the necessity of DT for achieving an optimal performance in DITEN systems through analysis and numerical results.

Flexible Spectrum Orchestration of Carrier Aggregation for 5G-Advanced

With increasing availability of spectrum in the market due to new spectrum allocation and re-farming bands from previous cellular generation networks, a more flexible, efficient and green usage of the spectrum becomes an important topic in 5G-Advanced. In this article, we provide an overview on the 3rd Generation Partnership Project (3GPP) work on flexible spectrum orchestration for carrier aggregation (CA). The configuration settings, requirements and potential specification impacts are analyzed. Some involved Release 18 techniques, such as multi-cell scheduling, transmitter switching and network energy saving, are also presented. Evaluation results show that clear performance gain can be achieved by these techniques.

6G Enabled Advanced Transportation Systems

The 6th generation (6G) wireless communication network is envisaged to be able to change our lives drastically, including transportation. In this paper, two ways of interactions between 6G communication networks and transportation are introduced. With the new usage scenarios and capabilities 6G is going to support, passengers on all sorts of transportation systems will be able to get data more easily, even in the most remote areas on the planet. The quality of communication will also be improved significantly, thanks to the advanced capabilities of 6G. On top of providing seamless and ubiquitous connectivity to all forms of transportation, 6G will also transform the transportation systems to make them more intelligent, more efficient, and safer. Based on the latest research and standardization progresses, technical analysis on how 6G can empower advanced transportation systems are provided, as well as challenges and insights for a possible road ahead.

Hybrid Active-Passive IRS Assisted Energy-Efficient Wireless Communication

Deploying active reflecting elements at the intelligent reflecting surface (IRS) increases signal amplification capability but incurs higher power consumption. Therefore, it remains a challenging and open problem to determine the optimal number of active/passive elements for maximizing energy efficiency (EE). To answer this question, we consider a hybrid active-passive IRS (H-IRS) assisted wireless communication system, where the H-IRS consists of both active and passive reflecting elements.Specifically, we study the optimization of the number of active/passive elements at the H-IRS to maximize EE. To this end, we first derive the closed-form expression for a near-optimal solution under the line-of-sight (LoS) channel case and obtain its optimal solution under the Rayleigh fading channel case. Then, an efficient algorithm is employed to obtain a high-quality sub-optimal solution for the EE maximization under the general Rician channel case. Simulation results demonstrate the effectiveness of the H-IRS for maximizing EE under different Rician factors and IRS locations.

Statistical Inference with Stochastic Gradient Methods under $φ$-mixing Data

Stochastic gradient descent (SGD) is a scalable and memory-efficient optimization algorithm for large datasets and stream data, which has drawn a great deal of attention and popularity. The applications of SGD-based estimators to statistical inference such as interval estimation have also achieved great success. However, most of the related works are based on i.i.d. observations or Markov chains. When the observations come from a mixing time series, how to conduct valid statistical inference remains unexplored. As a matter of fact, the general correlation among observations imposes a challenge on interval estimation. Most existing methods may ignore this correlation and lead to invalid confidence intervals. In this paper, we propose a mini-batch SGD estimator for statistical inference when the data is $\phi$-mixing. The confidence intervals are constructed using an associated mini-batch bootstrap SGD procedure. Using ``independent block'' trick from \cite{yu1994rates}, we show that the proposed estimator is asymptotically normal, and its limiting distribution can be effectively approximated by the bootstrap procedure. The proposed method is memory-efficient and easy to implement in practice. Simulation studies on synthetic data and an application to a real-world dataset confirm our theory.

Adaptive actuation of magnetic soft robots using deep reinforcement learning

Magnetic soft robots have attracted growing interest due to their unique advantages in terms of untethered actuation and excellent controllability. However, finding the required magnetization patterns or magnetic fields to achieve the desired functions of these robots is quite challenging in many cases. No unified framework for design has been proposed yet, and existing methods mainly rely on manual heuristics, which are hard to satisfy the high complexity level of the desired robotic motion. Here, we develop an intelligent method to solve the related inverse-design problems, implemented by introducing a novel simulation platform for magnetic soft robots based on Cosserat rod models and a deep reinforcement learning framework based on TD3. We demonstrate that magnetic soft robots with different magnetization patterns can learn to move without human guidance in simulations, and effective magnetic fields can be autonomously generated that can then be applied directly to real magnetic soft robots in an open-loop way.

Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure. Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

Calibrating multi-dimensional complex ODE from noisy data via deep neural networks

Ordinary differential equations (ODEs) are widely used to model complex dynamics that arises in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally very difficult. In this work, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary kernel method, and then feed them into a sparsely connected deep neural network with ReLU activation function. Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure. When the ODE possesses a general modular structure, with each modular component involving only a few input variables, and the network architecture is properly chosen, our method is proven to be consistent. Theoretical properties are corroborated by an extensive simulation study that demonstrates the validity and effectiveness of the proposed method. Finally, we use our method to simultaneously characterize the growth rate of Covid-19 infection cases from 50 states of the USA.