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As a revolutionary paradigm for controlling wireless channels, reconfigurable intelligent surface (RIS) has emerged as a candidate technology for future 6G networks. However, due to the multiplicative fading effect, the existing passive RISs only achieve a negligible capacity gain in many scenarios with strong direct links. In this paper, the concept of active RISs is proposed to overcome this fundamental limitation. Unlike the existing passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals actively through integrating amplifiers into their elements. To characterize the signal amplification and incorporate the noise introduced by active components, we develop a signal model for active RISs, which is validated through the experimental measurements on a fabricated active RIS element. Based on the developed signal model, we further analyze the asymptotic performance of active RISs to reveal its notable capacity gain for wireless communications. Finally, we formulate the sum-rate maximization problem for an active RIS aided multiple-input multiple-output (MIMO) system and a joint transmit beamforming and reflect precoding algorithm is proposed to solve this problem. Simulation results show that, in a typical wireless system, the existing passive RISs can realize only a negligible sum-rate gain of 3%, while the proposed active RISs can achieve a significant sum-rate gain of 108%, thus overcoming the multiplicative fading effect.

Distributed Artificial Intelligence (DAI) is regarded as one of the most promising techniques to provide intelligent services under strict privacy protection regulations for multiple clients. By applying DAI, training on raw data is carried out locally, while the trained outputs, e.g., model parameters, from multiple local clients, are sent back to a central server for aggregation. Recently, for achieving better practicality, DAI is studied in conjunction with wireless communication networks, incorporating various random effects brought by wireless channels. However, because of the complex and case-dependent nature of wireless channels, a generic simulator for applying DAI in wireless communication networks is still lacking. To accelerate the development of DAI applied in wireless communication networks, we propose a generic system design in this paper as well as an associated simulator that can be set according to wireless channels and system-level configurations. Details of the system design and analysis of the impacts of wireless environments are provided to facilitate further implementations and updates. We employ a series of experiments to verify the effectiveness and efficiency of the proposed system design and reveal its superior scalability.

The introduction of the mm-Wave spectrum into 5G NR promises to bring about unprecedented data throughput to future mobile wireless networks but comes with several challenges. Network densification has been proposed as a viable solution to increase RAN resilience, and the newly introduced Integrated-Access-and-Backhaul (IAB) is considered a key enabling technology with compelling cost-reducing opportunities for such dense deployments. Reconfigurable Intelligent Surfaces (RIS) have recently gained extreme popularity as they can create Smart Radio Environments by EM wave manipulation and behave as inexpensive passive relays. However, it is not yet clear what role this technology can play in a large RAN deployment. With the scope of filling this gap, we study the blockage resilience of realistic mm-Wave RAN deployments that use IAB and RIS. The RAN layouts have been optimised by means of a novel mm-Wave planning tool based on MILP formulation. Numerical results show how adding RISs to IAB deployments can provide high blockage resistance levels while significantly reducing the overall network planning cost.

A real-time motion training system for skydiving is proposed. Aerial maneuvers are performed by changing the body posture and thus deflecting the surrounding airflow. The natural learning process is extremely slow due to unfamiliar free-fall dynamics, stress induced blocking of kinesthetic feedback, and complexity of the required movements. The key idea is to augment the learner with an automatic control system that would be able to perform the trained activity if it had direct access to the learner's body as an actuator. The aiding system will supply the following visual cues to the learner: 1. Feedback of the current body posture; 2. The body posture that would bring the body to perform the desired maneuver; 3. Prediction of the future inertial position and orientation if the body retains its present posture. The system will enable novices to maintain stability in free-fall and perceive the unfamiliar environmental dynamics, thus accelerating the initial stages of skill acquisition. This paper presents results of a Proof-of-Concept experiment, whereby humans controlled a virtual skydiver free-falling in a computer simulation, by the means of their bodies. This task was impossible without the aiding system, enabling all participants to complete the task at the first attempt.

Beyond Visual Line of Sight operation enables drones to surpass the limits imposed by the reach and constraints of their operator's eyes. It extends their range and, as such, productivity, and profitability. Drones operating BVLOS include a variety of highly sensitive assets and information that could be subject to unintentional or intentional security vulnerabilities. As a solution, blockchain-based services could enable secure and trustworthy exchange and storage of related data. They also allow for traceability of exchanges and perform synchronization with other nodes in the network. However, most of the blockchain-based approaches focus on the network and the protocol aspects of drone systems. Few studies focus on the architectural level of on-chip compute platforms of drones. Based on this observation, the contribution of this paper is twofold: (1) a generic blockchain-based service architecture for on-chip compute platforms of drones, and (2) a concrete example realization of the proposed generic architecture.

We consider a coded compressed sensing approach for the unsourced random access and replace the outer tree code proposed by Amalladinne et al. with the list recoverable code capable of correcting t errors. A finite-length random coding bound for such codes is derived. The numerical experiments in the single antenna quasi-static Rayleigh fading MAC show that transition to list recoverable codes correcting t errors improves the performance of coded compressed sensing scheme by 7-10 dB compared to the tree code-based scheme. We propose two practical constructions of outer codes. The first is a modification of the tree code. It utilizes the same code structure, and a key difference is a decoder capable of correcting up to t errors. The second is based on the Reed-Solomon codes and Guruswami-Sudan list decoding algorithm. The first scheme provides an energy efficiency very close to the random coding bound when the decoding complexity is unbounded. But for the practical parameters, the second scheme is better and improves the performance of a tree code-based scheme when the number of active users is less than 200.

In this article, we investigate the behaviour of TMs with time limit and tape space limit. This problem is in P when the time limit is unary coded. If both limits go to infinity, it is undecidable which limit is exceeded first. Thus logspace-incomplete sets in P can be constructed. This implies L $\not=$ P.

The multi-link operation (MLO) is a new feature proposed to be part of the IEEE 802.11be Extremely High Throughput (EHT) amendment. Such feature represents a paradigm shift towards multi-link communications, as nodes will be allowed to transmit and receive data over multiple radio interfaces concurrently. To make it possible, the 802.11be Task Group has proposed different modifications in regards to nodes' architecture, transmission operation, and management functionalities. This article reviews such changes and tackles the question of how traffic should be distributed over multiple links, as it is still unresolved. To that end, we evaluate different load balancing strategies over the active links. Results show that in high load, dense and complex scenarios, implementing congestion-aware load balancing policies to significantly enhance next-generation WLAN performance using MLO is a must.

Integrated sensing and communication (ISAC) has been regarded as one of the most promising technologies for future wireless communications. However, the mutual interference in the communication radar coexistence system cannot be ignored. Inspired by the studies of reconfigurable intelligent surface (RIS), we propose a double-RIS-assisted coexistence system where two RISs are deployed for enhancing communication signals and suppressing mutual interference. We aim to jointly optimize the beamforming of RISs and radar to maximize communication performance while maintaining radar detection performance. The investigated problem is challenging, and thus we transform it into an equivalent but more tractable form by introducing auxiliary variables. Then, we propose a penalty dual decomposition (PDD)-based algorithm to solve the resultant problem. Moreover, we consider two special cases: the large radar transmit power scenario and the low radar transmit power scenario. For the former, we prove that the beamforming design is only determined by the communication channel and the corresponding optimal joint beamforming strategy can be obtained in closed-form. For the latter, we minimize the mutual interference via the block coordinate descent (BCD) method. By combining the solutions of these two cases, a low-complexity algorithm is also developed. Finally, simulation results show that both the PDD-based and low-complexity algorithms outperform benchmark algorithms.

Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification. However, previous works usually address one of two aspects: i) how to extract accurate rationales for explainability while being beneficial to prediction; ii) how to make the predictive model robust to different types of adversarial attacks. Intuitively, a model that produces helpful explanations should be more robust against adversarial attacks, because we cannot trust the model that outputs explanations but changes its prediction under small perturbations. To this end, we propose a joint classification and rationale extraction model named AT-BMC. It includes two key mechanisms: mixed Adversarial Training (AT) is designed to use various perturbations in discrete and embedding space to improve the model's robustness, and Boundary Match Constraint (BMC) helps to locate rationales more precisely with the guidance of boundary information. Performances on benchmark datasets demonstrate that the proposed AT-BMC outperforms baselines on both classification and rationale extraction by a large margin. Robustness analysis shows that the proposed AT-BMC decreases the attack success rate effectively by up to 69%. The empirical results indicate that there are connections between robust models and better explanations.