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Frequency-modulated continuous-wave (FMCW) radars are becoming increasingly important in industrial applications due to their low cost, simple signal processing, and high resolution. The research is driven by the growing trend of radar applications in industrial settings, including tasks like autonomous robot navigation, radar-based monitoring of humans, and enhancing safety across various scenarios, such as smart factories, traffic control, or environmental monitoring. The network of distributed sensor nodes is a feature to be implemented by the sixth generation of wireless mobile networks. As a result, 6G will become an enabler technology that will develop even further use cases and advance the expansion of these technologies. Therefore, the mutual interference of these sensor systems is becoming highly relevant since they can be avoided by scheduling the individual resources of the sensor nodes by a network structure. This paper investigates the interference behavior of FMCW radar sensors in a dense indoor scenario. In these environments, in particular, the reliability of the sensor system plays an important role, and disruption can have critical consequences like not recognizing a target, triggering a false alarm, or the complete failure of the sensing. To correctly classify the expected interference in the overall context, the paper investigates the physical layer effects on the performance, such as multipath propagation, channel spread, delay, Doppler shift, and attenuation. By evaluating these effects using state-of-the-art signal processing, it was revealed that a high channel spread in particular and the dynamics of the radar channel can significantly reduce the detection quality, as the interference power can exceed the received power by up to 20 dB, depending on the scenario. The simulation results given in this paper show that a potential communication between the radars and compliance with a synchronization delay, the interference problems can be reliably prevented with an appropriately designed anti-aliasing filter. Our simulations have quantified the detection performance under interference through the entire signal chain by evaluating the physical layer, the radar signal processing with non-idealities, and the hardware. The paper has investigated interference probability in indoor environments by examining radars with uncoordinated transmissions. Integrating findings from the physical propagation characteristics, the deviations in chirp durations, and the frequency of interference cases offers insights into the potential risk of interference.

Quantum computing, a transformative field that emerged from quantum mechanics and computer science, has gained immense attention for its potential to revolutionize computation. This paper aims to address the fundamentals of quantum computing and provide a comprehensive guide for both novices and experts in the field of quantum computing. Beginning with the foundational principles of quantum computing, we introduce readers to the fundamental concepts of qubits, superposition, entanglement, interference, and noise. We explore quantum hardware, quantum gates, and basic quantum circuits. This study offers insight into the current phase of quantum computing, including the noisy intermediate-scale quantum (NISQ) era and its potential for solving real-world problems. Furthermore, we discuss the development of quantum algorithms and their applications, with a focus on famous algorithms like Shor’s algorithm and Grover’s algorithm. We also touch upon quantum computing’s impact on various industries, such as cryptography, optimization, machine learning, and material science. By the end of this paper, readers will have a solid understanding of quantum computing’s principles, applications, and the steps involved in developing quantum circuits. Our goal is to provide a valuable resource for those eager to embark on their quantum computing journey and for researchers looking to stay updated on this rapidly evolving field.

Supporting high mobility is an important feature of the 6G mobile communication systems, which puts forward a great challenge for conventional orthogonal frequency division multiplexing (OFDM) systems due to the severe Doppler effects. Recently, orthogonal time frequency space (OTFS) has been proposed as a promising waveform for high-mobility scenarios, due to its excellent capability of exploiting delay-Doppler (DD) diversity to significantly improve the system performance. However, in multicell OTFS systems, intercell interference (ICI) is one of the major challenges, especially for cell-edge users (CEUs). In this article, the impacts of different types of interference on OTFS systems are first studied. Then, interference coordination and cooperative communications-based multicell OTFS systems are studied to overcome ICI, respectively. Next, simulation results are presented for the performance evaluation of these schemes. Finally, the main challenges are discussed and future research directions for multicell OTFS systems are highlighted. The performance evaluations demonstrate the effectiveness of interference coordination and cooperative communications on multicell OTFS systems. [ABSTRACT FROM AUTHOR]

The orthogonal time-frequency space (OTFS) modulation is resistant to double selective fading, making it a promising modulation scheme for next-generation integrated sensing and communication (ISAC) systems. Nonetheless, there exists a problem of mutual interference between uplink communication signals and sensing echo signals, which leads to the degradation of both information transmission and sensing performance. For this reason, this paper investigates a successive interference cancellation (SIC) method based on the delay-Doppler (DD) domain to suppress the interference between the sensing signals and communication signals, thereby ensuring the accuracy of communication signal detection and sensing parameter estimation. After receiving the mixed signals at the receiver, these signals are transformed from the time-frequency (TF) domain to the DD domain. Firstly, the sensing echo signal is treated as interference to the communication signal, which is detected using a threshold-based channel estimation method and the Message Passing (MP) algorithm. The communication signal is then reconstructed and subtracted from the mixed signal. At last, the remaining signal is then subjected to parameter estimation with the Maximum Likelihood (ML) algorithm. Simulation results demonstrate that this method allows the ISAC system to operate effectively in both communication and sensing tasks. Compared to the scenario without considering signal interference, the sensing parameter estimation is improved by approximately 6 dB. Additionally, compared to traditional interference suppression methods belonging to TF domain, the performance of sensing parameter estimation is improved by approximately 2 dB.

Transmit power control (PC) will become increasingly crucial in alleviating interference as the densification of the wireless networks continues towards 6G. However, the practicality of most PC methods suffers from high complexity, including the sensing and signalling overhead needed to obtain channel state information. In a highly dense scenario such as the deployment of short-range cells installed within production entities, termed in-factory subnetworks (InF-S), sensing and signalling overhead become a major limitation. In this paper, we represent the InF-S as a graph and resort to graph neural networks for solving the power control problem. We present four graph-attribution methods requiring different degrees of channel information corresponding to different levels of sensing and signalling overhead and study the complexity and performance tradeoffs of the resulting power control graph neural network (PCGNN) algorithms. We then propose a PCGNN method with scalable sensing and signalling graph attribution which can meet the stringent outage target while maximizing the global performance by 10% relative to fixed power control. We further verified the size generalizability and robustness of the PCGNN methods to different network settings.

For multi-transmission rate environments, access point (AP) connection methods have been proposed for maximizing system throughput, which is the throughput of an entire system, on the basis of the cooperative behavior of users. These methods derive optimal positions for the users’ cooperative behavior, which means that new users move to improve the system throughput when connecting to an AP. However, conventional methods only focus on increasing the transmission rate of new users without regard to the performance impact on existing users. In addition, these methods do not account for co-channel or stacked frequency interference between users. In this paper, we propose a novel approach to access point connectivity which maximizes the system throughput by taking into account the interference between users and the initial position of all users. In addition, our proposed method can improve system throughput by about 6% at most compared to previous methods.

Sarcasm, sentiment, and emotion are three typical kinds of spontaneous affective responses of humans to external events and they are tightly intertwined with each other. Such events may be expressed in multiple modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal conversations. Joint analysis of humans’ multi-modal sarcasm, sentiment, and emotion is an important yet challenging topic, as it is a complex cognitive process involving both cross-modality interaction and cross-affection correlation. From the probability theory perspective, cross-affection correlation also means that the judgments on sarcasm, sentiment, and emotion are incompatible. However, this exposed phenomenon cannot be sufficiently modelled by classical probability theory due to its assumption of compatibility. Neither do the existing approaches take it into consideration. In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis. Specifically, we propose a QUantum probabIlity driven multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET. Extensive experiments on two datasets and the results show that the effectiveness and advantages of QUIET in comparison with a wide range of the state-of-the-art baselines. We also show the great potential of QP in multi-affect analysis. [ABSTRACT FROM AUTHOR]