LoRa is one of the most prominent low-power wide area network (LPWAN) technologies for Internet of Things (IoT) applications. As the core technique of LoRa physical (PHY) layer, chirp spread spectrum (CSS) modulation is employed to support low power and long range communications. Although it provides a compelling tradeoff between coverage and data rate, the relatively low-spectral efficiency (SE) is still a limiting factor for its extensive applications. In this article, we propose two enhanced group-based CSS modulation schemes, named in-phase and quadrature group-based CSS (IQ-GCSS) and time domain multiplexed group-based CSS (TDM-GCSS), which can achieve much higher SE than the conventional LoRa modulation. The transmitter architectures of our proposed modulation schemes are presented along with both coherent and noncoherent detection methods. Moreover, an overall performance analysis of our proposed schemes is provided in terms of bit error rate (BER) and computational complexity. Numerical results not only validate the accuracy of our theoretical analysis but also demonstrate substantial performance improvements of our proposed schemes in terms of effective throughput compared to the classical counterparts.

In recent years, long-range (LoRa) has become one of the most prominent low-power wide-area network (LPWAN) technologies for the Internet of Things (IoT), which is based on a proprietary chirp spread spectrum (CSS) modulation (i.e., LoRa modulation). However, with the ever-increasing transmission demands of various IoT applications, the low-data-rate issue of LoRa modulation has become a critical bottleneck for its extensive deployment. To address this issue, we first formulate a unified framework for CSS-based waveform design and propose a novel layered group-based CSS (LGCSS) modulation scheme to achieve much higher spectral efficiency (SE) and data rate, thus accommodating a wider range of IoT applications. The complete transmitter architecture of LGCSS modulation is presented along with both coherent and non-coherent detection methods. Moreover, a comprehensive performance analysis of our proposed scheme is conducted in terms of orthogonality, bit error probability (BEP), and computational complexity. Extensive numerical simulations are conducted to verify the effectiveness of our theoretical analysis and the superiority of our proposed scheme compared to the traditional counterparts.

As reconfigurable intelligent surfaces (RIS) emerge as a pivotal technology in the upcoming sixth-generation (6G) networks, its deployment within practical multiple operator (OP) networks presents significant challenges, including the coordination of RIS configurations among OPs, interference management, and privacy maintenance. A promising strategy is to treat RIS as a public resource managed by an RIS provider (RP), which can enhance resource allocation efficiency by allowing dynamic access for multiple OPs. However, the intricate nature of coordinating management and optimizing RIS configurations significantly complicates the implementation process. In this paper, we propose a hierarchical deep reinforcement learning (HDRL) approach that decomposes the complicated RIS resource optimization problem into several subtasks. Specifically, a top-level RP-agent is responsible for RIS allocation, while low-level OP-agents control their assigned RISs and handle beamforming, RIS phase-shifts, and user association. By utilizing the semi-Markov decision process (SMDP) theory, we establish a sophisticated interaction mechanism between the RP and OPs, and introduce an advanced hierarchical proximal policy optimization (HPPO) algorithm. Furthermore, we propose an improved sequential-HPPO (S-HPPO) algorithm to address the curse of dimensionality encountered with a single RP-agent. Experimental results validate the stability of the HPPO algorithm across various environmental parameters, demonstrating its superiority over other benchmarks for joint resource optimization. Finally, we conduct a detailed comparative analysis between the proposed S-HPPO and HPPO algorithms, showcasing that the S-HPPO algorithm achieves faster convergence and improved performance in large-scale RIS allocation scenarios.

Owing to the promising ability of saving hardware cost and spectrum resources, integrated sensing and communication (ISAC) is regarded as a revolutionary technology for future sixth-generation (6G) networks. The mono-static ISAC systems considered in most of existing works can only achieve limited sensing performance due to the single observation angle and easily blocked transmission links, which motivates researchers to investigate cooperative ISAC networks. In order to further improve the degrees of freedom (DoFs) of cooperative ISAC networks, the transmitter-receiver selection, i.e., base station (BS) mode selection problem, is meaningful to be studied. However, to our best knowledge, this crucial problem has not been extensively studied in existing works. In this paper, we consider the joint BS mode selection, transmit beamforming, and receive filter designs for cooperative cell-free ISAC networks, where multi-BSs cooperatively serve communication users and detect targets. An efficient joint beamforming design algorithm and three different heuristic BS mode selection methods are proposed to solve the non-convex NP-hard problem. Simulation results demonstrates the advantages of cooperative ISAC networks, the importance of BS mode selection, and the effectiveness of proposed algorithms.

Reconfigurable intelligent surface (RIS) and ambient backscatter communication (AmBC) have been envisioned as two promising technologies due to their high transmission reliability as well as energy-efficiency. This paper investigates the secrecy performance of RIS assisted AmBC networks. New closed-form and asymptotic expressions of secrecy outage probability for RIS-AmBC networks are derived by taking into account both imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) cases. On top of these, the secrecy diversity order of legitimate user is obtained in high signal-to-noise ratio region, which equals zero and is proportional to the number of RIS elements for ipSIC and pSIC, respectively. Numerical results are provided to verify the accuracy of theoretical analyses and manifest that the secrecy performance of RIS-AmBC networks exceeds that of conventional AmBC networks. In addition, due to the mutual interference between direct and backscattering links, the number of RIS elements has an optimal value to minimise the secrecy system outage probability.

Reconfigurable Intelligent Surfaces (RIS) have been proposed as a revolutionary technology with the potential to address several critical requirements of 6G communication systems. Despite its powerful ability for radio environment reconfiguration, the “double fading” effect constricts the practical system performance enhancements due to the significant path loss. A new active RIS architecture has been recently proposed to overcome this challenge. However, existing active RIS studies rely on an ideal amplification model without considering the practical hardware limitation of amplifiers, which may cause performance degradation using such inaccurate active RIS mod-eling. Motivated by this fact, in this paper we first investigate the amplification principle of typical active RIS and propose a more accurate amplification model based on amplifier hardware characteristics. Then, based on the new amplification model, we propose a novel joint transmit beamforming and RIS reflection beamforming design considering the incident signal power on practical active RIS for multiuser multi-input single-output (MU-MISO) communication system. Fractional programming (FP), majorization minimization (MM) and block coordinate descent (BCD) methods are used to solve for the complex problem. Simulation results indicate the importance of the consideration of practical amplifier hardware characteristics in the joint beamforming designs and demonstrate the effectiveness of the proposed algorithm compared to other benchmarks.

Abstract Reconfigurable intelligent surface (RIS) has stimulated their potential applications for improving the performance of wireless communication networks. In this paper, the performance of RIS aided non‐orthogonal multiple access (NOMA) networks with hardware impairments over Rician fading channels is investigated. More specifically, the exact and asymptotic expressions of outage probability for a pair of users, that is, the nearby user n and distant user m are derived, where the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) are taken into consideration. According to the approximate analyses, the diversity orders of user n with ipSIC/pSIC and user m are obtained in the high signal‐to‐noise radio regime. It indicates that the diversity orders are in connection with reflecting elements and Rician factors except the channel ordering. The impact of these parameters on outage behaviors of RIS‐NOMA networks is also analysed. In addition, the system throughput of RIS‐NOMA networks with ipSIC/pSIC is surveyed in detail. Monte Carlo simulations are present to verify the correctness of theoretical analyses that: (1) The outage probability of user n with pSIC is superior to that of orthogonal user, while the outage probability of user m is inferior to that of orthogonal user and (2) as the number of reflecting elements and Rician factors increases, the outage behaviors of RIS‐NOMA networks are enhanced carefully.

In recent years, GNSS-ZTD/PWV has been attempted for exploring or predicting precipitation events. However, current GNSS precipitation prediction models take little account of short-period disturbances. In this study, GNSS is used to show the short-period tropospheric disturbance during strong convection for the first time. Taking Hong Kong as an example, several short-period before, during, and after precipitation events using GNSS-ZTD in April 2019 are analyzed using a band-pass filter. It is verified that the gravity waves-like disturbances are strongly correlated with heavy precipitation, which mainly occurs in the pre-precipitation (3 - 6 hour) and mid-precipitation periods. The probability of precipitation is 50%, 27.14%, and 22.86% for the early, middle, and late periods of precipitation events. The pre-precipitation disturbance demonstrated the ability of GNSS-ZTD used for heavy precipitation prediction. This also provides a new parameter for real-time precipitation forecasting in various machine/deep learning models.

Radio sensing can be integrated with communication in what the authors call future perceptive mobile networks. Due to the complicated signal structure, it is challenging to estimate sensing parameters such as delay, angle of arrival, and Doppler when joint communication and radar/radio sensing is applied in perceptive mobile networks. Radio sensing with signals compatible with a fifth‐generation (5G) new radio standard using one‐dimension (1D) to 3D compressive sensing (CS) techniques under 5G channel conditions is studied. In the case of 1D–3D CS techniques, they formulate the parameter estimation as a sparse signal recovery problem. These algorithms demonstrate respective advantages, but also show shortcomings in dealing with clustered channels. To effectively exploit the cluster structure in multipath channels, they also propose a 2D cluster Kronecker CS algorithm for significantly improved sensing parameter estimation via introducing a prior probability distribution. Simulation results are provided and they focus the respective advantages and disadvantages of these techniques that validate the effectiveness of the proposed algorithms.