Uncrewed aerial vehicles (UAVs) play a pivotal role in ensuring seamless connectivity for Internet of Things (IoT) devices, particularly in scenarios where conventional terrestrial networks are constrained or temporarily unavailable. However, traditional coverage-hole detection approaches, such as minimizing drive tests, are costly, time-consuming, and reliant on outdated radio-environment data, making them unsuitable for real-time applications. To address these limitations, this paper proposes a UAV-assisted framework for real-time detection and recovery of coverage holes in IoT networks. In the proposed scheme, a patrol UAV is first dispatched to identify coverage holes in regions where the operational status of terrestrial base stations (BSs) is uncertain. Once a coverage hole is detected, one or more UAVs acting as aerial BSs are deployed by a satellite or nearby operational BSs to restore connectivity. The UAV swarm is organized based on Delaunay triangulation, enabling scalable deployment and tractable analytical characterization using stochastic geometry. Moreover, a collision-avoidance mechanism grounded in multi-agent system theory ensures safe and coordinated motion among multiple UAVs. Simulation results demonstrate that the proposed framework achieves high efficiency in both coverage-hole detection and on-demand connectivity restoration while significantly reducing operational cost and time.

LoRa is a widely recognized modulation technology in the field of low power wide area networks (LPWANs). However, the data rate of LoRa is too low to satisfy the requirements of Internet of Things applications. To address this issue, we propose a novel high-data-rate LoRa scheme based on the spreading factor index (SFI). In the proposed SFI-LoRa scheme, the starting frequency bin of a chirp signal is used to transmit information bits, while the combinations of spreading factors are exploited as a set of indices to convey additional information bits. Moreover, the theoretical symbol error rate, data rate, transmission throughput, complexity and energy efficiency of the proposed SFI-LoRa scheme are carefully analyzed. Simulation results not only verify the accuracy of our theoretical analysis, but also demonstrate that the proposed SFI-LoRa scheme can improve the transmission throughput of existing LoRa schemes without sacrificing the BER performance over additive white Gaussian noise, Rayleigh fading, and multipath flat-fading channels. Therefore, the proposed SFI-LoRa scheme is a potential solution for applications requiring a high data rate in the LPWAN domain.

Low-altitude wireless networks are increasingly vital for the low-altitude economy, enabling wireless coverage in high-mobility and hard-to-reach environments. However, providing reliable connectivity to sparsely distributed aerial users in dynamic three-dimensional (3D) spaces remains a significant challenge. This paper investigates downlink coverage enhancement in vertical heterogeneous networks (VHetNets) beyond 5G, where unmanned aerial vehicles (UAVs) operate as emerging aerial base stations (ABSs) alongside legacy terrestrial base stations (TBSs). To improve coverage performance, we propose a coordinated multi-point (CoMP) transmission framework that enables joint transmission from ABSs and TBSs. This approach mitigates the limitations of non-uniform user distributions and enhances reliability for sparse aerial users. Two UAV deployment strategies are considered:i)random UAV placement, analyzed using stochastic geometry to derive closed-form coverage expressions, andii)optimized UAV placement using a coverage-aware weightedK-means clustering algorithm to maximize cooperative coverage in underserved areas. Theoretical analyses and Monte Carlo simulations demonstrate that the proposed CoMP-enabled VHetNet significantly improves downlink coverage probability, particularly in scenarios with sparse aerial users. These findings highlight the potential of intelligent UAV coordination and geometry-aware deployment to enable robust, adaptive connectivity in low-altitude wireless networks.

Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is challenging, and independent edge nodes can lead to inefficient resource utilization and reduced learning performance. To address these issues, this paper proposes a collaborative optimization framework for energy-efficient federated edge learning with small-scale datasets. We first derive an expected learning loss to quantify the relationship between the number of training samples and learning objectives. A stochastic online learning algorithm is then designed to adapt to data variations, and a resource optimization problem with a convergence bound is formulated. Finally, an online distributed algorithm efficiently solves large-scale optimization problems with high scalability. Extensive simulations and autonomous navigation case studies with collision avoidance demonstrate that the proposed approach significantly improves learning performance and resource efficiency compared to state-of-the-art benchmarks.

Nowadays, localization functionality is becoming increasingly critical in Internet of Things (IoT) applications. However, the localization accuracy of long-range IoT systems is fundamentally constrained by the limitations of narrowband transmission, low power consumption, and imperfect timing-frequency synchronization. To overcome these problems, this paper proposes a frequency-hopping (FH)-assisted localization system based on the existing LoRa technique under imperfect synchronization. The core idea is that localization accuracy can benefit from an equivalent wider bandwidth by transmitting symbols over multiple frequency bands and jointly processing the collectively received symbols. Specifically, we first develop a system model for the FH-assisted LoRa localization, incorporating the effects of imperfect timing-frequency synchronization. We then derive the Cramér-Rao Lower Bound (CRLB) for localization accuracy in the continuous-time domain, quantifying the theoretical performance limits with an infinitely wide sampling bandwidth. Furthermore, we propose a low-complexity 2D discrete Fourier transform (DFT)-based channel estimation algorithm. The estimation results are then used to localize the target device in a time-difference-of-arrival (TDoA)-based trilateration scheme. Numerical results demonstrate that our proposed FH-assisted LoRa localization system can achieve tens-of-meter-level accuracy in both LoS and multipath environments, significantly outperforming current LoRa localization systems with an accuracy of around 200 m.

Traditional low-power wide-area network (LPWAN) transceivers typically compromise data rates to achieve deep coverage. This paper presents a novel transceiver that achieves high receiver sensitivity and low computational complexity. At the transmitter, we replace the conventional direct sequence spread spectrum (DSSS) preamble with a chirp spread spectrum (CSS) preamble, consisting of a pair of down-chirp and up-chirp signals that are conjugate to each other, simplifying packet synchronization. For enhanced coverage, the payload incorporates continuous phase frequency shift keying (CPFSK) to maintain a constant envelope and phase continuity, in conjunction with DSSS to achieve a high spreading gain. At the receiver, we develop a double-peak detection method to improve synchronization and a non-coherent joint despreading and demodulation scheme that increases receiver sensitivity while maintaining simplicity in implementation. Furthermore, we optimize the preamble detection threshold and spreading sequences for maximum non-coherent receiver performance. The software-defined radio (SDR) prototype, developed using GNU Radio and USRP, along with operational snapshots, showcases its practical engineering applications. Extensive Monte Carlo simulations and field-test trials demonstrate that our transceiver outperforms traditional ones in terms of receiver sensitivity, while also being low in complexity and cost-effective for LPWAN requirements.

The evolution of Internet of Things technologies is driven by four key demands: ultra-low power consumption, high spectral efficiency, reduced implementation cost, and support for massive connectivity. To address these challenges, this paper proposes two novel modulation schemes that integrate continuous phase modulation (CPM) with spread spectrum (SS) techniques. We begin by establishing the quasi-orthogonality properties of CPM-SS sequences. The first scheme, termed IM-CPM-SS, employs index modulation (IM) to select spreading sequences from the CPM-SS set, thereby improving spectral efficiency while maintaining the constant-envelope property. The second scheme, referred to as CIM-CPM-SS, introduces code index modulation (CIM), which partitions the input bits such that one subset is mapped to phase-shift keying symbols and the other to CPM-SS sequence indices. Both schemes are applied to downlink non-orthogonal multiple access (NOMA) systems. We analyze their performance in terms of bit error rate (BER), spectral and energy efficiency, computational complexity, and peak-to-average power ratio characteristics under nonlinear amplifier conditions. Simulation results demonstrate that both schemes outperform conventional approaches in BER while preserving the benefits of constant-envelope, continuous-phase signaling. Furthermore, they achieve higher spectral and energy efficiency and exhibit strong resilience to nonlinear distortions in downlink NOMA scenarios.

Physical-layer authentication (PLA) can provide lightweight security solutions for the next-generation Internet of Things (IoT) networks. This paper adopts and modifies the spreading code authentication techniques initially designed for the Global Navigation Satellite System to apply PLA to narrowband IoT networks. In particular, the chip values of the PHY-layer preamble are replaced by a message-generated tag at the transmitter. At the receiver, two kinds of test statistics, cross-correlation and double-correlation values, are computed to decide the authenticity of a received signal. Also, the closed-form expressions of the corresponding optimal thresholds for the hypothesis tests are explicitly derived. Afterward, the single-frame authentication schemes are extended to multi-frame authentication, where several frames are jointly authenticated. Simulation results of the proposed schemes, along with the prototype validation of the double-correlation scheme based on the GNU Radio/USRP SDR platform, corroborate the effectiveness of the PLA strategies.

Narrowband Internet of Things (NB-IoT) is a powerful technology for massive machine-type communications, which is imperative in the forthcoming 5G wireless communications. Unlike the long-time evolution (LTE) protocol, in the NB-IoT protocol specified by the third generation partnership project (3GPP), the narrowband physical random access channel (NPRACH) is newly introduced and its receiver performance is critical to the success of an NB-IoT system. In this article, an optimal activity detection scheme is first designed by using the Neyman-Pearson criterion. Then, a low-complexity iterative search algorithm is developed for the joint estimation of residual carrier frequency offset (RCFO) and timing advanced (TA), avoiding the effect of phase ambiguity. Finally, simulation results collaborate on the effectiveness and efficiency of the proposed receiver.

Channel Estimation (CE) is one of the key components in wireless communication systems employing coherent demodulation and interference cancellation. Among the numerous CE techniques in the literature, Pilot-Assisted Channel Estimation (PACE) has been widely used in many practical systems, thanks to its simplicity and efficiency for implementation. In order to minimise pilot overhead, interpolation is typically mandatory in PACE. Conventional linear and Polar-Linear Interpolation (PLI) methods are easy to implement, but naturally result in residual interpolation errors that can not be mitigated even at high Signal-to-Noise Ratios (SNRs). In this paper, we propose a CE scheme equipped with a novel interpolation method referred to as Adaptive Polar-Linear Interpolation (APLI). The proposed APLI-aided CE scheme can achieve a good performance, thanks to its capability of adaptively tracking the instantaneous variation of channel's fading coefficients, while maintaining a low complexity that is similar to the simple linear interpolation. The benefits of our proposal are demonstrated by a range of numerical illustrations and simulation results.

In a typical wireless communication environment, radio channels need to be estimated for systems employing coherent detection. For implementation simplicity, Pilot-Assisted Channel Estimation (PACE) techniques have been widely used. An efficient interpolation method helps to estimate the channel fading coefficients between consecutive pilots with minimised pilot overhead. The simple linear interpolation method is easy to implement, but naturally results in residual interpolation errors that can not be mitigated even at high Signal-to-Noise Ratios (SNR). On the other hand, more sophisticated interpolation schemes usually require a high complexity that may be prohibited in real systems. In this paper, we propose a Channel Estimation (CE) scheme equipped with a novel interpolation method that is capable of achieving a good performance at a very low computational complexity. Numerical illustrations and simulation results provided show the benefits of the proposed CE technique.

The zero-padded block transmission with linear time-domain pre-equalizer is studied in this paper. A matched filter is exploited to guarantee the stability of the zero-forcing (ZF) and minimum mean square error (MMSE) pre-equalization. Then, in order to compute the pre-equalizers efficiently, an asymptotic decomposition is developed for the positive-definite Hermitian banded Toeplitz matrix. Compared to the direct matrix inverse methods or the Levinson-Durbin algorithm, the computational complexity of the proposed algorithm is significantly decreased and there is no bit error rate degradation when data block length is large.

Abstract A cross‐level pre‐RAKE combining (PRC) scheme for time hopping pulse amplitude modulation ultra wideband (TH‐PAM UWB) transmitter is studied in this paper. A two‐stage cross‐level PRC (CL‐PRC) scheme is proposed. The conventional PRC schemes suppress all the chip‐wise interference. However, the proposed scheme suppresses only the specific frame‐wise inter‐symbol interference (ISI) by exploiting the characteristic that the information bits are transmitted only at ultra short time slots. This results in a low complexity pre‐equalizer without bit error rate (BER) performance degradation. Furthermore, an order selection rule is presented to achieve the tradeoff between signal‐to‐interference ratio (SIR) and computational complexity. Simulation results illustrate the superior SIR and BER performance of our proposal. Copyright © 2009 John Wiley & Sons, Ltd.

Different from the opportunistic beamforming system (OBS) randomizing both amplitude and phase of its beamforming vector/matrix, the opportunistic cophasing system (OCS) randomizes the phase only. It significantly reduces the demands on the dynamic range of power amplifiers and even can be implemented just by a set of phase shifters. We first address the single-beam OCS in terms of its analytical system throughput. Then, we design a multi-beam OCS by exploiting the Fourier matrix and finally, we derive its closed-form upper bound on throughput, which is very tight with the Monte Carlo simulation results.

To avoid poor efficiency of the power amplifiers, the opportunistic cophasing communication system randomises only the phase of beamforming vector elements, instead of randomising both amplitude and phase. The opportunistic cophasing system with multi-beam transmission is focused. The cophasing matrix with a rotation matrix is first constructed. Then, the upper and lower bounds of asymptotic system throughput are analytically derived, based on the theory of order statistics. Finally, simulation results demonstrate the effectiveness of the proposed method in terms of system throughput.