arXiv subjects

Wenkun Wen

Publications and source records attributed to Wenkun Wen.

12 recordsLinked to original sources

arXiv2026

Generalized Two-Dimensional Index Modulation in the Code-Spatial Domain for LPWAN

Low-power wide-area networks (LPWANs) are crucial for large-scale Internet of Things (IoT) applications, yet they face increasing demands for higher data rates, improved reliability, and enhanced energy efficiency under stringent hardware constraints. To address these challenges, this paper introduces a generalized code-index modulation (CIM) transceiver that employs multiple-antenna index modulation (IM). The transmitter integrates spatial modulation (SM), space-time block coding (STBC), and CIM into a unified two-dimensional (2D) coding structure, where the spreading sequences -- realized via continuous phase modulation with spread spectrum (CPM-SS), chirp spread spectrum, or Zadoff-Chu sequences -- serve as spreading codes. Three specific schemes are proposed: SM-CIM, STBC-SM-CIM, and an enhanced STBC-SM-CIM (ESTBC-SM-CIM), designed to jointly improve data rate and energy efficiency. Closed-form expressions for the average bit error probability are derived, and system performance is analyzed in terms of data rate, energy efficiency, and computational complexity. Simulation results show that the proposed designs consistently outperform benchmark schemes, demonstrating their potential for enabling high-data-rate, energy-efficient LPWAN and IoT communications.

cs.IT↗

arXiv2026

Energy-Efficient Federated Edge Learning For Small-Scale Datasets in Large IoT 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.

cs.LG↗

arXiv2026

Air-to-Ground Communications for Internet of Things: UAV-based Coverage Hole Detection and Recovery

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.

cs.IT↗

arXiv2025

Vertical Heterogeneous Networks Beyond 5G: CoMP Coverage Enhancement and Optimization

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 uncrewed 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: \textit{i)} random UAV placement, analyzed using stochastic geometry to derive closed-form coverage expressions, and \textit{ii)} optimized UAV placement using a coverage-aware weighted $K$-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.

cs.IT↗

arXiv2025

Unified Block Signal Processing Framework for LPWANs: Sequence Index Modulation Spreading

Low-power wide-area networks (LPWANs) demand high receiver sensitivity and efficient physical-layer signal processing. This paper introduces a unified framework for generalized block signal transmission in LPWANs, addressing the limitations of conventional symbol-by-symbol approaches. The framework comprises three key components: the signal block vector, the intra-block structure generator, and the signal basis matrix, and leverages quasi-orthogonal codewords formed through cyclically shifted spreading sequences. The resulting quasi-orthogonality enables reliable multi-user separation, particularly under asynchronous access. The framework establishes a conceptual foundation for block synchronization and provides a unified demodulation structure based on block correlation matching. It further supports flexible and systematic implementation, as demonstrated through applications to frequency-shift keying and chirp spread spectrum. This work advances scalable and efficient physical-layer design for next-generation LPWANs.

cs.IT↗

arXiv2025

A Novel Spreading-Factor-Index-Aided LoRa Scheme: Design and Performance Analysis

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.

eess.SP↗

arXiv2025

Hybrid CNN-Transformer Based Sparse Channel Prediction for High-Mobility OTFS Systems

High-mobility scenarios in next-generation wireless networks, such as those involving vehicular communications, require ultra-reliable and low-latency communications (URLLC). However, rapidly time-varying channels pose significant challenges to traditional OFDM-based systems due to the Doppler effect and channel aging. Orthogonal time frequency space (OTFS) modulation offers resilience by representing channels in the quasi-static delay-Doppler (DD) domain. This letter proposes a novel channel prediction framework for OTFS systems using a hybrid convolutional neural network and transformer (CNN-Transformer) architecture. The CNN extracts compact features that exploit the DD-domain sparsity of the channel matrices, while the transformer models temporal dependencies with causal masking for consistency. Simulation experiments under extreme $500$ \si{km/h} mobility conditions demonstrate that the proposed method outperforms state-of-the-art baselines, reducing the root mean square error and mean absolute error by $12.2\%$ and $9.4\%$, respectively. These results demonstrate the effectiveness of DD-domain representations and the proposed model in accurately predicting channels in high-mobility scenarios, thereby supporting the stringent URLLC requirements in future wireless systems.

cs.IT↗

arXiv2025

Energy-Efficient Index and Code Index Modulations for Spread CPM Signals in Internet of Things

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.

cs.IT↗

arXiv2025

A CPFSK Transceiver with Hybrid CSS-DSSS Spreading for LPWAN PHY Communication

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.

cs.IT↗

arXiv2024

System-Level Simulation Framework for NB-IoT: Key Features and Performance Evaluation

Narrowband Internet of Things (NB-IoT) is a technology specifically designated by the 3rd Generation Partnership Project (3GPP) to meet the explosive demand for massive machine-type communications (mMTC), and it is evolving to RedCap. Industrial companies have increasingly adopted NB-IoT as the solution for mMTC due to its lightweight design and comprehensive technical specifications released by 3GPP. This paper presents a system-level simulation framework for NB-IoT networks to evaluate their performance. The system-level simulator is structured into four parts: initialization, pre-generation, main simulation loop, and post-processing. Additionally, three essential features are investigated to enhance coverage, support massive connections, and ensure low power consumption, respectively. Simulation results demonstrate that the cumulative distribution function curves of the signal-to-interference-and-noise ratio fully comply with industrial standards. Furthermore, the throughput performance explains how NB-IoT networks realize massive connections at the cost of data rate. This work highlights its practical utility and paves the way for developing NB-IoT networks.

cs.NI↗

arXiv2021

A Sparsity Adaptive Algorithm to Recover NB-IoT Signal from Legacy LTE Interference

As a forerunner in 5G technologies, Narrowband Internet of Things (NB-IoT) will be inevitably coexisting with the legacy Long-Term Evolution (LTE) system. Thus, it is imperative for NB-IoT to mitigate LTE interference. By virtue of the strong temporal correlation of the NB-IoT signal, this letter develops a sparsity adaptive algorithm to recover the NB-IoT signal from legacy LTE interference, by combining $K$-means clustering and sparsity adaptive matching pursuit (SAMP). In particular, the support of the NB-IoT signal is first estimated coarsely by $K$-means clustering and SAMP algorithm without sparsity limitation. Then, the estimated support is refined by a repeat mechanism. Simulation results demonstrate the effectiveness of the developed algorithm in terms of recovery probability and bit error rate, compared with competing algorithms.

cs.IT↗

arXiv2009

How to Fully Exploit the Degrees of Freedom in the Downlink of MISO Systems With Opportunistic Beamforming

The opportunistic beamforming in the downlink of multiple-input single-output (MISO) systems forms $N$ transmit beams, usually, no more than the number of transmit antennas $N_t$. However, the degrees of freedom in this downlink is as large as $N_t^2$. That is, at most $N_t^2$ rather than only $N_t$ users can be simultaneously transmitted and thus the scheduling latency can be significantly reduced. In this paper, we focus on the opportunistic beamforming schemes with $N_t<N\le N_t^2$ transmit beams in the downlink of MISO systems over Rayleigh fading channels. We first show how to design the beamforming matrices with maximum number of transmit beams as well as least correlation between any pair of them as possible, through Fourier, Grassmannian, and mutually unbiased bases (MUB) based constructions in practice. Then, we analyze their system throughput by exploiting the asymptotic theory of extreme order statistics. Finally, our simulation results show the Grassmannian-based beamforming achieves the maximum throughput in all cases with $N_t=2$, 3, 4. However, if we want to exploit overall $N_t^2$ degrees of freedom, we shall resort to the Fourier and MUB-based constructions in the cases with $N_t=3$, 4, respectively.

cs.IT↗