Minghua Xia

Haihui Xie, Wenkun Wen, Shuwu Chen et al.

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.

Long Yuan, Wenkun Wen, Junlin Liu et al.

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.

Shuai Wang, Yik-Chung Wu, Minghua Xia et al.

While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance.