The increasing demand for delay-sensitive services in industrial manufacturing, the Internet of Vehicles, and smart logistics imposes stringent delay requirements on intelligent machine (IM) networks. To reduce latency, short packet transmissions are widely used. However, their impact on network delay performance remains underexplored, particularly in large-scale deployments prone to packet collisions and queuing congestion. This paper develops a theoretical framework for modeling downlink communication and derives analytical expressions for three key delay metrics: transmission success probability, expected delay, and delay jitter. By incorporating finite blocklength constraints, we accurately characterize the effects of IM density and packet length on delay performance. Simulation results validate our model, offering valuable insights for optimizing IM network design and improving real-time communication efficiency.

A trade-off between instantaneous power variance (IPV) and ranging sidelobe was found when choosing the orthogonal signal base for integrated sensing and communication (ISAC) waveform. Recent study shows that the orthogonal frequency division multiplexing (OFDM) waveform achieves a lowest ranging sidelobe while single-carrier (SC) waveform achieves the highest under orthogonal signal bases. In this paper, we use IPV as a substitute metric for peak-to-average ratio (PAPR) to measure nonlinear power amplifier distortion and prove that SC waveform has the lowest PAPR while OFDM has highest. We give an efficient trade-off design through partial DFT-spread OFDM and prove that the interleaved DFT-spread OFDM achieves the Pareto optimal of this kind. We also utilize the metric in multi-antenna precoding design in order to reduce the IPV in each antenna. Simulation results are given that verify our analysis.

In perceptive mobile networks (PMNs), using 5G New Radio (NR) signals for direct sensing poses a significant challenge to practical implementation due to the high computational complexity involved in estimating sensing parameters. In this paper, an efficient sensing method is proposed to incorporate both downlink active sensing and passive sensing to estimate multiple sensing parameters, including delays, angle of arrival (AoA), angle of departure (AoD) and Doppler. In particular, it exploits the synchronization signal blocks (SSBs) to facilitate sensing with multiple remote radio units (RRUs). To reduce the computational complexity of direct sensing, a sparse model is developed to decouple multiple sensing parameter estimation, enabling efficient sensing method design. Then, leveraging unitary approximate message passing (UAMP) and sparse Bayesian learning (SBL), we propose an efficient method to achieve parameter estimation and association with corresponding RRUs. This method is further extended to general scenarios involving multiple path components with the same delay. Extensive simulations demonstrate the effectiveness of the proposed method, showing that it outperforms existing ones in terms of sensing accuracy and complexity.

Federated Learning (FL) is a communication-efficient distributed machine learning method that allows multiple devices to collaboratively train models without sharing raw data. FL can be categorized into centralized and decentralized paradigms. The centralized paradigm relies on a central server to aggregate local models, potentially resulting in single points of failure, communication bottlenecks, and exposure of model parameters. In contrast, the decentralized paradigm, which does not require a central server, provides improved robustness and privacy. The essence of federated learning lies in leveraging multiple local updates for efficient communication. However, this approach may result in slower convergence or even convergence to suboptimal models in the presence of heterogeneous and imbalanced data. To address this challenge, we study decentralized federated averaging via random walk (DFedRW), which replaces multiple local update steps on a single device with random walk updates. Traditional Federated Averaging (FedAvg) and its decentralized versions commonly ignore stragglers, which reduces the amount of training data and introduces sampling bias. Therefore, we allow DFedRW to aggregate partial random walk updates, ensuring that each computation contributes to the model update. To further improve communication efficiency, we also propose a quantized version of DFedRW. We demonstrate that (quantized) DFedRW achieves convergence upper bound of order$\mathcal {O}(\frac{1}{k^{1-q}})$under convex conditions. Furthermore, we propose a sufficient condition that reveals when quantization balances communication and convergence. Numerical analysis indicates that our proposed algorithms outperform (decentralized) FedAvg in both convergence rate and accuracy, achieving a 38.3% and 37.5% increase in test accuracy under high levels of heterogeneities, without increasing communication costs for the busiest device.

Space-time line code (STLC) constitutes an attractive multiple-input multiple-output (MIMO) transmission scheme conceived for full diversity gain, while reducing the complexity of the receiver substantially. In this paper, we investigate the influence of imperfect channel estimation to STLC when the channel is modeled by Rayleigh and Rician fading. Furthermore, based on the derived the signal-to-interference-plus-noise ratio (SINR), both the theoretical bit-error-rate (BER) bound and the outage probability of STLC are quantified subject to channel estimation errors. The numerical results finally confirm the reliability of the theoretical analysis.

In this paper, we consider the secure generalised precoding aided spatial modulation (GPSM) scheme, which is combined with artificial noise (AN) to resist unknown malicious eavesdropping. Given a strict power constraint for the transmit signal and a wiretap Rayleigh fading channel, the BER performances of both the desired receiver and malicious eavesdropper are derived. Simulation results validate the accuracy of the theoretical analysis and demonstrate the secrecy performance of the secure GPSM system.