As wireless communication systems evolve toward intelligent operation, the growing complexity of dynamic and non-stationary propagation environments inherent in large-scale and heterogeneous Internet of Things (IoT) scenarios imposes stringent demands on link adaptation robustness. To address these challenges, we propose a joint bandwidth and modulation and coding scheme (MCS) optimization framework that leverages autonomous scenario identification (ASI) and dueling double deep Q-network (D3QN), named as ASI-D3QN. Specifically, a lightweight deep convolutional neural network (CNN) is tailored for ASI to achieve high identification accuracy while maintaining low computational complexity. This design is particularly suited for resource-constrained devices. Then, a D3QN-based optimization strategy is developed to seamlessly integrate ASI-derived environmental context with intrinsic channel metrics. The proposed framework explicitly expands the decision space by integrating signal bandwidth as an additional optimization dimension, facilitating adaptive optimization over multi-dimensional parameters. Finally, an intelligent communication prototype is established to comprehensively validate the proposed approach under representative standardized channel models. Experimental results demonstrate that, compared to existing methods, the proposed ASI achieves at least a 0.27% accuracy improvement with fewer model parameters. Moreover, relative to conventional link adaptation schemes, the ASI-D3QN strategy achieves superior throughput gains in complex dynamic environments.

Affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) are two promising advanced waveforms proposed for reliable communications in high-mobility scenarios. In this paper, we introduce a simple transmit diversity technique, termed cyclic delay-Doppler shift (CDDS), for these two advanced waveforms to achieve ultra-reliable communications in doubly selective channels (DSCs). Two simple CDDS schemes, named modulation-domain CDDS (MD-CDDS) and time-domain CDDS (TD-CDDS), are proposed, which perform CDDS in advance at the transmitter before and after the modulation, respectively. We demonstrate that both of the two proposed CDDS schemes can be implemented efficiently and flexibly by multiplying the transmit vector with a well-designed precoding matrix, which is nothing but a sparse phase-compensated permutation matrix. Moreover, we theoretically and numerically prove that CDDS can provide MIMO-AFDM and MIMO-OTFS with optimal transmit diversity gain when a proper CDDS step is adopted. Compared to the conventional transmit diversity techniques, the proposed CDDS scheme enjoys the advantages of lower channel estimation overhead, implementation complexity, and signal processing latency, making it particularly suitable for ultra-reliable communications in high-mobility scenarios.

Affine frequency division multiplexing (AFDM) is a promising chirp-based waveform designed for communications in high-mobility scenarios. In this paper, the pulse shaping for AFDM over doubly selective channels (DSC) is investigated. We first develop the pulse-shaped AFDM (PS-AFDM) system, where different transmit pulses and receive pulses can be used for each chirp carrier. Based on that, we formulate the impacts of pulse shaping on the input-output relationship of PS-AFDM system with fractional delay and fractional Doppler shifts. In particular, we reveal that there exists inter-pulse interference (IPI) within the pilot region and inter-region interference (IRI) between the pilot region and the data region in the AFDM/PS-AFDM received symbols. To provide an instructive guideline for interference suppression, we elaborate how the adopted transmit and receive pulses determine the IPI and IRI. Furthermore, we demonstrate that applying the pulse-shaping window with low sidelobe levels in PS-AFDM can suppress the IPI and IRI, facilitating the channel estimation and signal detection processes significantly. Simulations verify that the proposed PS-AFDM systems can achieve lower overhead and higher accuracy channel estimation compared to the conventional AFDM systems.

Analogous to orthogonal time frequency space (OTFS), affine frequency division multiplexing (AFDM) emerges as a promising solution for achieving ultra-reliable communication under time-varying channels with large Doppler shifts. To apply this new modulation technique for next-generation communications, there is an expectation that it will be easily integrated into current systems without major modifications. In this paper, we propose a low-complexity waveform called simplified-AFDM (S-AFDM), which is more compatible with existing techniques by reducing the parameter settings in AFDM. First, We provide a general framework and formulate the input-output relation of the S-AFDM system in the discrete affine Fourier transform (DAFT) domain. Furthermore, we present a detailed analysis of the diversity order of S-AFDM in single-input single-output (SISO) setting with maximum likelihood (ML) detection. Numerical results demonstrate that the proposed modulation scheme exhibits the same performance of classic AFDM with commonly used detectors, while halving its addtional modulation complexity superimposed on the orthogonal frequency division multiplexing,

Orthogonal time frequency space (OTFS) is a promising waveform that modulates information in the delay-Doppler domain and enables robust transmission performance in high-mobility scenarios. However, similar to other multi-carrier schemes, OTFS has a non-negligible issue of high peak-to-average power ratio (PAPR), which may lead to signal distortion and performance degradation when using a power amplifier. In this paper, we employ a parameter-adjustable expanded precoding scheme, and generalize the precoding matrix into a scalable form, composed of a periodically expanded transformation matrix and an energy conservation matrix. The PAPR can be flexibly reduced by varying the dimension of the precoding matrix with only a slight increase in system complexity, thus improving the cost-efficiency of the system. Simulation results demonstrate that the proposed expanded precoding scheme can achieve a minimum reduction of 3 dB in PAPR and enhance the bit error rate (BER) performance of OTFS.

In simultaneous transmit and receive wireless communications, digital self-interference (SI) cancellation is required before estimating the remote transmission (RT) channel. Considering the inherent connection between SI channel reconstruction and RT channel estimation, we propose a multi-layered M-estimate total least mean squares (m-MTLS) joint estimator to estimate both channels. In each layer, our proposed m-MTLS estimator first employs an M-estimate total least mean squares (MTLS) algorithm to eliminate residual SI from the received signal and give a new estimation of the RT channel. Then, it gives the final RT channel estimation based on the average of the estimation values obtained from each layer. Compared to traditional minimum mean square error estimator and single-layered MTLS estimator, it demonstrates that the m-MTLS estimator has better performance of normalized mean squared difference. Besides, the simulation results also show the robustness of m-MTLS estimator even in scenarios where the local reference signal is contaminated with noise, and the received signal is impacted by strong impulse noise.