Constant envelope orthogonal frequency-division multiplexing (CE-OFDM) has attracted increasing attention as a power-efficient modulation scheme for Internet of Things (IoT) networks, particularly in energy-constrained scenarios such as space–air–ground integrated networks (SAGINs). Despite its inherently low peak-to-average power ratio (PAPR), CE-OFDM suffers from significant bit error rate (BER) degradation due to nonlinear phase wrapping caused by phase modulation. To address this limitation, we propose a selective mapping (SLM)-aided CE-OFDM scheme, in which the transmit sequence with the minimum number of phase jumps is selected from a pre-defined set of candidates. This strategy effectively mitigates the impact of phase wrapping and significantly improves BER performance. Furthermore, to eliminate the spectral overhead caused by conventional side information transmission, we propose an embedded side information (ESI) mechanism that seamlessly incorporates index data into the transmitted signal without requiring additional bandwidth. Simulation results verify that the proposed SLM-aided CE-OFDM scheme, equipped with ESI, achieves substantial BER improvements over conventional CE-OFDM systems, while maintaining the low-PAPR property essential for energy-constrained IoT devices.

As a promising candidate for high-mobility communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention owing to its robustness to Doppler shifts and efficient hardware implementation. In this paper, motivated by the urgent demand for seamless and reliable communications in high-speed railway (HSR) scenarios, we innovatively integrate OCDM into cell-free massive multiple-input multiple-output (CFmMIMO) systems and establish a novel transmission framework, termed CFmMIMO-OCDM. Within this framework, we conduct a comprehensive analysis of the doubly-dispersive HSR channel model and derive the input-output signal relation in HSR communications. Moreover, to address the challenges posed by high computational complexity and excessive data exchange inherent in centralized signal processing, we first reveal the quasi-sparsity of the Fresnel-domain channel matrix in HSR communications. Then, we develop a distributed baseband processing (DBP) architecture by leveraging the channel sparsity. Aimed at enhancing the signal detection efficiency and accuracy, we further design a distributed message passing (DMP)-based detection algorithm for CFmMIMO-OCDM in HSR communications, which achieves considerably reduced complexity and data exchange compared to the centralized detection. Numerical results confirm the superiority of CFmMIMO-OCDM over conventional orthogonal frequency division multiplexing (OFDM)-assisted CFmMIMO systems in HSR communications. Moreover, theoretical analysis and numerical results are provided to demonstrate that our proposed DMP detection can achieve attractive bit error rate (BER) and complexity performance compared to conventional centralized detection.

This paper addresses the problem of pilot contamination in single-cell networks, especially in dense-user areas where intra-cell pilot reuse is inevitable. We utilize fluid antennas (FAs) at the base station (BS) to improve the uplink (UL) channel estimation accuracy in terms of normalized mean square error (NMSE). To this end, we invoke channel spatial correlation as a key metric to characterize the similarity among the spatial structures of users’ channels. Inspired by the fact that the users with low spatial correlation experience less interference, we propose to mitigate pilot contamination by optimizing the FA positions with an innovative objective of minimizing the channel spatial correlation among the pilot-sharing users. To handle the resulting fractional programming problem, we recast it into a more tractable form via establishing upper and lower bounds for the objective function. Subsequently, the reformulated problem is effectively solved through an alternating optimization (AO) framework, where the position of each FA is iteratively optimized exploiting the successive convex approximation (SCA) method. Finally, simulation results validate the effectiveness of the proposed algorithm and demonstrate its remarkable performance in channel estimation and data transmission. In particular, the proposed 16-FA scheme achieves up to 71.2% NMSE reduction over its fixed-position antenna (FPA) counterpart, translating to an approximate 11 dB SNR gain in symbol detection.

This paper investigates the model-driven federated learning (FL) for channel estimation in multi-user millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Firstly, we formulate it as a sparse signal recovery problem by exploiting the beamspace domain sparsity of the mmWave channels. Then, we propose an FL-based learned approximate message passing (LAMP) channel estimation scheme, namely FL-LAMP, where the LAMP network is trained by an FL framework. Specifically, the base station (BS) and users jointly train the LAMP network, where the users update the local LAMP network parameters by local datasets consisting of measurement signals and beamspace channels, and the BS calculates the global LAMP network parameters by aggregating the local network parameters from all the users. The beamspace channel can thus be obtained in real time from the measurement signal based on the parameters of the trained LAMP network. Simulation results demonstrate that the proposed FL-LAMP scheme can achieve better channel estimation accuracy than the existing orthogonal matching pursuit (OMP) and approximate message passing (AMP) schemes, and provides satisfactory prediction capability for multipath channels.

Channel estimation for orthogonal frequency division multiplexing (OFDM) systems stands for an important issue in mobile broadband communications, where a class of methods based on training symbols, also known as pilots, have been widely studied. Aiming at improving the spectrum efficiency and reducing the transmission latency, we tend to approach the minimal pilot overhead for OFDM systems by proposing a two-stage channel estimation method. Specifically, in the first stage, the initial value of the channel frequency response is obtained by the discrete Fourier transform (DFT)-based estimator. Then, in the second stage, soft-decision is performed to select the components with high confidence, and thus the complete channel estimation value is reconstructed by employing the compressed sensing technology. Simulations show that the proposed scheme yields considerable improvements in both bit error rate (BER) performance and data transmission efficiency.

In order to mitigate the high peak-to-average power ratio (PAPR) of the uplink waveform, the frequency domain spectrum shaping (FDSS) and $\frac{\pi }{2}$ BPSK modulation are adopted to DFT spread orthogonal frequency-division multiplexing (DFT-s-OFDM) for both data and pilot transmission in 5G new radio (NR). However, generating such pilot sequences with acceptable orthogonality and reduced PAPR causes a computationally intensive search. This paper introduces the constant envelope (CE) OFDM waveform for both pilot and data transmitting with a PAPR of 0dB. Especially, an modified channel estimation scheme with the optimized selection of modulation index is proposed to overcome the non-flat power spectral density caused by phase modulation in CE-OFDM. Numerical results prove the superiority of the proposed low-PAPR waveform over the classical NR signal in terms of both PAPR and BER performance.

This paper shows how a novel infrastructure which makes use of the multiple antennas available on IEEE 802.11n Access Points can be combined with block erasure coding to multicast video with no packet loss. This paper also shows that this can be obtained while maintaining an efficient code rate, which is better than 1) packet repetition on the new infrastructure, 2) Block erasure coding on the legacy infrastructure and 3) Leader-Based Protocols used on the legacy infrastructure. This study employs physical layer data rates of 52Mbps, 58.5Mbps and 65Mbps. Hence the infrastructure proposed solves also the “Performance Anomaly Problem” which results with legacy multicast that transmits the data using the most robust modulation and coding scheme.