The low-altitude economy (LAE), an emerging economic paradigm encompassing various flight activities in low-altitude airspace, has attracted widespread attention from academia and industry due to its appealing economic and social benefits. In this paper, we investigate the design of integrated sensing and communication (ISAC) waveforms for LAE applications. Specifically, we propose an adaptive ISAC waveform, which integrates the maximum distance separable (MDS) code and index modulation (IM) into the orthogonal frequency division multiplexing (OFDM) waveform, namely A-MDS-OFDM-IM. This design combines the hybrid benefits of MDS code, IM, and OFDM techniques, i.e., the error detection capability of MDS code, the high spectral efficiency (SE) of IM, and the high sensing resolution of OFDM, thereby enabling robust communication and sensing. A comprehensive performance analysis of A-MDS-OFDM-IM is provided, including its bit error rate (BER), peak-to-sidelobe level (PSL), and peak-to-average power ratio (PAPR). Moreover, to address the high PAPR issue of A-MDS-OFDM-IM, we develop an adaptive design criterion based on the alternating direction method of multipliers (ADMM), which is capable of jointly optimizing the communication, sensing, and PAPR performance of the proposed system. Simulation results demonstrate that the proposed waveform achieves better BER performance than conventional OFDM-based waveforms under a non-ideal high power amplifier (HPA), owing to its low-PAPR characteristic. Additionally, the proposed waveform ensures robust sensing with satisfactory PSL performance, making it a promising ISAC waveform for LAE applications.

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.

In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform framework by capitalizing on the benefits of maximum distance separable (MDS) code and the reconfigurable intelligent surface (RIS). The proposed scheme is referred to as MDS-OFDM-RIS. The proposed design scheme consists of (i) an MDS code based amplitude and phase modulation scheme for OFDM transmission, which helps increase the minimum Hamming distance among symbols and improve on the error detection capabilities, (ii) a RIS that is placed near the radio frequency (RF) source, (iii) as well as a reduced-complexity maximum likelihood (RC-ML) detection algorithm at the receiver by utilizing the error detection ability of the MDS codes. We derive an upper bound for the bit error rate (BER) and a closed-form expression of the mutual information. Using the obtained analytical expressions, we formulate two optimization problems and derive the corresponding optimal solutions for RIS phase shifts. It is found that the two optimization problems share the same optimal solution, which indicates that the obtained RIS phase shifts optimize the system BER and channel capacity simultaneously. Simulation results show that compared with conventional OFDM systems, the proposed system can better combat multipath fading and provide higher channel capacity, especially when the RIS phase shifts are optimal. Moreover, the accuracy and low complexity of the proposed RC-ML detection scheme are demonstrated by numerical results.

In this paper, we propose a new framework by combining the concepts of maximum distance separable (MDS) code and generalized spatial modulation (GSM) for multiple-input multiple-output (MIMO) transmission, namely MDS-GSM-MIMO. In our design, we exploit the powerful MDS code to increase the minimum Hamming distance (MHD) of the code-words, up to 2, and use the concept of space-domain index modulation to enlarge the minimum Euclidean distance (MED) of the achieved multidimensional GSM constellations. Moreover, we add the maximum minimum distance (MMD) precoder and guaranteed Euclidean distance (GED) precoder to enhance the MED further. Therefore, the MDS-GSM-MIMO we proposed with precoding is capable of optimizing both the overall MHD and MED. Then, we conduct theoretical analyses and comparisons with regard to average bit error rate (ABER) bound, MHD and MED of the conventional GSM-MIMO and the proposed MDS-GSM-MIMO. Simulation results show that the MDS-GSM-MIMO exhibits a BER performance gain of 3 dB compared to conventional GSM-MIMO, while the proposed MDS-GSM-MIMO with precoding exhibits a BER performance gain of up to 4 dB compared to the counterpart without precoding.

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.

In this paper, we propose a novel maximum distance separable (MDS) code based and reconfigurable intelligent surface (RIS) assisted wireless communication system with orthogonal frequency division multiplexing (OFDM). Specifically, input bits are firstly divided into groups and their MDS codes are utilized to decide the amplitudes and phases of subcarriers. The introduction of the MDS code helps to increase the minimum Hamming distance between symbols and improve on the capability of error detection. Besides, the RIS is adopted to create additional paths between the radio frequency (RF) and the receiver as well as alter the signal phases with derived optimal solution. Benefiting from the strength of the RIS, the proposed system can better overcome multipath fading compared with conventional systems. Simulation results are presented to demonstrate the efficacy of the proposed system in terms of reducing bit error rate (BER) through multipath channels.