Nonorthogonal multiple access (NOMA) has been recognized as a key enabling techniques for future communication systems since it can take advantage of differences in users’ channel conditions to achieve higher spectral efficiency compared with orthogonal multiple access. However, employing such a capacity-centric framework can result in intragroup co-channel interference for those users whose symbols are intended to be first decoded, potentially causing a decrease in reliability. Therefore, the Alamouti encoding method has been widely used in the multiuser MISO-NOMA broadcast channels to improve reliability. Although the Alamouti-based NOMA paradigm can improve reliability by providing full spatial diversity, it comes at the cost of a significant loss in spectral efficiency compared to the capacity-centric NOMA framework. To overcome these limitations, this article presents a new approach for improving the performance of multiuser MISO-NOMA broadcast channels, in which the diversity-multiplexing tradeoff is leveraged more effectively by providing multiplexing for near users and diversity for far users at the transceiver. The closed-form expressions of ergodic sum-rate and outage probability for the proposed scheme are derived. Simulation results demonstrate that the proposed scheme is capable of performing well in a wide range of scenarios and it surpasses traditional methods in terms of reliability and effectiveness.

With the development of 6G wireless networks, non-fixed monitoring methods utilizing drone networks as monitoring carriers have been receiving increasing attention. However, due to significant environmental interference affecting the hovering of drones in the air, the video signals often contain substantial random interference, which impacts the performance of current multi-object and multi-camera tracking (MOMCT) algorithms. To address this issue, this paper proposes a digital-twin-driven multi-vehicle multi-drone monitoring system, in which a simulation scenario consistent with the actual environment is created. Furthermore, a large amount of vehicle monitoring video data from multiple drone perspectives with random interference is generated, thus compensating for the shortage of multi-camera vehicle monitoring data, especially data affected by random interference. Additionally, an online tracking framework for MOMCT based on the digital twin system is set up, forming a pipeline that fully utilizes the simulated data generated by the digital-twin system for training and testing. Using this pipeline, we find that a feature detection algorithm combining AttentionNet-CBAM and ResNext101-IBN-A can effectively enhance the ability to identify target features, thereby achieving better multi-vehicle tracking results. Experimental results on the CityFlow dataset verify that the proposed method has improved the IDF1 by 1.52% and IDR by 3.58% in comparison with the state-of-the-art online MOMCT methods.

In recent years, orthogonal chirp division multiplexing (OCDM) has gained attention as a robust communication waveform due to its strong resistance to both time-domain and frequency-domain interference. However, similar to orthogonal frequency division multiplexing (OFDM), OCDM suffers from a high peak-to-average power ratio (PAPR), resulting in increased hardware costs and reduced energy efficiency of the transmitter’s power amplifiers. In this work, we introduce a novel unitary transform called the generalized discrete Fresnel transform (GDFnT) and propose a new waveform based on this transform, named generalized OCDM (GOCDM). In GOCDM, data symbols from the constellation diagram are independently placed in the generalized Fresnel (GF) domain. We derive the system’s GF-domain channel matrix under a class of time-frequency doubly selective channels. These channels are characterized by multiple lags and multiple Doppler shifts (MLMDSs), making them suitable for application scenarios, such as vehicular mobile communication and narrowband underwater acoustic communication. We leverage the sparsity of the GF-domain channel matrix to design an iterative receiver based on the message-passing algorithm. Simulation results demonstrate that GOCDM achieves better PAPR performance than OCDM without compromising bit error rate (BER) performance.

Nonorthogonal multiple access (NOMA) has been widely recognized as a promising spectral efficiency technique for the next generation of wireless communication networks due to its ability to support multiple users in the same orthogonal resource block. In response to the increasing demands for extensive connectivity and high-volume data transmission, a novel index modulation (IM)-aided NOMA scheme has been conceived to improve downlink transmission by capitalizing on the flexibility provided by the constellation rotation design for superposition coding. In the proposed scheme, known as IM aided NOMA with the constellation rotation (IM-NOMA-CR), users are categorized into cell-edge group (far-user accommodated) and cell-center group (near-user accommodated) based on their channel conditions. The rotated constellation-based IM operation is exclusively applied to users in the near-user group, who receive lower power allocations compared to the far-user group, enabling them to transmit additional information while ensuring system reliability. This approach enhances spectral efficiency by activating all subcarriers (orthogonal resource blocks) to transmit information to scheduled users, in contrast to the conventional IM-NOMA scheme. Moreover, extra information can be transmitted through constellation rotation in the superposition coding process, setting it apart from traditional NOMA schemes. Subsequently, the maximum likelihood detector employing successive interference cancellation (ML-SIC) is utilized at the receiving end to decode the intended symbols for all users. Numerical simulations have been carried out to validate the efficacy of the proposed IM-NOMA-CR design, demonstrating a significant enhancement in spectral efficiency and error performance compared to existing NOMA schemes.

As a new candidate waveform for the next generation of mobile communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention for its high spectrum efficiency and robustness to narrow-band interference or impulsive noise. Under vehicular communication channels with multiple lags and multiple Doppler shifts (MLMD), the signal suffers doubly selective (DS) fadings in the time and frequency domain, and data symbols modulated on orthogonal chirps interfere with each other. To address the problem of symbol detection of OCDM over MLMD channels, under the assumption that path attenuation factors, delays, and Doppler shifts of the channel are available, we first derive the closed-form channel matrix in the Fresnel domain and then propose a low-complexity method to approximate it as a sparse matrix. Based on the approximated Fresnel-domain channel, we propose a message-passing (MP) based detector to estimate the transmit symbols iteratively. Finally, under two MLMD channels (an underspread channel for terrestrial vehicular communications and an overspread channel for narrow-band underwater acoustic communications), Monte Carlo simulation results and analyses are provided to validate its advantages as a promising detector for OCDM.

Maneuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes.

In this paper, we study the performance of a downlink non-orthogonal multiple access-based cooperative relay system with a single relay over Nakagami-m fading channels, where both decode-and-forward (DF) and amplify-and-forward (AF) protocols are considered. We assume that only statistical channel state information is available to the system and used to determine the decoding order of cell-edge users' data. For DF relaying, both ergodic sum rate and outage probability are solved in closed form. For AF relaying, closed-form asymptotic ergodic sum rate and outage probability are provided. Numerical results verify the accuracy of the analysis and demonstrate that the DF protocol significantly outperforms the AF one in terms of ergodic sum rate even the channel's near-far effect weakens. In addition, the DF protocol exhibits better outage performance than the AF one at low signal-to-noise ratio (SNR), though the superiority becomes negligible with the increasing SNR.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for future wireless communications as it results in high spectral efciency. In this paper, we study a NOMA based dual-hop X-relay system, where a decode-and- forward relay assists the communication between the sources and the destinations. We assume that the statistical channel state information is known to the network. In such an assumption, the achievable sum-rate at high signal-to-noise ratio is further solved in closed form. Especially, the corresponding power allocation schemes, including the theoretically optimal and practical suboptimal ones, are proposed. Analyses and simulation results show that in the considered system, NOMA signicantly outperforms the conventional orthogonal multiple access schemes in terms of sum-rate performance, especially when channel's near-far effect is signicant.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for future wireless communications as it results in high spectral efficiency. NOMA differs from recently adopted orthogonal multiple access techniques in that it superimposes multiple data flows at the same time and same frequency band but with different power levels, and resorts to the successive interference cancellation (SIC) for data decoding. This paper studies NOMA in a dual-hop decode-and-forward (DF) relaying where two relays cooperate to deliver messages to the destination. Specifically, the achievable sum-rate is analyzed and a optimal power allocation (PA) scheme is designed for the proposed system. Performance comparison with the two conventional cooperative schemes that rely on the maximal ratio combining (MRC) and the max-min criterion, respectively, is also made. Comparison results reveal the advantages of the proposed scheme on not only the sum-rate, but also the energy efficiency.