Massive machine-type communications (mMTC) in 6G requires supporting a massive number of devices with limited resources, posing challenges in efficient random access. Grant-free random access and uplink non-orthogonal multiple access (NOMA) are introduced to increase the overload factor and reduce transmission latency with signaling overhead in mMTC. Sparse code multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices (MTD) transmit information to the base station (BS) without a grant, creating a challenging task for the BS to identify the active MTD among all potential active devices. In this paper, a novel pre-activated residual neural network-based multi-user detection (MUD) scheme for the grant-free SCMA and MUSA system in an mMTC uplink framework is proposed to jointly identify the number of active MTDs and their respective messages in the received signal’s sparsity and the active MTDs in the absence of channel state information. A novel residual unit designed to learn the properties of multi-dimensional SCMA codebooks, MUSA spreading sequences, and corresponding combinations of active devices with diverse settings. The proposed scheme learns from the labeled dataset of the received signal and identifies the active MTDs from the received signal without any prior knowledge of the device sparsity level. A calibration curve is evaluated to verify the model’s calibration. The application of the proposed MUD scheme is investigated in an indoor factory setting using four different mmWave channel models. Numerical results show that when the number of active MTDs in the system is large, the proposed MUD has a significantly higher probability of detection compared to existing approaches over the signal-to-noise ratio range of interest.

Unmanned aerial vehicle (UAV) base stations (BSs) are reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide such requirements due to disasters. In this paper, we consider optimal UAV-deployment problem in 3D space for a mmWave network. The objective is to deploy multiple aerial BSs simultaneously to completely serve the ground users. We develop a novel algorithm to find the feasible positions for a set of UAV-BSs from a predefined set of locations, subject to a signal-to-interference-plus-noise ratio (SINR) constraint of every associated user, UAV-BS’s limited hovering altitude constraint and restricted operating zone constraint. We cast this 3D positioning problem as an $\ell_{0}$ minimization problem. This is a combinatorial, NP-hard problem. We approximate the $\ell_{0}$ minimization problem as non-combinatorial $\ell_{1} -$norm problem. Therefore, we provide a suboptimal algorithm to find a set of feasible locations for the UAV-BSs to operate. The analysis shows that the proposed algorithm achieves a set of the location to deploy multiple UVABSs simultaneously while satisfying the constraints.

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to $50\%$ and in received image quality with an image coding gain of up to 17 dB.

Semantic communication (SemCom) systems enhance transmission efficiency by conveying semantic information in lieu of raw data. However, challenges arise when designing these systems due to the need for robust semantic source coding for information representation extending beyond the training dataset, maintaining channel-agnostic performance, and ensuring robustness to channel and semantic noise. We propose a novel generative artificial intelligence (AI) based SemCom architecture conditioned on quantized latent. The system reduces the communication overhead of the wireless channel by transmitting the index of the quantized latent over the communication channel by mapping the quantized vector to the learned codebook vectors. The learned codebook is the shared knowledge base. The encoder is designed with a novel spatial attention mechanism based on image energy, focusing on object edges. The critic assesses the realism of generated data relative to the original distribution, with the Wasserstein distance. The model introduces novel contrastive objectives at multiple levels, including pixel, latent, perceptual, and task output, tailored for noisy wireless semantic communication. We validated the proposed model for transmission quality and robustness with low-density parity-check (LDPC), which outperforms the baselines of better portable graphics (BPG), specifically at low signal-to-noise ratio (SNR) levels ( $ {\lt }~ {5}$ dB). Additionally, it shows comparable results with joint source-channel coding (JSCC) with lower complexity and latency. The model is validated for human perception and machine perception-oriented task utility. The model effectively transmits high-resolution images without requiring additional error correction at the receiver. We propose a novel semantic-based matrix to evaluate the robustness to noise and task-specific semantic distortion.

Emerging massive machine-type communications service class needs to support many devices while ensuring that scarce radio resources are utilized efficiently. Nonorthogonal multiple access is proposed to minimize the signaling overhead and optimize resource allocation. However, during the initial access, the base station (BS) is presented with the challenge of identifying sparsely active devices in the absence of knowledge about the sparsity and channel state information. The user channels in most practical scenarios have common reflection paths, making them partially correlated, which can be exploited to improve the detection performance at the BS. In this context, we formulate a novel multiuser detection (MUD) problem in spatially correlated Rician channels, which we reformulate as a multilabel classification problem utilizing deep learning techniques. We propose two diverse approaches to tackle this problem: 1) ViT-Net, a vision transformer-based architecture, and 2) FAR-Net, a fully activated deep neural network featuring residual connections. Our analysis highlights the significance of spatial correlation for MUD, which can accord around 13% higher overloading ratio compared to the noncorrelated scenario. Numerical evaluations demonstrate the effectiveness of the proposed model in addressing spatial correlation compared to the existing deep-learning models.