The 3GPP standardized the physical layer specification in 5G New Radio to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) coexistence in usage scenarios including aerial vehicles (AVs). Dynamic multiplexing of URLLC traffic was standardized to increase the outage capacity. DM allocates a fully overlapped bandwidth part (BWP) of eMBB and URLLC AVs to perform the immediate scheduling of URLLC traffic by puncturing ongoing eMBB traffic. However, DM often suffers from a significant frame error incurred by puncturing. Meanwhile, BWP can be sliced orthogonally for eMBB and URLLC AVs, possibly preventing overdimensioning the resources depending on the eMBB and URLLC traffic loads. In this paper, we propose a dynamic BWP allocation scheme that switches between two multiplexing methods, dynamic multiplexing (DM) and orthogonal slicing (OS), so as to minimize an impact of uRLLC traffic on eMBB traffic. To implement efficient BWP allocation, the capacity region is analyzed by considering the effect of physical layer parameters, such as modulation and coding scheme (MCS) levels and code block group size on DM and OS. OS is effective for improving the eMBB throughput under a URLLC latency constraint for deterministic and predictable URLLC traffic, whereas DM has limited error-correcting capability against the URLLC’s puncturing effect. The relative MCS level of eMBB and URLLC is critical in determining the eMBB traffic tolerance against puncturing. Identifying the performance tradeoff between DM and OS, the tolerance level is quantified by a URLLC load threshold. It is given in an approximate closed form, which is an essential reference for selecting DM over OS, enabling dynamic BWP allocation for the URLLC AV.

A general form of codebook design for code-domain non-orthogonal multiple access (CD-NOMA) can be considered equivalent to an autoencoder (AE)-based constellation design for multi-user multidimensional modulation (MU-MDM). Due to a constrained design space for optimal constellation, e.g., fixed resource mapping and equal power allocation to all codebooks, however, existing AE architectures produce constellations with suboptimal bit-error-rate (BER) performance. Accordingly, we propose a new architecture for MU-MDM AE and underlying training methodology for joint optimization of resource mapping and a constellation design with bit-to-symbol mapping, aiming at approaching the BER performance of a single-user MDM (SU-MDM) AE model with the same spectral efficiency. The core design of the proposed AE architecture is dense resource mapping combined with the novel power allocation layer that normalizes the sum of user codebook power across the entire resources. This globalizes the domain of the constellation design by enabling flexible resource mapping and power allocation. Furthermore, it allows the AE-based training to approach a global optimal MU-MDM constellations for CD-NOMA. Extensive BER simulation results demonstrate that the proposed design outperforms the existing CD-NOMA designs while approaching the single-user BER performance achieved by the equivalent SU-MDM AE within 0.3dB over the additive white Gaussian noise channel.

The adoption of one-bit analog-to-digital converters (ADCs) in wireless communication has become increasingly popular owing to their potential to reduce power consumption. However, there is a decreased spectral efficiency (SE) associated with their use, as they are unable to take advantage of high-modulation-order signaling. Faster-than-Nyquist (FTN) signaling along with oversampling are promising means of improving SE at the expense of additional inter-symbol interference (ISI). This study establishes a common platform for design and evaluation by providing a general system model and problem formulation, as well as proposing an alternative solution framework for the one-bit quantization system. By systematically evaluating optimizable factors that impact performance, such as signaling rate, transmit sequences, and error correction blocks, this study proposes a deep learning (DL)-based architecture, specifically channel autoencoders, for end-to-end communication over a one-bit quantization channel. Various transceiver designs are proposed for performance comparison as well as enabling one-bit quantized communication at previously unattainable information rates through conventional means. Numerical results showcase the superiority of jointly optimizing all blocks in an additive white Gaussian noise (AWGN) channel. The DL-based scheme operationalizes Bit Error Rate (BER) performance at information rates scaling up to 80% of Shamai’s limit. Beyond AWGN, the autoencoder-based transceiver design extends to make one-bit quantization operational over a challenging Rayleigh multipath fading channel. A detailed analysis compares a pilot-based scheme for one-bit quantization with a pilotless option, thereby revealing their SE and BER relationship. The pilot-based quantized scheme outperforms conventional fading channel transmission without quantization by up to 10 dB.

In grant-free sparse code multiple access (SCMA) systems, each active user transmits data using randomly selected SCMA codebook along with its associated preamble. When multiple users select the same codebook, i.e., leading to codebook collisions, the detection of channel-modulated codebooks is still possible through collision-aware multi-user detection (CA-MUD) using their associated preambles. However, traditional CA-MUDs are designed with unique configurations tailored to each of the extensive codebook activity scenarios, thereby significantly enhancing the detection complexity and limiting the practical implementation of GF-SCMA systems. In this paper, our objective is to propose a deep learning (DL)-based CA-MUD capable of efficiently handling diverse codebook activities with a single detector, even in the presence of codebook collisions. Toward this end, we propose a multi-task learning-based DL architecture for CA-MUD that can tolerate codebook collisions, without resorting to distinct CA-MUD processes for individual collision scenarios. A key innovation in our approach is an input pre-processing method for efficient CA-MUD training that generates a channel-modulated codebook vector at the receiving end, enhancing the learning process. Simulation results demonstrate that our proposed approach enables a single CA-MUD network to manage various codebook activity scenarios, including 2-fold codebook collision, within a limited number of active users, while ensuring robustness against channel estimation errors.

In grant-free sparse code multiple access (GF-SCMA) system, active user detection (AUD) is a major performance bottleneck as it involves complex combinatorial problem, which makes joint design of contention resources for users and AUD at the receiver a crucial but a challenging problem. To this end, we propose autoencoder (AE)-based joint optimization of both preamble generation networks (PGNs) in the encoder side and data-aided AUD in the decoder side. The core architecture of the proposed AE is a novel user activity extraction network (UAEN) in the decoder that extracts a priori user activity information from the SCMA codeword data for the data-aided AUD. An end-to-end training of the proposed AE enables joint optimization of the contention resources, i.e., preamble sequences, each associated with one of the codebooks, and extraction of user activity information from both preamble and SCMA-based data transmission. Furthermore, we propose a self-supervised pre-training scheme for the UAEN prior to the end-to-end training, to ensure the convergence of the UAEN which lies deep inside the AE network. Simulation results demonstrated that the proposed AUD scheme achieved 3 to 5dB gain at a target activity detection error rate of $\bf{{10}^{-3}}$ compared to the state-of-the-art DL-based AUD schemes.

The recent works on a deep learning (DL)-based joint design of preamble set for the transmitters and data-aided active user detection (AUD) in the receiver has demonstrated a significant performance improvement for grant-free sparse code multiple access (GF-SCMA) system. The autoencoder for the joint design can be trained only in a given environment, but in an actual situation where the operating environment is constantly changing, it is difficult to optimize the preamble set for every possible environment. Therefore, a conventional, yet general approach may implement the data-aided AUD while relying on the preamble set that is designed independently rather than the joint design. In this paper, the activity detection error rate (ADER) performance of the data-aided AUD subject to the two preamble designs, i.e., independently designed preamble and jointly designed preamble, were directly compared. Fortunately, it was found that the performance loss in the data-aided AUD induced by the independent preamble design is limited to only 1dB. Furthermore, such performance characteristics of jointly designed preamble set is interpreted through average cross-correlation among the preambles associated with the same codebook (CB) (average intra-CB cross-correlation) and average cross-correlation among preambles associated with the different CBs (average inter-CB cross-correlation).