Search arXivSearch

arXiv · 2007.09179

A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System

Abstract

This paper proposes a novel hybrid-domain (HD) non-orthogonal multiple access (NOMA) approach to support a larger number of uplink users than the recently proposed code-domain NOMA approach, i.e., sparse code multiple access (SCMA). HD-NOMA combines the code-domain and power-domain NOMA schemes by clustering the users in small path loss (strong) and large path loss (weak) groups. The two groups are decoded using successive interference cancellation while within the group users are decoded using the message passing algorithm. To further improve the performance of the system, a spectral-efficiency maximization problem is formulated under a user quality-of-service constraint, which dynamically assigns power and subcarrier to the users. The problem is non-convex and has sparsity constraints. The alternating optimization procedure is used to solve it iteratively. We apply successive convex approximation and reweighted $\ell_1$ minimization approaches to deal with the non-convexity and sparsity constraints, respectively. The performance of the proposed HD-NOMA is evaluated and compared with the conventional SCMA scheme through numerical simulation. The results show the potential of HD-NOMA in increasing the number of uplink users.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chen Quan, Animesh Yadav, Baocheng Geng, Pramod K. Varshney, H. Vincent Poor. 2020-07-27. A Novel Spectrally-Efficient Uplink Hybrid-Domain NOMA System. https://arxiv.org/abs/2007.09179

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Scensory: Real-Time Robotic Olfactory Perception for Joint Identification and Source Localization

Olfaction offers robots access to chemical information that is largely inaccessible to vision, touch, and audition, yet using airborne chemical signals for spatial perception remains challenging because local volatile organic compound (VOC) measurements are shaped by complex chemical transport and sensor dynamics. We introduce Scensory, a robotic olfaction framework that learns to jointly infer biological source identity and relative location from short temporal VOC measurements. Using a robot-automated data collection platform, we pair VOC dynamics from cross-sensitive gas sensor arrays with spatial supervision and train models to predict fungal identity, source direction, and distance. We show that a single sensor array can extract all three quantities from only 3 s of local measurements under ambient environmental conditions, achieving species classification accuracy of up to 80.13%, directional accuracy of up to 68.65%, and mean absolute distance errors of 0.110-0.131 m. Incorporating measurements from multiple spatial locations further reduces ambiguity, improving peak species and directional accuracies by 9.72 and 18.66 percentage points, respectively. We then embody this learned olfactory perception on a mobile robot, where successive local predictions acquired during motion are transformed into a world-frame evidence map, allowing observations from different positions and headings to reinforce persistent source hypotheses and guide closed-loop localization. Across eight selected indoor runs, the robot achieves a planar endpoint error of 0.606 +/- 0.294 m. Our results establish airborne chemical dynamics as a viable perceptual signal for robots to recognize biological sources, reason about their spatial origin, and autonomously navigate toward them under ambient environments.

eess.SP

Distribution-Aware GMD Transceiver Design for Probabilistic Shaping in MIMO

Multiple-input multiple-output (MIMO) transceiver and probabilistic shaping (PS) are key enablers for high spectral efficiency in 6G wireless networks. This work proposes a distribution-aware MIMO transceiver optimized for PS constellation symbols, including a Bayesian geometric mean decomposition (BGMD) precoder and a maximum a posteriori VBLAST (MAP-VBLAST) detector. The BGMD precoder uses PS priors in the derivation and equalizes layer gains to facilitate a single modulation and coding scheme for low-complexity transmissions while preserving channel capacity. MAP-VBLAST leverages these PS priors for optimal MAP detection within a successive interference cancellation (SIC) framework. Furthermore, a new codeword-to-layer mapping scheme, termed layer-contained MIMO (LC-MIMO), is proposed. By containing each codeblock (CB) within a single layer, LC-MIMO enables SIC at the CB level, allowing the receiver to exploit the error-correction capability of channel coding to mitigate error propagation. Numerical results show that the BGMD transceiver with LC-MIMO achieves notable performance gains over state-of-the-art methods.

eess.SP

Subject-Specific Analysis of Self-Initiated Attention Shifts from EEG with Controlled Internal and External Attention Conditions

Self-initiated attention shifts play a critical role in voluntary behavior but are difficult to study due to the absence of explicit temporal markers. While previous studies have examined their neural correlates, it remains unclear how multi-dimensional electroencephalography (EEG) features contribute to their characterization within an interpretable computational framework. In this study, we build on an experimental paradigm developed in our previous work, which enables controlled comparison between task-constrained self-initiated shifts and externally instructed shifts under identical visual stimulation. Within this setting, we investigate whether preparatory EEG activity can distinguish these two types of attention shifts. We adopt a machine learning-based approach and conduct two complementary analyses: (1) a performance-oriented assessment of frequency-specific topographic patterns, and (2) a model-based feature attribution analysis using SHapley Additive exPlanations (SHAP). These analyses provide a structured view of how spectral features across regions of interest contribute to model behavior. Our results demonstrate reliable within-subject classification performance, indicating that preparatory EEG activity contains subject-specific discriminative information within this paradigm. The analysis shows that higher-frequency bands and frontal regions contribute strongly to model decisions, although such contributions should be interpreted cautiously due to the potential influence of non-neural artifacts in high-frequency EEG signals. Overall, this work highlights the value of interpretable machine learning for analyzing subject-specific EEG signal patterns in a controlled experimental setting, with potential applications in personalized and asynchronous brain-machine interface systems.

eess.SP