Fractional Doppler Effects on OTFS-NOMA HetNets with Mixed-Mobility Users

Heterogeneous networks (HetNets) are considered a promising approach to meet the increasing throughput requirements of 6G vehicular networks. The integration of orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access (NOMA) has demonstrated significant improvements in the reliability of wireless networks under mixed-mobility. Applying this combination in HetNets helps accommodate high-mobility (HM) and low-mobility (LM) users while effectively addressing high Doppler shifts. However, in practical scenarios, fractional Doppler arises from the mismatch between the actual Doppler frequency and its discrete representation on the DD grid. This effect leads to inter-Doppler interference (IDI) that can significantly degrade detection performance. In our work, we investigate the effect of fractional Doppler on OTFS-NOMA systems under mixed-mobility conditions and analyze how NOMA power allocation can be optimized to mitigate the resulting performance degradation. OTFS modulation is used for the HM user, while the LM users' symbols are embedded in the time-frequency (TF) domain. Minimum mean square error (MMSE) detection is utilized under multiple assumptions regarding the knowledge of the IDI parameters. We investigate the impact of optimizing NOMA power allocation on the overall system performance. The purpose of this work is to analyze the impact of fractional Doppler in OTFS-NOMA systems in HetNets rather than proposing new detection algorithms for OTFS modulation. The results validate the significant performance impact of fractional Doppler on the system and demonstrate the role of power allocation in mitigating IDI effects in terms of spectral efficiency and number of users served.

Adaptive Wavelet Division Multiplexing for Heterogeneous Mobility Users

This paper proposes an adaptive wavelet division multiplexing scheme for wireless systems serving users with heterogeneous mobility profiles over frequency-selective Rayleigh fading channels. By exploiting the multiresolution structure of the discrete wavelet transform (DWT), users are adaptively assigned to different decomposition levels according to their channel dynamics and Doppler conditions. A single-tap minimum mean square error (MMSE) equalizer is applied in the frequency domain, and the system performance is evaluated under realistic time-varying multipath fading environments. Simulation results demonstrate that the proposed adaptive allocation achieves balanced bit error rate (BER) across all user mobility classes while delivering substantial peak-to-average power ratio (PAPR) reductions relative to both conventional orthogonal frequency division multiplexing (OFDM) and orthogonal time-frequency space (OTFS) modulation. The proposed framework is further validated in a four-user heterogeneous-mobility scenario, confirming its scalability and effectiveness to mixed-mobility multi-user scenarios.

RSMA-Assisted OFDM-OTFS Hybrid Framework for Mixed-Mobility Multiuser Systems

In future 6G vehicular networks, users employing orthogonal frequency division multiplexing (OFDM) and orthogonal time frequency space (OTFS) waveforms may coexist under diverse mobility conditions, where both can experience high-mobility and low-mobility profiles. Since OFDM users can suffer severe inter-carrier interference (ICI) and OTFS users occupy larger spectrum resources, rate-splitting multiple access (RSMA) is a flexible framework that can efficiently handle these heterogeneous aspects. In this work, we propose a novel RSMA-assisted system to provide downlink communication to multiple OFDM and OTFS users. A common stream comprising the common messages of OFDM users spans the whole bandwidth to help OFDM users manage the ICI induced by potential high Doppler effects. OTFS users do not participate in the common stream. The private streams of OFDM users and the streams of OTFS users are transmitted over disjoint frequency bands. During the SIC process implemented at all receivers, channel estimation errors are taken into account. The simulation results highlight the impact of the power allocation factors and channel estimation errors on the system performance, and demonstrate the superiority of the proposed framework over orthogonal multiplexing in terms of outage probability and rate performance.

Constructing Composite Features for Interpretable Music-Tagging

Combining multiple audio features can improve the performance of music tagging, but common deep learning-based feature fusion methods often lack interpretability. To address this problem, we propose a Genetic Programming (GP) pipeline that automatically evolves composite features by mathematically combining base music features, thereby capturing synergistic interactions while preserving interpretability. This approach provides representational benefits similar to deep feature fusion without sacrificing interpretability. Experiments on the MTG-Jamendo and GTZAN datasets demonstrate consistent improvements compared to state-of-the-art systems across base feature sets at different abstraction levels. It should be noted that most of the performance gains are noticed within the first few hundred GP evaluations, indicating that effective feature combinations can be identified under modest search budgets. The top evolved expressions include linear, nonlinear, and conditional forms, with various low-complexity solutions at top performance aligned with parsimony pressure to prefer simpler expressions. Analyzing these composite features further reveals which interactions and transformations tend to be beneficial for tagging, offering insights that remain opaque in black-box deep models.

Performance Analysis of OTFS-NOMA System with Fractional Doppler

In this work, we investigate the effect of fractional Doppler on the performance of a system using orthogonal time frequency space (OTFS) modulation and non-orthogonal multiple access (NOMA) where users have different mobility profiles. Fractional Doppler results in inter-Doppler interference (IDI) and degrades the quality of OTFS-modulated signals. We consider a downlink (DL) communication scenario where multiple users are distinguished based on their mobility profiles into a single high-mobility (HM) user and multiple low-mobility (LM) users. OTFS modulation is implemented for the HM user by embedding its information symbols in the delay-Doppler domain, while LM users' symbols are represented in the time-frequency (TF) domain. The LM users' signals are kept orthogonal to each other in the frequency domain by accessing disjoint subcarriers. Further, NOMA spectrum sharing is implemented between the HM user and the KM users to achieve higher spectral efficiency. Performance analysis in terms of DL spectral efficiency and outage probability is conducted for different system parameters. The numerical results show that IDI has a noticeable performance impact on the HM user, depending on the NOMA parameters.

OTFS-NOMA System for MIMO Communication Networks with Spatial Diversity

In this work, we study the use of non-orthogonal multiple access (NOMA) and orthogonal time frequency space (OTFS) modulation in a multiple-input multiple-output (MIMO) communication network where mobile users (MUs) with different mobility profiles are grouped into clusters. We consider a downlink scenario where a base station (BS) communicates with multiple users that have diverse mobility profiles. High-mobility (HM) users' signals are placed in the delay-Doppler (DD) domain using OTFS modulation in order to transform their time-varying channel into a sparse static channel, while low-mobility (LM) users signals are placed in the time-frequency (TF) domain. Precoding is adopted at the BS to direct focused beams towards each cluster of users. Moreover, NOMA spectrum sharing is used in each cluster to allow the coexistence of a single HM user and multiple LM users within the same resource block. LM users access disjoint subchannels to ensure their orthogonality. All users within the same cluster first detect the HM user's signal. Afterward, LM users suppress the interference from the HM user and detect their own signals. Closed-form expressions of the detection signal-to-noise ratios (SNRs) are derived. The numerical results showed that the performance of the proposed system highly depends on the number of LM users, the number of clusters and the power allocation factors between HM and LM users.

Multi-Agent Deep Reinforcement Learning in Vehicular OCC

Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison.

Viewport-Aware Deep Reinforcement Learning Approach for 360$^o$ Video Caching

360$^o$ video is an essential component of VR/AR/MR systems that provides immersive experience to the users. However, 360$^o$ video is associated with high bandwidth requirements. The required bandwidth can be reduced by exploiting the fact that users are interested in viewing only a part of the video scene and that users request viewports that overlap with each other. Motivated by the findings of recent works where the benefits of caching video tiles at edge servers instead of caching entire 360$^o$ videos were shown, in this paper, we introduce the concept of virtual viewports that have the same number of tiles with the original viewports. The tiles forming these viewports are the most popular ones for each video and are determined by the users' requests. Then, we propose a proactive caching scheme that assumes unknown videos' and viewports' popularity. Our scheme determines which videos to cache as well as which is the optimal virtual viewport per video. Virtual viewports permit to lower the dimensionality of the cache optimization problem. To solve the problem, we first formulate the content placement of 360$^o$ videos in edge cache networks as a Markov Decision Process (MDP), and then we determine the optimal caching placement using the Deep Q-Network (DQN) algorithm. The proposed solution aims at maximizing the overall quality of the 360$^o$ videos delivered to the end-users by caching the most popular 360$^o$ videos at base quality along with a virtual viewport in high quality. We extensively evaluate the performance of the proposed system and compare it with that of known systems such as LFU, LRU, FIFO, over both synthetic and real 360$^o$ video traces. The results reveal the large benefits coming from proactive caching of virtual viewports instead of the original ones in terms of the overall quality of the rendered viewports, the cache hit ratio, and the servicing cost.

Design of Capacity-Approaching Low-Density Parity-Check Codes using Recurrent Neural Networks

In this paper, we model Density Evolution (DE) using Recurrent Neural Networks (RNNs) with the aim of designing capacity-approaching Irregular Low-Density Parity-Check (LDPC) codes for binary erasure channels. In particular, we present a method for determining the coefficients of the degree distributions, characterizing the structure of an LDPC code. We refer to our RNN architecture as Neural Density Evolution (NDE) and determine the weights of the RNN that correspond to optimal designs by minimizing a loss function that enforces the properties of asymptotically optimal design, as well as the desired structural characteristics of the code. This renders the LDPC design process highly configurable, as constraints can be added to meet applications' requirements by means of modifying the loss function. In order to train the RNN, we generate data corresponding to the expected channel noise. We analyze the complexity and optimality of NDE theoretically, and compare it with traditional design methods that employ differential evolution. Simulations illustrate that NDE improves upon differential evolution both in terms of asymptotic performance and complexity. Although we focus on asymptotic settings, we evaluate designs found by NDE for finite codeword lengths and observe that performance remains satisfactory across a variety of channels.