arXiv subjects

Sumita Majhi

Publications and source records attributed to Sumita Majhi.

7 recordsLinked to original sources

arXiv2026

RL+AHP: A Novel Reinforcement Learning driven AHP for Slice Aware mode selection in D2D enabled Heterogeneous Networks

The mode selection problem in device-to-device communication (D2D) enabled Fifth generation (5G) heterogeneous networks (HetNet) aims prioritizing four key performance indicators (KPIs) namely data rate, latency, reliability and jitter across three slices: enhanced mobile broadband (eMBB), ultra reliable low latency (uRLLc) and massive machine type communications (mMTC). Such priority assignment must be \emph{traded off} among three access technologies, i.e., Long Term Evolution advanced (LTE-A), New Radio (NR) and D2D, while minimizing handover frequency. In existing mode selection approaches for HetNet, slice specific quality of service (QoS) requirements are largely ignored. In this work, a novel mode selection algorithm is proposed by combining a two level Analytic Hierarchy Process (AHP) with a Reinforcement Learning (RL) method. While the two level AHP facilitates decision making based on multiple criteria (i.e., KPIs) and options (i.e., LTE-A, NR, D2D mode), the RL approach computes the weights of each criteria based on the feedback from the environment. Simulation results show that our proposed algorithm outperforms related works in terms of the major KPIs for all three slices. For eMBB applications, our approach increases throughput by $33\%$; for uRLLc applications, our approach significantly decreases latency and BER ($27\%$ and $10\%$ respectively) and for mMTc applications, our approach significantly decreases latency ($44\%$). Moreover, it has been shown that the proposed RL+AHP approach outperforms the existing DRL based approaches in terms of CPU usage when the number of criteria is reasonably low ($<6$).

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arXiv2026

Reinforcement Learning-Enabled Dynamic Code Assignment for Ultra-Dense IoT Networks: A NOMA-Based Approach to Massive Device Connectivity

Ultra-dense IoT networks require an effective non-orthogonal multiple access (NOMA) scheme, yet they experience intense interference because of fixed code assignment. We suggest a reinforcement learning (RL) model of dynamic Gold code assignment in IoT-NOMA networks. Our Markov Decision Process which is IoT aware is a joint optimization of throughput, energy efficiency, and fairness. Two RL algorithms are created, including Natural Policy Gradient (NPG) to learn stable discrete actions and Deep Deterministic Policy Gradient (DDPG) with continuous code embedding. Under smart city conditions, NPG can attain throughput of 11.6% and energy efficiency of 15.8 likewise superior to its performance with a static allocation. Nonetheless, the performance is worse in organized industrial settings, and the reliability is minimal (0-2%), which points to the fact that dynamic code assignment is not a sufficient measure of ultra-reliable IoT and needs to be supplemented by power control or retransmission schemes. The work offers a basis to the RL-based resource allocation in massive IoT network.

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arXiv2026

Enhancing NOMA Handover Performance Using Hybrid AI-Driven Modulated Deterministic Sequences

Non-Orthogonal Multiple Access (NOMA) is an information-theoretical approach used in 5G networks to improve spectral efficiency, but it is prone to interference during handovers. In this work, we propose a hybrid method that combines Gold-Walsh modulated sequences with Deep Q-Networks (DQN) to intelligently manage interference during NOMA handovers. This method optimizes sequence selection and power allocation dynamically. As a result, it achieves a 95.2\% handover success rate, which is an improvement of up to 23.1 percentage points. It also delivers up to 28\% throughput gain and reduces interference by up to 41\% in various mobility scenarios. All improvements are statistically significant ($p < 0.001$). The DQN trains in $4{,}200 \pm 400$ episodes with a complexity of $O(N \log N + d \cdot h + \log B)$ and can be deployed in real-time.

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arXiv2025

A Deep-SIC Channel Estimator Scheme in NOMA Network

In 5G and next-generation mobile ad-hoc networks, reliable handover is a key requirement, which guarantees continuity in connectivity, especially for mobile users and in high-density scenarios. However, conventional handover triggers based on instantaneous channel measurements are prone to failures and the ping-pong effect due to outdated or inaccurate channel state information. To address this, we introduce Deep-SIC, a knowledge-based channel prediction model that employs a Transformer-based approach to predict channel quality and optimise handover decisions. Deep-SIC is a unique model that utilises Partially Decoded Data (PDD), a byproduct of successive interference cancellation (SIC) in NOMA, as a feedback signal to improve its predictions continually. This special purpose enables learners to learn quickly and stabilise their learning. Our model learns 68\% faster than existing state-of-the-art algorithms, such as Graph-NOMA, while offering verifiable guarantees of stability and resilience to user mobility (Theorem~2). When simulated at the system level, it can be shown that our strategy can substantially enhance network performance: the handover failure rate can be reduced by up to 40\%, and the ping-pong effect can be mitigated, especially at vehicular speeds (e.g., 60 km/h). Moreover, Deep-SIC has a 20\% smaller normalised root mean square error (NRMSE) in low-SNR situations than state-of-the-art algorithms with linear computational complexity, $O(K)$. This work has introduced a new paradigm for robust and predictive mobility management in dynamic wireless networks.

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arXiv2025

Improving Channel Estimation Through Gold Sequences

This study evaluates Non-Orthogonal Multiple Access (NOMA) systems using Gold coding and Conventional-V-BLAST (C-V-BLAST). Superimposed signals on shared subcarriers make NOMA user separation difficult, unlike MIMO. Gold sequences' orthogonal features may enhance user separation and channel estimation. A novel channel estimation approach uses fractional power allocation and partially decoded data symbols. A realistic simulation environment was created using AWGN, Rayleigh fading, and shadowing. Using pilot signals, power allocation, and data symbols, our Channel Prediction Function (CPF) surpasses pilot-based techniques.

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arXiv2025

Holographic MIMO Empowered NOMA-ISAC for 6G: Rate-Splitting Enhanced Near-Field Modeling, Multi-Objective Optimization, and Statistical Performance Validation

Holographic multiple-input multiple-output (MIMO) systems with extremely large apertures enable transformational capabilities for sixth-generation (6G) integrated sensing and communications (ISAC). However, existing non-orthogonal multiple access (NOMA) ISAC works inadequately address: (i) holographic near-field propagation with sub-wavelength antenna spacing; (ii) rate-splitting multiple access (RSMA) integration for interference management; (iii) statistical validation under realistic impairments. This paper presents a comprehensive holographic MIMO NOMA-ISAC framework featuring: \textbf{(1)} Unified near-field modeling incorporating spatially-correlated Rayleigh fading, spherical wavefront propagation, and sub-wavelength antenna coupling effects; \textbf{(2)} Novel rate-splitting enhanced NOMA (RS-NOMA) architecture enabling flexible interference management between sensing and communication; \textbf{(3)} Multi-objective optimization suite comparing hybrid alternating optimization with successive convex approximation (HAO-SCA), weighted minimum mean square error (WMMSE), semidefinite relaxation (SDR), fractional programming (FP), and deep reinforcement learning (DRL); \textbf{(4)} Rigorous statistical validation over 5000 Monte Carlo runs with significance testing across massive MIMO scenarios (up to 1024 antennas). Results demonstrate that RS-NOMA achieves \SI{11.7}{\percent} higher sum-rate than conventional NOMA and \SI{18.8}{\percent} over WMMSE at matched sensing utility. Sensing CRLB improvements of \SI{2.4}{\decibel} are confirmed with 99\% statistical confidence. The framework establishes rigorous foundations for practical 6G holographic MIMO ISAC deployment.

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arXiv2024

A PDD-Inspired Channel Estimation Scheme in NOMA Network

In 5G networks, non-orthogonal multiple access (NOMA) provides a number of benefits by providing uneven power distribution to multiple users at once. On the other hand, effective power allocation, successful successive interference cancellation (SIC), and user fairness all depend on precise channel state information (CSI). Because of dynamic channels, imperfect models, and feedback overhead, CSI prediction in NOMA is difficult. Our aim is to propose a CSI prediction technique based on an ML model that accounts for partially decoded data (PDD), a byproduct of the SIC process. Our proposed technique has been shown to be efficient in handover failure (HOF) prediction and reducing pilot overhead, which is particularly important in 5G. We have shown how machine learning (ML) models may be used to forecast CSI in NOMA handover.

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