arXiv subjects

Eman Alqudah

Publications and source records attributed to Eman Alqudah.

3 recordsLinked to original sources

arXiv2026

Efficient Parallel Implementation of the Pilot Assignment Problem in Massive MIMO Systems

The assignment of the pilot sequence is a critical challenge in massive MIMO systems, as sharing the same pilot sequence among multiple users causes interference, which degrades the accuracy of the channel estimation. This problem, equivalent to the NP-hard graph coloring problem, directly impacts real-time applications such as autonomous driving and industrial IoT, where minimizing channel estimation time is crucial. This paper proposes an optimized hybrid K-means clustering and Genetic Algorithm (SK-means GA) to improve the pilot assignment efficiency, achieving a 29.3% reduction in convergence time (82s vs. 116s for conventional GA). A parallel implementation (PK-means GA) is developed on an FPGA using Vivado High-Level Synthesis Tools (HLST) to further enhance the run-time performance, accelerating convergence to 3.5 milliseconds. Within Vivado implementation, different optimization techniques such as loop unrolling, pipelining, and function inlining are applied to realize the reported speedup. This significant improvement of PK-means GA in execution speed makes it highly suitable for low-latency real-time wireless networks (6G)

cs.DC↗

arXiv2025

CNN-Enabled Scheduling for Probabilistic Real-Time Guarantees in Industrial URLLC

Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a CNN-based dynamic priority prediction mechanism for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach uses a Convolutional Neural Network and graph coloring to adaptively assign link priorities based on real-time traffic, transmission opportunities, and network conditions. Assuming that first training phase is performed offline, our approach introduced minimal overhead, while enabling more efficient resource allocation, boosting network capacity, SINR, and schedulability. Simulation results show SINR gains of up to 113\%, 94\%, and 49\% over LDP across three network configurations, highlighting its effectiveness for complex URLLC scenarios.

cs.NI↗

arXiv2025

GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC

Ensuring packet-level communication quality is vital for ultra-reliable, low-latency communications (URLLC) in large-scale industrial wireless networks. We enhance the Local Deadline Partition (LDP) algorithm by introducing a Graph Convolutional Network (GCN) integrated with a Deep Q-Network (DQN) reinforcement learning framework for improved interference coordination in multi-cell, multi-channel networks. Unlike LDP's static priorities, our approach dynamically learns link priorities based on real-time traffic demand, network topology, remaining transmission opportunities, and interference patterns. The GCN captures spatial dependencies, while the DQN enables adaptive scheduling decisions through reward-guided exploration. Simulation results show that our GCN-DQN model achieves mean SINR improvements of 179.6\%, 197.4\%, and 175.2\% over LDP across three network configurations. Additionally, the GCN-DQN model demonstrates mean SINR improvements of 31.5\%, 53.0\%, and 84.7\% over our previous CNN-based approach across the same configurations. These results underscore the effectiveness of our GCN-DQN model in addressing complex URLLC requirements with minimal overhead and superior network performance.

cs.NI↗