arXiv subjects

Jeffrey Reed

Publications and source records attributed to Jeffrey Reed.

5 recordsLinked to original sources

arXiv2026

ML and Smartphones Assisted Real-Time Uplink Performance Prediction in 5G Cellular System

We propose a machine learning (ML) and smartphone-assisted framework for uplink performance prediction in a private, realistic 5G cellular system using real-time measurements in both indoor and outdoor settings. This work presents a comprehensive data-driven evaluation of 5G performance prediction using a controllable software-defined radio test environment. The experimental platform is built on srsRAN 5G NR stack running on a Dell workstation configured as a gNB and 5G core operating at 3.4 GHz. Two commercial Google Pixel 7a devices are instrumented to capture uplink metrics, including channel quality indicator (CQI), modulation and coding scheme (MCS), throughput, transmission time interval (TTI), and block error rate (BLER). Different types of traffic are generated using industry-standard tools such as Ookla and iperf, spanning stationary, pedestrian, and mobility cases under both line-of-sight (LOS) and non-line-of-sight (nLOS) propagation environments. Additional datasets include YouTube video sessions and global server endpoints to introduce variability in path characteristics. The resulting measurements, including multi-UE interference conditions, serve as training data for several supervised regression models. Five learning algorithms-linear regression, decision tree, random forest, XGBoost, and LightGBM-are benchmarked for prediction accuracy. The study shows that reliable forecasting of throughput and BLER is feasible using only COTS smartphones and widely available ML methods, offering a practical pathway for real-world 5G network performance estimation.

cs.NI↗

arXiv2019

CogRF: A New Frontier for Machine Learning and Artificial Intelligence for 6G RF Systems

The concept of CogRF, a novel tunable radio frequency (RF) frontend that uses artificial intelligence (AI) to meet mission requirements for beyond 5G and 6G systems, is introduced. CogRF utilizes AI as the core to control and operate RF system components with the objective of optimizing the overall system performance. An overview of the vital elements that make up CogRF as well as the overall hierarchy of the envisioned CogRF system is provided, and potential RF components and control parameters are discussed. AI-powered flexible RF front ends, provide new opportunities to identify to enhance security, speed up optimization of device configurations, further refine radio design, improve existing spectrum sharing operations, and develop device health analytics. Top research challenges for CogRF systems have also been described and potential research directions are provided.

eess.SP↗

arXiv2019

Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G

Mobile Network Operators (MNOs) are in process of overlaying their conventional macro cellular networks with shorter range cells such as outdoor pico cells. The resultant increase in network complexity creates substantial overhead in terms of operating expenses, time, and labor for their planning and management. Artificial intelligence (AI) offers the potential for MNOs to operate their networks in a more organic and cost-efficient manner. We argue that deploying AI in 5G and Beyond will require surmounting significant technical barriers in terms of robustness, performance, and complexity. We outline future research directions, identify top 5 challenges, and present a possible roadmap to realize the vision of AI-enabled cellular networks for Beyond-5G and 6G.

cs.IT↗

arXiv2019

Self-Tuning Sectorization: Deep Reinforcement Learning Meets Broadcast Beam Optimization

Beamforming in multiple input multiple output (MIMO) systems is one of the key technologies for modern wireless communication. Creating appropriate sector-specific broadcast beams are essential for enhancing the coverage of cellular network and for improving the broadcast operation for control signals. However, in order to maximize the coverage, patterns for broadcast beams need to be adapted based on the users' distribution and movement over time. In this work, we present self-tuning sectorization: a deep reinforcement learning framework to optimize MIMO broadcast beams autonomously and dynamically based on user' distribution in the network. Taking directly UE measurement results as input, deep reinforcement learning agent can track and predict the UE distribution pattern and come up with the best broadcast beams for each cell. Extensive simulation results show that the introduced framework can achieve the optimal coverage, and converge to the oracle solution for both single sector and multiple sectors environment, and for both periodic and Markov mobility patterns.

eess.SP↗

arXiv2018

A Digital Predistortion Scheme Exploiting Degrees-of-Freedom for Massive MIMO Systems

The primary source of nonlinear distortion in wireless transmitters is the power amplifier (PA). Conventional digital predistortion (DPD) schemes use high-order polynomials to accurately approximate and compensate for the nonlinearity of the PA. This is not practical for scaling to tens or hundreds of PAs in massive multiple-input multiple-output (MIMO) systems. There is more than one candidate precoding matrix in a massive MIMO system because of the excess degrees-of-freedom (DoFs), and each precoding matrix requires a different DPD polynomial order to compensate for the PA nonlinearity. This paper proposes a low-order DPD method achieved by exploiting massive DoFs of next-generation front ends. We propose a novel indirect learning structure which adapts the channel and PA distortion iteratively by cascading adaptive zero forcing precoding and DPD. Our solution uses a 3rd order polynomial to achieve the same performance as the conventional DPD using an 11th order polynomial for a 100x10 massive MIMO configuration. Experimental results show a 70% reduction in computational complexity, enabling ultra-low latency communications.

eess.SP↗