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Milad Ganjalizadeh

Publications and source records attributed to Milad Ganjalizadeh.

8 recordsLinked to original sources
arXiv2026

Holistic Energy Performance Management: Enablers, Capabilities, and Features

Energy consumption is a significant concern for mobile network operators, and to enable further network energy improvements it is also an important target when developing the emerging 6G standard. In this paper we show that, despite the existence of many energy-saving features in 5G new radio (NR) networks, activating them in isolation yields only suboptimal savings and often compromises other network key performance indicators (KPIs) such as coverage or latency. We first introduce a compact taxonomy that distinguishes hardware capabilities from higher-layer features. Features fall into two classes: (i) signaling and scheduling mechanisms that create idle windows, and (ii) features that utilize those windows to save energy. We then present a feature orchestrator as a logical node to coordinate between features to maximize the gain. Using a 3GPP-aligned simulator with product-realistic parameters, we show that coordinating lean NR, scheduling, and advanced sleep modes significantly reduces gNodeB (gNB) energy consumption with negligible throughput loss, compared to the uncoordinated scenario. We conclude by outlining open issues in observability, system dynamics, coordination, and intelligent automation for energy performance management.

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arXiv2026

Collective Grid: Privacy-Preserved Multi-Operator Energy Sharing Optimization via Federated Energy Prediction

Electricity consumption in mobile networks is increasing with the continued 5G expansion, rising data traffic, and more complex infrastructures. However, energy management is often handled independently by each mobile network operator (MNO), leading to limited coordination and missed opportunities for collective efficiency gains. To address this gap, we propose a privacy-preserving framework for automated energy infrastructure sharing among co-located MNOs. Our framework consists of three modules: (i) a federated learning-based privacy-preserving site energy consumption forecasting module, (ii) an orchestration module in which a mixed-integer linear program is solved to schedule energy purchases from the grid, utilization of renewable sources, and shared battery charging or discharging, based on real-time prices, forecasts, and battery state, and (iii) an energy source selection module which handles the selection of cost-effective power sources and storage actions based on predicted demand across MNOs for the next control window. Using data from operational networks, our experiments confirm that the proposed solution substantially reduces operational costs and outperforms non-sharing baselines, with gains that increase as network density rises in 5G-and-beyond deployments.

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arXiv2025

BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection

Recent advances in distributed intelligence have driven impressive progress across a diverse range of applications, from industrial automation to autonomous transportation. Nevertheless, deploying distributed learning services over wireless networks poses numerous challenges. These arise from inherent uncertainties in wireless environments (e.g., random channel fluctuations), limited resources (e.g., bandwidth and transmit power), and the presence of coexisting services on the network. In this paper, we investigate a mixed service scenario wherein high-priority ultra-reliable low latency communication (URLLC) and low-priority distributed learning services run concurrently over a network. Utilizing device selection, we aim to minimize the convergence time of distributed learning while simultaneously fulfilling the requirements of the URLLC service. We formulate this problem as a Markov decision process and address it via BSAC-CoEx, a framework based on the branching soft actor-critic (BSAC) algorithm that determines each device's participation decision through distinct branches in the actor's neural network. We evaluate our solution with a realistic simulator that is compliant with 3GPP standards for factory automation use cases. Our simulation results confirm that our solution can significantly decrease the training delays of the distributed learning service while keeping the URLLC availability above its required threshold and close to the scenario where URLLC solely consumes all wireless resources.

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arXiv2023

Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning

Ultra-reliable low latency communications (URLLC) service is envisioned to enable use cases with strict reliability and latency requirements in 5G. One approach for enabling URLLC services is to leverage Reinforcement Learning (RL) to efficiently allocate wireless resources. However, with conventional RL methods, the decision variables (though being deployed at various network layers) are typically optimized in the same control loop, leading to significant practical limitations on the control loop's delay as well as excessive signaling and energy consumption. In this paper, we propose a multi-agent Hierarchical RL (HRL) framework that enables the implementation of multi-level policies with different control loop timescales. Agents with faster control loops are deployed closer to the base station, while the ones with slower control loops are at the edge or closer to the core network providing high-level guidelines for low-level actions. On a use case from the prior art, with our HRL framework, we optimized the maximum number of retransmissions and transmission power of industrial devices. Our extensive simulation results on the factory automation scenario show that the HRL framework achieves better performance as the baseline single-agent RL method, with significantly less overhead of signal transmissions and delay compared to the one-agent RL methods.

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arXiv2022

Interplay between Distributed AI Workflow and URLLC

Distributed artificial intelligence (AI) has recently accomplished tremendous breakthroughs in various communication services, ranging from fault-tolerant factory automation to smart cities. When distributed learning is run over a set of wireless connected devices, random channel fluctuations, and the incumbent services simultaneously running on the same network affect the performance of distributed learning. In this paper, we investigate the interplay between distributed AI workflow and ultra-reliable low latency communication (URLLC) services running concurrently over a network. Using 3GPP compliant simulations in a factory automation use case, we show the impact of various distributed AI settings (e.g., model size and the number of participating devices) on the convergence time of distributed AI and the application layer performance of URLLC. Unless we leverage the existing 5G-NR quality of service handling mechanisms to separate the traffic from the two services, our simulation results show that the impact of distributed AI on the availability of the URLLC devices is significant. Moreover, with proper setting of distributed AI (e.g., proper user selection), we can substantially reduce network resource utilization, leading to lower latency for distributed AI and higher availability for the URLLC users. Our results provide important insights for future 6G and AI standardization.

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arXiv2020

Translating Cyber-Physical Control Application Requirements to Network level Parameters

Cyber-physical control applications impose strict requirements on the reliability and latency of the underlying communication system. Hence, they have been mostly implemented using wired channels where the communication service is highly predictable. Nevertheless, fulfilling such stringent demands is envisioned with the fifth generation of mobile networks (5G). The requirements of such applications are often defined on the application layer. However, cyber-physical control applications can usually tolerate sparse packet loss, and therefore it is not at all obvious what configurations and settings these application level requirements impose on the underlying wireless network. In this paper, we apply the fundamental metrics from reliability literature to wireless communications and derive a mapping function between application level requirements and network level parameters for those metrics under deterministic arrivals. Our mapping function enables network designers to realize the end-to-end performance (as the target application observes it). It provides insights to the network controller to either enable more reliability enhancement features (e.g., repetition), if the metrics are below requirements, or to enable features increasing network utilization, otherwise. We evaluate our theoretical results by realistic and detailed simulations of a factory automation scenario. Our simulation results confirm the viability of the theoretical framework under various burst error tolerance and load conditions.

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arXiv2019

Impact of Correlated Failures in 5G Dual Connectivity Architectures for URLLC Applications

Achieving end-to-end ultra-reliability and resiliency in mission critical communications is a major challenge for future wireless networks. Dual connectivity has been proposed by 3GPP as one of the viable solutions to fulfill the reliability requirements. However, the potential correlation in failures occurring over different wireless links is commonly neglected in current network design approaches. In this paper, we investigate the impact of realistic correlation among different wireless links on end-to-end reliability for two selected architectures from 3GPP. In ultra-reliable use-cases, we show that even small values of correlation can increase the end-to-end error rate by orders of magnitude. This may suggest alternative feasible architecture designs and paves the way towards serving ultra-reliable communications in 5G networks.

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